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4th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2021

作者： Ha, Thi-Oanh Tran, Hoang-Nhat Nguyen, Hong-Quan Tran, Thanh-Hai Nguyen, Phuong-Dung Doan, Huong-Giang Nguyen, Van-Toi Vu, Hai Le, Thi-Lan Hanoi University of Science and Technology MICA Hanoi Viet Nam Hanoi University of Science and Technology School of Electronics and Telecommunications Hanoi Viet Nam Hanoi University of Science and Technology MICA Hanoi Viet Nam Electric Power University Control and Automation Faculty Hanoi Viet Nam Phenikaa University Faculty of Electrical and Electronics Engineering Hanoi Viet Nam

ISBN: (纸本)9781665419109

Video or image-based people counting in real-time has multiple applications in intelligent transportation, density estimation or class management, and so on. This problem is usually carried out by detecting people using conventional detectors. However, this approach can be failed when people stay in various postures or are occluded by each other. In this paper, we notice that even a main part of human body is occluded, their face and head are still observable. We then propose a method that counts people based on face and head detection and pairing. Instead of deploying only face or head detector, we apply both detectors as in many cases the human does not turn his/her face to camera then head detector takes advantage. Otherwise, face detector produces reliable results. The fact of combining both head and face detection results will lead to duplicated responses for one person. We then propose a simple yet effective alignment technique to pair a face with a head of a person. Subsequently, the remaining heads and faces which are not paired with any other faces or heads will be added to our people counter to increase the true positive rate. We evaluate our proposed method on four datasets (Hollywood, Casablanca, Wider Face, and our own dataset). The experimental results show an improvement of average precision and recall comparing to the original head or face detectors. © 2021 IEEE.

关键词： Face recognition

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Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme

arXiv

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arXiv 2021年

作者： Weibel, Jean-Baptiste Patten, Timothy Vincze, Markus Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney Australia

While object semantic understanding is essential for most service robotic tasks, 3D object classification is still an open problem. Learning from artificial 3D models alleviates the cost of annotation necessary to approach this problem, but most methods still struggle with the differences existing between artificial and real 3D data. We conjecture that the cause of those issue is the fact that many methods learn directly from point coordinates, instead of the shape, as the former is hard to center and to scale under variable occlusions reliably. We introduce spherical kernel point convolutions that directly exploit the object surface, represented as a graph, and a voting scheme to limit the impact of poor segmentation on the classification results. Our proposed approach improves upon state-of-the-art methods by up to 36% when transferring from artificial objects to real objects. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Super-klust: Another way of piecewise linear classification

arXiv

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arXiv 2021年

作者： Zengin, Rahman Salim Sezer, Volkan Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Autonomous Mobility Group Electrical and Electronics Engineering Faculty Istanbul Technical University Istanbul Turkey

With our previous study, the Super-k algorithm, we have introduced a novel way of piecewise-linear classification. While working on the Super-k algorithm, we have found that there is a similar, and simpler way to explain for obtaining a piecewise-linear classifier based on Voronoi tessellations. Replacing the multidimensional voxelization and expectation-maximization stages of the algorithm with a distance-based clustering algorithm, preferably k-means, works as well as the prior approach. Since we are replacing the voxelization with the clustering, we have found it meaningful to name the modified algorithm, with respect to Super-k, as Supervised k Clusters or in short Super-klust. Similar to the Super-k algorithm, the Super-klust algorithm covers data with a labeled Voronoi tessellation, and uses resulting tessellation for classification. According to the experimental results, the Super-klust algorithm has similar performance characteristics with the Super-k algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Piecewise linear techniques

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Modeling and fuzzy control of permanent magnet synchronous motors 10

Modeling and fuzzy control of permanent magnet synchronous m...

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10th International Conference on Power Electronics, Machines and Drives, PEMD 2020

作者： Juki, Domagoj-Kreimir Varga, Toni Beni, Tin Štil, Vedrana Jerkovi Baruki, Marinko Automation and Control Institute TU Wien Vienna Austria Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia

ISBN: (纸本)9781839535635

This paper deals with Permanent Magnet Synchronous Motor modeling and control. The derived nonlinear dq model is a combination of Finite Element Method simulation data with the classical dq model. The so derived model keeps the precision of the Finite Element Method simulations and also the fast execution times of the dq model. The Finite Element Method data is included in the dq model using Artificial Neural Networks. The derived model is also used for controller design. The chosen controller is a PI like Fuzzy Logic controller, which is tuned using Genetic Algorithm optimization. The Permanent Magnet Synchronous Motor with Fuzzy Logic controller operation is tested using Real-Time (Model-in-the-Loop) simulations. © PEMD *** right reserved.

关键词： Permanent magnets

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Implementation of smart manufacturing maturity assessment framework: A socio-technical perspective

Implementation of smart manufacturing maturity assessment fr...

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2020 International Conference on Environment and Water Resources engineering, EWRE 2020

作者： Yue, Lei Wang, Zhou Fang, Yifang Han, Zhonghua Beijing100055 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang110016 China

Smart manufacturing capability maturity assessment has been widely carried out in order to help enterprises to identify the direction and route of smart transformation by stationing in enterprises. However, many maturity models for industrial enterprises can not reflect their industrial realities more accurately. Therefore, the aim of this paper attempts to make the smart manufacturing maturity assessment operational, and implement a specific assessment framework based on an existing general conceptual framework. This implementation involved two top-down approaches that one is socio-technical system as a top-level design of factors, and the other is refining assessment domains based on lifecycle and activity models. Results of this implementation showed a two-dimensional assessment framework instance and collections of definitions for capability maturity levels. To conclude, this implementation may be a reference for work specification of smart manufacturing maturity assessment. Three possible future research in the area was suggested at last. © The Authors, published by EDP Sciences, 2020.

关键词： Flow control

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment

Integration of ANN for Accurate Estimation and Control in Wa...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Andreea Elena Țîru Iulian Vasiliev Larisa Diaconu Ramon Vilanova Daniel Voipan Harsha Ratnaweera Department of Automatic Control and Electrical Engineering "Dunărea de Jos" University of Galaţi Galaţi Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain Department of Computer Science and Information Technology "Dunărea de Jos" University of Galaţi Galaţi Romania Faculty of Science and Technology (Realtek) Norwegian University of Life Sciences Ås Norway

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so.

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Reduced order modelling for spatial-temporal temperature and property estimation in a multi-stage hot sheet metal forming process

arXiv

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arXiv 2021年

作者： Kloeser, Daniel Martschin, Juri Meurer, Thomas Tekkaya, Erman Chair of Automatic Control Faculty of Engineering Kiel University Kiel Germany Institute of Forming Technology and Lightweight Components Technische Universität Dortmund Dortmund Germany

A concise approach is proposed to determine a reduced order control design oriented dynamical model of a multi-stage hot sheet metal forming process starting from a high-dimensional coupled thermo-mechanical model. The obtained reduced order nonlinear parametric model serves as basis for the design of an Extended Kalman filter to estimate the spatial-temporal temperature distribution in the sheet metal blank during the forming process based on sparse local temperature measurements. To address modeling and approximation errors and to capture physical effects neglected during the approximation such as phase transformation from austenite to martensite a disturbance model is integrated into the Kalman filter to achieve joint state and disturbance estimation. The extension to spatial-temporal property estimation is introduced. The approach is evaluated for a hole-flanging process using a thermo-mechanical simulation model evaluated using LS-DYNA. Here, the number of states is reduced from approximately 17 000 to 30 while preserving the relevant dynamics and the computational time is 1000 times shorter. The performance of the combined temperature and disturbance estimation is validated in different simulation scenarios with three spatially fixed temperature *** Codes 35Q93 Copyright © 2021, The Authors. All rights reserved.

关键词： State estimation

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Krasovskii and Shifted Passivity Based Output Consensus

arXiv

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arXiv 2022年

作者： Kawano, Yu Cucuzzella, Michele Feng, Shuai Scherpen, Jacquelien M.A. Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia PV Pavia27100 Italy School of Automation Nanjing University of Science and Technology Nanjing210094 China Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands

Motivated by current sharing in power networks, we consider a class of output consensus (also called agreement) problems for nonlinear systems, where the consensus value is determined by external disturbances, e.g., power demand. This output consensus problem is solved by a simple distributed output feedback controller if a system is either Krasovskii or shifted passive, which is the only essential requirement. The effectiveness of the proposed controller is shown in simulation on an islanded DC power network. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear systems

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Energy-Saving Practice of Variable Frequency Drive Technology in Data Center HVAC System

Energy-Saving Practice of Variable Frequency Drive Technolog...

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International Conference on Power and Energy Technology (ICPET)

作者： Chuanxian Ai Jinpeng Yang Rui Chen Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Branch The National Computer Network Emergency Response Technical Team/Coordination Center of China Kunming China Power Dispatch Control Center Guizhou Power Grid Co. Ltd. China Southern Power Grid Co. Ltd Guiyang China

The variable-frequency drive technology (VFD) can modify the motor speed or torque by varying the frequency and voltage of the AC motor input power according to the load demand as a way to save energy. To analyse the energy saving effect of variable-frequency drive technology applied to data centre heating, ventilation, and air conditioning (HVAC) systems, this paper applies VFD technology to data centre fluorine pump-compressor composite air conditioning systems, implements monitoring and analysis of the operational energy efficiency of this system before and after retrofit, and combines the actual application of this technology. The findings indicate that the VFD technology can substantially reduce the number of compressor starts and stops, reduce the compressor start-up inrush current, and improve the power system’s stability. Furthermore, utilising this technology, the HVAC system can effectively synchronise with the load and fluctuations in outdoor temperature and humidity, resulting in accurate temperature regulation and optimal utilisation of outdoor cooling resources. This approach can lead to significant energy conservation of up to 42.95% when compared to extreme hot and cold weather conditions. The findings of the study indicate that the utilisation of VFD technology exhibits commendable adaptability and yields favourable outcomes in regions with varying temperature ranges. The minimum PUE value recorded was 1.228.

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