To see the other types of publications on this topic, follow the link: Artificial Neural Networking (ANN).
Dissertations / Theses on the topic 'Artificial Neural Networking (ANN)'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Artificial Neural Networking (ANN).'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Perchiazzi, Gaetano. "Artificial Neural Networks (ANN) in the Assessment of Respiratory Mechanics." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4665.
Full text
2
Li, Tan. "Tire-Pavement Interaction Noise (TPIN) Modeling Using Artificial Neural Network (ANN)." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/87417.
Full textAbstract:
Tire-pavement interaction is a dominant noise source for passenger cars and trucks above 25 mph (40 km/h) and 43 mph (70 km/h), respectively. For the same pavement, tires with different tread pattern and construction generate noise of different levels and frequencies. In the present study, forty-two different tires were tested over a range of speeds (45-65 mph, i.e., 72-105 km/h) on a non-porous asphalt pavement (a section of U.S. Route 460, both eastbound and westbound). An On-Board Sound Intensity (OBSI) system was instrumented on the test vehicle to collect the tire noise data at both the leading and trailing edge of the tire contact patch. An optical sensor recording the once-per-revolution signal of the wheel was also installed to monitor the vehicle speed and, more importantly, to provide the data needed to perform the order tracking analysis in order to break down the tire noise into two components. These two components are: the tread pattern and the non-tread pattern noise. Based on the experimental noise data collected, two artificial neural networks (ANN) were developed to predict the tread pattern (ANN1) and the non-tread pattern noise (ANN2) components, separately. The inputs of ANN1 are the coherent tread profile spectrum and the air volume velocity spectrum calculated from the digitized 3D tread pattern. The inputs of ANN2 are the tire size and tread rubber hardness. The vehicle speed is also included as input for the two ANN's. The optimized ANN's are able to predict the tire-pavement interaction noise well for different tires on the pavement tested. Another outcome of this work is the complete literature review on Tire-Pavement Interaction Noise (TPIN), as an appendix of this dissertation and covering ~1000 references, which might be the most comprehensive compilation of this topic.<br>PHD
3
Sun, Chang. "Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85020.
Full textAbstract:
Artificial Neural Networks (ANNs) have been established as one of the most important algorithmic tools in the Machine Learning (ML) toolbox over the past few decades. ANNs' recent rise to widespread acceptance can be attributed to two developments: (1) the availability of large-scale training and testing datasets; and (2) the availability of new computer architectures for which ANN implementations are orders of magnitude more efficient. In this thesis, I present research on two aspects of the second development. First, I present a portable, open source implementation of ANNs in OpenCL and MPI. Second, I present performance and scaling models for ANN algorithms on state-of-the-art Graphics Processing Unit (GPU) based parallel compute clusters.<br>Master of Science
4
Lundin, Johan. "Prediction of Protein Mutations Using Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-400.
Full textAbstract:
<p>This thesis is concerned with the prediction of protein mutations using artificial neural networks. From the biological perspective it is of interest to investigate weather it is possible to find rules of mutation between evolutionary adjacent (or closely related) proteins. Techniques from computer science are used in order to see if it is possible to predict protein mutations i.e. using artificial neural networks. The computer science perspective of this work would be to try optimizing the results from the neural networks. However, the focus of this thesis is primarily on the biological perspective and the performance of the computer science methods are secondary objective i.e. the primary interest is to show the existence of rules for protein mutations.</p><p>The method used in this thesis consists two neural networks. One network is used to predict the actual protein mutations and the other network is used to make a compressed representation of each amino acid. By using a compression network it is possible to make the prediction network much smaller (each amino acid is represented by 3 nodes instead of 22 nodes). The compression network is an auto associative network and the prediction network is a standard feed-forward network. The prediction network predicts a block of amino acids at a time and for comparison a sliding window technique has also been tested.</p><p>It is my belief that the results in this thesis indicate that there exists rules for protein mutations. However, the tests done in this thesis is only performed on a small portion of all proteins. Some protein families tested show really good results while other families are not as good. I believe that extended work using optimized neural networks would improve the predictions further.</p>
5
Denecour, Micah D. "Artificial Neural Networking as a Decision Tool for Natural Gas Investment." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/487.
Full textAbstract:
With the growing interest in the Marcellus Shale and its natural gas deposits, there are opportunities to purchase and hold land for investment purposes. A robust decision tool is needed to help guide investors towards the most profitable properties. Artificial neural networks have many unique benefits that make them an ideal candidate for this purpose.
The artificial neural networks created in this study had nine independent variables. Combinations of these nine variables were created to describe 300 theoretical properties available for purchase. Each of these properties were then evaluated by an expert in the field and given a score from one to five to rate its investment potential, which was the dependent variable.
Sixteen different network architectures were used to create over 200 neural networks. However, none of these networks met the criteria established to determine success. This is likely due to the unreliability in the data used to train the network, evidenced by the expert’s inability to reproduce previously assigned scores.
6
Rodríguez, Villegas Antoni. "Polyp segmentation using artificial neural networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-98001.
Full textAbstract:
Colorectal cancer is the second cause of cancer death in the world. Aiming to early detect and prevent this type of cancer, clinicians perform screenings through the colon searching for polyps (colorectal cancer precursor lesions).If found, these lesions are susceptible of being removed in order to further ana-lyze their malignancy degree. Automatic polyp segmentation is of primary impor-tance when it comes to computer-aided medical diagnosis using images obtained in colonoscopy screenings. These results allow for more precise medical diagnosis which can lead to earlier detection.This project proposed a neural network based solution for semantic segmenta-tion, using the U-net architecture.Combining different data augmentation techniques to alleviate the problem of data scarcity and conducting experiments on the different hyperparameters of the network, the U-net scored a mean Intersection over Union (IoU) of 0,6814. A final approach that combines prediction maps of different models scored a mean IoU of 0,7236.
7
Ghosh, Ranadhir, and n/a. "A Novel Hybrid Learning Algorithm For Artificial Neural Networks." Griffith University. School of Information Technology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030808.162355.
Full textAbstract:
Last few decades have witnessed the use of artificial neural networks (ANN) in many real-world applications and have offered an attractive paradigm for a broad range of adaptive complex systems. In recent years ANN have enjoyed a great deal of success and have proven useful in wide variety pattern recognition or feature extraction tasks. Examples include optical character recognition, speech recognition and adaptive control to name a few. To keep the pace with its huge demand in diversified application areas, many different kinds of ANN architecture and learning types have been proposed by the researchers to meet varying needs. A novel hybrid learning approach for the training of a feed-forward ANN has been proposed in this thesis. The approach combines evolutionary algorithms with matrix solution methods such as singular value decomposition, Gram-Schmidt etc., to achieve optimum weights for hidden and output layers. The proposed hybrid method is to apply evolutionary algorithm in the first layer and least square method (LS) in the second layer of the ANN. The methodology also finds optimum number of hidden neurons using a hierarchical combination methodology structure for weights and architecture. A learning algorithm has many facets that can make a learning algorithm good for a particular application area. Often there are trade offs between classification accuracy and time complexity, nevertheless, the problem of memory complexity remains. This research explores all the different facets of the proposed new algorithm in terms of classification accuracy, convergence property, generalization ability, time and memory complexity.
8
Bonagura, Mario <1982>. "Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amsdottorato.unibo.it/4880/.
Full textAbstract:
The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT.
The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations.
Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.
9
Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics." University of Sydney, 2003. http://hdl.handle.net/2123/488.
Full textAbstract:
Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.
10
Xu, Siyao. "THE RECONSTRUCTION OF CLOUD-FREE REMOTE SENSING IMAGES: AN ARTIFICIAL NEURAL NETWORKS (ANN) APPROACH." [Kent, Ohio] : Kent State University, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=kent1248112891.
Full textAbstract:
Thesis (M.A.)--Kent State University, 2009.<br>Title from PDF t.p. (viewed Mar. 11, 2010). Advisor: Mandy Munro-Stasiuk. Keywords: Remote Sensing Image; Cloud-free; Artificial Neural Networks. Includes bibliographical references (p. 57-59).
11
Mbandi, Aderiana Mutheu. "Using linear regression and ANN techniques in determining variable importance." Thesis, Cape Peninsula University of Technology, 2009. http://hdl.handle.net/20.500.11838/879.
Full textAbstract:
Thesis (MTech (Chemical Engineering))--Cape Peninsula University of Technology, 2009.
Includes bibliographical references (leaves 95-100).<br>The use of Neural Networks in chemical engineering is well documented. There has
also been an increase in research concerned with the explanatory capacity of Neural
Networks although this has been hindered by the regard of Artificial Neural Networks
(ANN’s) as a black box technology.
Determining variable importance in complex systems that have many variables as
found in the fields of ecology, water treatment, petrochemical production, and
metallurgy, would reduce the variables to be used in optimisation exercises, easing
complexity of the model and ultimately saving money. In the process engineering
field, the use of data to optimise processes is limited if some degree of process
understanding is not present.
The project objective is to develop a methodology that uses Artificial Neural Network
(ANN) technology and Multiple Linear Regression (MLR) to identify explanatory
variables in a dataset and their importance on process outputs. The methodology is
tested by using data that exhibits defined and well known numeric relationships. The
numeric relationships are presented using four equations.
The research project assesses the relative importance of the independent variables
by using the “dropping method” on a regression model and ANN’s. Regression used
traditionally to determine variable contribution could be unsuccessful if a highly nonlinear
relationship exists. ANN’s could be the answer for this shortcoming.
For differentiation, the explanatory variables that do not contribute significantly
towards the output will be named “suspect variables”. Ultimately the suspect
variables identified in the regression model and ANN should be the same, assuming
a good regression model and network. The dummy variables introduced to the four equations are successfully identified as
suspect variables. Furthermore, the degree of variable importance was determined
using linear regression and ANN models. As the equations complexity increased, the
linear regression models accuracy decreased, thus suspect variables are not
correctly identified. The complexity of the equations does not affect the accuracy of
the ANN model, and the suspect variables are correctly identified.
The use of R2 and average error in establishing a criterion for identifying suspect
variables is explored. It is established that the cumulative variable importance
percentage (additive percentage), has to be below 5% for the explanatory variable to
be considered a suspect variable. Combining linear regression and ANN provides insight into the importance of explanatory variables and indeed suspect variables and their contribution can be determined. Suspect variables can be eliminated from the model once identified
simplifying the model, and increasing accuracy of the model.
12
Zhu, Xuesong. "Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets." FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/77.
Full textAbstract:
Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.
13
Norman, Gustaf. "Sensor Validation Using Linear Parametric Models, Artificial Neural Networks and CUSUM." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119004.
Full textAbstract:
Siemens gas turbines are monitored and controlled by a large number of sensors and actuators. Process information is stored in a database and used for offline calculations and analyses. Before storing the sensor readings, a compression algorithm checks the signal and skips the values that explain no significant change. Compression of 90 % is not unusual. Since data from the database is used for analyses and decisions are made upon results from these analyses it is important to have a system for validating the data in the database. Decisions made on false information can result in large economic losses. When this project was initiated no sensor validation system was available. In this thesis the uncertainties in measurement chains are revealed. Methods for fault detection are investigated and finally the most promising methods are put to the test. Linear relationships between redundant sensors are derived and the residuals form an influence structure allowing the faulty sensor to be isolated. Where redundant sensors are not available, a gas turbine model is utilized to state the input-output relationships so that estimates of the sensor outputs can be formed. Linear parametric models and an ANN (Artificial Neural Network) are developed to produce the estimates. Two techniques for the linear parametric models are evaluated; prediction and simulation. The residuals are also evaluated in two ways; direct evaluation against a threshold and evaluation with the CUSUM (CUmulative SUM) algorithm. The results show that sensor validation using compressed data is feasible. Faults as small as 1% of the measuring range can be detected in many cases.
14
Hussain, Tayyab. "Checking the integrity of Global Positioning Recommended Minimum (GPRMC) sentences using Artificial Neural Network (ANN)." Thesis, University of Gävle, Ämnesavdelningen för samhällsbyggnad, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-5205.
Full textAbstract:
<p>In this study, Artificial Neural Network (ANN) is used to check the integrity of the Global Positioning Recommended Minimum (GPRMC) sentences. The GPRMC sentences are the most common sentences transmitted by the Global Positioning System (GPS) devices. This sentence contains nearly every thing a GPS application needs. The data integrity is compared on the basis of the classification accuracy and the minimum error obtained using the ANN. The ANN requires data to be presented in a certain format supported by the learning process of the network. Therefore a certain amount of data processing is needed before training patterns are presented to the network. The data pre processing is done by the design and development of different algorithms in C# using Visual Studio.Net 2003. This study uses the BackPropagation (BP) feed forward multilayer ANN algorithm with the learning rate and the momentum as its parameters. The results are analyzed based on different ANN architectures, classification accuracy, Sum of Square Error (SSE), variables sensitivity analysis and training graph. The best obtained ANN architecture shows a good performance with the selection classification of 96.79 % and the selection sum of square error 0.2022. This study uses the ANN tool Trajan 6.0 Demonstrator.</p>
15
Krishnamurthy, Raju Chemical Sciences & Engineering Faculty of Engineering UNSW. "Prediction of consumer liking from trained sensory panel information: evaluation of artificial neural networks (ANN)." Awarded by:University of New South Wales. Chemical Sciences & Engineering, 2007. http://handle.unsw.edu.au/1959.4/40746.
Full textAbstract:
This study set out to establish artificial neural networks (ANN) as an alternate to regression methods (multiple linear, principal components and partial least squares regression) to predict consumer liking from trained sensory panel data. The study has two parts viz., I) Flavour study - evaluation of ANNs to predict consumer flavour preferences from trained sensory panel data and 2) Fragrance study ??? evaluation of different ANN architectures to predict consumer fragrance liking from trained sensory panel data. In this study, a multi-layer feedforward neural network architecture with input, hidden and output layer(s) was designed. The back-propagation algorithm was utilised in training of neural networks. The network learning parameters such as learning rate and momentum rate were optimised by the grid experiments for a fixed number of learning cycles. In flavour study, ANNs were trained using the trained sensory panel raw data as well as transformed data. The networks trained with sensory panel raw data achieved 98% correct learning, whereas the testing was within the range of 28 -35%. A suitable transformation methods were applied to reduce the variations in trained sensory panel raw data. The networks trained with transformed sensory panel data achieved between 80-90% correct learning and 80-95% correct testing. In fragrance study, ANNs were trained using the trained sensory panel raw data as well as principal component data. The networks trained with sensory panel raw data achieved 100% correct learning, and testing was in a range of 70-94%. Principal component analysis was applied to reduce redundancy in the trained sensory panel data. The networks trained with principal component data achieved about 100% correct learning and 90% correct testing. It was shown that due to its excellent noise tolerance property and ability to predict more than one type of consumer liking using a single model, the ANN approach promises to be an effective modelling tool.
16
Joy, Karen. "Evaluating Input Variable Effects of an Artificial Neural Network Modeling Facial Attractiveness." VCU Scholars Compass, 2005. http://scholarscompass.vcu.edu/etd_retro/128.
Full textAbstract:
Artificial Neural Networks (ANNs) are powerful predictors, however, they essentially function like 'black boxes' because they lack explanatory power. Various algorithms have been developed to examine input influences and interactions thus enhancing understanding of the function being modeled. The study of facial attractiveness is one domain that could potentially benefit from ANN models. The literature shows that the relationship between attractiveness and facial attributes is complex and not yet fully understood. In this project, a feed-forward ANN was trained with backpropagation to 0.86 classification using 8-fold cross validation. The dataset consisted of 88 female facial images, each containing 17 geofacial measurements, a random noise variable, and a rating. Input 'clamping' and the Connection Weight Approach (Olden & Jackson, 2002), were implemented and the results were examined in terms of the facial attractiveness domain. In general, the results suggest that more feminized and asymmetrical features enhance facial attractiveness.
17
Lynch, Dustin Shane. "Asset Allocation Technique for a Diversified Investment Portfolio Using Artificial Neural Networks." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1432805760.
Full text
18
Prisilla, L., P. Simon Vasantha Rooban, and L. Arockiam. "A Novel Method for Water irrigation System for paddy fields using ANN." IJCSN, 2012. http://hdl.handle.net/10150/219532.
Full textAbstract:
In our country farmers have to face many difficulties
because of the poor irrigation system. During flood
situation, excessive waters will be staged in paddy field
producing great loss and pain to farmers. So, proper
Irrigation mechanism is an essential component of paddy
production. Poor irrigation methods and crop management
are rapidly depleting the country’s water table. Most small
hold farmers cannot afford new wells or lawns and they are
looking for alternative methods to reduce their water
consumption. So proper irrigation mechanism not only leads
to high crop production but also pave a way for water saving
techniques. Automation of irrigation system has the
potential to provide maximum water usage efficiency by
monitoring soil moistures. The control unit based on
Artificial Neural Network is the pivotal block of entire
irrigation system. Using this control unit certain factors like
temperature, kind of the soil and crops, air humidity,
radiation in the ground were estimated and this will help to
control the flow of water to acquire optimized results.<br>Water is an essential resource in the earth. It is also essential for
irrigation, so irrigation technique is essential for agriculture. To
irrigate large area of lands is a tedious process. In our country
farmers are not following proper irrigation techniques. Currently,
most of the irrigation scheduling systems and their corresponding
automated tools are in fixed rate. Variable rate irrigation is very
essential not only for the improvement of irrigation system but also
to save water resource for future purpose. Most of the irrigation
controllers are ON/OFF Model. These controllers cannot give
optimal results for varying time delays and system parameters.
Artificial Neural Network (ANN) based intelligent control system
is used for effective irrigation scheduling in paddy fields. The
input parameters like air, temperature, soil moisture, radiations and
humidity are modeled. Using appropriate method, ecological
conditions, Evapotranspiration, various growing stages of crops are
considered and based on that the amount of water required for
irrigation is estimated. Using this existing ANN based intelligent
control system, the water saving procedure in paddy field can be
achieved. This model will lead to avoid flood in paddy field during
the rainy seasons and save that water for future purposes.
19
Miranda, Trujillo Luis Carlos. "Artificial Neural Networks in Greenhouse Modelling." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19354.
Full textAbstract:
Moderne Präzisionsgartenbaulicheproduktion schließt hoch technifizierte Gewächshäuser, deren Einsatz in großem Maße von der Qualität der Sensorik- und Regelungstechnik abhängt, mit ein. Zu den Regelungsstrategien gehören unter anderem Methoden der Künstlichen Intelligenz, wie z.B. Künstliche Neuronale Netze (KNN, aus dem Englischen).
Die vorliegende Arbeit befasst sich mit der Eignung KNN-basierter Modelle als Bauelemente von Klimaregelungstrategien in Gewächshäusern. Es werden zwei Modelle vorgestellt: Ein Modell zur kurzzeitigen Voraussage des Gewächshausklimas (Lufttemperatur und relative Feuchtigkeit, in Minuten-Zeiträumen), und Modell zur Einschätzung von phytometrischen Signalen (Blatttemperatur, Transpirationsrate und Photosyntheserate). Eine Datenbank, die drei Kulturjahre umfasste (Kultur: Tomato), wurde zur Modellbildung bzw. -test benutzt.
Es wurde festgestellt, dass die ANN-basierte Modelle sehr stark auf die Auswahl der Metaparameter und Netzarchitektur reagieren, und dass sie auch mit derselben Architektur verschiedene Kalkulationsergebnisse liefern können. Nichtsdestotrotz, hat sich diese Art von Modellen als geeignet zur Einschätzung komplexer Pflanzensignalen sowie zur Mikroklimavoraussage erwiesen. Zwei zusätzliche Möglichkeiten zur Erstellung von komplexen Simulationen sind in der Arbeit enthalten, und zwar zur Klimavoraussage in längerer Perioden und zur Voraussage der Photosyntheserate. Die Arbeit kommt zum Ergebnis, dass die Verwendung von KNN-Modellen für neue Gewächshaussteuerungstrategien geeignet ist, da sie robust sind und mit der Systemskomplexität gut zurechtkommen. Allerdings muss beachtet werden, dass Probleme und Schwierigkeiten auftreten können. Diese Arbeit weist auf die Relevanz der Netzarchitektur, die erforderlichen großen Datenmengen zur Modellbildung und Probleme mit verschiedenen Zeitkonstanten im Gewächshaus hin.<br>One facet of the current developments in precision horticulture is the highly technified production under cover. The intensive production in modern greenhouses heavily relies on instrumentation and control techniques to automate many tasks. Among these techniques are control strategies, which can also include some methods developed within the field of Artificial Intelligence. This document presents research on Artificial Neural Networks (ANN), a technique derived from Artificial Intelligence, and aims to shed light on their applicability in greenhouse vegetable production. In particular, this work focuses on the suitability of ANN-based models for greenhouse environmental control. To this end, two models were built: A short-term climate prediction model (air temperature and relative humidity in time scale of minutes), and a model of the plant response to the climate, the latter regarding phytometric measurements of leaf temperature, transpiration rate and photosynthesis rate. A dataset comprising three years of tomato cultivation was used to build and test the models.
It was found that this kind of models is very sensitive to the fine-tuning of the metaparameters and that they can produce different results even with the same architecture. Nevertheless, it was shown that ANN are useful to simulate complex biological signals and to estimate future microclimate trends. Furthermore, two connection schemes are proposed to assemble several models in order to generate more complex simulations, like long-term prediction chains and photosynthesis forecasts. It was concluded that ANN could be used in greenhouse automation systems as part of the control strategy, as they are robust and can cope with the complexity of the system. However, a number of problems and difficulties are pointed out, including the importance of the architecture, the need for large datasets to build the models and problems arising from different time constants in the whole greenhouse system.
20
Tarullo, Viviana. "Artificial Neural Networks for classification of EMG data in hand myoelectric control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19195/.
Full textAbstract:
This thesis studies the state-of-the-art in myoelectric control of active hand prostheses for people with trans-radial amputation using pattern recognition and machine learning techniques. Our work is supported by Centro Protesi INAIL in Vigorso di Budrio (BO). We studied the control system developed by INAIL consisting in acquiring EMG signals from amputee subjects and using pattern recognition methods for the classifcation of acquired signals, associating them with specifc gestures and consequently commanding the prosthesis. Our work consisted in improving classifcation methods used in the learning phase. In particular, we proposed a classifer based on a neural network as a valid alternative to the INAIL one-versus-all approach to multiclass classifcation.
21
Chamanirad, Mohsen. "Design and implementation of controller for robotic manipulators using Artificial Neural Networks." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-6297.
Full textAbstract:
<p>In this thesis a novel method for controlling a manipulator with arbitrary number of Degrees of freedom is proposed, the proposed method has the main advantages of two common controllers, the simplicity of PID controller and the robustness and accuracy of adaptive controller. The controller architecture is based on an Artificial Neural Network (ANN) and a PID controller.</p><p>The controller has the ability of solving inverse dynamics and inverse kinematics of robot with two separate Artificial Neural Networks. Since the ANN is learning the system parameters by itself the structure of controller can easily be changed to</p><p>improve the performance of robot.</p><p>The proposed controller can be implemented on a FPGA board to control the robot in real-time or the response of the ANN can be calculated offline and be reconstructed by controller using a lookup table. Error between the desired trajectory path and the path of the robot converges to zero rapidly and as the robot performs its tasks the controller learns the robot parameters and generates better control signal. The performance of controller is tested in simulation and on a real manipulator with satisfactory results.</p>
22
Sharaf, Taysseer. "Statistical Learning with Artificial Neural Network Applied to Health and Environmental Data." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5866.
Full textAbstract:
The current study illustrates the utilization of artificial neural network in statistical methodology. More specifically in survival analysis and time series analysis, where both holds an important and wide use in many applications in our real life. We start our discussion by utilizing artificial neural network in survival analysis. In literature there exist two important methodology of utilizing artificial neural network in survival analysis based on discrete survival time method. We illustrate the idea of discrete survival time method and show how one can estimate the discrete model using artificial neural network. We present a comparison between the two methodology and update one of them to estimate survival time of competing risks.
To fit a model using artificial neural network, you need to take care of two parts; first one is the neural network architecture and second part is the learning algorithm. Usually neural networks are trained using a non-linear optimization algorithm such as quasi Newton Raphson algorithm. Other learning algorithms are base on Bayesian inference. In this study we present a new learning technique by using a mixture of the two available methodologies for using Bayesian inference in training of neural networks. We have performed our analysis using real world data. We have used patients diagnosed with skin cancer in the United states from SEER database, under the supervision of the National Cancer Institute.
The second part of this dissertation presents the utilization of artificial neural to time series analysis. We present a new method of training recurrent artificial neural network with Hybrid Monte Carlo Sampling and compare our findings with the popular auto-regressive integrated moving average (ARIMA) model. We used the carbon dioxide monthly average emission to apply our comparison, data collected from NOAA.
23
Edossa, D. C., and M. S. Babel. "Development of streamflow forecasting model using artificial neural network in the Awash River Basin, Ethiopia." Interim : Interdisciplinary Journal, Vol 10 , Issue 1: Central University of Technology Free State Bloemfontein, 2011. http://hdl.handle.net/11462/332.
Full textAbstract:
Published Article<br>Early indication of possible drought can help in developing suitable drought mitigation strategies and measures in advance. Therefore, drought forecasting plays an important role in the planning and management of water resource in such circumstances. In this study, a non-linear streamflow forecasting model was developed using Artificial Neural Network (ANN) modeling technique at the Melka Sedi stream gauging station, Ethiopia, with adequate lead times. The available data was divided into two independent sets using a split sampling tool of the neural network software. The first data set was used for training and the second data set, which is normally about one fourth of the total available data, was used for testing the model. A one year data was set aside for validating the ANN model. The streamflow predicted using the model on weekly time step compared favorably with the measured streamflow data (R2 = 75%) during the validation period. Application of the model in assessing appropriate agricultural water management strategies for a large-scale irrigation scheme in the Awash River Basin, Ethiopia, has already been considered for publication in a referred journal.
24
Aslan, Muhittin. "Modeling The Water Quality Of Lake Eymir Using Artificial Neural Networks (ann) And Adaptive Neuro Fuzzy Inference System (anfis)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610211/index.pdf.
Full textAbstract:
Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate, Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: to develop models with ANN to predict DO concentration in Lake Eymir with high fidelity to actual DO data, to compare the success (prediction capacity) of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. For modeling studies &ldquo<br>Matlab R 2007b&rdquo<br>software was used.
The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.
25
Shu, Jiangpeng, and Ziye Zhang. "Damage detection on railway bridges using Artificial Neural Network and train induced vibrations." Thesis, KTH, Bro- och stålbyggnad, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99387.
Full textAbstract:
A damage detection approach based on Artificial Neural Network (ANN), using the statistics of structural dynamic responses as the damage index, is proposed in this study for Structural Health Monitoring (SHM). Based on the sensitivity analysis, the feasibility of using the changes of variances and covariance of dynamic responses of railway bridges under moving trains as the indices for damage detection is evaluated. A FE Model of a one-span simply supported beam bridge is built, considering both single damage case and multi-damage case. A Back-Propagation Neural Network (BPNN) is designed and trained to simulate the detection process. A series of numerical tests on the FE model with different train properties prove the validity and efficiency of the proposed approach. The results show not only that the trained ANN together with the statistics can correctly estimate the location and severity of damage in the structure, but also that the identification of the damage location is more difficult than that of the damage severity. In summary, it is concluded that the use of statistical property of structural dynamic response as damage index with the Artificial Neural Network as detection tool for damage detection is reliable and effective.
26
Kim, Jun Ha. "Artificial neural network (ANN) based decision support model for alternative workplace arrangements (AWA) readiness assessment and type selection." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31830.
Full textAbstract:
Thesis (Ph.D)--Building Construction, Georgia Institute of Technology, 2010.<br>Committee Chair: Roper, Kathy; Committee Co-Chair: Kangari, Roozbeh; Committee Member: Ashuri, Baabak; Committee Member: Castro, Daniel; Committee Member: Rouse, William. Part of the SMARTech Electronic Thesis and Dissertation Collection.
27
Zainun, Noor Y. B. "Computerized model to forecast low-cost housing demand in urban area in Malaysia using Artificial Neural Networks (ANN)." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/9833.
Full textAbstract:
The forecasted proportions of urban population to total population in Malaysia are steadily increasing from 26% in 1965 to 70% in 2020. Therefore, there is a need to fully appreciate the legacy of the urbanization of Malaysia by providing affordable housing. The main aim of this study is to focus on developing a model to forecast the demand of low cost housing in urban areas. The study is focused on eight states in Peninsular Malaysia, as most of these states are among the areas predicted to have achieved the highest urbanization level in the country. The states are Kedah, Penang, Perlis, Kelantan, Terengganu, Perak, Pahang and Johor. Monthly time-series data for six to eight years of nine indicators including: population growth; birth rate; child mortality rate; unemployment rate; household income rate; inflation rate; GDP; poverty rate and housing stocks have been used to forecast the demand on low cost housing using Artificial Neural Network (ANN) approach. The data is collected from the Department of Malaysian Statistics, the Ministry of Housing and the Housing Department of the State Secretary. The Principal Component Analysis (PCA) method has been adopted to analyze the data using SPSS 18.0 package. The performance of the Neural Network is evaluated using R squared (R2) and the accuracy of the model is measured using the Mean Absolute Percentage Error (MAPE). Lastly, a user friendly interface is developed using Visual Basic. From the results, it was found that the best Neural Network to forecast the demand on low cost housing in Kedah is 2-16-1, Pahang 2-15-1, Kelantan 2-25-1, Terengganu 2-30-1, Perlis 3-5-1, Pulau Pinang 3-7-1, Johor 3-38-1 and Perak 3-24-1. In conclusion, the evaluation performance of the model through the MAPE value shows that the NN model can forecast the low-cost housing demand very good in Pulau Pinang, Johor, Pahang and Kelantan, where else good in Kedah and Terengganu while in Perlis and Perak it is not accurate due to the lack of data. The study has successfully developed a user friendly interface to retrieve and view all the data easily.
28
Zare, Kourosh Abbas. "Development of a Predictive Control Model for a Heat Pump System Based on Artificial Neural Networks (ANN) approach." Thesis, Högskolan Dalarna, Energiteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:du-30957.
Full text
29
Drezga, Irislav. "A generalized ANN-based model for short-term load forecasting." Diss., This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-06062008-151653/.
Full text
30
Souza, João Pedro Carvalho de. "Pouso autônomo de VANTs baseado em rede neural artificial supervisionada por lógica fuzzy." Universidade Federal de Juiz de Fora (UFJF), 2018. https://repositorio.ufjf.br/jspui/handle/ufjf/6512.
Full textAbstract:
Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-03-27T14:38:40Z
No. of bitstreams: 1
joaopedrocarvalhodesouza.pdf: 18276186 bytes, checksum: 402b9ec7121d8ad1bc3f51202005d04e (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-03-27T18:00:24Z (GMT) No. of bitstreams: 1
joaopedrocarvalhodesouza.pdf: 18276186 bytes, checksum: 402b9ec7121d8ad1bc3f51202005d04e (MD5)<br>Made available in DSpace on 2018-03-27T18:00:24Z (GMT). No. of bitstreams: 1
joaopedrocarvalhodesouza.pdf: 18276186 bytes, checksum: 402b9ec7121d8ad1bc3f51202005d04e (MD5)
Previous issue date: 2018-02-08<br>Os Veículos Aéreos Não Tripulados (VANTs) demonstram-se como tecnologia promissora visto sua alta aplicabilidade e custos reduzidos. Assim, esses veículos são estudados por engenheiros e pesquisadores que visam, além de aplicá-los, melhorar seu desempenho, segurança e torná-los autônomos e de fácil interação.
Etapas de voos como decolagem, subida, cruzeiro, descida e aterrissagem são objetos
de estudos para melhoria de perfomance dessas aeronaves. A aterrissagem é uma etapa delicada para o veículo, cuja operação inadequada pode resultar em acidentes e perdas. Com esse intuito, a presente dissertação propõe uma técnica para o pouso autônomo/assistido de VANTs embarcado ao veículo, sem a necessidade de estações base de processamento. Para o sensoriamento, é utilizado o algoritmo de visão computacional denominado Ar Track Alvar para identificação de marcadores artificiais, utilizados como local de pouso. A configuração do local de pouso visa a aplicação da aterrissagem em alturas mais elevadas, pois são utilizados diferentes marcadores artificiais para a sua composição.
O algoritmo de pouso também é uma contribuição do presente trabalho, no qual a execução é realizada por uma Rede Neural Artificial (RNA), do tipo Multilayer Perceptron, cujo treinamento é supervisionado por uma lógica fuzzy que utiliza a inferência Mamdani. A utilização do fuzzy torna-se viável devido a sua característica não determinística, sendo menos susceptível a ruídos de sensoriamento. Outro ponto importante é a não necessidade de se ajustar ganhos para o procedimento para cada aeronave usada, tornando-se o processo perigoso e trabalhoso. Esse revés é visto em controladores clássicos como o PID. Apesar das vantagens da lógica fuzzy, essa se mostra computacionalmente custosa devido a seu processo Mamdani. Como uma RNA treinada é um conjunto de operações matriciais, é proposto o treinamento da mesma supervisionada pelo algoritmo fuzzy já funcional. Assim se reduz a complexidade computacional do algoritmo embarcado facilitando o processsamento de imagem.
O firmware de aterrissagem proposto é desenvolvido sobre o framework Robot Operation System (ROS) e focado para replicação em dispositivos reais e embarcados. Os resultados são apresentados em Software in the Loop (SITL) e em experimentos reais em ambientes externos para locais de pouso estáticos e dinâmicos. A comparação de desempenhos dos algoritmos é mostrada. O desempenho atingido foi satisfatório e a capacidade da RNA, além da redução da complexidade computacional, foram verificadas.<br>Unmanned Aerial Vehicles (UAVs) are shown as promising technology because of their
high applicability and low costs. Thus, these vehicles are engineers and researchers studies targets that aim, in addition to applying them, to improve their performance, safety and make them autonomous and easily interaction.
Flight stages such as takeoff, ascent, cruise, descent and landing are objects of studies to
improve these aircrafts performance. Landing is a delicate stage for the vehicle, whose
improper operation can result in accidents and losses. With this purpose, the present
dissertation proposes a technique for the UAVs autonomous/assisted landing onboard the vehicle, without the use of ground control stations. As a sensing, the Ar Track Alvar
computational vision algorithm is used to identify artificial markers used as a landing
site. The landing site configuration aims the application of landing at higher altitudes, as different artificial markers are used for its composition.
The landing algorithm is also a contribution of the present work, in which the execution is performed by an Multilayer Perceptron Artificial Neural Network (ANN) whose training
is supervised by a logic fuzzy that uses the Mamdani inference. The use of fuzzy becomes viable due to non-deterministic characteristic and is less susceptible to sensing noise. Another important point is the no need to adjust gains for the procedure for each aircraft used, making the process dangerous and laborious. This setback is seen in classic controllers like the PID. Despite the advantages of fuzzy logic, this is computationally costly due to its Mamdani process. As a trained RNA is a set of matrix operations, it is proposed to train it supervised by the already functional fuzzy algorithm. This reduces the computational complexity of the embedded algorithm, facilitating image processing. The proposed landing firmware is developed on the Robot Operation System (ROS) and is focused on replication on real and embedded devices.
The results are presented in Software in the Loop (SITL) and in real experiments at
outdoor environments for static and dynamic landing spots. Comparison of algorithm
performances is also shown. The performance was satisfactory and the RNA capacity and computational complexity reduction were verified.
31
Coughlin, Michael J., and n/a. "Calibration of Two Dimensional Saccadic Electro-Oculograms Using Artificial Neural Networks." Griffith University. School of Applied Psychology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030409.110949.
Full textAbstract:
The electro-oculogram (EOG) is the most widely used technique for recording eye movements in clinical settings. It is inexpensive, practical, and non-invasive. Use of EOG is usually restricted to horizontal recordings as vertical EOG contains eyelid artefact (Oster & Stern, 1980) and blinks. The ability to analyse two dimensional (2D) eye movements may provide additional diagnostic information on pathologies, and further insights into the nature of brain functioning. Simultaneous recording of both horizontal and vertical EOG also introduces other difficulties into calibration of the eye movements, such as different gains in the two signals, and misalignment of electrodes producing crosstalk. These transformations of the signals create problems in relating the two dimensional EOG to actual rotations of the eyes. The application of an artificial neural network (ANN) that could map 2D recordings into 2D eye positions would overcome this problem and improve the utility of EOG. To determine whether ANNs are capable of correctly calibrating the saccadic eye movement data from 2D EOG (i.e. performing the necessary inverse transformation), the ANNs were first tested on data generated from mathematical models of saccadic eye movements. Multi-layer perceptrons (MLPs) with non-linear activation functions and trained with back propagation proved to be capable of calibrating simulated EOG data to a mean accuracy of 0.33° of visual angle (SE = 0.01). Linear perceptrons (LPs) were only nearly half as accurate. For five subjects performing a saccadic eye movement task in the upper right quadrant of the visual field, the mean accuracy provided by the MLPs was 1.07° of visual angle (SE = 0.01) for EOG data, and 0.95° of visual angle (SE = 0.03) for infrared limbus reflection (IRIS®) data. MLPs enabled calibration of 2D saccadic EOG to an accuracy not significantly different to that obtained with the infrared limbus tracking data.
32
Chokshi, Prasun. "Development of an artificial neural network (ANN) based phase distribution prediction model for 22MnB5 boron steel during tailored hot stamping." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/90156/.
Full textAbstract:
Due to demand for lower emissions and better crashworthiness, the use of boron ultra high strength steel (UHSS) has greatly increased in manufacturing of automotive components. However in many cases an idealized component has got different mechanical properties in different regions. For example in an automotive structural component such as B-pillar, which may undergo impact loading, it is desirable that there are certain regions in it which are softer and more ductile so that component's overall energy absorption is improved. The innovative process of tailored hot stamping allows for this by controlling the localized cooling rates, through actively dividing the tooling into heated and cooled zones. A barrier to optimal application of the technique is that a reliable phase distribution model is required to predict the distribution of different phases which occur in the different regions of a tailored hot stamped component. Currently most of the existing physical models for phase distribution prediction in boron steel after hot stamping only take into account the thermal history of the region while not accounting for the effect of deformation and thus have had only limited success so far. This research has developed a novel state-of-the-art Artificial Neural Network (ANN) based phase distribution prediction model for 22MnB5 boron UHSS steel, which is able to successfully take into account both the thermal and mechanical history while making final phase distribution predictions during tailored hot stamping. The model was developed and validated using data generated from extensive tailored hot stamping thermo-mechanical physical simulation experiments and scanned surface instrumented nanoindentation based phase quantification method. For the development of the ANN based model, the backpropagation algorithm was deployed on the available experimental data from 40 thermo-mechanical physical simulation experiments to learn the complex multivariate functional relationship between the thermal and mechanical history of the samples and the final resulting phase distributions in them. Advanced statistical techniques were used for preventing overfitting in the ANN based model while learning, for making the optimal use of limited available experimental data and for quantification of uncertainties in the predictions made by the model. After the ANN based model had been developed, its prediction performance was rigorously measured and analyzed. During measuring its prediction performance over the data used for its development, it had a prediction root mean square error of just 5.4% over 120 phase volume fraction predictions. During its validation over the completely new independent experimental data, the ANN based model had root mean square prediction error of just 7.7% over 30 phase volume fraction predictions. This excellent prediction performance of the developed ANN based model demonstrated its reliability and robustness and established the potential for ANN model to be used in future computer aided engineering applications for tailored hot stamping process.
33
Ozturk, Hayrullah Ugras. "Discharge Predictions Using Ann In Sloping Rectangular Channels With Free Overfall." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606706/index.pdf.
Full textAbstract:
In recent years, artificial neural networks (ANNs) have been applied to estimate in many areas of hydrology and hydraulic engineering. In this thesis, multilayered feedforward backpropagation algorithm was used to establish for the prediction of unit discharge q (m3/s/m) in a rectangular free overfall. Researchers&rsquo<br>experimental data were used to train and validate the network with high reliability. First, an appropriate ANN model has been established by considering determination of hidden layer and node numbers related to training function and training epoch number. Then by applying sensitivity analysis, parameters involved in and their effectiveness relatively has been determined in the phenomenon. In the scope of the thesis, there are two case studies. In the first case study, ANN models reliability has been investigated according to the training data clustered and the results are given by comparing to regression analysis. In the second case, ANN models&rsquo<br>ability in establishing relations with different data clusters is investigated and effectiveness of ANN is scrutinized.
34
Dravenstott, Ronald W. "Restaurant Industry Stock Price Forecasting Model Utilizing Artificial Neural Networks to Combine Fundamental and Technical Analysis." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1337780178.
Full text
35
Fletcher, Eric Matthew. "FE-ANN based modeling of 3D simple reinforced concrete girders for objective structural health evaluation." Thesis, Kansas State University, 2016. http://hdl.handle.net/2097/32497.
Full textAbstract:
Master of Science<br>Department of Civil Engineering<br>Hayder A. Rasheed<br>The structural deterioration of aging infrastructure systems is becoming an increasingly important issue worldwide. To compound the issue, economic strains limit the resources available for repair or replacement of such systems. Over the past several decades, structural health monitoring (SHM) has proved to be a cost-effective method for detection and evaluation of damage in structures. Visual inspection and condition rating is one of the most commonly applied SHM techniques, but the effectiveness of this method suffers due to its reliance on the availability and experience of qualified personnel performing largely qualitative damage evaluations. The artificial neural network (ANN) approach presented in this study attempts to augment visual inspection methods by developing a crack-induced damage quantification model for reinforced concrete bridge girders that requires only the results of limited field measurements to operate. Simply-supported three-dimensional reinforced concrete T-beams with varying geometric, material, and cracking properties were modeled using Abaqus finite element (FE) analysis software. Up to five cracks were considered in each beam, and the ratios of stiffness between cracked and healthy beams with the same geometric and material parameters were measured at nine equidistant nodes along the beam. Two feedforward ANNs utilizing backpropagation learning algorithms were then trained on the FE model database with beam properties serving as inputs for both neural networks. The outputs for the first network consisted of the nodal stiffness ratios, and the sole output for the second ANN was a health index parameter, computed by normalizing the area under the stiffness ratio profile over the span length of the beam. The ANNs achieved excellent prediction accuracies with coefficients of determination (R²) exceeding 0.99 for both networks. Additional FE models were created to further assess the networks’ prediction capabilities on data not utilized in the training process. The ANNs displayed good prediction accuracies (R² > 0.8) even when predicting damage levels in beams with geometric, material, and cracking parameters dissimilar from those found in the training database. A touch-enabled user interface was developed to allow the ANN models to be utilized for on-site damage evaluations. The results of this study indicate that application of ANNs with FE modeling shows great promise in SHM for damage evaluation.
36
Sabih, Ann Faik. "Cognitive smart agents for optimising OpenFlow rules in software defined networks." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15743.
Full textAbstract:
This research provides a robust solution based on artificial intelligence (AI) techniques to overcome the challenges in Software Defined Networks (SDNs) that can jeopardise the overall performance of the network. The proposed approach, presented in the form of an intelligent agent appended to the SDN network, comprises of a new hybrid intelligent mechanism that optimises the performance of SDN based on heuristic optimisation methods under an Artificial Neural Network (ANN) paradigm. Evolutionary optimisation techniques, including Particle Swarm Optimisation (PSO) and Genetic Algorithms (GAs) are deployed to find the best set of inputs that give the maximum performance of an SDN-based network. The ANN model is trained and applied as a predictor of SDN behaviour according to effective traffic parameters. The parameters that were used in this study include round-trip time and throughput, which were obtained from the flow table rules of each switch. A POX controller and OpenFlow switches, which characterise the behaviour of an SDN, have been modelled with three different topologies. Generalisation of the prediction model has been tested with new raw data that were unseen in the training stage. The simulation results show a reasonably good performance of the network in terms of obtaining a Mean Square Error (MSE) that is less than 10−6 [superscript]. Following the attainment of the predicted ANN model, utilisation with PSO and GA optimisers was conducted to achieve the best performance of the SDN-based network. The PSO approach combined with the predicted SDN model was identified as being comparatively better than the GA approach in terms of their performance indices and computational efficiency. Overall, this research demonstrates that building an intelligent agent will enhance the overall performance of the SDN network. Three different SDN topologies have been implemented to study the impact of the proposed approach with the findings demonstrating a reduction in the packets dropped ratio (PDR) by 28-31%. Moreover, the packets sent to the SDN controller were also reduced by 35-36%, depending on the generated traffic. The developed approach minimised the round-trip time (RTT) by 23% and enhanced the throughput by 10%. Finally, in the event where SDN controller fails, the optimised intelligent agent can immediately take over and control of the entire network.
37
Guan, Zhengyuan. "A New Islanding Detection Method Based On Wavelet-transform and ANN for Inverter Assisted Distributed Generator." UKnowledge, 2015. http://uknowledge.uky.edu/ece_etds/72.
Full textAbstract:
Nowadays islanding has become a big issue with the increasing use of distributed generators in power system. In order to effectively detect islanding after DG disconnects from main source, author first studied two passive islanding methods in this thesis: THD&VU method and wavelet-transform method. Compared with other passive methods, each of them has small non-detection zone, but both of them are based on the threshold limit, which is very hard to set. What’s more, when these two methods were applied to practical signals distorted with noise, they performed worse than anticipated.
Thus, a new composite intelligent based method is presented in this thesis to solve the drawbacks above. The proposed method first uses wavelet-transform to detect the occurrence of events (including islanding and non-islanding) due to its sensitivity of sudden change. Then this approach utilizes artificial neural network (ANN) to classify islanding and non-islanding events. In this process, three features based on THD&VU are extracted as the input of ANN classifier. The performance of proposed method was tested on two typical distribution networks. The obtained results of two cases indicated the developed method can effectively detect islanding with low misclassification.
38
Nigrini, L. B., and G. D. Jordaan. "Short term load forecasting using neural networks." Journal for New Generation Sciences, Vol 11, Issue 3: Central University of Technology, Free State, Bloemfontein, 2013. http://hdl.handle.net/11462/646.
Full textAbstract:
Published Article<br>Several forecasting models are available for research in predicting the shape of electric load curves. The development of Artificial Intelligence (AI), especially Artificial Neural Networks (ANN), can be applied to model short term load forecasting. Because of their input-output mapping ability, ANN's are well-suited for load forecasting applications.
ANN's have been used extensively as time series predictors; these can include feed-forward networks that make use of a sliding window over the input data sequence. Using a combination of a time series and a neural network prediction method, the past events of the load data can be explored and used to train a neural network to predict the next load point.
In this study, an investigation into the use of ANN's for short term load forecasting for Bloemfontein, Free State has been conducted with the MATLAB Neural Network Toolbox where ANN capabilities in load forecasting, with the use of only load history as input values, are demonstrated.
39
Yerrabolu, Pavan. "Correction model based ANN modeling approach for the estimation of Radon concentrations in Ohio." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341604941.
Full text
40
Bhat, Chandrashekhar. "Artificial Neural Network Approach For Characterization Of Acoustic Emission Sources From Complex Noisy Data." Thesis, Indian Institute of Science, 2001. http://hdl.handle.net/2005/251.
Full textAbstract:
Safety and reliability are prime concerns in aircraft performance due to the involved costs and risk to lives. Despite the best efforts in design methodology, quality evaluation in production and structural integrity assessment in-service, attainment of one hundred percent safety through development and use of a suitable in-flight health monitoring system is still a farfetched goal. And, evolution of such a system requires, first, identification of an appropriate Technique and next its adoption to meet the challenges posed by newer materials (advanced composites), complex structures and the flight environment. In fact, a quick survey of the available Non-Destructive Evaluation (NDE) techniques suggests Acoustic Emission (AE) as the only available method. High merit in itself could be a weakness - Noise is the worst enemy of AE. So, while difficulties are posed due to the insufficient understanding of the basic behavior of composites, growth and interaction of defects and damage under a specified load condition, high in-flight noise further complicates the issue making the developmental task apparently formidable and challenging.
Development of an in-flight monitoring system based on AE to function as an early warning system needs addressing three aspects, viz., the first, discrimination of AE signals from noise data, the second, extraction of required information from AE signals for identification of sources (source characterization) and quantification of its growth, and the third, automation of the entire process. And, a quick assessment of the aspects involved suggests that Artificial Neural Networks (ANN) are ideally suited for solving such a complex problem. A review of the available open literature while indicates a number of investigations carried out using noise elimination and source characterization methods such as frequency filtering and statistical pattern recognition but shows only sporadic attempts using ANN. This may probably be due to the complex nature of the problem involving investigation of a large number of influencing parameters, amount of effort and time to be invested, and facilities required and multi-disciplinary nature of the problem. Hence as stated in the foregoing, the need for such a study cannot be over emphasized.
Thus, this thesis is an attempt addressing the issue of analysis and automation of complex sets of AE data such as AE signals mixed with in-flight noise thus forming the first step towards in-flight monitoring using AE. An ANN can in fact replace the traditional algorithmic approaches used in the past. ANN in general are model free estimators and derive their computational efficiency due to large connectivity, massive parallelism, non-linear analog response and learning capabilities. They are better suited than the conventional methods (statistical pattern recognition methods) due to their characteristics such as classification, pattern matching, learning, generalization, fault tolerance and distributed memory and their ability to process unstructured data sets which may be carrying incomplete information at times and hence chosen as the tool. Further, in the current context, the set of investigations undertaken were in the absence of sufficient a priori information and hence clustering of signals generated by AE sources through self-organizing maps is more appropriate. Thus, in the investigations carried out under the scope of this thesis, at first a hybrid network named "NAEDA" (Neural network for Acoustic Emission Data Analysis) using Kohonen self-organizing feature map (KSOM) and multi-layer perceptron (MLP) that learns on back propagation learning rule was specifically developed with innovative data processing techniques built into the network. However, for accurate pattern recognition, multi-layer back propagation NN needed to be trained with source and noise clusters as input data. Thus, in addition to optimizing the network architecture and training parameters, preprocessing of input data to the network and multi-class clustering and classification proved to be the corner stones in obtaining excellent identification accuracy.
Next, in-flight noise environment of an aircraft was generated off line through carefully designed simulation experiments carried out in the laboratory (Ex: EMI, friction, fretting and other mechanical and hydraulic phenomena) based on the in-flight noise survey carried out by earlier investigators. From these experiments data was acquired and classified into their respective classes through MLP. Further, these noises were mixed together and clustered through KSOM and then classified into their respective clusters through MLP resulting in an accuracy of 95%- 100%
Subsequently, to evaluate the utility of NAEDA for source classification and characterization, carbon fiber reinforced plastic (CFRP) specimens were subjected to spectrum loading simulating typical in-flight load and AE signals were acquired continuously up to a maximum of three designed lives and in some cases up to failure. Further, AE signals with similar characteristics were grouped into individual clusters through self-organizing map and labeled as belonging to appropriate failure modes, there by generating the class configuration. Then MLP was trained with this class information, which resulted in automatic identification and classification of failure modes with an accuracy of 95% - 100%. In addition, extraneous noise generated during the experiments was acquired and classified so as to evaluate the presence or absence of such data in the AE data acquired from the CFRP specimens.
In the next stage, noise and signals were mixed together at random and were reclassified into their respective classes through supervised training of multi-layer back propagation NN. Initially only noise was discriminated from the AE signals from CFRP failure modes and subsequently both noise discrimination and failure mode identification and classification was carried out resulting in an accuracy of 95% - 100% in most of the cases. Further, extraneous signals mentioned above were classified which indicated the presence of such signals in the AE signals obtained from the CFRP specimen.
Thus, having established the basis for noise identification and AE source classification and characterization, two specific examples were considered to evaluate the utility and efficiency of NAEDA. In the first, with the postulation that different basic failure modes in composites have unique AE signatures, the difference in damage generation and progression can be clearly characterized under different loading conditions. To examine this, static compression tests were conducted on a different set of CFRP specimens till failure with continuous AE monitoring and the resulting AE signals were classified through already trained NAEDA. The results obtained shows that the total number of signals obtained were very less when compared to fatigue tests and the specimens failed with hardly any damage growth. Further, NAEDA was able to discriminate the"noise and failure modes in CFRP specimen with the same degree of accuracy with which it has classified such signals obtained from fatigue tests.
In the second example, with the same postulate of unique AE signatures for different failure modes, the differences in the complexion of the damage growth and progression should become clearly evident when one considers specimens with different lay up sequences. To examine this, the data was reclassified on the basis of differences in lay up sequences from specimens subjected to fatigue. The results obtained clearly confirmed the postulation.
As can be seen from the summary of the work presented in the foregoing paragraphs, the investigations undertaken within the scope of this thesis involve elaborate experimentation, development of tools, acquisition of extensive data and analysis. Never the less, the results obtained were commensurate with the efforts and have been fruitful. Of the useful results that have been obtained, to state in specific, the first is, discrimination of simulated noise sources achieved with significant success but for some overlapping which is not of major concern as far as noises are concerned. Therefore they are grouped into required number of clusters so as to achieve better classification through supervised NN. This proved to be an innovative measure in supervised classification through back propagation NN.
The second is the damage characterization in CFRP specimens, which involved imaginative data processing techniques that proved their worth in terms of optimization of various training parameters and resulted in accurate identification through clustering. Labeling of clusters is made possible by marking each signal starting from clustering to final classification through supervised neural network and is achieved through phenomenological correlation combined with ultrasonic imaging.
Most rewarding of all is the identification of failure modes (AE signals) mixed in noise into their respective classes. This is a direct consequence of innovative data processing, multi-class clustering and flexibility of grouping various noise signals into suitable number of clusters.
Thus, the results obtained and presented in this thesis on NN approach to AE signal analysis clearly establishes the fact that methods and procedures developed can automate detection and identification of failure modes in CFRP composites under hostile environment, which could lead to the development of an in-flight monitoring system.
41
Anderson, Jerone S. "A Study of Nutrient Dynamics in Old Woman Creek Using Artificial Neural Networks and Bayesian Belief Networks." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1242921000.
Full text
42
Dahlberg, Emil, Mattias Mineur, Linus Shoravi, and Holger Swartling. "Replacing Setpoint Control with Machine Learning : Model Predictive Control Using Artificial Neural Networks." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413003.
Full textAbstract:
Indoor climate control is responsible for a substantial amount of the world's total energy expenditure. In a time of climate crisis where a reduction of energy consumption is crucial to avoid climate disaster, indoor climate control is a ripe target for eliminating energy waste. The conventional method of adjusting the indoor climate with the use of setpoint curves, based solely on outdoor temperature, may lead to notable inefficiencies. This project evaluates the possibility to replace this method of regulation with a system based on model predictive control (MPC) in one of Uppsala University Hospitals office buildings. A prototype of an MPC controller using Artificial Neural Networks (ANN) as its system model was developed. The system takes several data sources into account, including indoor and outdoor temperatures, radiator flowline and return temperatures, current solar radiation as well as forecast for both solar radiation and outdoor temperature. The system was not set in production but the controller's predicted values correspond well to the buildings current thermal behaviour and weather data. These theoretical results attest to the viability of using the method to regulate the indoor climate in buildings in place of setpoint curves.<br>Bibehållande av inomhusklimat står för en avsevärd del av världens totala energikonsumtion. Med dagens klimatförändringar där minskad energikonsumtion är viktigt för den hållbara utvecklingen så är inomhusklimat ett lämpligt mål för att förhindra slösad energi. Konventionell styrning av inomhusklimat använder sig av börvärdeskurvor, baserade enbart på utomhustemperatur, vilket kan leda till anmärkningsvärt energispill. Detta projekt utvärderar möjligheten att ersätta denna styrmetod med ett system baserat på model predictive control (MPC) och använda detta i en av Akademiska sjukhusets lokaler i Uppsala. En MPC styrenhet som använder Artificiella Neurala Nätverk (ANN) som sin modell utvecklades. Systemet använder sig av flera datakällor däribland inomhus- och utomhustemperatur, radiatorslingornas framlednings- och returtemperatur, rådande solinstrålning såväl som prognoser för solinstrålning och utomhustemperatur. Systemet sattes inte i produktion men dess prognos stämmer väl överens med tillgänglig väderdata och husets termiska beteende. De presenterade resultaten påvisar metoden vara ett lämpligt substitut för styrning med börvärdeskurvor.
43
Rosquist, Parker Gary. "Modeling Three Dimensional Ground Reaction Force Using Nanocomposite Piezoresponsive Foam Sensors." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6390.
Full textAbstract:
Three dimensional (3D) ground reaction force (GRF) are an essential component for gait analysis. Current methods for measuring 3D GRF involve using a stationary force plate embedded in the ground, which captures the forces as subjects walk across the platform. This approach has several limitations, a few being: it can only capture a few steps at a time, it is expensive to purchase and maintain, it can't reflect forces caused by natural uneven surfaces, etc. Previous research has attempted to develop wearable force sensors to overcome these problems; however, these endeavors have resulted in devices that are expensive, bulky, and fail to accurately measure forces when compared to static force plates. This thesis presents the implementation and validation of novel nanocomposite piezoresponsive foam (NCPF) sensors for measuring 3D GRF. Four NCPF sensors were embedded in a shoe sole at four locations: heel, arch, ball, and toe. The signals from each sensor were used in a functional data analysis (FDA) to develop a statistical model for estimating 3D GRF. The process of calibrating the sensors to model GRF was validated through a study where 9 subjects (4 females, 5 males) walked on a force-sensing treadmill for two minutes. Two approaches were used to model the GRF response. The first approach was based on functional decomposition of the data. Using a tenfold cross validation process a statistical model was developed for each subject with the ability to predict walking 3D GRF with less than 7% error. The second approach used machine learning to model 3D GRF. Using the same walking data for the statistical models, an artificial neural network (ANN) was used to create subject-specific models that could predict walking 3D GRF with less than 11% error. The predictive capabilities of ANN were tested using a pilot study where a single subject performed a calibration procedure by running at seven different speeds for thirty seconds each on the force-sensing treadmill. This calibration data was used to train a model, which was then used to estimate vertical GRF (VGRF) for three additional running trials at randomly selected speeds from within the calibration range. The ANN model was able to predict VGRF for three running speeds after calibration with less than 4% error. The use of NCPF sensors to estimate 3D GRF was shown to be a viable alternative to static force plates. It is recommended, in future work, that 3D GRF and subsequent sensor data be collected from a large sample of subjects to create a baseline of 3D GRF characteristics for a population that will enable a robust cross-subject model capable of performing real-time ground reaction force analysis across the general population, which will greatly benefit our understanding of human gait.
44
Hensman, Paulina. "Intra-prediction for Video Coding with Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224197.
Full textAbstract:
Intra-prediction is a method for coding standalone frames in video coding. Until now, this has mainly been done using linear formulae. Using an Artificial Neural Network (ANN) may improve the prediction accuracy, leading to improved coding efficiency. In this degree project, Fully Connected Networks (FCN) and Convolutional Neural Networks (CNN) were used for intra-prediction. Experiments were done on samples from different image sizes, block sizes, and block contents, and their effect on the results were compared and discussed. The results show that ANN methods have the potential to perform better or on par with the video coder High Efficiency Video Coding (HEVC) in the intra-prediction task. The proposed ANN designs perform better on smaller block sizes, but different designs could lead to better performance on larger block sizes. It was found that training one network for each HEVC mode and using the most suitable network to predict each block improved performance of the ANN approach.<br>Intra-prediktion är en metod för kodning av stillbilder i videokodning. Hittills har detta främst gjorts med hjälp av linjära formler. Användning av artificialla neuronnät (ANN) skulle kunna öka prediktionsnoggrannheten och ge högre effektivitet vid kodning. I detta examensarbete användes fully connected networks (FCN) och convolutional neural networks (CNN) för att utföra intra-prediktion. Experiment gjordes på prover från olika bildstorlekar, blockstorlekar och blockinnehåll, och de olika parametrarnas effekt på resultaten jämfördes och diskuterades. Resultaten visar att ANN-metoder har potential att prestera bättre eller lika bra som videokodaren High Efficiency Video Coding (HEVC) för intra-prediktion. De föreslagna ANN-designerna presterar bättre på mindre blockstorlekar, men andra ANN-designs skulle kunna ge bättre prestanda för större blockstorlekar. Det konstaterades att prestandan för ANN-metoderna kunde ökas genom att träna ett nätverk för varje HEVC-mode och använda det mest passande nätverket för varje block.
45
Moayed, Farman Amin. "Constructing the Function of “Magnitude-of-Effect” for Artificial Neural Network (ANN) Models and Their Application in Occupational Safety and Health (OSH) Engineering." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1217519927.
Full text
46
Joosep, Henno. "Empirical Evaluation of Approaches for Digit Recognition." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-46676.
Full textAbstract:
Optical Character Recognition (OCR) is a well studied subject involving variousapplication areas. OCR results in various limited problem areas are promising,however building highly accurate OCR application is still problematic in practice.This thesis discusses the problem of recognizing and confirming Bingo lottery numbersfrom a real lottery field, and a prototype for Android phone is implementedand evaluated. An OCR library Tesseract and two Artificial Neural Network (ANN)approaches are compared in an experiment and discussed. The results show thattraining a neural network for each number gives slightly higher results than Tesseract.
47
Patterson, James Cameron. "Managing a real-time massively-parallel neural architecture." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/managing-a-realtime-massivelyparallel-neural-architecture(dfab5ca7-fcd5-4ebe-887b-0a7c330c7206).html.
Full textAbstract:
A human brain has billions of processing elements operating simultaneously; the only practical way to model this computationally is with a massively-parallel computer. A computer on such a significant scale requires hundreds of thousands of interconnected processing elements, a complex environment which requires many levels of monitoring, management and control. Management begins from the moment power is applied and continues whilst the application software loads, executes, and the results are downloaded. This is the story of the research and development of a framework of scalable management tools that support SpiNNaker, a novel computing architecture designed to model spiking neural networks of biologically-significant sizes. This management framework provides solutions from the most fundamental set of power-on self-tests, through to complex, real-time monitoring of the health of the hardware and the software during simulation. The framework devised uses standard tools where appropriate, covering hardware up / down events and capacity information, through to bespoke software developed to provide real-time insight to neural network software operation across multiple levels of abstraction. With this layered management approach, users (or automated agents) have access to results dynamically and are able to make informed decisions on required actions in real-time.
48
Carn, Cheril, and cheril Carn@dsto defence gov au. "The inverse determination of aircraft loading using artificial neural network analysis of structural response data with statistical methods." RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080109.090600.
Full textAbstract:
An artificial Neural Network (ANN) system has been developed that can analyse aircraft flight data to provide a reconstruction of the aerodynamic loads experienced by the aircraft during flight, including manoeuvre, buffet and distributed loading. For this research data was taken from the International Follow-On Structural Test Project (IFOSTP) F/A-18 fatigue test conducted by the Royal Australian Air Force and Canadian Forces. This fatigue test involved the simultaneous application of both manouevre and buffet loads using airbag actuators and shakers. The applied loads were representative of the actual loads experienced by an FA/18 during flight tests. Following an evaluation of different ANN types an Ellman network with three linear layers was selected. The Elman back-propagation network was tested with various parameters and structures. The network was trained using the MATLAB 'traingdx' function with is a gradient descent with momentum and adaptive learning rate back-propagation algorithm. The ANN was able to provide a good approximation of the actual manoeuvre or buffet loads at the location where the training loads data were recorded even for input values which differ from the training input values. In further tests the ability to estimate distributed loading at locations not included in the training data was also demonstrated. The ANN was then modified to incorporate various methods for the calculation and prediction of output error and reliability Used in combination and in appropriate circumstances, the addition of these capabilities significantly increase the reliability, accuracy and therefore usefulness of the ANN system's ability to estimate aircraft loading.To demonstrate the ANN system's usefulness as a fatigue monitoring tool it was combined with a formulae for crack growth analysis. Results inficate the ANN system may be a useful fatigue monitoring tool enabling real time monitoring of aircraft critical components using existing strain gauge sensors.
49
Wilson, Brittany Michelle. "Evaluating and Improving the SEU Reliability of Artificial Neural Networks Implemented in SRAM-Based FPGAs with TMR." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8619.
Full textAbstract:
Artificial neural networks (ANNs) are used in many types of computing applications. Traditionally, ANNs have been implemented in software, executing on CPUs and even GPUs, which capitalize on the parallelizable nature of ANNs. More recently, FPGAs have become a target platform for ANN implementations due to their relatively low cost, low power, and flexibility. Some safety-critical applications could benefit from ANNs, but these applications require a certain level of reliability. SRAM-based FPGAs are sensitive to single-event upsets (SEUs), which can lead to faults and errors in execution. However there are techniques that can mask such SEUs and thereby improve the overall design reliability. This thesis evaluates the SEU reliability of neural networks implemented in SRAM-based FPGAs and investigates mitigation techniques against upsets for two case studies. The first was based on the LeNet-5 convolutional neural network and was used to test an implementation with both fault injection and neutron radiation experiments, demonstrating that our fault injection experiments could accurately evaluate SEU reliability of the networks. SEU reliability was improved by selectively applying TMR to the most critical layers of the design, achieving a 35% improvement reliability at an increase in 6.6% resources. The second was an existing neural network called BNN-PYNQ. While the base design was more sensitive to upsets than the CNN previous tested, the TMR technique improved the reliability by approximately 7× in fault injection experiments.
50
Cherif, Wael. "Adaptation de contexte basée sur la Qualité d'Expérience dans les réseaux Internet du Futur." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00940287.
Full textAbstract:
Pour avoir une idée sur la qualité du réseau, la majorité des acteurs concernés (opérateurs réseau, fournisseurs de service) se basent sur la Qualité de Service (Quality of Service). Cette mesure a montré des limites et beaucoup d'efforts ont été déployés pour mettre en place une nouvelle métrique qui reflète, de façon plus précise, la qualité du service offert. Cette mesure s'appelle la qualité d'expérience (Quality of Experience). La qualité d'expérience reflète la satisfaction de l'utilisateur par rapport au service qu'il utilise. Aujourd'hui, évaluer la qualité d'expérience est devenu primordiale pour les fournisseurs de services et les fournisseurs de contenus. Cette nécessité nous a poussés à innover et concevoir des nouvelles méthodes pour estimer la QoE. Dans cette thèse, nous travaillons sur l'estimation de la QoE (1) dans le cas des communications Voix sur IP et (2) dans le cas des services de diffusion Vidéo sur IP. Nous étudions les performances et la qualité des codecs iLBC, Speex et Silk pour la VoIP et les codecs MPEG-2 et H.264/SVC pour la vidéo sur IP. Nous étudions l'impact que peut avoir la majorité des paramètres réseaux, des paramètres sources (au niveau du codage) et destinations (au niveau du décodage) sur la qualité finale. Afin de mettre en place des outils précis d'estimation de la QoE en temps réel, nous nous basons sur la méthodologie Pseudo-Subjective Quality Assessment. La méthodologie PSQA est basée sur un modèle mathématique appelé les réseaux de neurones artificiels. En plus des réseaux de neurones, nous utilisons la régression polynomiale pour l'estimation de la QoE dans le cas de la VoIP.