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Yousefi, Mahdi. "Performance assessment and online input design for closed-loop identification of machine directional properties on paper machines." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50053.
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Model-based controllers based on incorrect estimates of the true plant behaviour can be expected to perform badly. Due to the fact that machine directional proper- ties in paper machines can be controlled by model predictive control, it is important for us to use a valid model of the process in the controller to keep controller per- formance high. Performance is measured to detect model-plant mismatch using a minimum variance index and a closely related user-specified criterion. In this the- sis, we define a sensitivity measure that relates system performance to model-plant mismatch, and use it to explore this sensitivity for three realistic types of paramet- ric modelling errors. This analysis shows the power of the indices to detect model plant mismatch. In addition, the effect of model-plant mismatch on the closed loop behaviour is discussed.
To compensate controller performance in the case of model-plant mismatch, the process needs to be re-identified to update the process model. This thesis presents a new approach to input design for closed loop identification. The idea is to maximize the trace of the Fisher information matrix associated with the plant model in a moving horizon framework, while enforcing explicit constraints on both inputs and outputs. The result is the richest possible excitation signal for which the operation of a running closed-loop system remains within acceptable bounds. The method can be combined with a fixed model variable regressor technique to esti- mate time delays.
The suggested technique is implemented and used to monitor performance of machine-directional processes in an industrial paper machine and identify the pro- cess if any degradation in controller performance because of model-plant mismatch is detected.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate
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Borngrund, Carl. "Machine vision for automation of earth-moving machines : Transfer learning experiments with YOLOv3." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-75169.
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This master thesis investigates the possibility to create a machine vision solution for the automation of earth-moving machines. This research was done as without some type of vision system it will not be possible to create a fully autonomous earth moving machine that can safely be used around humans or other machines. Cameras were used as the primary sensors as they are cheap, provide high resolution and is the type of sensor that most closely mimic the human vision system. The purpose of this master thesis was to use existing real time object detectors together with transfer learning and examine if they can successfully be used to extract information in environments such as construction, forestry and mining. The amount of data needed to successfully train a real time object detector was also investigated. Furthermore, the thesis examines if there are specifically difficult situations for the defined object detector, how reliable the object detector is and finally how to use service-oriented architecture principles can be used to create deep learning systems. To investigate the questions formulated above, three data sets were created where different properties were varied. These properties were light conditions, ground material and dump truck orientation. The data sets were created using a toy dump truck together with a similarly sized wheel loader with a camera mounted on the roof of its cab. The first data set contained only indoor images where the dump truck was placed in different orientations but neither the light nor the ground material changed. The second data set contained images were the light source was kept constant, but the dump truck orientation and ground materials changed. The last data set contained images where all property were varied. The real time object detector YOLOv3 was used to examine how a real time object detector would perform depending on which one of the three data sets it was trained using. No matter the data set, it was possible to train a model to perform real time object detection. Using a Nvidia 980 TI the inference time of the model was around 22 ms, which is more than enough to be able to classify videos running at 30 fps. All three data sets converged to a training loss of around 0.10. The data set which contained more varied data, such as the data set where all properties were changed, performed considerably better reaching a validation loss of 0.164 compared to the indoor data set, containing the least varied data, only reached a validation loss of 0.257. The size of the data set was also a factor in the performance, however it was not as important as having varied data. The result also showed that all three data sets could reach a mAP score of around 0.98 using transfer learning.
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Liang, Jiongqian. "Human-in-the-loop Machine Learning: Algorithms and Applications." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523988406039076.
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Bajay, Abel. "Identify customization, module opportunities for machines and parameterize the construction Case of the TransCent TCR loom." Thesis, KTH, Industriell produktion, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105739.
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Paper is one of the elements used in our everyday life under its different forms; from the office use, newspapers, books, post-it, different packaging… it is ever present. The mass production of paper is made possible today by the industrial paper mill. One of a key component affecting the quality of the paper is the forming fabric used as a filter in the preliminary stages of paper manufacturing. The TCR Transcent is such machine used to produce the forming fabric and TEXO-AB assures two third of the world production of the machine. Given the ever increasing customer demand, machine variants and technological need, it is imperial for TEXO-AB to implement a time-efficient and responsive design system. This is made possible by shifting from traditional design to a logic based parametric design. The TCR loom model provided by the method followed in this report allows among other benefit a reduction of up to 28% in the time taken to design a new machine while still attending to the customization element providing a unique machine for every customer. The use of the model will afford designers more time to focus on other essential tasks, schemes and strive toward continuous improvement in terms of quality and technology.
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Sun, Hui. "Computer aided design and analysis of loom beating-up mechanisms." Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/9162.
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Wild, Harald G. "Robust control of high dynamic machine drives employing linear motors." Thesis, University of East London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323529.
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Varanasi, Kripa K. (Kripa Kiran) 1977. "On the design of a precision machine for closed-loop performance." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/89334.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (p. 205-209).<br>by Kripa K. Varanasi.<br>S.M.
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Fadhloun, Karim. "Modeling Human And Machine-In-The-Loop In Car-Following Theory." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/95208.
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Most phenomena in engineering fields involve physical variables that can potentially be predicted using simple or complex mathematical models. However, traffic engineers and researchers are faced with a complex challenge since they have to deal with the human element. For instance, it can be stated that the biggest challenge facing researchers in the area of car-following theory relates to accounting for the human-in-the-loop while modeling the longitudinal motion of the vehicles. In fact, a major drawback of existing car-following models is that the human-in-the-loop is not modeled explicitly. This is specifically important since the output from car-following models directly impacts several other factors and measures of effectiveness, such as vehicle emissions and fuel consumption levels.
The main contribution of this research relates to modeling and incorporating, in an explicit and independent manner, the human-in-the-loop component in car-following theory in such a way that it can be either activated or deactivated depending on if a human driver is in control of the vehicle. That would ensure that a car-following model is able to reflect the different control and autonomy levels that a vehicle could be operated under. Besides that, this thesis offers a better understanding of how humans behave and differ from each other. In fact, through the implementation of explicit parameters representing the human-in-the-loop element, the heterogeneity of human behavior, in terms of driving patterns and styles, is captured.
To achieve its contributions, the study starts by modifying the maximum acceleration vehicle-dynamics model by explicitly incorporating parameters that aim to model driver behavior in its expression making it suitable for the representation of typical acceleration behavior. The modified variant of the model is demonstrated to have a flexible shape that allows it to model different types of variations that drivers can generate, and to be superior to other similar models in that it predicts more accurate acceleration levels in all domains. The resulting model is then integrated in the Rakha-Pasumarthy-Adjerid car-following model, which uses a steady-state formulation along with acceleration and collision avoidance constraints to model the longitudinal motion of vehicles. The validation of the model using a naturalistic dataset found that the modified formulation successfully integrated the human behavior component in the model and that the new formulation decreases the modeling error.
Thereafter, this dissertation proposes a new car-following model, which we term the Fadhloun-Rakha model. Even though structurally different, the developed model incorporates the key components of the Rakha-Pasumarthy-Adjerid model in that it uses the same steady state formulation, respects vehicle dynamics, and uses very similar collision-avoidance strategies to ensure safe following distances between vehicles. Besides offering a better fit to empirical data, the Fadhloun-Rakha model is inclusive of the following characteristics: (1) it models the driver throttle and brake pedal input; (2) it captures driver variability; (3) it allows for shorter than steady-state following distances when following faster leading vehicles; (4) it offers a much smoother acceleration profile; and (5) it explicitly captures driver perception and control inaccuracies and errors. Through a quantitative and qualitative evaluation using naturalistic data, the new model is demonstrated to outperform other state-of-the-practice car-following models. In fact, the model is proved to result in a significant decrease in the modeling error, and to generate trajectories that are highly consistent with the observed car-following behavior.
The final part of this study investigates a case in which the driver is excluded and the vehicles are operating in a connected environment. This section aims to showcase a scenario in which the human-in-the-loop is deactivated through the development of a platooning strategy that governs the motion of connected cooperative multi-vehicle platoons.<br>Doctor of Philosophy<br>Even though the study of the longitudinal motion of vehicles spanned over several decades leading to the development of more precise and complex car-following models, an important aspect was constantly overlooked in those models. In fact, due to the complexity of modeling the human-in-the-loop, the vehicle and the driver were almost always assumed to represent a single entity. More specifically, ignoring driver behavior and integrating it to the vehicle allowed avoiding to deal with the challenges related to modeling human behavior. The difficulty of mathematically modeling the vehicle and the driver as two independent components rather than one unique system is due to two main reasons. First, there are numerous car models and types that make it difficult to determine the different parameters impacting the performance of the vehicle as they differ from vehicle to vehicle. Second, different driving patterns exist and the fact that they are mostly dependent on human behavior and psychology makes them very difficult to replicate mathematically. The research presented in this thesis provides a comprehensive investigation of the human-in-the-loop component in car-following theory leading to a better understanding of the human-vehicle interaction. This study was initiated due to the noticeable overlooking of driver behavior in the existing literature which, as a result, fails to capture the effect of human control and perception errors.
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Porr, Bernd. "Sequence-learning in a self-referential closed-loop behavioural system." Thesis, University of Stirling, 2003. http://hdl.handle.net/1893/2582.
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This thesis focuses on the problem of "autonomous agents". It is assumed that such agents want to be in a desired state which can be assessed by the agent itself when it observes the consequences of its own actions. Therefore the feedback from the motor output via the environment to the sensor input is an essential component of such a system. As a consequence an agent is defined in this thesis as a self-referential system which operates within a closed sensor- mot or-sensor feedback loop. The generic situation is that the agent is always prone to unpredictable disturbances which arrive from the outside, i.e. from its environment. These disturbances cause a deviation from the desired state (for example the organism is attacked unexpectedly or the temperature in the environment changes, ...). The simplest mechanism for managing such disturbances in an organism is to employ a reflex loop which essentially establishes reactive behaviour. Reflex loops are directly related to closed loop feedback controllers. Thus, they are robust and they do not need a built-in model of the control situation. However, reflexes have one main disadvantage, namely that they always occur 'too late'; i.e., only after a (for example, unpleasant) reflex eliciting sensor event has occurred. This defines an objective problem for the organism. This thesis provides a solution to this problem which is called Isotropic Sequence Order (ISO-) learning. The problem is solved by correlating the primary reflex and a predictive sensor input: the result is that the system learns the temporal relation between the primary reflex and the earlier sensor input and creates a new predictive reflex. This (new) predictive reflex does not have the disadvantage of the primary reflex, namely of always being too late. As a consequence the agent is able to maintain its desired input-state all the time. In terms of engineering this means that ISO learning solves the inverse controller problem for the reflex, which is mathematically proven in this thesis. Summarising, this means that the organism starts as a reactive system and learning turns the system into a pro-active system. It will be demonstrated by a real robot experiment that ISO learning can successfully learn to solve the classical obstacle avoidance task without external intervention (like rewards). In this experiment the robot has to correlate a reflex (retraction after collision) with signals of range finders (turn before the collision). After successful learning the robot generates a turning reaction before it bumps into an obstacle. Additionally it will be shown that the learning goal of 'reflex avoidance' can also, paradoxically, be used to solve an attraction task.
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Eren, Recep. "An integrated electronic control of take-up and let-off motions in a weaving machine." Thesis, University of Manchester, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295662.
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Stewart, Gregory Edward. "Two dimensional loop shaping controller design for paper machine cross-directional processes." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0015/NQ56628.pdf.
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Zvokel, Kenneth M. "Can a rhino be taught to draw? : a look at path control algorithms." Virtual Press, 1989. http://liblink.bsu.edu/uhtbin/catkey/543993.
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In today's high-tech industrialized world, we are always looking for faster, and more reliable ways to produce goods. Robotics offers us a possible replacement for the human worker, but can a robot reliably perform the same tasks as a human arm, for example?The complex problem of teaching a robot to move it's hand in some well defined path can be broken down into a variety of algorithms. These path control algorithms generally compute some path description equation, which is used to generate path points either in terms of the Cartesian coordinates of the robot's work cell or the robot's joint variables. Common functions used in the path generation process include cubic spline functions and linear functions.This research project tests a variety of algorithms on a relatively simple robot in order to perform the task of drawing shapes (lines, squares, circles) on planes (horizontal and vertical) in the workcell. By studying the paths drawn we can determine the effect of each algorithm on the path control process, as well as the effect of plane positioning, robot structure, and the robot's controller.<br>Department of Computer Science
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Holmberg, Lars. "Human In Command Machine Learning." Licentiate thesis, Malmö universitet, Malmö högskola, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-42576.
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Machine Learning (ML) and Artificial Intelligence (AI) impact many aspects of human life, from recommending a significant other to assist the search for extraterrestrial life. The area develops rapidly and exiting unexplored design spaces are constantly laid bare. The focus in this work is one of these areas; ML systems where decisions concerning ML model training, usage and selection of target domain lay in the hands of domain experts. This work is then on ML systems that function as a tool that augments and/or enhance human capabilities. The approach presented is denoted Human In Command ML (HIC-ML) systems. To enquire into this research domain design experiments of varying fidelity were used. Two of these experiments focus on augmenting human capabilities and targets the domains commuting and sorting batteries. One experiment focuses on enhancing human capabilities by identifying similar hand-painted plates. The experiments are used as illustrative examples to explore settings where domain experts potentially can: independently train an ML model and in an iterative fashion, interact with it and interpret and understand its decisions. HIC-ML should be seen as a governance principle that focuses on adding value and meaning to users. In this work, concrete application areas are presented and discussed. To open up for designing ML-based products for the area an abstract model for HIC-ML is constructed and design guidelines are proposed. In addition, terminology and abstractions useful when designing for explicability are presented by imposing structure and rigidity derived from scientific explanations. Together, this opens up for a contextual shift in ML and makes new application areas probable, areas that naturally couples the usage of AI technology to human virtues and potentially, as a consequence, can result in a democratisation of the usage and knowledge concerning this powerful technology.
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shahdoostfard, shahabedin. "A MINIATURIZED BRAIN-MACHINE-SPINAL CORD INTERFACE (BMSI) FOR CLOSED-LOOP INTRASPINAL MICROSTIMULATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1502108119503029.
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Edgar, Alexander Montero Vera. "Virtual Commissioning of an industrialwood cutter machine : A software in the loop simulation." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77401.
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The methods used today for the commissioning and validation of industrial machines requires theconstruction of physical prototypes. Those prototypes help the engineers to e.g. validate if theprogram code meant to control a machine works as intended. In recent years the development ofnew techniques for the commissioning and validation of industrial machines has changed rapidlythanks to the development of new software. The method used in this thesis is called simulationin the loop. Another method that can be benecial to use is hardware in the loop. Using thosemethods for the commissioning of a machine is called virtual commissioning. The simulation inthe loop method is used to simulate both the machine and the control system that operate thatmachine. This is called a digital twin, a virtual copy of the physical hardware and its control systemthat can be used without the need for a real prototype to be available.The software used in this thesis comes all from the company Siemens and those are TIA Portal,Mechatronics Concept Designer, SIMIT and PLCSim Advanced. By using those programs it waspossible to build a digital twin with rigid body dynamics and its control system of the industrialmodel that was given by the company Renholmen AB. This model contained all the necessarycomponents needed for a virtual commissioning project to be done without the need to be at thefactory oor.The results showed that it was possible to achieve a real time simulation, allowing the possibilityto trim the controller parameters without the need of a physical prototype. Design errors were alsofound thanks to the results of the simulation.This new technique has shown to be a useful tool due to most of the work could be done on a digitalmodel of the machine. Simulations can reduce the time to market for industrial machines and alsohelp engineers to validate and optimize the product at an early stage. This tool that can be usedto validate industrial machines before they are created.
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Laspas, Theodoros. "Closed Force Loop Evaluation of Machining Systems." Licentiate thesis, KTH, Maskin- och processteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227664.
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Machine tools accuracy has been a cornerstone in defining machining system capability and directly affecting dimensional and geometrical tolerance of machined parts. The effort for achieving higher precision and accuracy can be distinguished in two general ideas. One is to improve performance of machine tools by designing better, stiffer and more accurate machine tools. The other is to develop tools and methods for evaluating and measuring their behaviour and gain knowledge of their performance. In order to properly control the machine tool characteristics that can affect and improve their accuracy such as static stiffness and static accuracy.This thesis is aiming at establishing and further advancing the idea of loaded testing of machining systems and the concept of Elastically Linked Systems as a framework for measuring, identifying and characterising quasi-static stiffness of machine tools under loaded conditions. This will allow the creation of an improved capability profile of machining system accuracy. The focus is on the implementation of Elastically Linked Systems concept (ELS) through the Loaded Double Bar system, a measurement method that can partly simulate the process-machine interaction by reproducing forces exerted on the machine tool structure for the evaluation of system characteristics under loaded condition. This allows the qualitative and quantitative evaluation and comparison of machine tools for the purpose of accuracy enhancement, identification of weak directions with potential utilization in process planning, machine procurement and maintenance.Through two case studies, the capability to identify the effect of machine components and structure behaviour is shown.<br><p>QC 20180514</p>
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Giudiceandrea, Roberto. "Virtual Commissioning of a crown-caps manufacturing machine through hardware in the loop architecture." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
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Questo progetto di tesi, svolto in collaborazione con l’azienda SACMI S.C. di Imola e sviluppato all’interno del laboratorio industriale di ricerca LIAM Lab, ha come obiettivo la realizzazione di un Digital Twin di una sezione di macchina in fase di costruzione in azienda, attraverso l’ausilio del Software commerciale Virtuos. In particolare, dopo aver studiato le caratteristiche del Software, le possibili configurazioni e le tecnologie di comunicazione con PLC di terze parti, è stata eseguita una modellazione cinematica e logica della macchina. Ottenuto il modello, lo si è messo in comunicazione con il codice di controllo fornito dall’azienda. Questa comunicazione può avvenire in locale, ovvero con il codice di controllo eseguito sulla stessa macchina dove si trova Virtuos («PC Virtuos»), oppure in remoto, dove il codice viene eseguito su un IPC Beckhoff connesso al «PC Virtuos» simulando la configurazione Hardware del progetto reale. Il tutto si concretizza nella visualizzazione delle movimentazioni comandate. A questo livello, il Digital Twin si presta supportare la fase di debug e all’ottimizzazione del codice macchina, presupponendo però un corretto dimensionamento degli azionamenti. Successivamente si è valutata la possibilità di estendere Virtuos a meccanismi di co-simulazione al fine estendere le caratteristiche del modello con proprietà dinamiche, con l’obiettivo di verificare la bontà di Virtuos come strumento per validare le assunzioni fatte nella fase di dimensionamento del sistema. La co-simulazione dinamica viene valutata anche per modellare la flessibilità di alcune parti critiche della macchina oppure l’effetto degli urti tra gli oggetti movimentati durante il ciclo di lavoro, fornendo un intuitivo feedback grafico.
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Rifkin, Ryan Michael 1972. "Everything old is new again : a fresh look at historical approaches in machine learning." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/17549.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2002.<br>Includes bibliographical references (leaves 213-225).<br>This thesis shows that several old, somewhat discredited machine learning techniques are still valuable in the solution of modern, large-scale machine learning problems. We begin by considering Tikhonov regularization, a broad framework of schemes for binary classification. Tikhonov regularization attempts to find a function which simultaneously has small empirical loss on a training set and small norm in a Reproducing Kernel Hilbert Space. The choice of loss function determines the learning scheme. Using the hinge loss gives rise to the now well-known Support Vector Machine algorithm. We present SvmFu, a state-of-the-art SVM solver developed as part of thesis. We discuss the design and implementation issues involved in SvmFu, present empirical results on its performance, and offer general guidance on the use of SVMs to solve machine learning problems. We also consider, and advocate in many cases, the use of the more classical square loss, giving rise to the Regularized Least Squares Classifiation algorithm. RLSC is "trained" by solving a single system of linear equations. While it is widely believed that the SVM will perform substantially better than RLSC, we note that the same generalization bounds that apply to SVMs apply to RLSC, and we demonstrate empirically on both toy and real-world examples that RLSC's performance is essentially equivalent to SVMs across a wide range of problems, implying that the choice between SVM and RLSC should be based on computational tractability considerations. We demonstrate the empirical advantages and properties of RLSC, discussing the tradeoffs between RLSC and SVMs.<br>(cont.) We also prove leave-one-out bounds for RLSC classification. Next, we turn to the problem of multiclass classification. Although a large body of recent literature exists suggesting the use of sophisticated schemes involving joint optimization of multiple discriminant functions or the use of error-correcting codes, we instead advocate a simple "one-vs-all" approach in which one classifier is trained to discriminate each class from all the others, and a new point is classified according to which classifier fires most strongly. We present empirical evidence of the strength of this scheme on real-world problems, and, in the context of RLSC as the base classifier, compelling arguments as to why the simple one-vs-all scheme is hard to beat. We also present leave-one-out bounds for multiclass classification where the base learners belong to a broad class of Tikhonov regularizers. Finally, we consider algorithmic stability, a relatively new theory that results in very elegant generalization bounds for algorithms which are "stable". We compare and contrast Tikhonov regularization, to which algorithmic stability applies, with Ivanov regularization, the form of regularization that is the basis for structural risk minimization and its related generalization bounds. We present some interesting examples which highlight the differences between the Tikhonov and Ivanov approaches, showing that the Tikhonov form has a much stronger stability than the Ivanov form ...<br>by Ryan Michael Rifkin.<br>Ph.D.
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Ostheimer, Julia. "Human-in-the-loop Computing : Design Principles for Machine Learning Algorithms of Hybrid Intelligence." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-94051.
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Artificial intelligence (AI) is revolutionizing contemporary industries and being applied in application domains ranging from recommendation systems to self-driving cars. In scenarios in which humans are interacting with an AI, inaccurate algorithms could lead to human mistreatment or even harmful events. Human-in-the-loop computing is a machine learning approach desiring hybrid intelligence, the combination of human and machine intelligence, to achieve accurate and interpretable results. This thesis applies human-in-the-loop computing in a Design Science Research project with a Swedish manufacturing company to make operational processes more efficient. The thesis aims to investigate emerging design principles useful for designing machine learning algorithms of hybrid intelligence. Hereby, the thesis has two key contributions: First, a theoretical framework is built that comprises general design knowledge originating from Information Systems (IS) research. Second, the analysis of empirical findings leads to the review of general IS design principles and to the formulation of useful design principles for human-in-the-loop computing. Whereas the principle of AI-readiness improves the likelihood of strategical AI success, the principle of hybrid intelligence shows how useful it can be to trigger a demand for human-in-the-loop computing in involved stakeholders. The principle of use case-marketing might help designers to promote the customer benefits of applying human-in-the-loop computing in a research setting. By utilizing the principle of power relationship and the principle of human-AI trust, designers can demonstrate the humans’ power over AI and build a trusting human-machine relationship. Future research is encouraged to extend and specify the formulated design principles and employ human-in-the-loop computing in different research settings. With regard to technological advancements in brain-machine interfaces, human-in-the-loop computing might even become much more critical in the future.
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Abbasi, Mohammad Aamir. "Integration of sensory feedback in a closed-loop cortical brain-machine interface requires somatotopy." Thesis, Université de Paris (2019-....), 2019. https://theses.md.univ-paris-diderot.fr/ABBASI_MohammadAamir_va1.pdf.
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Le développement des interfaces cerveau-machine (ICM) apporte une nouvelle perspective aux patients en perte d'autonomie motrice. En combinant des enregistrements en ligne de l'activité cérébrale avec un algorithme de décodage, les patients peuvent apprendre à contrôler un bras robotique afin d'effectuer des actions simples. Cependant, contrairement à la grande quantité d'informations somatosensorielles fournie par les membres physiologiques vers le cerveau, les ICM actuelles sont dépourvues de capteurs de toucher et de force. Les patients doivent donc se fier uniquement à la vision et à l'audition, qui sont inadaptées au contrôle d'une prothèse. Cela contraste avec le fait que dans le cas d’un membre sain, les entrées somatosensorielles seules peuvent guider efficacement la manipulation d'un objet fragile, ou assurer une trajectoire précise. Une caractéristique intéressante des entrées somatosensorielles est leur organisation topologique à la surface corticale. Cette carte corticale semble jouer un rôle déterminant dans la perception sensorielle. Par conséquent, l'intégration d'une rétroaction somatosensorielle artificielle alignée sur cette carte corticale pourrait aider grandement l’intégration avoir un impact déterminant dans le transporter un grand nombre d'informations. Pour vérifier cette hypothèse, nous avons développé un ICM chez la souris qui inclut une riche rétroaction corticale artificielle de type somatosensoriel. Notre installation comprend des enregistrements en ligne de l'activité de plusieurs neurones dans le cortex moteur primaire du whisker (wM1), et fournit une rétroaction simultanée via une photostimulation du cortex somatosensoriel primaire du whisker (wS1), à faible latence, haute fréquence et structure spatiale, basée sur une cartographie obtenue par imagerie intrinsèque. Nous démontrons le fonctionnement de la boucle et montrons que les souris peuvent détecter l’activité des neurons dans wS1 déclenchée par les photostimulations. Surtout, nous montrons qu'en utilisant l'ICM en boucle fermée, les souris peuvent avoir une meilleure performance dans une tâche comportementale lorsque la structure du feedback artificiel est respecté somatotopie connue de wS1<br>The development of brain-machine interfaces (BMIs) brings a new perspective to patients with a loss of motor autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis. In contrast, in a healthy limb, somatosensory inputs alone can efficiently guide the handling of a brittle object, or ensure a smooth trajectory. One interesting feature of somatosensory inputs is its topological organization at the cortical surface. This cortical map plays a role in sensory perception. Therefore, integrating artificial somatosensory feedback aligned and consistent with this cortical map could potentially help the subject to decode the information conveyed by the feedback. To test this hypothesis, we have developed a BMI in the mouse model that includes a rich artificial somatosensory-like cortical feedback. Our setup includes online recordings of the activity of multiple neurons in the whisker primary motor cortex (wM1), and delivers feedback simultaneously via a low-latency, high-refresh rate photo-stimulation of the whisker primary somatosensory cortex (wS1) that is spatially structured at the mesoscopic scale, based on a mapping obtained by intrinsic imaging. We demonstrate the operation of the loop and show that mice can detect the wS1 neuronal spiking triggered by the photostimulations. Remarkably, we show that in the closed loop BMI, mice can have a significantly better performance in a behavioral task when the structure of the artificial feedback abides to the known wS1 somatotopy
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Schmitz, Michael Glenn. "Key Tension Points of creative Machine Learning applications keeping a Human-in-the-Loop." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264570.
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Machine learning (ML) and artificial intelligence (AI), might have earlier primarily found industrial use, improving production chains, efficiency and the like but are now an integral part of private and commercial application. Many systems are using, or are claiming to use, machine learning to improve the end user's experience. This study aims to explore applications that are using creative ML, in which output might have a plethora of solutions instead of a single correct one. More specifically the focus is to evaluate which Key Tension Points, central lesser components of a complex and bigger issue, arise for researchers, designers and users coming into contact with this technology. The goal is to draw upon these Key Tension Points and attempt to frame guidelines which researchers and designers can use to further their understanding of the relationship between ML and design and how they can be accounted for to build and develop better application. The study found that these tension points (Impersonality, passive consumers & transparency) vary considerably depending on the application and presents how designers can account for them.<br>Maskininlärning (ML) och artificiell intelligens (AI) har sedan tidigare oftast använts på en industriell skala, för att effektivisera produktionskedjor eller förfina dessa. Dock har det skett ett skifte och nuförtiden är ML en betydande del i applikationer som har privatpersoner som målgrupp. Den här studien undersöker kreativa maskininlärningslösningar, sådana som kommer med fler än ett förslag. Mer specifikt så undersöker den här studien vilka Key Tension Points, dvs. betydande mindre komponenter av komplexa stora problem, som forskare, designers eller användare kommer i kontakt med. Målet är att ta fram Key Tension Points och sedan undersöka huruvida riktlinjer kan formuleras som underlättar för forskare och designers att hantera frågor rörande design och tillgänglighet av ML. Dessutom underlättar användandet av Key Tension Points vid byggandet och utvecklingen av kreativa ML applikationer. Studien fann att Key Tension Points varierar betydande beroende på vilken typ av applikation som används av konsumenten.
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Ballerini, Simone. "Virtual commissioning of an automatic boxing machine for hardware-in-the-loop control logic validation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
Find full textAbstract:
Today the increase in complexity within the development process of automatic machines and automated systems and delivery times of the control project and the various mechanical movements. This is done by creating a virtual replica of the entire machine or only by the critical parts of the same through a physical simulation software.
The objective of this thesis project is the design using a virtual commissioning software useful for testing the control logic, creating a virtual 3D model of a machine and connecting a real PLC to it.
Corazza S.p.a. world leader in the construction of machines for soup cubes, melted cheeses and butter, which for the project it has provided the CAD design of the machine and the instrumentation as PC and motion controller.
The software on which the 3D model was virtualized was delivered by Siemens and its name is NX-MCD, which provided support in the first phase of the virtual modeling.
The project was divided into two parts:
The generation of the stand-alone virtual model from the 3D CAD design.
Hardware-in-the-loop connection with Schneider PLC to move the virtual model and test the control logic.
Below there is a brief introduction to the chapters of the thesis:
The first chapter presents the meaning of virtual commissioning, considering the various technologies used and what is the goal of this type of activity.
In the second chapter there is an explanation of the software used to perform the thesis activity, studying its potential and its functionality. In he last part of the chapter there is a brief introduction of the competitors on the market.
The third chapter presents the automatic boxing machine on which the project of virtual commissioning is done, studying the task of the machine and the critic part.
In the fourth chapter explain how the virtual machine was connected to the real PLC.
In the fifth chapter we describe the pros and cons that were found during this project on virtual commissioning.
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Razzak, Muhammed T. "Reducing the Burden of Aerial Image Labelling Through Human-in-the-Loop Machine Learning Methods." Master's thesis, Faculty of Engineering and the Built Environment, 2021. http://hdl.handle.net/11427/33908.
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This dissertation presents an introduction to human-in-the-loop deep learning methods for remote sensing applications. It is motivated by the need to decrease the time spent by volunteers on semantic segmentation of remote sensing imagery. We look at two human-in-the-loop approaches of speeding up the labelling of the remote sensing data: interactive segmentation and active learning. We develop these methods specifically in response to the needs of the disaster relief organisations who require accurately labelled maps of disaster-stricken regions quickly, in order to respond to the needs of the affected communities. To begin, we survey the current approaches used within the field. We analyse the shortcomings of these models which include outputs ill-suited for uploading to mapping databases, and an inability to label new regions well, when the new regions differ from the regions trained on. The methods developed then look at addressing these shortcomings. We first develop an interactive segmentation algorithm. Interactive segmentation aims to segment objects with a supervisory signal from a user to assist the model. Work within interactive segmentation has focused largely on segmenting one or few objects within an image. We make a few adaptions to allow an existing method to scale to remote sensing applications where there are tens of objects within a single image that needs to be segmented. We show a quantitative improvements of up to 18% in mean intersection over union, as well as qualitative improvements. The algorithm works well when labelling new regions, and the qualitative improvements show outputs more suitable for uploading to mapping databases. We then investigate active learning in the context of remote sensing. Active learning looks at reducing the number of labelled samples required by a model to achieve an acceptable performance level. Within the context of deep learning, the utility of the various active learning strategies developed is uncertain, with conflicting results within the literature. We evaluate and compare a variety of sample acquisition strategies on the semantic segmentation tasks in scenarios relevant to disaster relief mapping. Our results show that all active learning strategies evaluated provide minimal performance increases over a simple random sample acquisition strategy. However, we present analysis of the results illustrating how the various strategies work and intuition of when certain active learning strategies might be preferred. This analysis could be used to inform future research. We conclude by providing examples of the synergies of these two approaches, and indicate how this work, on reducing the burden of aerial image labelling for the disaster relief mapping community, can be further extended.
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Günther, Johannes [Verfasser], Klaus [Akademischer Betreuer] Diepold, Patrick M. [Gutachter] Pilarski, and Klaus [Gutachter] Diepold. "Machine intelligence for adaptable closed loop and open loop production engineering systems / Johannes Günther ; Gutachter: Patrick M. Pilarski, Klaus Diepold ; Betreuer: Klaus Diepold." München : Universitätsbibliothek der TU München, 2018. http://d-nb.info/1153122413/34.
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Günther, Johannes Verfasser], Klaus [Akademischer Betreuer] [Diepold, Patrick M. [Gutachter] Pilarski, and Klaus [Gutachter] Diepold. "Machine intelligence for adaptable closed loop and open loop production engineering systems / Johannes Günther ; Gutachter: Patrick M. Pilarski, Klaus Diepold ; Betreuer: Klaus Diepold." München : Universitätsbibliothek der TU München, 2018. http://d-nb.info/1153122413/34.
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Le, Chenadec Jean-Yves. "Minimisation de l'ondulation de couple des machines à reluctance variable à double saillance, influence des caractéristiques de la machine et des limites de l'onduleur." Cachan, Ecole normale supérieure, 1993. http://www.theses.fr/1993DENS0006.
Full textAbstract:
Les nombreux avantages des machines a reluctance variable expliquent le développement de leur utilisation, principalement pour des applications a fortes contraintes économiques ou en milieu difficile. Malheureusement, de part sa structure, ce type de moteur présente un couple instantané naturellement pulsatoire. Nous avons recherche un mode de commande permettant d'annuler -théoriquement- l'ondulation du couple polyphasé instantané, en produisant une forme d'onde de couple monophase trapézoïdale. Après avoir défini des coefficients de dimensionnement permettant de caractériser le rapport puissance silicium du convertisseur/puissance mécanique de la machine, nous avons optimise les formes d'onde de courant d'alimentation et chiffre l'influence de la forme de la caractéristique magnétique de la machine (et donc de sa structure) sur ces coefficients. Dans un deuxième temps, nous avons cherche qu'elle était l'influence des limitations -en tension- du convertisseur statique de la machine sur la forme du couple. En effet, ces limitations ne permettront pas d'injecter les formes d'onde de courant optimale, des que la vitesse augmente. Afin de valider le mode d'alimentation que nous avons utilise dans nos calculs, nous avons recherche, pour une machine existante, des lois de commande permettant de minimiser les pulsations du couple polyphasé. Ces lois de commande ont été testées sur un banc d'essai, l'ensemble des boucles de contrôle étant confié à un processeur de traitement du signal. Afin de vérifier les taux d'ondulation de couple calcules, nous avons utilise un système de mesure direct du couple instantané fourni par la machine. Nous avons alors obtenu, à basse vitesse, des taux d'ondulation de couple tout a fait satisfaisants
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Krivosheev, Evgeny. "Crowd and Hybrid Algorithms for Cost-Aware Classification." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263787.
Full textAbstract:
Classification is a pervasive problem in research that aims at grouping items in categories
according to established criteria. There are two prevalent ways to classify items of interest: i) to
train and exploit machine learning (ML) algorithms or ii) to resort to human classification (via
experts or crowdsourcing).
Machine Learning algorithms have been rapidly improving with an impressive performance in
complex problems such as object recognition and natural language understanding.
However, in many cases they cannot yet deliver the required levels of precision and recall, typically
due to difficulty of the problem and (lack of) availability of sufficiently large and clean datasets.
Research in crowdsourcing has also made impressive progress in the last few years, and the
crowd has been shown to perform well even in difficult tasks [Callaghan et al., 2018; Ranard et al.,
2014]. However, crowdsourcing remains expensive, especially when aiming at high levels of
accuracy, which often implies collecting more votes per item to make classification more robust to
workers' errors.
Recently, we witness rapidly emerging the third direction of hybrid crowd-machine classification
that can achieve superior performance by combining the cost-effectiveness of automatic machine
classifiers with the accuracy of human judgment.
In this thesis, we focus on designing crowdsourcing strategies and hybrid crowd-machine
approaches that optimize the item classification problem in terms of results and budget. We start
by investigating crowd-based classification under the budget constraint with different loss
implications, i.,e., when false positive and false negative errors carry different harm to the task.
Further, we propose and validate a probabilistic crowd classification algorithm that iteratively
estimates the statistical parameters of the task and data to efficiently manage the accuracy vs. cost
trade-off. We then investigate how the crowd and machines can support each other in tackling
classification problems. We present and evaluate a set of hybrid strategies balancing between
investing money in building machines and exploiting them jointly with crowd-based classifiers.
While analyzing our results of crowd and hybrid classification, we found it is relevant to study the
problem of quality of crowd observations and their confusions as well as another promising
direction of linking entities from structured and unstructured sources of data. We propose crowd
and neural network grounded algorithms to cope with these challenges followed by rich evaluation
on synthetic and real-world datasets.
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Krivosheev, Evgeny. "Crowd and Hybrid Algorithms for Cost-Aware Classification." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263787.
Full textAbstract:
Classification is a pervasive problem in research that aims at grouping items in categories
according to established criteria. There are two prevalent ways to classify items of interest: i) to
train and exploit machine learning (ML) algorithms or ii) to resort to human classification (via
experts or crowdsourcing).
Machine Learning algorithms have been rapidly improving with an impressive performance in
complex problems such as object recognition and natural language understanding.
However, in many cases they cannot yet deliver the required levels of precision and recall, typically
due to difficulty of the problem and (lack of) availability of sufficiently large and clean datasets.
Research in crowdsourcing has also made impressive progress in the last few years, and the
crowd has been shown to perform well even in difficult tasks [Callaghan et al., 2018; Ranard et al.,
2014]. However, crowdsourcing remains expensive, especially when aiming at high levels of
accuracy, which often implies collecting more votes per item to make classification more robust to
workers' errors.
Recently, we witness rapidly emerging the third direction of hybrid crowd-machine classification
that can achieve superior performance by combining the cost-effectiveness of automatic machine
classifiers with the accuracy of human judgment.
In this thesis, we focus on designing crowdsourcing strategies and hybrid crowd-machine
approaches that optimize the item classification problem in terms of results and budget. We start
by investigating crowd-based classification under the budget constraint with different loss
implications, i.,e., when false positive and false negative errors carry different harm to the task.
Further, we propose and validate a probabilistic crowd classification algorithm that iteratively
estimates the statistical parameters of the task and data to efficiently manage the accuracy vs. cost
trade-off. We then investigate how the crowd and machines can support each other in tackling
classification problems. We present and evaluate a set of hybrid strategies balancing between
investing money in building machines and exploiting them jointly with crowd-based classifiers.
While analyzing our results of crowd and hybrid classification, we found it is relevant to study the
problem of quality of crowd observations and their confusions as well as another promising
direction of linking entities from structured and unstructured sources of data. We propose crowd
and neural network grounded algorithms to cope with these challenges followed by rich evaluation
on synthetic and real-world datasets.
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Karran, Alexander John. "Exploring the biocybernetic loop : classifying psychophysiological responses to cultural artefacts using physiological computing." Thesis, Liverpool John Moores University, 2014. http://researchonline.ljmu.ac.uk/4563/.
Full textAbstract:
The aim of this research project was to provide a bio-sensing component for a real-time adaptive technology in the context of cultural heritage. The proposed system was designed to infer the interest or intention of the user and to augment elements of the cultural heritage experience interactively through implicit interaction. Implicit interaction in this context is the process whereby the system observes the user while they interact with artefacts; recording psychophysiological responses to cultural heritage artefacts or materials and acting upon these responses to drive adaptations in content in real-time. Real-time biocybernetic control is the central component of physiological computing wherein physiological data are converted into a control input for a technological system. At its core the bio-sensing component is a biocybernetic control loop that utilises an inference of user interest as its primary driver. A biocybernetic loop is composed of four main stages: inference, classification, adaptation and interaction. The programme of research described in this thesis is concerned primarily with exploration of the inference and classification elements of the biocybernetic loop but also encompasses an element of adaptation and interaction. These elements are explored first through literature review and discussion (presented in chapters 1-5) and then through experimental studies (presented in chapters 7-11).
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Shkarin, Roman [Verfasser]. "Machine learning-based automated segmentation with a feedback loop for 3D synchrotron micro-CT / Roman Shkarin." Karlsruhe : KIT-Bibliothek, 2021. http://d-nb.info/1228439400/34.
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Noon, John Patrick. "Development of a Power Hardware-in-the-Loop Test Bench for Electric Machine and Drive Emulation." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/101498.
Full textAbstract:
This work demonstrates the capability of a power electronic based power hardware-inthe-
loop (PHIL) platform to emulate electric machines for the purpose of a motor drive
testbench with a particular focus on induction machine emulation. PHIL presents advantages
over full-hardware testing of motor drives as the PHIL platform can save space and
cost that comes from the physical construction of multiple electric machine test configurations.
This thesis presents real-time models that were developed for the purpose of PHIL
emulation. Additionally, real-time modeling considerations are presented as well as the modeling
considerations that stem from implementing the model in a PHIL testbench. Next, the
design and implementation of the PHIL testbench is detailed. This thesis describes the
design of the interface inductor between the motor drive and the emulation platform. Additionally,
practical implementation challenges such as common mode and ground loop noise
are discussed and solutions are presented. Finally, experimental validation of the modeling
and emulation of the induction machine is presented and the performance of the machine
emulation testbench is discussed.<br>Master of Science<br>According to the International Energy Agency (IEA), electric power usage is increasing
across all sectors, and particularly in the transportation sector [1]. This increase is apparent
in one's daily life through the increase of electric vehicles on the road. Power electronics
convert electricity in one form to electricity in another form. This conversion of power is
playing an increasingly important role in society because examples of this conversion include
converting the dc voltage of a battery to ac voltage in an electric car or the conversion of
the ac power grid to dc to power a laptop. Additionally, even within an electric car, power
converters transform the battery's electric power from a higher dc voltage into lower voltage
dc power to supply the entertainment system and into ac power to drive the car's motor.
The electrification of the transportation sector is leading to an increase in the amount
of electric energy that is being consumed and processed through power electronics. As was
illustrated in the previous examples of electric cars, the application of power electronics
is very wide and thus requires different testbenches for the many different applications.
While some industries are used to power electronics and testing converters, transportation
electrification is increasing the number of companies and industries that are using power
electronics and electric machines.
As industry is shifting towards these new technologies, it is a prime opportunity to change
the way that high power testing is done for electric machines and power converters. Traditional
testing methods are potentially dangerous and lack the flexibility that is required
to test a wide variety of machines and drives. Power hardware-in-the-loop (PHIL) testing
presents a safe and adaptable solution to high power testing of electric machines. Traditionally,
electric machines were primarily used in heavy industry such as milling, processing, and
pumping applications. These applications, and other applications such as an electric motor
in a car or plane are called motor drive systems. Regardless of the particular application
of the motor drive system, there are generally three parts: a dc source, an inverter, and
the electric machine. In most applications, other than cars which have a dc battery, the
dc source is a power electronic converter called a rectifier which converts ac electricity from
the grid to dc for the motor drive. Next, the motor drive converts the dc electricity from
the first stage to a controlled ac output to drive the electric machine. Finally, the electric
machine itself is the final piece of the electrical system and converts the electrical energy to
mechanical energy which can drive a fan, belt, or axle. The fact that this motor drive system
can be generalized and applied to a wide range of applications makes its study particularly
interesting.
PHIL simplifies testing of these motor drive systems by allowing the inverter to connect
directly to a machine emulator which is able to replicate a variety of loads. Furthermore,
this work demonstrates the capability of PHIL to emulate both the induction machine load
as well as the dc source by considering several rectifier topologies without any significant
adjustments from the machine emulation platform.
This thesis demonstrates the capabilities of the EGSTON Power Electronics GmbH COMPISO
System Unit to emulate motor drive systems to allow for safer, more flexible motor drive system testing. The main goal of this thesis is to demonstrate an accurate PHIL emulation
of a induction machine and to provide validation of the emulation results through
comparison with an induction machine.
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Nguyen, Ngoc Linh. "Predictive control of two synchronous machines in parallel supplied by a standard three phase static converter." Phd thesis, Toulouse, INPT, 2013. http://oatao.univ-toulouse.fr/9906/1/nguyen.pdf.
Full textAbstract:
Nowadays, embedded systems are more and more numerous that impacted strongly energy conversion systems. Associated constraints translates into a reduction of the masses and the losses to improve energy efficiency in the conversion chain. This is the case in the field of aeronautics or the concept of ″More Electric Aircraft″ now becomes a reality. Therefore, the permanent magnet synchronous machine becomes an actuator of excellence because of its important mass power, its low maintenance cost and its dynamic qualities. When these machines are associated to carry out cooperative functions (for example flight surfaces) can still reduce the mass embedded in pooling power electronics. It is precisely in this context that localizes our work by offering structures power electronics-based, reduced to power for two or more electric machines in parallel and providing control laws aimed at improving energy efficiency. We we are interested specifically in the Predictive Control of two Synchronous Machines connected in parallel with a 3-Phase Converter. These machines have identical characteristics and must follow a same velocity profile with a torque of different load and in any case independent. The predictive control approach leads us to consider the voltage inverter as a device having a finite number of input states and we need to select every moment the best control solution to minimize a cost function. This cost function on one or two machines, is composed of a portion which represents the quality of the produced torque (Iq current) and another party representative quality of the conversion via produced losses (Id current). This approach works naturally for variable switching frequency. Thus the document state of different solutions studied showing the limits of such an approach both dynamic in terms of losses. To improve this basic solution we develop an approach based on the use of virtual vector which increase the possibilities of control and led to operation at constant frequency through a SVM solution. The search for an optimum virtual vector is proposed and applied to a device consisting of two low-power machines. The various proposals are validated through numerical simulation and consolidated by experimental results.
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Alharbi, W. N. H. "Development of a closed loop control system for vibratory milling." Thesis, Liverpool John Moores University, 2018. http://researchonline.ljmu.ac.uk/8456/.
Full textAbstract:
Manufacturing makes ever-increasing demands for higher machining speeds. This is particularly true in car and aircraft production, but also for cutting tools. Vibration is used in various technological processes to improve the performance of the machines by intelligently exploiting the synergy of the oscillations. Vibration provides several benefits for various technologies, such as manufacturing, medical, communications, transport, industries, etc. Vibration assisted machining techniques have recently become an area of interest for many engineering applications. In machining processes, vibration can lead to improvements when applied in a controlled manner. Vibration assisted machining is a technique in which a certain frequency of vibration is applied to the cutting tool or the Workpiece to achieve better cutting performance The aim of this project is to apply vibration to the work-piece during milling process in order to improve the machining performance. In this project, a theoretical modelling and experimental implementation of vibratory milling process are presented and explored in depth. The modelling focused on the control system which tracked and regulated the vibration amplitude in the cutting zone during machining. Here, hardware and software of advanced technology of LabVIEW applications were used to develop implement and optimise the control system. The machine tool static, dynamic and compliance characteristics were investigated in terms of static analysis, natural frequencies and dynamic stiffness, using harmonic excitation, hammer impact test and the application of external forces. Preliminary studies were undertaken, where, the effect of cutting parameters were evaluated and the optimal cutting conditions were determined. Series of machining tests were undertaken, with the aim of recording process performance data in terms of cutting forces that were used for the development of the control system. A closed loop PID controller was developed using advanced Field-Programming-Gate-Array (FPGA) and Real-time Labview applications, using a non-interrupted real time target PC. An innovative and unique combination of FPGA and target PC allow the control system to have a very fast response in keeping the set amplitude of the vibration whilst recording simultaneously the machining data for further analysis. Aluminium and mild Steel were using in this investigation, along with a comparative study between conventional and vibratory milling and between open loop and closed loop control systems. The results of this investigation show the benefits of the superimposed vibration. The outperformance of the vibratory machining over the conventional milling provides a very promising outlook for the application of subsonic vibration into machining as an alternative to ultrasonic process.
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Thorniley, James. "Information transfer and causality in the sensorimotor loop." Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/57186/.
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This thesis investigates information-theoretic tools for detecting and describing causal influences in embodied agents. It presents an analysis of philosophical and statistical approaches to causation, and in particular focuses on causal Bayes nets and transfer entropy. It argues for a novel perspective that explicitly incorporates the epistemological role of information as a tool for inference. This approach clarifies and resolves some of the known problems associated with such methods. Here it is argued, through a series of experiments, mathematical results and some philosophical accounts, that universally applicable measures of causal influence strength are unlikely to exist. Instead, the focus should be on the role that information-theoretic tools can play in inferential tests for causal relationships in embodied agents particularly, and dynamical systems in general. This thesis details how these two approaches differ. Following directly from these arguments, the thesis proposes a concept of “hidden” information transfer to describe situations where causal influences passing through a chain of variables may be more easily detected at the end-points than at intermediate nodes. This is described using theoretical examples, and also appears in the information dynamics of computer-simulated and real robots developed herein. Practical examples include some minimal models of agent-environment systems, but also a novel complete system for generating locomotion gait patterns using a biologically-inspired decentralized architecture on a walking robotic hexapod.
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Arslan, Oktay. "Machine learning and dynamic programming algorithms for motion planning and control." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54317.
Full textAbstract:
Robot motion planning is one of the central problems in robotics, and has received considerable amount of attention not only from roboticists but also from the control and artificial intelligence (AI) communities. Despite the different types of applications and physical properties of robotic systems, many high-level tasks of autonomous systems can be decomposed into subtasks which require point-to-point navigation while avoiding infeasible regions due to the obstacles in the workspace. This dissertation aims at developing a new class of sampling-based motion planning algorithms that are fast, efficient and asymptotically optimal by employing ideas from Machine Learning (ML) and Dynamic Programming (DP). First, we interpret the robot motion planning problem as a form of a machine learning problem since the underlying search space is not known a priori, and utilize random geometric graphs to compute consistent discretizations of the underlying continuous search space. Then, we integrate existing DP algorithms and ML algorithms to the framework of sampling-based algorithms for better exploitation and exploration, respectively. We introduce a novel sampling-based algorithm, called RRT#, that improves upon the well-known RRT* algorithm by leveraging value and policy iteration methods as new information is collected. The proposed algorithms yield provable guarantees on correctness, completeness and asymptotic optimality. We also develop an adaptive sampling strategy by considering exploration as a classification (or regression) problem, and use online machine learning algorithms to learn the relevant region of a query, i.e., the region that contains the optimal solution, without significant computational overhead. We then extend the application of sampling-based algorithms to a class of stochastic optimal control problems and problems with differential constraints. Specifically, we introduce the Path Integral - RRT algorithm, for solving optimal control of stochastic systems and the CL-RRT# algorithm that uses closed-loop prediction for trajectory generation for differential systems. One of the key benefits of CL-RRT# is that for many systems, given a low-level tracking controller, it is easier to handle differential constraints, so complex steering procedures are not needed, unlike most existing kinodynamic sampling-based algorithms. Implementation results of sampling-based planners for route planning of a full-scale autonomous helicopter under the Autonomous Aerial Cargo/Utility System Program (AACUS) program are provided.
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Stang, Patricia A. "A critical look at the validity of studies that focus on humans versus machines teaching grammer." Online version, 2002. http://www.uwstout.edu/lib/thesis/2002/2002stangp.pdf.
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Xia, Chunming. "Control loop measurement based isolation of faults and disturbances in process plants." Thesis, University of Glasgow, 2003. http://theses.gla.ac.uk/4080/.
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This thesis focuses on the development of data-driven automated techniques to enhance performance assessment methods. These techniques include process control loop status monitoring, fault localisation in a number of interacting control loops and the detection and isolation of multiple oscillations in a multi-loop situation. Not only do they make use of controlled variables, but they also make use of controller outputs, indicator readings, set-points and controller settings. The idea behind loop status is that knowledge of the current behaviour of a loop is important when assessing MVC-based performance, because of the assumptions that are made in the assessment. Current behaviour is defined in terms of the kind of deterministic trend that is present in the loop at the time of assessment. When the status is other than steady, MVC-based approaches are inappropriate. Either the assessment must be delayed until steady conditions are attained or other methods must be applied. When the status is other than steady, knowledge of current behaviour can help identify the possible cause. One way of doing this is to derive another statistic, the overall loop performance index (OLPI), from loop status. The thesis describes a novel fault localisation technique, which analyses this statistic to find the source of a plant-wide disturbance, when a number of interacting control loops are perturbed by a single dominant disturbance/fault. Although the technique can isolate a single dominant oscillation, it is not able to isolate the sources of multiple, dominant oscillations. To do this, a novel technique is proposed that is based on the application of spectral independent component analysis (ICA). Spectral independent component analysis (spectral ICA) is based on the analysis of spectra derived via a discrete Fourier transform from time domain process data. The analysis is able to extract dominant spectrum-like independent components each of which has a narrow-bank peak that captures the behaviour of one of the oscillation sources. It is shown that the extraction of independent components with single spectral peaks can be guaranteed by an ICA algorithm that maximises the kurtosis of the independent components (ICs). This is a significant advantage over spectral principle component analysis (PCA), because multiple spectral peaks could be present in the extracted principle components (PCs), and the interpretation of detection and isolation of oscillation disturbances based on spectral PCs is not straightforward. The novel spectral ICA method is applied to a simulated data set and to real plant data obtained from an industrial chemical plant. Results demonstrate its ability to detect and isolate multiple dominant oscillations in different frequency ranges.
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Gilmore, Eugene M. "Learning Interpretable Decision Tree Classifiers with Human in the Loop Learning and Parallel Coordinates." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/418633.
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The Machine Learning (ML) community has recently started to recognise the importance of model interpretability when using ML techniques. In this work, I review the literature on Explainable Artificial Intelligence (XAI) and interpretability in ML and discuss several reasons why interpretability is critical for many ML applications. Although there is now increased interest in XAI, there are significant issues with the approaches taken in a large portion of the research in XAI. In particular, the popularity of techniques that try to explain black-box models often leads to misleading explanations that are not faithful to the model being explained.
The popularity of black-box models is, in large part, due to the immense size and complexity of many datasets available today. The high dimensionality of many datasets has encouraged research in ML and particular techniques such as Artificial Neural Networks (ANNs). However, I argue in this work that the high dimensionality of a dataset should not, in itself, be a reason to settle for black-box models that humans cannot understand. Instead, I argue for the need to learn inherently interpretable models, rather than black-box models with post-hoc explanations of their results.
One of the most well-known ML models for supervised learning tasks that remains interpretable to humans is the Decision Tree Classifier (DTC). The DTC's interpretability is due to its simple tree structure where a human can individually inspect the splits at each node in the tree. Although a DTC's fundamental structure is interpretable to humans, even a DTC can effective become a black-box model. This may be due to the size of a DTC being too large for a human to comprehend. Alternatively, a DTC may use uninterpretable oblique splits at each node. These oblique splits most often use a hyperplane through the entire attributes space of a dataset to construct a split which is impossible for a human to interpret past three dimensions.
In this work, I propose techniques for learning and visualising DTCs and datasets to produce interpretable classifiers that do not sacrifice predictive power. Moreover, I combine such visualisation with an interactive DTC building strategy and enable productive and effective Human-In-the-Loop-Learning (HILL). Not only do classifiers learnt with human involvement have the natural requirement of being humanly interpretable, but there are also several additional advantages to be gained by involving human expertise. These advantages include the ability for a domain expert to contribute their domain knowledge to a model. We can also exploit the highly sophisticated visual pattern recognition capabilities of the human to learn models that more effectively generalise to unseen data. Despite limitations of current HILL systems, a user study conducted as part of this work provides promising results for the involving the human in the construction of DTCs.
However, to effective employ this learning style, we need powerful visualisation techniques for both high dimensional datasets and DTCs. Remarkably, despite being ideally suited for high dimensional datasets, the use of Parallel Coordinates (||-coords) by the ML community is minimal. First proposed by Alfred Inselberg, ||-coords is a revolutionary visualisation technique that uses parallel axis to display a dataset of an arbitrary number of dimensions.
Using ||-coords, I propose a HILL system for the construction of DTCs. This work also exploits the ||-coords visualisation system to facilitate human input to the splits of internal nodes in a DTC. In addition, I propose a new form of oblique split for DTCs that uses the properties of the ||-coords plane. Unlike other oblique rules, this oblique rule can be easily visualised using ||-coords.
While there has recently been renewed interest in XAI and HILL, the research that evaluates systems that facilitate XAI and HILL is limited. I report on an online survey that gathers data from 104 participants. This survey examines participants' use of visualisation systems which I argue are ideally suited for HILL and XAI. The results support my hypothesis and the proposals for HILL.
I further argue that for a HILL system to succeed, comprehensive algorithm support is critical. As such, I propose two new DTC induction algorithms. These algorithms are designed to be used in conjunction with the HILL system developed in this work to provide algorithmic assistance in the form of suggestions of splits for a DTC node. The first proposed induction algorithm uses the newly proposed form of oblique split with ||-coords to learn interpretable splits that can capture correlations between attributes. The second induction algorithm advances the nested cavities algorithm originally proposed by Inselberg for classification tasks using ||-coords. Using these induction algorithms enables learning of DTCs with oblique splits that remain interpretable to a human without sacrificing predictive performance.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Info & Comm Tech<br>Science, Environment, Engineering and Technology<br>Full Text
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Botha, Hermanus Van Niekerk. "A Closed Loop Research Platform That Enables Dynamic Control Of Wing Gait Patterns In A Vertically Constrained Flapping Wing - Micro Air Vehicle." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1462801627.
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Zhang, Yunfan. "Optimization and control of a dual-loop EGR system in a modern diesel engine." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8598/.
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Focusing on the author's research aspects, the intelligent optimization algorithm and advanced control methods of the diesel engine's air path have been proposed in this work. In addition, the simulation platform and the HIL test platform are established for research activities on engine optimization and control. In this thesis, it presents an intelligent transient calibration method using the chaos-enhanced accelerated particle swarm optimization (CAPSO) algorithm. It is a model-based optimization approach. The test results show that the proposed method could locate the global optimal results of the controller parameters within good speed under various working conditions. The engine dynamic response is improved and a measurable drop of engine fuel consumption is acquired. The model predictive control (MPC) is selected for the controllers of DLEGR and VGT in the air-path of a diesel engine. Two MPC-based controllers are developed in this work, they are categorized into linear MPC and nonlinear MPC. Compared with conventional PIO controller, the MPC-based controllers show better reference trajectory tracking performance. Besides, an improvement of the engine fuel economy is obtained. The HIL test indicates the two controllers could be implemented on the real engine.
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Khademolama, Ehsan. "Vision in the Loop for Force and Position Control of the Robot Manipulators." Doctoral thesis, Università degli studi di Bergamo, 2018. http://hdl.handle.net/10446/104935.
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Over the last decades, both force sensors and cameras have developed as useful sensors for different applications in robotics. This thesis considers a number of dynamic visual tracking and control problems, as well as the integration of these techniques with contact force control. Different topics
ranging from basic theory to system implementation and applications are treated. It addresses the use of monocular eye-in-hand machine vision to control the position of a robot manipulator for dynamically challenging tasks. Such tasks are defined as those where the robot motion required approaches or exceeds the performance limits stated by the manufacturer.
Computer vision systems have been used for robot control for over four decades now, but have rarely been used for high-performance visual closed-loop control. This has largely been due to technological limitations in image processing, but since the mid 2010s advances have made it feasible to apply computer vision techniques at a sufficiently high rate to guide a robot or close a feedback control loop. Visual servoing is the use of computer vision for closed-loop control of a robot manipulator, and has the potential to solve a number of problems that currently limit the potential of robots in industry and advanced applications. In this thesis we have developed an algorithm that can extract high accurate position of object from vision data. This can be used as proximity sensor, in harsh environments.
In order to achieve high-performance it is necessary to have accurate models of the system to be controlled (the robot) and the sensor (the camera and vision system). Despite the long history of research in these areas individually, and combined in visual servoing, it is apparent that many issues have not been addressed in sufficient depth, and that much of the relevant information is spread through a very diverse literature. A new filter based on the wavelet multi resolution structures has been developed that can fuse position from camera and acceleration data from MEMS and produce velocity estimations which have lowest delay and drift with highest resolution at output.
Also in the empirical and theoretical way, we have studied over robotic actuators specially brushless DC motors. Outputs of these studies are one designed and implemented advanced brushless driver, which can control the brushless motors of medium power around $300[W]$ in position and velocity mode.
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Chipalkatty, Rahul. "Human-in-the-loop control for cooperative human-robot tasks." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43649.
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Even with the advance of autonomous robotics and automation, many automated tasks still require human intervention or guidance to mediate uncertainties in the environment or to execute the complexities of a task that autonomous robots are not yet equipped to handle. As such, robot controllers are needed that utilize the strengths of both autonomous agents, adept at handling lower level control tasks, and humans, superior at handling higher-level cognitive tasks.
To address this need, we develop a control theoretic framework that seeks to incorporate user commands such that user intention is preserved while an automated task is carried out by the controller. This is a novel approach in that system theoretic tools allow for analytic guarantees of feasibility and convergence to goal states which naturally lead to varying levels of autonomy. We develop a model predictive controller that takes human input, infers human intent, then applies a control that minimizes deviations from the intended human control while ensuring that the lower-level automated task is being completed.
This control framework is then evaluated in a human operator study involving a shared control task with human guidance of a mobile robot for navigation. These theoretical and experimental results lay the foundation for applying this control method for human-robot cooperative control to actual human-robot tasks. Specifically, the control is applied to a Urban Search and Rescue robot task where the shared control of a quadruped rescue robot is needed to ensure static stability during human-guided leg placements in uneven terrain. This control framework is also extended to a multiple user and multiple agent system where the human operators control multiple agents such that the agents maintain a formation while allowing the human operators to manipulate the shape of the formation. User studies are also conducted to evaluate the control in multiple operator scenarios.
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Kuwornu, Delali Korku. "Virtual commissioning of automatic machines: performance evaluation and robotic integration." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
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This thesis pertains to the virtual commissioning process executed for an Automatic machine, the E-contiuous for TELEROBOT to test the Controller Software of the said machine’s Motion controller. It also looks at how this virtual commissioning process and results affected the real machine, while focusing on the benefits of the particular platform used, and its ability to capture all necessary behavior of the real machine model into the virtual one. The process was divided into three main stages and then the model was passed through each stage to obtain the final model which was tested and results posted. Also integration of industrial manipulators into virtual environment mainly for experimental analysis and virtual commissioning was looked at to obtain data on the feasibility of these robots for specific functions. Finally, there was the discussion of the future possibilities of virtual commissioning and what could yet be achieved.
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Brusamento, Donato. "Improving pattern recognition based myocontrol of prosthetic hands via user-in-the-loop." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
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Il controllo di mani protesiche basato su elettromiografia (EMG) ha le potenzialità di ristabilire funzioni motorie ai pazienti che hanno subito un’amputazione, migliorando sensibilimente la qualità della vita. Tuttavia rimangono problemi aperti nell’ottenere un controllo ricco di movimenti e stabile, fra cui la presenza del limb position effect.
La tesi si concentra nel cercare di ridurre questa causa di instabilità, proponendo una versione modificata dell’algoritmo Ridge Regression with Random Fourier Features, reso incrementale e arricchito di feedback all’utente. Questo approccio viene poi validato tramite un esperimento su 12 soggetti intatti, per verificare l’incremento di performance, e tramite un ulteriore studio pilota su un soggetto amputato, a seguito dell’adattamento del software ad una mano protesica in via di sviluppo.
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Hill, John Michael. "Simulation of a variable speed air conditioner as a multiple loop thermal system." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/16754.
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Sahaï, Aïsha. "Agentivité conjointe lors des interactions homme-machine : comment concevoir des agents plus coopératifs?" Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE025.
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À travers l’automatisation des systèmes, les mutations technologiques ont progressivement éloigné l’opérateur humain des actions et des effets générés par les machines. Ce phénomène de « sortie de boucle » (Kaber, Onal, & Endsley, 2000) a généré un ensemble de difficultés pour l’opérateur humain qui reste encore aujourd’hui difficile à appréhender et compenser. Au cours de cette thèse, nous avons proposé le cadre théorique de l’Agentivité pour tenter d’expliquer et de compenser ces difficultés. De manière intéressante, il a été montré qu’au cours des interactions humaines, les individus pouvaient ressentir un sens d’agentivité pour des actions et effets générés par autrui (ou « sens d’agentivité conjoint »). En revanche, la capacité à développer un sens d’agentivité conjoint et à se représenter les actions générées par une machine semble altérée lors des interactions homme-machine (Obhi & Hall, 2011b). La première partie de la thèse a consisté à examiner les mécanismes de cette perte d’agentivité lors de tâches conjointes avec des systèmes automatisés, aux niveaux comportemental (Expérience 1) et cérébral (Expérience 2). La deuxième partie de la thèse visait à déterminer sur quelles propriétés de la machine il était possible d’agir pour recréer un sens d’agentivité conjoint, en utilisant une approche top-down (Expérience 3) puis bottom-up (Expérience 4). Les implications de nos résultats sont discutées au regard de la littérature sur l’Agentivité et de la problématique opérationnelle du phénomène de « sortie de boucle »<br>System automation has steadily created a gap between the human operators and the loop of control (i.e., “out-of-the-loop” (OOTL) problem), disconnecting them from the machines’ actions and outcomes (Kaber, Onal, & Endsley, 2000). In this thesis, we aimed at investigating how to keep the human operators in the loop of control. We based our investigations on the theoretical framework of the science of Agency. Interestingly, it has been shown that during human-human interactions, individuals could exhibit a sense of agency for other-generated actions and outcomes (or sense of “we-agency”) while such ability was impaired for machine-generated actions and outcomes (Obhi & Hall, 2011b). The first stage of the thesis sought to finely examine the cognitive processes underlying individuals’ loss of agency during joint tasks with automated artificial systems, both at the behavioral (Experiment 1) and at the cerebral (Experiment 2) levels. The second stage of the thesis sought to investigate on which characteristics of the machine it was possible to act in order to regain the human operator’s sense of agency using a top-down approach (Experiment 3) and a bottom-up approach (Experiment 4). Implications of our findings are discussed in regard with the literature on the sense of agency and the operational OOTL issue
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Cappon, Giacomo. "Open-loop insulin dosing personalization in type 1 diabetes using continuous glucose monitoring data and patient characteristics." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3425798.
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Patients with type 1 diabetes (T1D) require lifelong insulin therapy in order to maintain their blood glucose (BG) concentration within the euglycemic range preventing long-term complications associated with hyperglycemia and avoid- ing dangerous episodes of hypoglycemia. To achieve proper glycemic con- trol, people with T1D need to perform a constant learning process about how daily conditions (e.g. insulin administrations, meals schedule and composi- tion, physical activity, and illness) affect BG levels. More than 500,000 op- erations can be needed during the lifetime of a T1D patient to manage the therapy. For this reason, management of diabetes is burdensome for patients, and results in deteriorating their quality of life. One of the major issues in the daily management of T1D concerns with the amount of insulin that has to be administered, by a subcutaneous bolus injection, in order to compensate the increase of BG associated with meals. So far, a standard simple mathematical formula (SF), designed by clinical investigators on an empirical basis, is com- monly used by patients to calculate the size of insulin boluses. SF leverages on the current BG level obtained from self monitoring blood glucose (SMBG) samples, the estimated amount of carbohydrates (CHOs) present in the meal, and patient specific therapy parameters. While the SF is well-established in clinical practice, the insulin amount determined through its use could be sub- optimal due to several reasons, including the error patients make in estimating CHO, the intrinsical sparseness of SMBG, and the inability of accounting for many important factors such as patients’ intra-/interday variability.
Margins of improvement over the SMBG-based SF emerged in the past decade, when diabetes management has been transformed by the introduction of min- imally invasive continuous glucose monitoring (CGM) sensors, which have been recently approved by regulatory agencies, such as the Food and Drug Ad- ministration (FDA), to be usable to make treatment decisions, such as insulin dosing. Of course, CGM provides an increased amount of available features on BG, such as the rate of change (ROC), that could be exploited to improve insulin standard therapy. As a matter of fact, several attempts have been pro- posed in the literature to account for CGM-derived information and adjust SF accordingly, but unfortunately, they fall short in personalizing such an adjust- ment patient-by-patient. In this thesis we propose new methodologies for determining a dose of in- sulin bolus able to effectively account for the "dynamic" information on BG provided by the ROC and patient characteristics, the final aim being to per- sonalize the standard insulin therapy and eventually improve the glycemic control. In particular, to identify the possible margins of improvement, in the first part of the thesis we assess and analyze the criticalities of three popular literature techniques that exploit the ROC magnitude and direction to adjust the insulin bolus amount computed through SF. To such a scope, we designed ad-hoc in silico clinical trials implemented using a popular powerful simula- tion tool, i.e. the UVa/Padova T1D Simulator. Then, in the second part, we propose two novel machine learning based algorithms that, being fed by in- formation on current patient status and characteristics, provide patients with new tools to adjust SF in a personalized manner. Finally, in the third part of the thesis, we abandon the idea of using the insulin bolus provided by SF as a sort of initial estimate to be simply adjusted, and we design a brand new formula for insulin bolus determination that naturally takes into account for CGM-derived information and current patient status and characteristics. This represents an innovation in the literature because no insulin bolus formulae specifically designed for use with CGM have been proposed yet.
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Mantovani, Alessandro. "An Analysis of Human-in-the-loop Approaches for Reverse Engineering Automation." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS052.pdf.
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En matière de sécurité des systèmes et des logiciels, l'un des premiers critères avant d'appliquer une méthodologie d'analyse est de distinguer selon la disponibilité ou non du code source. Lorsque le logiciel que nous voulons investiguer est présent sous forme binaire, la seule possibilité que nous avons est d'en extraire des informations en observant son code machine, en effectuant ce qui est communément appelé Binary Analysis (BA). Les artisans de ce secteur sont chargés de mêler leur expérience personnelle à un arsenal d'outils et de méthodologies pour comprendre certains aspects intrinsèques et cachés du binaire cible, par exemple pour découvrir de nouvelles vulnérabilités ou détecter des comportements malveillants. Bien que cette configuration humaine dans la boucle soit bien consolidée au fil des ans, l'explosion actuelle des menaces et des vecteurs d'attaque tels que les logiciels malveillants, les exploits armés, etc. met implicitement à l'épreuve ce modèle de BA, exigeant en même temps une grande précision de l'analyse ainsi qu'une évolutivité appropriée des binaires pour contrer les acteurs adverses. C'est pourquoi, malgré les nombreux progrès réalisés dans le domaine de la BA au cours des dernières années, nous sommes toujours obligés de chercher de nouvelles solutions. Dans cette thèse, nous faisons un pas de plus sur ce problème et nous essayons de montrer ce qui manque aux paradigmes actuels pour augmenter le niveau d'automatisation. Pour ce faire, nous avons isolé trois cas d'utilisation classiques de l'analyse binaire et nous avons démontré comment l'analyse en pipeline bénéficie de l'intervention humaine. En d'autres termes, nous avons considéré trois systèmes "human-in-the-loop" et nous avons décrit le rôle de l'homme dans le pipeline en nous concentrant sur les types de feedback que l'analyste "échange" avec sa chaîne d'outils. Ces trois exemples nous ont fourni une vue complète de l'écart entre les solutions actuelles d'analyse binaire et les solutions idéalement plus automatisées, suggérant que la principale caractéristique à la base du retour d'information humain correspond à la compétence humaine à comprendre des portions de code binaire. Cette tentative de systématisation du rôle de l'homme dans les approches modernes de l'analyse binaire tente d'élever la barre vers des systèmes plus automatisés en tirant parti de la composante humaine qui, jusqu'à présent, est toujours inévitable dans la majorité des scénarios d'analyse binaire. Bien que notre analyse montre que les machines ne peuvent pas remplacer les humains au stade actuel, nous ne pouvons pas exclure que les approches futures seront en mesure de combler cette lacune et de faire évoluer les outils et les méthodologies vers un niveau supérieur. Par conséquent, nous espérons avec ce travail, inspirer les recherches futures dans le domaine pour atteindre des techniques d'analyse binaire toujours plus sophistiquées et automatisées<br>In system and software security, one of the first criteria before applying an analysis methodology is to distinguish according to the availability or not of the source code. When the software we want to investigate is present in binary form, the only possibility that we have is to extract some information from it by observing its machine code, performing what is commonly referred to as Binary Analysis (BA). The artisans in this sector are in charge of mixing their personal experience with an arsenal of tools and methodologies to comprehend some intrinsic and hidden aspects of the target binary, for instance, to discover new vulnerabilities or to detect malicious behaviors. Although this human-in-the-loop configuration is well consolidated over the years, the current explosion of threats and attack vectors such as malware, weaponized exploits, etc. implicitly stresses this binary analysis model, demanding at the same time for high accuracy of the analysis as well as proper scalability over the binaries to counteract the adversarial actors. Therefore, despite the many advances in the BA field over the past years, we are still obliged to seek novel solutions. In this thesis, we take a step more on this problem, and we try to show what current paradigms lack to increase the automation level. To accomplish this, we isolated three classical binary analysis use cases, and we demonstrated how the pipeline analysis benefits from the human intervention. In other words, we considered three human-in-the-loop systems, and we described the human role inside the pipeline with a focus on the types of feedback that the analyst ``exchanges'' with her toolchain. These three examples provided a full view of the gap between current binary analysis solutions and ideally more automated ones, suggesting that the main feature at the base of the human feedback corresponds to the human skill at comprehending portions of binary code. This attempt to systematize the human role in modern binary analysis approaches tries to raise the bar towards more automated systems by leveraging the human component that, so far, is still unavoidable in the majority of the scenarios. Although our analysis shows that machines cannot replace humans at the current stage, we cannot exclude that future approaches will be able to fill this gap as well as evolve tools and methodologies to the next level. Therefore, we hope with this work to inspire future research in the field to reach always more sophisticated and automated binary analysis techniques
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MOCERA, FRANCESCO. "Study of hybrid electric architectures for industrial vehicle applications using Hardware In the Loop techniques." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2734720.
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Buttar, Sarpreet Singh. "Applying Machine Learning to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-77201.
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Self-adaptive systems are capable of autonomously adjusting their behavior at runtime to accomplish particular adaptation goals. The most common way to realize self-adaption is using a feedback loop(s) which contains four actions: collect runtime data from the system and its environment, analyze the collected data, decide if an adaptation plan is required, and act according to the adaptation plan for achieving the adaptation goals. Existing approaches achieve the adaptation goals by using formal methods, and exhaustively verify all the available adaptation options, i.e., adaptation space. However, verifying the entire adaptation space is often not feasible since it requires time and resources. In this thesis, we present an approach which uses machine learning to reduce the adaptation space in self-adaptive systems. The approach integrates with the feedback loop and selects a subset of the adaptation options that are valid in the current situation. The approach is applied on the simulator of a self-adaptive Internet of Things application which is deployed in KU Leuven, Belgium. We compare our results with a formal model based self-adaptation approach called ActivFORMS. The results show that on average the adaptation space is reduced by 81.2% and the adaptation time by 85% compared to ActivFORMS while achieving the same quality guarantees.