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9th IEEE International Conference on Communications and Electronics, ICCE 2022

作者： Luckose, Vinukumar Kannan, Ramani Hasan, Khairul Nisak Md Ibrahim, Taib B SEGi University Center for Advanced Electrical and Electronic System Faculty of Engineering Built Environment and Information Technology Petaling Jaya Malaysia Universiti Teknologi Petronas Power Control and Instrumetnation Cluster Department of Electrical and Electronics Engineering Perak Seri Iskandar Malaysia

ISBN: (数字)9781665497459

ISBN: (纸本)9781665497442

Electric vehicles are become more popular nowadays due to negligible gas emissions and less dependent on fossil fuel. However, the battery charging process is the main challenge that influences the electric vehicle (EV) transportation system. The power factor correction (PFC) and dc-dc converter stages are the main phases in the EV charging system. Various electric vehicle charging technologies are developed using different power electronics converter topologies, such as cuck, Zeta, SEPIC, and buck-boost converter. SEPIC (Single Ended Primary Inductor Converter) converter topology is the most preferred topology for battery charging applications due to providing non-inverting output with buck boost operation and reduced input and output ripple current. Although the SEPIC convertor topology has many advantages still possess a lot of challenges. The main challenges are reducing the converter size, conduction loss, voltage stress and increasing the efficiency when compared with the existing recent developed Bridgeless SEPIC topologies with interleaved concept. In this proposed research, a modified interleaved bridgeless, SEPIC based converter is proposed to improve the Power Quality (PQ) for vehicle charging station. The proposed topology is able to reduce the voltage stress, power loss, increase the converter efficiency and improve the power factor due to the bridgeless with interleaved configuration compared to other topologies. The preliminary design of the of the bridgeless interleaved topology is developed with the help of MATLAB Simulink software. The various preliminary results were obtained at the input supply voltage of 70-230 V ac supply. © 2022 IEEE.

关键词： Topology

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Assessment of Depression Level Based on Multi-Scale 3D Convolution and Expression Videos 3

Assessment of Depression Level Based on Multi-Scale 3D Convo...

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Cao, Qishuang Gao, Junlong Lv, Zeying Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781510667563

The automatic diagnosis of depression using deep learning has recently shown significant progress, and 3DCNN has been applied to assessing depression levels from videos. There are two problems to be encountered applying 3DCNN, including insufficient data to train the model and an insufficient diversity of features extracted on a single scale. An approach is presented in this paper to address these issues. Firstly, we augmented the raw data by adding noise and applying rotations to increase the amount of the training set. Secondly, we constructed a 3DCNN to extract more diverse features from multiscales. We evaluated our proposed method on the AVEC 2014. Our approach achieved MAE=7.13 and RMSE=9.16, demonstrating its effectiveness in improving the accuracy of depression assessment compared to existing approaches. © 2023 SPIE.

关键词： Deep learning

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Short-Term Load Forecasting for Buildings Based on Dynamic Weighted edRVFL Model

Short-Term Load Forecasting for Buildings Based on Dynamic W...

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Chinese automation Congress (CAC)

作者： Junwei Xie Meng Yuan Zhezhuang Xu Chi Liu Yuxiong Xia Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Fuzhou China Fujian Huading Intelligent Manufacturing Technology Co. Ltd Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Short-term load forecasting of buildings plays a key role in demand management, however, when dealing with massive data, the prevalent practice of training and testing the system as a whole overlooks pattern variations inside data and potentially limits predictive performance. To address this issue, this paper proposes an ensemble deep random vector functional link (edRVFL) model that combines adaptive ensemble clustering and dynamic weighting strategy for load forecasting of an industrial building. Firstly, the adaptive ensemble clustering that combines ordering points to identify the clustering structure and K nearest neighbor is employed to determine the electricity load usage patterns. Subsequently, the improved particle swarm optimization algorithm is applied to adjust the weights of load curves in different typical day scenarios. Finally, a dynamic weighting strategy is designed to dynamically assign model weights to two clusters, where one matches the type of predicted load pattern for the day being forecasted and another for the cluster of the previous day. The experiment in a real building verifies the effectiveness of the proposed algorithm, which has higher prediction accuracy than other comparison algorithms including one-week interval and different ensemble clustering.

关键词： Adaptation models Load forecasting Heuristic algorithms Buildings Clustering algorithms Predictive models Prediction algorithms Vectors Forecasting Load modeling

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Automated Depression Diagnosis Based on Facial Expression and Deep Dual-Stream Convolutional Neural Networks 3

Automated Depression Diagnosis Based on Facial Expression an...

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Chen, Yangyang Chen, Yingyu Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781510667563

Depression is a common mental illness characterized by symptoms such as low mood, pessimism, and insomnia. In this study, we developed a deep Dual-Stream CNN to automatically diagnose and classify depression in expression video sequences. The network has two branches that extract static features and dynamic features from static and dynamic expressions, respectively, which are then fused for depression classification. The experiments were performed on the AVEC2014 database, and the results showed that the Dual-Stream model significantly improved the classification performance of depression, achieving an accuracy of 69.08% in depression categorization. © 2023 SPIE.

关键词： Convolutional neural networks

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Fast and Robust Energy Router control in Dynamic Conditions Using Flatness-Based control Theory

Fast and Robust Energy Router Control in Dynamic Conditions ...

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Compatibility in Power Electronics (CPE)

作者： Mohammadreza Azizi Oleksandr Husev Carlos Roncero-Clemente Oleksandr Veligorskyi Ryszard Strzelecki Department of Radiotechnic and Embedded Systems Chernihiv Polytechnic National University Chernihiv Ukraine Department of Electric Electronic and Automation Engineering University of Extremadura Badajoz Spain Faculty of Electrical and Control Engineering Gdansk University of Technology Gdansk Poland

ISBN: (数字)9798331515171

ISBN: (纸本)9798331515188

In this paper, flatness-based control theory is developed to enhance the dynamic performance of a multiport energy router. The energy router integrates multiple power sources and sinks, including photovoltaic systems, battery storage, the grid, and various loads. Consequently, any sudden change in a subsystem can induce dynamic conditions across the entire system. The presented method controls the grid-side inverter and regulates the de link voltage. To implement this control approach, the system equations have been established, and it has been demonstrated that the system is differentially flat. Afterward, the controller design has been addressed. The simulation results confirm the proper performance of this method, and the comparisons made validate the high speed and accuracy of the system responses compared to conventional solutions.

关键词： Photovoltaic systems Power engineering Accuracy Simulation Modulation control systems Inverters Mathematical models control theory Voltage control

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information Presentation Strategies to Promote Pedestrian Behavior Change in Mixed Spaces with Automated Vehicle

Information Presentation Strategies to Promote Pedestrian Be...

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IEEE/SICE International Symposium on System Integration

作者： Yuki Nishiura Hiroyuki Yamada Ryo Kurazume Graduate School of Information Science and Electrical Engineering Kyushu University Japan Autonomous Control Research Department Center for Sustainability - Connective Automation Hitachi Ltd. Research and Development Group Ibaraki Japan Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

This paper introduces an information presentation strategy for pedestrians, aiming to enhance traffic efficiency in a mixed pedestrian-automated vehicle environment, such as a public road. While automated driving technology has made remarkable progress, interactions with pedestrians on regular roads have mostly been studied in virtual environments using virtual reality goggles. According to these studies, potential traffic efficiency and safety issues arise from pedestrians' limited understanding of automated vehicle behavior. To address this, we propose a human-machine interface employing a head-mounted display (HMD) to mitigate traffic efficiency degradation caused by pedestrians. The proposed system draws upon behavioral economics principles to encourage pedestrians to modify their behavior and develop better interactions with automated vehicles. Simulations were conducted to identify an information presentation strategy that strongly supports learning, and its effectiveness was further validated through experiments involving a real vehicle. Notably, the experimental results confirmed that the information presentation strategies proven effective in simulations also facilitated pedestrian learning during real-world interactions.

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Multi-population particle swarm optimization concerning the worst information 3

Multi-population particle swarm optimization concerning the ...

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Li, Haiyan Chen, Yanbo Sun, Jiulin Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781510667563

Currently, the majority of multi-population algorithms ignore the worst particles and instead learn from the gbest or lbest. Hence, it is simple for these strategies to result in inadequate population variety, which causes early convergence. A multi-population particle swarm optimization considering the worst particles into account (CWMPSO) is suggested to demonstrate the importance of the worst particle information. In addition to considering the best particle, the CWMPSO algorithm concentrates on the worst particle globally. The algorithm divides the population into three subgroups, which learns from the gbest, gworst, and gbest + gworst, respectively. The development of the global worst particle information can be achieved by group learning, allowing for a more thorough exploration of potential optimum solutions and an avoidance of local functional ones. 30 complex CEC2017 benchmark functions were used in this work to test a range of algorithms including CWMPSO with various dimensions. The outcomes demonstrate that, in every dimension, the CWMPSO algorithm is better than other advanced PSOs. © 2023 SPIE.

关键词： Particle swarm optimization (PSO)

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无人飞行器在不确定扰动下的动态目标跟踪

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engineering 2024年第4期35卷 74-85页

作者： Yanjie Chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao Hui Zhang Yaonan Wang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer Science Aberystwyth UniversityAberystwyth SY233DBUK School of Robotics Hunan UniversityChangsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle Visual servoing Velocity observer Target tracking

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Modeling the Correlation of Human Vertebral Body Volumes*

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IFAC-PapersOnLine 2023年第2期56卷 9030-9035页

作者： Bálint Szabó Ákos Szlávecz Sándor Bazsó Péter Somogyi Katalin Kovács Árpád Viola Balázs István Benyó Budapest University of Technology and Economics Faculty of Electrical Engineering and Information Technology Department of Control Engineering and Information Technology Budapest Hungary. Department of Informatics Széchenyi István University Győr Hungary Péterfy Sándor Street Hospital Department of Neurosurgery Budapest Hungary

Anatomical parameters of the human body strongly correlate with each other. Modelling these dependencies enables the creation of a realistic anatomical human body model that can be parameterized. Such a model can be used for several diagnostic processes to identify abnormalities or even give guidance in surgical interventions. This paper proposes a probabilistic model describing the dependencies between the vertebral body volumes of humans from the Caucasian human race. As demonstrated, the proposed model can accurately describe the relationship between the vertebral body volumes and is used for the prediction of an unknown vertebral volume based on a known one. The probabilistic model is created by using the CT segmentation of 37 patients.

关键词： Biomedical engineering Statistical Modelling Human Body Modelling Spinal Chord Segmentation Probabilistic Model

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Prototype for Current and Voltage Monitoring of Distribution Transformer Based on WiFi

Prototype for Current and Voltage Monitoring of Distribution...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial automation (ICAMIMIA)

作者： Firman Yudianto Teguh Herlambang Mukhtar Adinugroho Fajar Annas Susanto Tri Deviasari Wulan Imam Wahyudi Farid Department of Information System Faculty of Business Economics and Digital Technology UNUSA Indonesia Department of Management Faculty of Business Economics and Digital Technology UNUSA Indonesia Department of Electrical Automation Engineering Vocational Faculty ITS Indonesia

A transformer is a key component in the electrical power distribution system. To maintain the continuity and reliability of the power system, it is essential to ensure the reliability of the energy distribution system. Factors affecting the quality of electrical energy include current, voltage, and temperature. With an increase in load, the transformer's temperature and current can rise, and the increase in current can be influenced by excessive load, thus raising the transformer's temperature. The temperature rise can also be influenced by the environmental conditions inside the transformer room. Excessive temperature in the transformer room can lead to explosions. Therefore, this thesis research aims to design a prototype monitoring device based on WiFi for parameters such as current, voltage, and temperature, to provide real-time information about the transformer's condition. The results of this research show that the prototype monitoring device for the transformer provides values very close to manual calculations, with the largest error percentages being 2.18% for current measurement, 1.04% for voltage measurement, and 3.3% for temperature measurement. This prototype monitoring device also provides information to the user when the current value in the circuit reaches 2A, making it easier for the user to identify potential transformer faults before they lead to damage. The transformer's efficiency ranges from 99.54% to 99.82%. Maintenance is required for the transformer as it approaches high temperatures to ensure optimal transformer performance.

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