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1
Möller, Klaus, and Daniel Marks. "Flexible Fortschrittssteuerung als Target Operating Model der finanziellen Führung." Controlling 33, no. 2 (2021): 14–21. http://dx.doi.org/10.15358/0935-0381-2021-2-14.
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In einem VUCA-Umfeld ist ein rigides Planungs- und Steuerungssystem hinderlich und erschwert die dynamische Anpassung an veränderte Kontexte. Erforderlich ist eine fundamentale Neuausrichtung, weg von einer Plan-Ist-Orientierung und hin zu einer flexiblen, eher auf Fortschritt und an der Strategie ausgerichteten ganzheitlichen Steuerung. Der Beitrag liefert einen solchen Alternativvorschlag, in dessen Kern kein Budgetierungssystem steht, sondern ein dynamischer auf Fortschritt ausgelegter Kreislauf, der in ein Target Operating Model der finanziellen Führung eingebettet ist und damit eine Blaupause für eine Performance Management Organisation bietet.
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Verma, M. K., and R. K. Shrivastava. "Min-max goal programming: an application to Mahanadi Reservoir Project Complex." Canadian Journal of Civil Engineering 28, no. 4 (August 1, 2001): 684–90. http://dx.doi.org/10.1139/l01-026.
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An application of the min-max goal programming methodology to a system of multipurpose reservoirs for optimal monthly operation has been presented in this paper. The goal programming approach possesses significant advantages because of the fact that it may be based on physical operating criteria. The system goals and constraints are expressed deterministically. A constraint must be strictly satisfied, while for a goal it is desired to achieve the solution, which is as close as possible to the specified target. The min-max goal programming model is developed and applied to the Mahanadi Reservoir Project (MRP) Complex comprising of six multipurpose reservoirs in the state of Madhya Pradesh, India. The MRP Complex operations resulting from the use of the min-max goal programming model are compared to the operations resulting from three other reported optimization models with the same data set for the same operation period. The set of operations resulting from various models are comparable in their effectiveness, and in most aspects the min-max goal programming model operations are better.Key words: optimization, reservoir operation, goal programming, min-max goal programming.
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Wei, Li Xin, Lu Ying Zhang, and Yu Wang. "Simulation of Operation Scheme of Su Cuo Buried Oil Pipeline." Advanced Materials Research 356-360 (October 2011): 3023–27. http://dx.doi.org/10.4028/www.scientific.net/amr.356-360.3023.
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Aim at the actual operating condition of Su Cuo buried oil pipeline, the simulation model of this pipeline operation was built by the simulation software of TLNET to simulate the running states of pipeline in different operating conditions. With the target of saving energy and reducing consumption, the operating parameters of this pipeline with different throughput and different seasons were optimized, and the optimization results can be used to guide the pipeline operation.
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Hinov, Nikolay, Bogdan Gilev, and Tsveti Hranov. "Model-Based Optimization of an LLC-Resonant DC-DC Converter." Electronics 8, no. 7 (July 17, 2019): 799. http://dx.doi.org/10.3390/electronics8070799.
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The study presented in the paper is to guarantee the performance of the LLC DC-DC converter using model-based optimization. The primary scope of the study is to maintain the output parameters regardless of the variation of the values of the circuit elements. In engineering practice, it is known that any schematic element cannot be reproduced with an absolute accuracy of features. In addition, its main parameters change during operation due to changes in operating temperature, aging, operating modes and so on. Optimization procedures are a tool for finding the most appropriate values for circuit elements, with selected constraints, target functions and operating modes. In electronic converters, these are most often: minimal loss, maximum efficiency, the critical-aperiodic transition process, realization of certain dynamics, appropriate modes of operation and so on. The results obtained show that using the proposed approach produces more robustness to disturbances and tolerances, with improved dynamics and faster transient processes. On the other hand, the value of the circuit elements is smaller, and reliable operation of the protection and automatic regulation systems is achieved.
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Nam, Jae-Won, Young-Kyun Cho, and Youn Kyu Lee. "Regression Model-Based AMS Circuit Optimization Technique Utilizing Parameterized Operating Condition." Electronics 11, no. 3 (January 29, 2022): 408. http://dx.doi.org/10.3390/electronics11030408.
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An analog and mixed-signal (AMS) circuit that draws on machine learning while using a regression model differs in terms of the design compared to more sophisticated circuit designs. Technology structures that are more advanced than conventional CMOS processes, specifically the fin field-effect transistor (FinFET) and silicon-on-insulator (SOI), have been proposed to provide the higher computation performance required to meet various design specifications. As a result, the latest research on AMS design optimization has enabled enormous resource savings in AMS design procedures but remains limited with regard to reflecting the intended operating conditions in the design parameters. Hereby, we propose what is termed a supervised learning artificial neural network (ANN) as a means by which to define an AMS regression model. This approach allows for rapid searches of complex design dimensions, including variations in performance metrics caused by process–voltage–temperature (PVT) changes. The method also reduces the considerable computation expense compared to that of simulation-program-with-integrated-circuit-emphasis (SPICE) simulations. Hence, the proposed AMS circuit design flow generates highly promising output to meet target requirements while showing an excellent ability to match the design target performance. To verify the potential and promise of our design flow, a successive approximation register analog-to-digital converter (SAR ADC) is designed with a 14 nm process design kit. In order to show the maximum single ADC performance (6-bit∼8-bit resolution and few GS/s conversion speed), we have set three different ADC performance targets. Under all SS/TT/FF corners, ±6.25% supply voltage variation, and temperature variation from −40 ∘C to 80 ∘C, the designed SAR ADC using our proposed AMS circuit optimization flow yields remarkable figure-of-merit energy efficiency (approximately 15 fJ/conversion step).
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Bai, Tao, Xia Liu, Yan-ping HA, Jian-xia Chang, Lian-zhou Wu, Jian Wei, and Jin Liu. "Study on the Single-Multi-Objective Optimal Dispatch in the Middle and Lower Reaches of Yellow River for River Ecological Health." Water 12, no. 3 (March 24, 2020): 915. http://dx.doi.org/10.3390/w12030915.
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Given the increasingly worsening ecology issues in the lower Yellow River, the Xiaolangdi reservoir is chosen as the regulation and control target, and the single and multi-objective operation by ecology and power generation in the lower Yellow River is studied in this paper. This paper first proposes the following three indicators: the ecological elasticity coefficient (f1), the power generation elasticity coefficient (f2), and the ecological power generation profit and loss ratio (k). This paper then conducts a multi-target single dispatching study on ecology and power generation in the lower Yellow River. A genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II) combining constraint processing and feasible space search techniques were used to solve the single-objective model with the largest power generation and the multi-objective optimal scheduling model considering both ecology and power generation. The calculation results show that: (1) the effectiveness of the NSGA-Ⅱcombined with constraint processing and feasible spatial search technology in reservoir dispatching is verified by an example; (2) compared with the operation model of maximizing power generation, the power generation of the target model was reduced by 0.87%, the ecological guarantee rate was increased by 18.75%, and the degree of the impact of ecological targets on the operating results was quantified; (3) in each typical year, the solution spatial distribution and dimensions of the single-target and multi-target models of change are represented by the Pareto-front curve, and a multi-objective operation plan is generated for decision makers to choose; (4) the f1, f2, and k indicators are selected to analyze the sensitivity of the five multi-objective plans and to quantify the interaction between ecological targets and power generation targets. Ultimately, this paper discusses the conversion relationship and finally recommends the best equilibrium solution in the multi-objective global equilibrium solution set. The results provide a decision-making basis for the multi-objective dispatching of the Xiaolangdi reservoir and have important practical significance for further improving the ecological health of the lower Yellow River.
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Qiang, Bo, Junyong Lai, Hongwei Jin, Liangren Zhang, and Zhenming Liu. "Target Prediction Model for Natural Products Using Transfer Learning." International Journal of Molecular Sciences 22, no. 9 (April 28, 2021): 4632. http://dx.doi.org/10.3390/ijms22094632.
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A large proportion of lead compounds are derived from natural products. However, most natural products have not been fully tested for their targets. To help resolve this problem, a model using transfer learning was built to predict targets for natural products. The model was pre-trained on a processed ChEMBL dataset and then fine-tuned on a natural product dataset. Benefitting from transfer learning and the data balancing technique, the model achieved a highly promising area under the receiver operating characteristic curve (AUROC) score of 0.910, with limited task-related training samples. Since the embedding distribution difference is reduced, embedding space analysis demonstrates that the model’s outputs of natural products are reliable. Case studies have proved our model’s performance in drug datasets. The fine-tuned model can successfully output all the targets of 62 drugs. Compared with a previous study, our model achieved better results in terms of both AUROC validation and its success rate for obtaining active targets among the top ones. The target prediction model using transfer learning can be applied in the field of natural product-based drug discovery and has the potential to find more lead compounds or to assist researchers in drug repurposing.
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Araujo, Gustavo F., Renato Machado, and Mats I. Pettersson. "Non-Cooperative SAR Automatic Target Recognition Based on Scattering Centers Models." Sensors 22, no. 3 (February 8, 2022): 1293. http://dx.doi.org/10.3390/s22031293.
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This article proposes an Automatic Target Recognition (ATR) algorithm to classify non-cooperative targets in Synthetic Aperture Radar (SAR) images. The scarcity or nonexistence of measured SAR data demands that classification algorithms rely only on synthetic data for training purposes. Based on a model represented by the set of scattering centers extracted from purely synthetic data, the proposed algorithm generates hypotheses for the set of scattering centers extracted from the target under test belonging to each class. A Goodness of Fit test is considered to verify each hypothesis, where the Likelihood Ratio Test is modified by a scattering center-weighting function common to both the model and target. Some algorithm variations are assessed for scattering center extraction and hypothesis generation and verification. The proposed solution is the first model-based classification algorithm to address the recently released Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset on a 100% synthetic training data basis. As a result, an accuracy of 91.30% in a 10-target test within a class experiment under Standard Operating Conditions (SOCs) was obtained. The algorithm was also pioneered in testing the SAMPLE dataset in Extend Operating Conditions (EOCs), assuming noise contamination and different target configurations. The proposed algorithm was shown to be robust for SNRs greater than −5 dB.
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Premchand, Anshu, M. Sandhya, and Sharmila Sankar. "Simplification of application operations using cloud and DevOps." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (January 1, 2019): 85. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp85-93.
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<span>In our experience of having worked with financial institutions for over 19 calendar years, we have seen that application development, maintenance and operations teams have a very silo-ed existence. In today's ever more fluid market, it is imperative for these teams to be very agile in their response to business needs. While development teams are trying to adopt agile and devops practices, operations and maintenance teams are sometimes forgotten in this transformation journey even though they play a very crucial role in managing customer facing systems. In this paper, we focus on application operations for their simplification, outline the post production challenges faced by the operations teams, assistance required from the development teams and need for coordination & harmony between application development and operations teams. We use the example of a financial organization where we are currently helping in simplification of application support processes using two key levers of cloud and devops adoption to highlight the path to simplification. We outline the solution levers we used, solution tenets, solution approach and considerations. We also share a target state model and regulated key functions & activities within the model as well as an interim operating model and a target operating model.</span>
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Zhou, Guanghui, Fengtian Chang, Changle Tian, and Qi Lu. "A reliability optimization allocation method for the hydraulic system of CNC honing machine considering the operating condition information." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 12 (July 6, 2017): 2244–64. http://dx.doi.org/10.1177/0954406217719475.
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In recent years, reliability optimization allocation has drawn a broad spectrum of attentions, especially for complex systems consisting of a large number of subsystems and components. Hydraulic system, deemed as a complex system, plays a great role in reliability increment of the CNC honing machine. In order to realize the CNC honing machine tool’s balanced operating performance, the crucial hydraulic system is designed to obtain the optimal and well-balanced allocation solution under the constrains of cost and reliability. Meanwhile, the quantified operating factor that influences the allocation solution is rarely considered adequately when operating samples are insufficient. So, if hydraulic systems are used in a new required longer operating mission duration or a new severer operating environment, the dissatisfied reliability could not be better reallocated to adapt to this environment with appropriate methods. Aimed at overcoming the abovementioned shortcomings and optimizing the dissatisfied reliability for existing hydraulic systems, this paper proposes a reliability optimization allocation method considering operating the condition information. From the standpoint of the complex system, this method firstly builds a three-level reliability optimization allocation model based on the analytic target cascading for the specific hydraulic system of CNC honing machine 2MK2263 × 200. It transforms the complex reliability allocation problem to a set of smaller and coordinated optimization sub-problems. Secondly, through studying the operating reliability problems of small samples in the new operating scene, the reliability prediction results based on the operating condition information are applied to build the initial reliability targets, which are regarded as the initial constrains for the optimization. With the help of analytic target cascading model and initial target reliability, the optimal reliability for the complex hydraulic system would be obtained. It is also shown to provide an innovative route for the reliability redesign of the related complex systems for this new required operating scene.
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Tan, Jian, Xiangtao Fan, Shenghua Wang, and Yingchao Ren. "Target Recognition of SAR Images via Matching Attributed Scattering Centers with Binary Target Region." Sensors 18, no. 9 (September 10, 2018): 3019. http://dx.doi.org/10.3390/s18093019.
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A target recognition method of synthetic aperture radar (SAR) images is proposed via matching attributed scattering centers (ASCs) to binary target regions. The ASCs extracted from the test image are predicted as binary regions. In detail, each ASC is first transformed to the image domain based on the ASC model. Afterwards, the resulting image is converted to a binary region segmented by a global threshold. All the predicted binary regions of individual ASCs from the test sample are mapped to the binary target regions of the corresponding templates. Then, the matched regions are evaluated by three scores which are combined as a similarity measure via the score-level fusion. In the classification stage, the target label of the test sample is determined according to the fused similarities. The proposed region matching method avoids the conventional ASC matching problem, which involves the assignment of ASC sets. In addition, the predicted regions are more robust than the point features. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used for performance evaluation in the experiments. According to the experimental results, the method in this study outperforms some traditional methods reported in the literature under several different operating conditions. Under the standard operating condition (SOC), the proposed method achieves very good performance, with an average recognition rate of 98.34%, which is higher than the traditional methods. Moreover, the robustness of the proposed method is also superior to the traditional methods under different extended operating conditions (EOCs), including configuration variants, large depression angle variation, noise contamination, and partial occlusion.
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Zhao, Rui Qi, Fu Xian Liu, Bo Zhang, and Yong Ge Chen. "Model of Fire Co-Operation Based on DCPN." Applied Mechanics and Materials 198-199 (September 2012): 848–51. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.848.
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To meet the need of deploying decision, co-operating decision, calculating the intercepting probability of target and realizing the simulation of firing in air-defense, we established the Dynamic Colored Petri Net, DCPN model, to solve the questions above. The model is definitely intuitionistic, convenient to realize in computer and clear to understand.
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Ying, Zilu, Chen Xuan, Yikui Zhai, Bing Sun, Jingwen Li, Wenbo Deng, Chaoyun Mai, et al. "TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR." Sensors 20, no. 6 (March 19, 2020): 1724. http://dx.doi.org/10.3390/s20061724.
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Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.
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Che, Changchang, Huawei Wang, Qiang Fu, and Xiaomei Ni. "Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions." Advances in Mechanical Engineering 11, no. 12 (December 2019): 168781401989721. http://dx.doi.org/10.1177/1687814019897212.
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Rolling bearings are the vital components of rotary machines. The collected data of rolling bearing have strong noise interference, massive unlabeled samples, and different fault features. Thus, a deep transfer learning method is proposed for rolling bearings fault diagnosis under variable operating conditions. To obtain robust feature representation, the denoising autoencoder is used to denoise and reduce dimension of unlabeled rolling bearing signals. For those unlabeled target domain signals, a feature matching method based on multi-kernel maximum mean discrepancies between source domain and target domain is adopted to get enough labeled target domain samples. Then, these rolling bearing signals are converted to multi-dimensional graph samples and fed into a convolutional neural network model for fault diagnosis. To improve the generalization of convolutional neural network under variable operating conditions, we combine model-based transfer learning with feature-based transfer learning to initialize and optimize the convolutional neural network parameters. The effectiveness of the proposed method is validated through several comparative experiments of Case Western Reserve University data. The results demonstrate that the proposed method can learn features adaptively from noisy data and increase the accuracy rate by 2%–8% comparing with other models.
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Paul, Michael, Ido Silverman, Shlomi Halfon, Semion Sukoriansky, Boris Mikhailovich, Tala Palchan, Arkady Kapusta, et al. "A 50 kW Liquid-Lithium Target for BNCT and Material-Science Applications." EPJ Web of Conferences 231 (2020): 03004. http://dx.doi.org/10.1051/epjconf/202023103004.
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A compact Liquid Lithium Target (LiLiT) has been operating at SARAF for several years with beam power of several kW (1.9-2.5 MeV, up to 2 mA). When bombarding the lithium with low energy protons neutrons are generated. The neutron source, mainly used for nuclear astrophysics research, was decommissioned in 2016 towards an upgraded model - with possible applications to Boron Neutron Capture Therapy (BNCT) and material-science studies. The improved version has been designed to sustain 50 kW proton beam power (2.5 MeV, ~20 mA) to provide sufficient neutron flux required for clinical BNCT application. The new model has a 50 mm wide lithium jet to enable dissipation of the higher beam power and an improved heat exchanger to remove the power to a secondary cooling loop. A new Annular Linear INduction electro-magnetic pump (ALIN) has been designed and built to provide the required lithium flow rate. Other mechanical improvements facilitate the maintenance of the system and the robustness of operation. Radiological risks due to the 7Be produced in the reaction are reduced by using an integrated lead shielding of the lithium reservoir. An integrated neutron moderator is being designed to adjust the neutron energy to the spectrum best suited to BNCT. A low power (6 kW) model of the new design with a narrower nozzle (18 mm wide) and a rotating-magnet electro-magnetic pump is operating at SARAF to support the ongoing astrophysics and nuclear research program [1], [2]. To fulfill clinical BNCT, the upgraded LiLiT model will require an accelerator of appropriate energy and intensity. The design features of the new system are presented in this paper.
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Luo, Haifeng. "Automatic Manipulator Tracking Control Based on Moving Target Trajectory Prediction." Scientific Programming 2021 (November 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/7944300.
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The core issue of automatic manipulator tracking control is how to ensure the given moving target follows the expected trajectory and adapts to various uncertain factors. However, the existing moving target trajectory prediction methods rely on highly complex and accurate models, lacking the ability to generalize different automatic manipulator tracking scenarios. Therefore, this study tries to find a way to realize automatic manipulator tracking control based on moving target trajectory prediction. In particular, a moving target trajectory prediction model was established, and its parameters were optimized. Next, a tracking-training-testing algorithm was proposed for manipulator’s automatic moving target tracking, and the operating flows were detailed for training module, target detection module, and target tracking module. The proposed model and algorithm were proved effective through experiments.
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Wang, Yingxu, Cyprian F. Ngolah, Guangping Zeng, Philip C. Y. Sheu, C. Philip Choy, and Yousheng Tian. "The Formal Design Model of a Real-Time Operating System (RTOS+)." International Journal of Software Science and Computational Intelligence 2, no. 2 (April 2010): 105–22. http://dx.doi.org/10.4018/jssci.2010040106.
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A real-time operating system (RTOS) provides a platform for the design and implementation of a wide range of applications in real-time systems, embedded systems, and mission-critical systems. This paper presents a formal design model for a general RTOS known as RTOS+ that enables a specific target RTOS to be rigorously and efficiently derived in real-world applications. The methodology of a denotational mathematics, Real-Time Process Algebra (RTPA), is described for formally modeling and refining architectures, static behaviors, and dynamic behaviors of RTOS+. The conceptual model of the RTOS+ system is introduced as the initial requirements for the system. The architectural model of RTOS+ is created using RTPA architectural modeling methodologies and refined by a set of Unified Data Models (UDMs). The static behaviors of RTOS+ are specified and refined by a set of Unified Process Models (UPMs). The dynamic behaviors of the RTOS+ system are specified and refined by the real-time process scheduler and system dispatcher. This work is presented in two papers; the conceptual and architectural models of RTOS+ is described in this paper, while the static and dynamic behavioral models of RTOS+ will be elaborated in a forthcoming paper.
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Latif, Sarmad Dashti, Suzlyana Marhain, Md Shabbir Hossain, Ali Najah Ahmed, Mohsen Sherif, Ahmed Sefelnasr, and Ahmed El-Shafie. "Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia." Sustainability 13, no. 11 (May 24, 2021): 5900. http://dx.doi.org/10.3390/su13115900.
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In planning and managing water resources, the implementation of optimization techniques in the operation of reservoirs has become an important focus. An optimal reservoir operating policy should take into consideration the uncertainty associated with uncontrolled reservoir inflows. The charged system search (CSS) algorithm model is developed in the present study to achieve optimum operating policy for the current reservoir. The aim of the model is to minimize the cost of system performance, which is the sum of square deviations from the distinction between the release of the target and the actual demand. The decision variable is the release of a reservoir with an initial volume of storage, reservoir inflow, and final volume of storage for a given period. Historical rainfall data is used to approximate the inflow volume. The charged system search (CSS) is developed by utilizing a spreadsheet model to simulate and perform optimization. The model gives the steady-state probabilities of reservoir storage as output. The model is applied to the reservoir of Klang Gates for the development of an optimal reservoir operating policy. The steady-state optimal operating system is used in this model.
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Acarlar Barlas, Merve, Haluk Gozde, and Semih Ozden. "Modelling and Optimisation of UHF band EW Based WTA Problem within the Scope of Threat Assessment." Defence Science Journal 72, no. 1 (January 5, 2022): 73–82. http://dx.doi.org/10.14429/dsj.72.17218.
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The classical weapon target allocation (WTA) problem has been evaluated within the scope of electronic warfare (EW) threat assessment with an electromagnetic effect-based jammer- tactical radio engagement approach. As different from the literature, optimum allocation of non-directional jammers operating at different operating UHF frequencies under constraints to RF emitters is aimed in this study. The values of the targets are modelled using an original threat assessment algorithm developed that takes into account operating frequencies, jamming distance, and weather conditions. The computed jammer-target effect matrix has been solved under different scenarios according to the efficiency and cost constraints. It is seen at the end of the simulations that the allocation results for EW applications largely depend on the effect ratio used. The better results are taken in the case of under 0.5 effect ratio. Finally, jammer-radio allocation problem specified at the suggested model is solved successfully and effectively.
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Abdulla, Hind, Heungjo An, Imad Barsoum, and Maher Maalouf. "Mathematical Modeling of Multi-Performance Metrics and Process Parameter Optimization in Laser Powder Bed Fusion." Metals 12, no. 12 (December 6, 2022): 2098. http://dx.doi.org/10.3390/met12122098.
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This study aims to develop mathematical models to improve multi-performance metrics, such as relative density and operating costs, in laser powder bed fusion (LPBF), also known as selective laser melting, a metallic additive manufacturing technique, by optimizing the printing process parameters. The work develops a data-driven model for relative density based on measurements and an analytical model for operating costs related to the process parameters. Optimization models are formulated to maximize relative density or minimize operating costs by determining the optimal set of process parameters, while meeting a target level of the other performance metrics (i.e., relative density or operating costs). Furthermore, new metrics are devised to test the sensitivity of the optimization solutions, which are used in a novel robust optimization model to acquire less sensitive process parameters. The sensitivity analysis examines the effect of varying some parameters on the relative density of the fabricated specimens. Samples with a relative density greater than 99% and a machine operating cost of USD 1.00 per sample can be produced, utilizing a combination of low laser power (100 W), high scan speed (444 mm/s), moderate layer thickness (0.11 mm), and large hatch distance (0.4 mm). This is the first work to investigate the relationship between the quality of the fabricated samples and operating cost in the LPBF process. The formulated robust optimization model achieved less sensitive parameter values that may be more suitable for real operations. The equations used in the models are verified via 10-fold cross-validation, and the predicted results are further verified by comparing them with the experimental data in the literature. The multi-performance optimization models and framework presented in this study can pave the way for other additive manufacturing techniques and material grades for successful industrial-level implementation.
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Quartagno, Matteo, James R. Carpenter, A. Sarah Walker, Michelle Clements, and Mahesh KB Parmar. "The DURATIONS randomised trial design: Estimation targets, analysis methods and operating characteristics." Clinical Trials 17, no. 6 (August 16, 2020): 644–53. http://dx.doi.org/10.1177/1740774520944377.
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Background: Designing trials to reduce treatment duration is important in several therapeutic areas, including tuberculosis and bacterial infections. We recently proposed a new randomised trial design to overcome some of the limitations of standard two-arm non-inferiority trials. This DURATIONS design involves randomising patients to a number of duration arms and modelling the so-called ‘duration-response curve’. This article investigates the operating characteristics (type-1 and type-2 errors) of different statistical methods of drawing inference from the estimated curve. Methods: Our first estimation target is the shortest duration non-inferior to the control (maximum) duration within a specific risk difference margin. We compare different methods of estimating this quantity, including using model confidence bands, the delta method and bootstrap. We then explore the generalisability of results to estimation targets which focus on absolute event rates, risk ratio and gradient of the curve. Results: We show through simulations that, in most scenarios and for most of the estimation targets, using the bootstrap to estimate variability around the target duration leads to good results for DURATIONS design-appropriate quantities analogous to power and type-1 error. Using model confidence bands is not recommended, while the delta method leads to inflated type-1 error in some scenarios, particularly when the optimal duration is very close to one of the randomised durations. Conclusions: Using the bootstrap to estimate the optimal duration in a DURATIONS design has good operating characteristics in a wide range of scenarios and can be used with confidence by researchers wishing to design a DURATIONS trial to reduce treatment duration. Uncertainty around several different targets can be estimated with this bootstrap approach.
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Kim, J. H., T. W. Song, T. S. Kim, and S. T. Ro. "Model Development and Simulation of Transient Behavior of Heavy Duty Gas Turbines." Journal of Engineering for Gas Turbines and Power 123, no. 3 (October 1, 2000): 589–94. http://dx.doi.org/10.1115/1.1370973.
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This paper describes models for the transient analysis of heavy duty gas turbines, and presents dynamic simulation results of a modern gas turbine for electric power generation. Basic governing equations have been derived from integral forms of unsteady conservation equations. Mathematical models of each component are described with the aid of unsteady one-dimensional governing equations and steady-state component characteristics. Special efforts have been made to predict compressor characteristics including the effect of movable vanes, which govern the operating behavior of a whole engine. The derived equation sets are solved numerically by a fully implicit method. A controller model that maintains constant rotational speed and target temperature (turbine inlet or exhaust temperature) is used to simulate practical operations. Component models, especially those of the compressor, are validated through comparison with test data. The dynamic behavior of a 150 MW class engine is simulated. The time-dependent variations of engine parameters such as power, rotational speed, fuel, temperature, and guide vane angles are compared with field data. Simulated results are fairly close to the operation data.
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Zhao, Lijun, Qingsheng Li, and Bingbing Li. "SAR Target Recognition via Monogenic Signal and Gaussian Process Model." Mathematical Problems in Engineering 2022 (September 13, 2022): 1–7. http://dx.doi.org/10.1155/2022/3086486.
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The monogenic signal and Gaussian process model are applied to synthetic aperture radar (SAR) target recognition. The monogenic signal is used to extract the features of the SAR image. The Gaussian process model is a statistical learning algorithm based on the Bayesian theory, which constructs a classification model by combining the kernel function and the probability judgement. Compared with the traditional classification model, the Gaussian process model can obtain higher classification efficiency and accuracy. During the implementation, the monogenic feature vector of the SAR image is used as the input, and the target label is used as the output to train the Gaussian process model. For the test sample to be classified, the target label is determined by calculating the posterior probability of each class using the Gaussian process model. In the experiments, the validations are carried out under typical conditions based on the MSTAR dataset. According to the experimental results, the proposed method maintains the highest performance under the standard operating condition, depression angle differences, and noise corruption, which verifies its effectiveness and robustness.
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Wang, Yingxu, Guangping Zeng, Cyprian F. Ngolah, Philip C. Y. Sheu, C. Philip Choy, and Yousheng Tian. "The Formal Design Model of a Real-Time Operating System (RTOS+)." International Journal of Software Science and Computational Intelligence 2, no. 3 (July 2010): 79–105. http://dx.doi.org/10.4018/jssci.2010070106.
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A real-time operating system (RTOS) provides a platform for the design and implementation of a wide range of applications in real-time systems, embedded systems, and mission-critical systems. This paper presents a formal design model for a general RTOS known as RTOS+ that enables a specific target RTOS to be rigorously and efficiently derived in real-world applications. The methodology of a denotational mathematics, Real-Time Process Algebra (RTPA), is described for formally modeling and refining architectures, static behaviors, and dynamic behaviors of RTOS+. The conceptual model of the RTOS+ system is introduced as the initial requirements for the system. The architectural model of RTOS+ is created using RTPA architectural modeling methodologies and refined by a set of Unified Data Models (UDMs). The static behaviors of RTOS+ are specified and refined by a set of Unified Process Models (UPMs). The dynamic behaviors of the RTOS+ system are specified and refined by the real-time process scheduler and system dispatcher. This work is presented in two papers in serial due to its excessive length. The static and dynamic behavioral models of RTOS+ is described in this paper; while the conceptual and architectural models of RTOS+ has been published in IJSSCI 2(2).
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Стародубцев, V. Starodubtsev, Фарафонов, and A. Farafonov. "IDENTIFICATION OF CONSTANTLY OPERATING MATHEMATICAL MODELS FOR QUALITY CONTROL OF UNIVERSITY STUDENTS’ KNOWLEDGE." Modeling of systems and processes 7, no. 2 (October 30, 2014): 36–40. http://dx.doi.org/10.12737/6079.
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Based on the theory of self-organization has developed an original method for identification of target functions students’ knowledge quality (SKQ) in the face of influencing factors uncertainty. The proposed system of external criteria for the models selection and control parameters allows to use the model for the effective management of the SKQ
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Dong, Fei, Xiao Yu, Xinguo Shi, Ke Liu, Zhaoli Wu, and Wanli Yu. "A New Transferable Fault Diagnosis Approach of Rotating Machinery Based on Deep Autoencoder and Dominant Features Selection under Different Operating Conditions." Shock and Vibration 2021 (October 30, 2021): 1–21. http://dx.doi.org/10.1155/2021/7383255.
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In the actual industrial scenarios, most existing fault diagnosis approaches are faced with two challenges, insufficient labeled training data and distribution divergences between training and testing datasets. For the above issues, a new transferable fault diagnosis approach of rotating machinery based on deep autoencoder and dominant features selection is proposed in this article. First, maximal overlap discrete wavelet packet transform is applied for signals processing and mix-domains statistical feature extraction. Second, dominant features selection by importance score and differences between domains is proposed to select dominant features with high fault-discriminative ability and domain invariance. Then, selected dominant features are used for pretraining deep autoencoder (source model), which helps in enhancing the fault representative ability of deep features. The parameters of the source model are transferred to the target model, and normal state features from target domain are adopted for fine-tuning the target model. Finally, the target model is applied for fault patterns classification. Motor and bearing fault datasets are used for a series of experiments, and the results verify that the proposed methods have better cross-domain diagnosis performance than comparative models.
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Hu, Dawei, Gangyan Li, and Feng Deng. "Gain-Scheduled Model Predictive Control for a Commercial Vehicle Air Brake System." Processes 9, no. 5 (May 20, 2021): 899. http://dx.doi.org/10.3390/pr9050899.
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This paper presents a control-oriented Linear Parameter-Varying (LPV) model for commercial vehicle air brake systems with the electro-pneumatic proportional valve based on the nonlinear mathematical model, a set of discrete-time linearized models at different target pressures with the q-Markov Cover system identification method. The scheduled parameters for the LPV model were the brake chamber pressure, which was controlled by the electro-pneumatic proportional valve. On the basis of the LPV model, a family of Model Predictive Control (MPC) controllers with a Kalman filter was designed at each operation point. Then, the gain-scheduled MPC was designed over the entire operating range with the switched strategy, which was validated by experimental data. Furthermore, compared with the PID controller, the performance of the system was improved with a gain-scheduled MPC controller.
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Choi, Woo Yeong, Kyung Soo Kook, and Hyeong-Jun Yoo. "Effect Quantification of BESS Providing Frequency Response on Penetration Limit of VER in Power Systems." Energies 15, no. 24 (December 11, 2022): 9380. http://dx.doi.org/10.3390/en15249380.
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Increasing the penetration of variable energy resources (VER) can reduce the inertia and frequency response performance of power systems supported by replacement synchronous power generation. Therefore, it is necessary to manage the VER penetration limit in power systems for stable operation and to increase the operability to the desired level. This study proposes a method to evaluate and quantify the effect of increasing the penetration limit of VER by controlling a battery energy storage system (BESS). The BESS can provide a fast response, but frequency response performance varies depending on the operating conditions. In the proposed quantification method, various control methods of a BESS, operating conditions of the power system, and penetration conditions of additional VER were analyzed, and the effect of the BESS on increasing the penetration limit of VER was evaluated. This evaluation and analysis enabled the selection of the BESS operating conditions to achieve the target VER capacity in the power system. The proposed quantification method was analyzed through simulations based on the Korean power system model. Therefore, it can contribute to estimating the required performance of the BESS for each system operating condition required to achieve the VER target.
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Huang, Kun, Jingtao Zhao, Xiaohan Sun, Wei Li, and Shu Zheng. "Analysis of Optimal Operation of Charging Stations Based on Dynamic Target Tracking of Electric Vehicles." Electronics 11, no. 19 (October 2, 2022): 3175. http://dx.doi.org/10.3390/electronics11193175.
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In view of the large impact of traditional charging stations on the power grid and the investment in the construction of charging stations for electric vehicle infrastructure services, this paper considers the configuration of optical storage equipment in charging stations from a practical point of view and proposes an economic operation strategy for charging stations to meet the economically optimal requirements of different scenarios. First, we analyze the behavioral characteristics of multiple types of electric vehicles, consider the influence of charging queues, and establish a daily load model of charging stations by taking into account the daily monitoring load and nighttime lighting load of charging stations. Then, considering the electric vehicle (EV) charging demand, photovoltaic (PV) output and energy storage charging and discharging power, the daily economic optimal operation problem based on the dynamic target tracking of charging stations is established; the objective is to maximize the daily operating revenue of charging stations under the condition of satisfying the EV charging demand and PV consumption. Secondly, the objective function is linearized, and the economic operation model is transformed into a mixed integer linear programming model for solving, and the simulation is verified under different scenarios. The results show that the economic optimal operation strategy can adapt to the economic operation requirements of charging stations in different scenarios and maximize the charging station revenue.
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Kang, Hyeunseok, Sungwoo Goo, Hyunjung Lee, Jung-woo Chae, Hwi-yeol Yun, and Sangkeun Jung. "Fine-tuning of BERT Model to Accurately Predict Drug–Target Interactions." Pharmaceutics 14, no. 8 (August 16, 2022): 1710. http://dx.doi.org/10.3390/pharmaceutics14081710.
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The identification of optimal drug candidates is very important in drug discovery. Researchers in biology and computational sciences have sought to use machine learning (ML) to efficiently predict drug–target interactions (DTIs). In recent years, according to the emerging usefulness of pretrained models in natural language process (NLPs), pretrained models are being developed for chemical compounds and target proteins. This study sought to improve DTI predictive models using a Bidirectional Encoder Representations from the Transformers (BERT)-pretrained model, ChemBERTa, for chemical compounds. Pretraining features the use of a simplified molecular-input line-entry system (SMILES). We also employ the pretrained ProBERT for target proteins (pretraining employed the amino acid sequences). The BIOSNAP, DAVIS, and BindingDB databases (DBs) were used (alone or together) for learning. The final model, taught by both ChemBERTa and ProtBert and the integrated DBs, afforded the best DTI predictive performance to date based on the receiver operating characteristic area under the curve (AUC) and precision-recall-AUC values compared with previous models. The performance of the final model was verified using a specific case study on 13 pairs of subtrates and the metabolic enzyme cytochrome P450 (CYP). The final model afforded excellent DTI prediction. As the real-world interactions between drugs and target proteins are expected to exhibit specific patterns, pretraining with ChemBERTa and ProtBert could teach such patterns. Learning the patterns of such interactions would enhance DTI accuracy if learning employs large, well-balanced datasets that cover all relationships between drugs and target proteins.
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Sepci, Anthony, Peter Heim, and Arsen Babayan. "Lessons Learned from the Crisis and Thoughts on a New Non-Agency Mortgage Target Operating Model." Journal of Structured Finance 20, no. 2 (July 31, 2014): 19–27. http://dx.doi.org/10.3905/jsf.2014.20.2.019.
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Kapil, Sheeba, and Gaurav Barick. "Post-acquisition Performance Analysis of Indian Target Firms: The Role of Deal Characteristics." Indian Economic Journal 66, no. 3-4 (December 2018): 250–69. http://dx.doi.org/10.1177/0019466220938042.
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The aim of this article is to identify and determine various dimensions of deal characteristics affecting the post-acquisition performance of Indian target firms. Available researches have focussed on the stock market reaction as the indicator of target firm performance. Nonetheless, various determinants are found to affect the post-acquisition performance of target firms. Bearing that in mind, this article focusses on the significance of deal characteristics while assessing the performance of the Indian target firms post-acquisition. The article attempts to analyse the post-acquisition performance of target firms independent of the acquirer firm to get a more appropriate perception of their position. The change model, generalised method of moments (GMM) model and the regression model has been employed to assess the changes in operating performance, analyse the importance of financial variables in post-acquisition performance and study the impact of deal characteristics in the post-acquisition performance of the Indian target firms. The outcomes reveal that there has been a significant difference in the improvement of targets depending on whether it has been taken over by a domestic acquirer or inbound acquirer.
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Cheng, Shicheng, Liang Zhang, Bo Jin, Qiang Zhang, Xinjiang Lu, Mao You, and Xueqing Tian. "GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures." Applied Sciences 11, no. 7 (April 4, 2021): 3239. http://dx.doi.org/10.3390/app11073239.
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The prediction of drug–target interactions is always a key task in the field of drug redirection. However, traditional methods of predicting drug–target interactions are either mediocre or rely heavily on data stacking. In this work, we proposed our model named GraphMS. We merged heterogeneous graph information and obtained effective node information and substructure information based on mutual information in graph embeddings. We then learned high quality representations for downstream tasks, and proposed an end–to–end auto–encoder model to complete the task of link prediction. Experimental results show that our method outperforms several state–of–the–art models. The model can achieve the area under the receiver operating characteristics (AUROC) curve of 0.959 and area under the precise recall curve (AUPR) of 0.847. We found that the mutual information between the substructure and graph–level representations contributes most to the mutual information index in a relatively sparse network. And the mutual information between the node–level and graph–level representations contributes most in a relatively dense network.
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Levy, Chagai, Monika Pinchas, and Yosef Pinhasi. "Coherent Integration Loss Due to Nonstationary Phase Noise in High-Resolution Millimeter-Wave Radars." Remote Sensing 13, no. 9 (April 30, 2021): 1755. http://dx.doi.org/10.3390/rs13091755.
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Phase noise refers to the instability of an oscillator, which is the cause of instantaneous phase and frequency deviations in the carrier wave. This unavoidable instability adversely affects the performance of range–velocity radar systems, including synthetic aperture radars (SARs) and ground-moving target indicator (GMTI) radars. Phase noise effects should be considered in high-resolution radar designs, operating in millimeter wavelengths and terahertz frequencies, due to their role in radar capability during the reliable identification of target location and velocity. In general, phase noise is a random process consisting of nonstationary terms. It has been shown that in order to optimize the coherent detection of stealthy, fast-moving targets with a low radar cross-section (RCS), it is required to evaluate the integration gain and to determine the incoherent noise effects for resolving target location and velocity. Here, we present an analytical expression for the coherent integration loss when a nonstationary phase noise is considered. A Wigner distribution was employed to derive the time–frequency expression for the coherent loss when nonstationary conditions were considered. Up to now, no analytical expressions have been developed for coherent integration loss when dealing with real nonstationary phase noise mathematical models. The proposed expression will help radar systems estimate the nonstationary integration loss and adjust the decision threshold value in order to maximize the probability of detection. The effect of nonstationary phase noise is demonstrated for studying coherent integration loss of high-resolution radar operating in the W-band. The investigation indicates that major degradation in the time-frequency coherent integration due to short-term, nonstationary phase noise instabilities arises for targets moving at low velocities and increases with range. Opposed to the conventional model, which assumes stationarity, a significant difference of up to 25 dB is revealed in the integration loss for radars operating in the millimeter wave regime. Moreover, for supersonic moving targets, the loss peaks at intermediate distances and then reduces as the target moves away.
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Cho, Sunghyun, Dongwoo Kang, Joseph Sang-Il Kwon, Minsu Kim, Hyungtae Cho, Il Moon, and Junghwan Kim. "A Framework for Economically Optimal Operation of Explosive Waste Incineration Process to Reduce NOx Emission Concentration." Mathematics 9, no. 17 (September 6, 2021): 2174. http://dx.doi.org/10.3390/math9172174.
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Explosives, especially those used for military weapons, have a short lifespan and their performance noticeably deteriorates over time. These old explosives need to be disposed of safely. Fluidized bed incinerators (FBIs) are safe for disposal of explosive waste (such as TNT) and produce fewer gas emissions compared to conventional methods, such as the rotary kiln. However, previous studies on this FBI process have only focused on minimizing the amount of NOx emissions without considering the operating and unitality costs (i.e., total cost) associated with the process. It is important to note that, in general, a number of different operating conditions are available to achieve a target NOx emission concentration and, thus, it requires a significant computational requirement to compare the total costs among those candidate operating conditions using a computational fluid dynamics simulation. To this end, a novel framework is proposed to quickly determine the most economically viable FBI process operating condition for a target NOx concentration. First, a surrogate model was developed to replace the high-fidelity model of an FBI process, and utilized to determine a set of possible operating conditions that may lead to a target NOx emission concentration. Second, the candidate operating conditions were fed to the Aspen Plus™ process simulation program to determine the most economically competitive option with respect to its total cost. The developed framework can provide operational guidelines for a clean and economical incineration process of explosive waste.
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Janzing, Dominik, and Paweł Wocjan. "Does Universal Controllability of Physical Systems Prohibit Thermodynamic Cycles?" Open Systems & Information Dynamics 25, no. 03 (September 2018): 1850016. http://dx.doi.org/10.1142/s1230161218500166.
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Here we study the thermodynamic cost of computation and control using ‘physically universal’ cellular automata (CAs) or Hamiltonians. The latter were previously defined as systems that admit the implementation of any desired transformation on a finite target region by first initializing the state of the surrounding and then letting the system evolve according to its autonomous dynamics. This way, one obtains a model of control where each region can play both roles, the controller or the system to be controlled. In physically universal systems every degree of freedom is indirectly accessible by operating on the remaining degrees of freedom. In a nutshell, the thermodynamic cost of an operation is then given by the size of the region around the target region that needs to be initialized. In the meantime, physically universal CAs have been constructed by Schaeffer (in two dimensions) and Salo & Törmä (in one dimension). Here we show that in Schaeffer’s CA the cost for implementing n operations grows linearly in n, while operating in a thermodynamic cycle requires sublinear growth to ensure zero cost per operation in the limit n → ∞. Although this particular result need not hold for general physically universal CAs, this strong notion of universality does imply a certain kind of instability of information, which could result in lower bounds on the cost of protecting information from its noisy environment. The technical results of the paper are sparse and quite simple. The contribution of the paper is mainly conceptual and consists in illustrating the type of thermodynamic questions raised by models of control that rely on the concept of physical universality.
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Zhang, Dewei, Hui Qi, Xiande Wu, Yaen Xie, and Jiangtao Xu. "The Quadrotor Dynamic Modeling and Indoor Target Tracking Control Method." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/637034.
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A reliable nonlinear dynamic model of the quadrotor is presented. The nonlinear dynamic model includes actuator dynamic and aerodynamic effect. Since the rotors run near a constant hovering speed, the dynamic model is simplified at hovering operating point. Based on the simplified nonlinear dynamic model, the PID controllers with feedback linearization and feedforward control are proposed using the backstepping method. These controllers are used to control both the attitude and position of the quadrotor. A fully custom quadrotor is developed to verify the correctness of the dynamic model and control algorithms. The attitude of the quadrotor is measured by inertia measurement unit (IMU). The position of the quadrotor in a GPS-denied environment, especially indoor environment, is estimated from the downward camera and ultrasonic sensor measurements. The validity and effectiveness of the proposed dynamic model and control algorithms are demonstrated by experimental results. It is shown that the vehicle achieves robust vision-based hovering and moving target tracking control.
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Wu, Wen Rong, Da Hai Yu, Lin Zhang, Zu Rong Qiu, and Xin Han Huang. "Development of a Precision Micro-Assembly Machine for ICF Targets." Advanced Materials Research 605-607 (December 2012): 1431–35. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1431.
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The Precision Micro-Assembly Machine was developed to manufacture the ICF targets, which have submillimeter-sized components with micrometer sized features. The machine provides simultaneous manipulation of three objects in a 3-cm3 operating arena and can stitch together multiple millimeter-scales operating arenas over distances spanning tens of centimeters with micrometer-level accuracy. In this report, we will discuss the technologies that are merged to help assist the research community with micro-component fabrication. Furthermore, we also present the design of the online monitoring system, which contains two kinds of non-contact measurement system, microscopic image and laser confocal. The online monitoring system can characterize the positions of the target components during the assembly process. The effect and mathematic model of on-line measuring was analyzed. The results of target assembly experiments show position tolerance of the assembly is not more than 10μm.
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Wang, Xuan, Yuliang Lu, Xuehu Yan, Da Li, and Chunqian He. "Synthetic Aperture Radar Target Recognition Based on Multimodule Image Enhancement Network." Security and Communication Networks 2022 (November 21, 2022): 1–16. http://dx.doi.org/10.1155/2022/8465543.
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Synthetic aperture radar (SAR) has been widely used in recent years, and SAR automatic target recognition (ATR) has become a research hotspot. Most of the existing SAR ATR methods focus on the network structure design and increasing data volume but omit image quality and real-time processing. We design a multimodule image enhancement network (MMIE-Net) to solve these problems, which include the target extraction module, the image processing module, and convolutional neural networks (CNNs). First, we use the target extraction module and the image processing module to enhance the quality of raw SAR images. Then we design a suitable network for SAR image recognition, which is simple, lightweight, and recognizable. The experiment was mainly carried out on the MSTAR dataset, which can be divided into two categories, Standard Operating Condition (SOC) and Extended Operating Condition (EOC). The identification accuracy, the parameter storage space, and the depth of the model are considered as the criterion. The experimental results show that, compared with other methods, the proposed method not only ensures the simple structure of the network model but also has better recognition accuracy. Additionally, our method is robust and stable to large depression angle variation as well.
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Aybar, M., M. Carvallo, F. Fabacher, G. Pizarr, and P. Pastén. "Towards a benchmarking model for winery wastewater treatment and disposal." Water Science and Technology 56, no. 2 (July 1, 2007): 153–60. http://dx.doi.org/10.2166/wst.2007.484.
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We propose a benchmarking model for winery wastewater treatment systems and use it to quantitatively compare the performance of Chilean wine-making operations. The benchmarking model integrates three components: the influent characteristics, the wastewater treatment alternatives, and the location constraints. Four performance levels may be defined when plotting the available data of the wine production versus the ratio of wastewater to wine, for the French, US, and Chilean industries. Knowing where a certain system lies in this diagram helps to quantify the gap between the current and a target performance, and to set performance goals for planned expansions. The analysis of construction and operating costs of treatment systems currently in operation in Chile shows that similar compliance levels can be achieved at remarkably different costs. A steep decrease in the unitary cost is observed as wastewater flow increases; yet, the treatment alternative for achieving that cost may change. Further selection is obtained when location constraints are considered, including stringent discharge standards and proximity to urban settlements. The application of this simple benchmark model to three Chilean winery facilities shows how it produces meaningful quantitative and qualitative results. However, there is still ample room to improve this benchmarking model by considering additional complexity, including technical detail in the treatment options and costs related to technology conversion.
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Luo, Xin. "Research on Testing System of Multi-Target Machine Based on Virtual Network Technology." Applied Mechanics and Materials 539 (July 2014): 964–67. http://dx.doi.org/10.4028/www.scientific.net/amm.539.964.
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Research and development of independent brands enterprises need to experience complicated process. Therefore it needs to increase the value of own brand products. Only using diversified business model, it can achieve long-term strategic development of independent brands. Based on optimization mathematical of Gauss equations we establish diversified business model of independent brand, and design the objective function to optimize the process of product. Combining the computer internet system to do virtual simulation on the R & D products and diversified business model of machine independent product, the multiple operating profit and vibration table can be obtained, which provides the theory reference for the independent brand development and management research.
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Billatos, S. B., and L. A. Kendall. "A Replacement Model for Multi-Tool Transfer Lines." Journal of Engineering for Industry 112, no. 3 (August 1, 1990): 253–59. http://dx.doi.org/10.1115/1.2899583.
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Transfer lines have long been used for machining a single product at high production rates. This study deals with the transfer line, in particular, estimating the production time per part and the effect of using probability models in describing tool and machine lives. The production time is estimated using three major causes of line stoppage. An aspiration level criterion is used to establish a scheduled tool replacement interval. The aspiration decision parameter is system reliability. It is shown how a scheduled tool replacement interval could be established to obtain this reliability level. A methodology for selecting the replacement interval for a group of tools is developed and the impact of changing this interval on tool changing costs and tool failure costs is discussed. In this study, only a single premature tool failure is assumed to occur; however, the results are compared with multiple tool failures using the renewal function. It is shown that this single premature failure analysis is equivalent to the renewal function approach for machining systems having a reliability greater than 50 percent and tool failure variabilities having a coefficient of variation less than 1.0. Using the model developed in this paper, an example problem is presented. For this problem, electromechanical equipment failure and tool wear failures are modeled using the exponential and the Weibull probability distributions, respectively. Scheduled tool replacement intervals are developed for a range of target reliabilities. The part cycle time and operating cost for the transfer line based on these reliabilities are computed. These results are compared to the minimum cost reliability level. The minimum cost operating condition is dependent on the ratio of the scheduled replacement costs over the failure replacement costs. For the example presented, the reliability level at minimum cost is much lower than the desired target value of .9. This result illustrates the importance of conducting a careful analysis and using the results to help guide operating practices toward more efficient tool change practices and maintenance policies.
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Balal, Yael, Nezah Balal, Yair Richter, and Yosef Pinhasi. "Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar." Sensors 20, no. 17 (August 19, 2020): 4660. http://dx.doi.org/10.3390/s20174660.
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We present a technique for the identification of human and animal movement and height using a low power millimeter-wave radar. The detection was based on the transmission of a continuous wave and heterodyning the received signal reflected from the target to obtain micro-Doppler shifts associated with the target structure and motion. The algorithm enabled the extraction of target signatures from typical gestures and differentiated between humans, animals, and other ‘still’ objects. Analytical expressions were derived using a pendulum model to characterize the micro-Doppler frequency shifts due to the periodic motion of the human and animal limbs. The algorithm was demonstrated using millimeter-wave radar operating in the W-band. We employed a time–frequency distribution to analyze the detected signal and classify the type of targets.
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Tian, Hong Fang, Ying Hong Li, Li Zhi Shan, and Xiao Qing Hao. "Semi-Physical Simulation Platform Design Base on FMI." Applied Mechanics and Materials 336-338 (July 2013): 498–505. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.498.
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FMI is put forward by ITEA2, the target is the realization of cross platform, many areas of joint simulation. FMI is a standard interface which is used to implement the simulation. It is complete the following parts of specific functions: To load FMU model which is generated by other software , edit and compile the model , then download to target machine which VxWorks is running on; Realization of seamless connection with VxWorks target machine; Supporting the simulation model which is build by C/C++ language . Through this platform, FMU model and C/C++ model can be used in the Real time operating system of VxWorks, and realize the real time monitoring of simulation data, which make it becoming possible to realization of joint simulation.
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Feng, Wang, and Luo Yutao. "Modelling of a Power Converter with Multiple Operating Modes." World Electric Vehicle Journal 9, no. 1 (June 5, 2018): 7. http://dx.doi.org/10.3390/wevj9010007.
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In order to achieve DC voltage matching, on-board charging, and DC/AC power inversion, three independent power converters are often needed in traditional Distributed Power Converter (DPC) systems of electric vehicles (EVs): bidirectional DC/DC (Bi-DC/DC), AC/DC, and DC/AC. The requirement of electronic devices such as power switches, inductors, and capacitors make the converter costly and complicated in structure. In this paper, a power converter with multi-operating mode (PCMM) is presented. The proposed PCMM can work in Bi-DC/DC, AC/DC, and DC/AC modes. The state-space averaging model of PCMM considering resistance of Insulated Gate Bipolar Transistor (IGBT) and the inductor is presented. Based on this model, the transfer function of the system is derived and the controller is designed. The simulation and experimental results show that PCMM can meet the design target and verify the feasibility of the model. The measurement results show that the weight of PCMM proposed in this paper is reduced by 51.2% compared with the traditional structure.
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Jang, Gye-Bong, and Sung-Bae Cho. "Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions." Sensors 21, no. 4 (February 18, 2021): 1417. http://dx.doi.org/10.3390/s21041417.
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In recent years, various deep learning models have been developed for the fault diagnosis of rotating machines. However, in practical applications related to fault diagnosis, it is difficult to immediately implement a trained model because the distribution of source data and target domain data have different distributions. Additionally, collecting failure data for various operating conditions is time consuming and expensive. In this paper, we introduce a new transformation method for the latent space between domains using the source domain and normal data of the target domain that can be easily collected. Inspired by semantic transformations in an embedded space in the field of word embedding, discrepancies between the distribution of the source and target domains are minimized by transforming the latent representation space in which fault attributes are preserved. To match the feature area and distribution, spatial attention is applied to learn the latent feature spaces, and the 1D CNN LSTM architecture is implemented to maximize the intra-class classification. The proposed model was validated for two types of rotating machines such as a dataset of rolling bearings as CWRU and a gearbox dataset of heavy machinery. Experimental results show the proposed method has higher cross-domain diagnostic accuracy than others, therefore showing reliable generalization performance in rotating machines operating under various conditions.
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Jägersberg, Max, Michael Kosterhon, and Florian Ringel. "Caliper navigation for craniotomy planning of convexity targets." PLOS ONE 16, no. 5 (May 20, 2021): e0251023. http://dx.doi.org/10.1371/journal.pone.0251023.
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Introduction A technique to localize a radiological target on the head convexity fast and with acceptable precision is sufficient for surgeries of superficial intracranial lesions, and of help in the setting of emergency surgery, computer navigation breakdown, limited resources and education. We present a caliper technique based on fundamental geometry, with inexpensive and globally available tools (conventional CT or MRI image viewer, calculator, caliper). Methods The distances of the radiological target from two landmarks (nasion and porus acusticus externus) are assessed with an image viewer and Pythagoras’ theorem. The two distances are then marked around the landmarks onto the head of the patient with help of a caliper. The intersection defines the target. We tested the technique in a saw bone skull model and afterwards in the operating room. Convexity targets were localized with the caliper navigation technique and then with computer navigation as ground truth. Results In the saw bone model, the mean offset between the caliper navigated target and the real target was 2.9 ± 2.8 mm, 95% CI (1.6 mm; 4.2 mm). The mean offset between computer navigated target and real target was 1.6 ± 0.9 mm, 95% CI (1.2 mm; 2 mm) (ns). In 15 patients undergoing navigated cranial procedures, 100 targets were assessed in reference to computer navigation. The mean offset of the caliper navigation was 11 ± 5.2 mm, 95% CI (9.9 mm; 12 mm). Conclusion This is a low-tech approach for translation of a radiological target to the patient’s head in short time and with globally available inexpensive tools, with satisfying precision for many procedures.
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Ma, Ling. "Design and Discussion on Information Management and Control System of Agricultural Machinery and Equipment." Journal of Control Science and Engineering 2022 (October 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/5748161.
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In order to solve the problem of low development efficiency of agricultural mechanization information network system, a design and discussion method of agricultural machinery equipment information management and control system is proposed. This paper introduces the technologies required to realize the agricultural machinery operation management control system: Android development technology required to develop mobile phone positioning software, Internet map API for displaying location data, and construction of agricultural machinery resource allocation model for the realization of the scheduling module. Different objective functions are set, and the model results in different situations are obtained through experimental data. The experimental data are in the case of schedulable agricultural machinery, if the target is short-distance short-distance operation, it can save time by 0.16 h, and the existing M1 model is in Changying. Village work, the work end time is about 2.5 hours; M4 and M2 models work in the Xuzhuang Village, and the work end time is about 0.5 h; M3 model works in the Jiangtang Village, and the calculation results for different goals are as follows: M1 model first dispatches short-distance operations; the M3 type is dispatched first for short-distance operations; the M4 type operating in Xuzhuang Village is given priority to dispatch short-distance and short-distance operations. According to the analysis of system requirements, each submodule of the system is designed, including the function description and realization method of the module. The history track query module, mobile phone positioning module, and agricultural machinery scheduling module are introduced in detail. Using the Android development technology required by mobile phone positioning software, the information management of user interface, server system, and database is realized, which provides conditions for the collection of agricultural machinery and equipment information and the generation of information management control.
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Li, Shuo, Luqing Zhang, Aikang Chen, Yanchi Zhang, and Da Xie. "Bi-level Planning Model for Integrated Energy Network in Solid Waste Treatment Park." E3S Web of Conferences 118 (2019): 04026. http://dx.doi.org/10.1051/e3sconf/201911804026.
Full textAbstract:
In this paper, a bi-level planning model was proposed for the integrated energy network in solid waste treatment park to achieve optimal energy planning in the park. Firstly, the factors affecting planning were analyzed from the aspects of energy and environment. Then, the physical architecture of the integrated energy network planning of the solid waste treatment park was proposed, and the typical energy component models of the solid waste treatment park were proposed. This paper took the annual total cost as the first-level target and the annual operating cost as the second-level target, then proposed the bi-level planning model of the integrated energy network. Finally, the advantages of the model were verified by example. The example shows that under the action of the bi-level planning model, the energy, environment and economic benefits of the solid waste treatment park can be significantly improved.
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Absalan, Faezeh, Fatemeh Hatam, Benoit Barbeau, Michèle Prévost, and Françoise Bichai. "Predicting Chlorine and Trihalomethanes in a Full-Scale Water Distribution System under Changing Operating Conditions." Water 14, no. 22 (November 15, 2022): 3685. http://dx.doi.org/10.3390/w14223685.
Full textAbstract:
Predicting free chlorine residual and Trihalomethanes (THMs) in water distribution systems (DS) is challenging, given the variability and imprecise description of the chlorination conditions prevailing in full-scale systems. In this work, we used the variable reaction rate constant (VRRC) model, which offers the advantage of describing variable applied dosage and rechlorination conditions without the need for model recalibration. The VRRC model successfully predicted chlorine decay and THMs formation in ammonia-containing water at the lab scale. Comparing the goodness of fit results showed a better fit by the VRRC model than the 1st-order and an equally good fit compared to the parallel 1st-order model. However, the independence of the VRRC coefficients upon chlorine dosage made it a better choice for full-scale implementation than the parallel 1st-order model. Chlorine and THMs predictions in the DS were performed in 22 locations from a full-scale DS in southern Quebec (Canada). Chlorine predictions by VRRC were conducted in the spring and fall of 2021 under changing water quality conditions (temperature, DOC, dosage). With a prediction target of 0.1 mg/L absolute error, the VRRC model met this target in 77% of the points in the spring and 73% in the fall. While the predictions were comparable and slightly better than those of the 1st-order model, the main advantage of the VRRC was its applicability under variable dosage and rechlorination conditions (e.g., booster chlorination). THMs predictions in the DS were successfully performed in fall 2021. While 91% of the nodes had less than 5 μg/L of absolute prediction error with the VRRC model, the 1st-order model only met this target in 1 out of 22 points. In addition to its high precision, the VRRC can predict THMs using only the lab scale experiments for model parametrization. This enables small utilities with limited resources to predict the possibility of THMs non-compliances under changing water quality conditions with simple lab-based experiments. Changing climatic conditions can deteriorate drinking water quality, raise regulatory concerns for chlorine and THMs, and threaten public health. Water utilities can use the simple approach proposed in this work to assess the possibility of non-compliance under changing conditions. Moreover, the efficiency of different interventions or mitigation strategies to resolve or avoid non-compliance can be evaluated with this approach.