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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Zheyuan Yi Kai Sun Qiang Li Zengyang Liu Department of Electrical Engineering State Key Laboratory of Power System Tsinghua University Beijing China Delta Electronics Shanghai Co. Ltd. Shanghai China The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State Center for Power Electronics Systems University Blacksburg Virginia USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Thermal analysis is a crucial and time-consuming step in the optimization design process of high-frequency transformers (HFTs). This paper proposes an improved thermal analysis method based on heat dissipation-conduction two-step simulation, significantly reducing the computation time while ensuring the accuracy of thermal analysis. Simulation results validate the accuracy and of the proposed method and demonstrate that the proposed method saves 82% of computation time for a single thermal analysis case compared to direct CFD. Experimental results further verify the accuracy of the proposed method. An exemplary application of the proposed method in the optimization design of the HFT illustrates a 98% reduction in computation time for thermal analysis during the optimization process.

关键词： Heating systems Temperature distribution Accuracy Simulation Computational modeling Thermal analysis High-frequency transformers Optimization Surface treatment Heat transfer

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LSTM-based Energy Management for Electric Vehicle Charging in Commercial-building Prosumers

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Journal of Modern Power systems and Clean Energy 2021年第5期9卷 1205-1216页

作者： Huayanran Zhou Yihong Zhou Junjie Hu Guangya Yang Dongliang Xie Yusheng Xue Lars Nordström State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Center for Electric Power and Energy Technical University of DenmarkLyngbyDenmark State Grid Electric Power Research Institute NanjingChina Division of Electric Power and Energy Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden

As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management ***,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online *** address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building *** the proposed system control structure,the LSTM algorithm can be separated into offline and online *** the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network *** the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM *** simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.

关键词： Building energy management system(BEMS) electric vehicle(EV) long short-term memory(LSTM) recurrent neural network machine learning prosumer

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, engineering, Science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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Improved Action Recognition Algorithm for Video Surveillance Based on PoseC3D

Improved Action Recognition Algorithm for Video Surveillance...

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International Conference on computer and Communications (ICCC)

作者： Ruifeng Guo Chao Yang Changyi Deng Ying Bai Xiaoxing Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

The precise recognition of human action is of great significance for intelligent monitoring and video analysis. Previous researchers have utilized many methods to detect human action, but the accuracy and robustness of these methods remain to be improved. Skeleton-based action recognition methods have reported high accuracy and robustness, but these methods have not fully utilized multiple modalities for feature fusion. In order to solve the above problems, a multi-modality feature fusion based video surveillance personnel action recognition method is proposed by combining the two methods. The SlowOnly network is used to extract RGB modality features. The Faster R-CNN and Lite-HRNet networks are used to obtain skeleton modality data. The PoseC3D network is used to extract skeleton modality features. The early and late fusion of RGB modality features and skeleton modality features are performed. Then the action recognition results of video surveillance personnel are obtained. The experimental results on the NTU RGB+D public dataset under the X-Sub standard show the following points. In the action recognition model based on a single skeleton modality, PoseC3D has a higher recognition accuracy than GCN methods, reaching 92.8%. In the action recognition model based on RGB modality, recognition accuracy has reached 95.4%. The action recognition model based on multi-modality feature fusion has a higher recognition accuracy than the recognition model based on a single modality, reaching 96.1%.

关键词： Accuracy Computational modeling Feature extraction Video surveillance Skeleton Robustness Personnel Standards Monitoring

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Distributed Optimal Formation Control for an Uncertain Multiagent system in the Plane

arXiv

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arXiv 2023年

作者： Enwerem, Clinton Baras, John Romero, Danilo Department of Electrical & Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States Department of Electrical & Computer Engineering University of Maryland College ParkMD United States University System of Maryland Southern Maryland California MD20619 United States

In this paper, we present a distributed optimal multiagent control scheme for quadrotor formation tracking under localization errors. Our control architecture is based on a leader-follower approach, where a single leader quadrotor tracks a desired trajectory while the followers maintain their relative positions in a triangular formation. We begin by modeling the quadrotors as particles in the YZ-plane evolving under dynamics with uncertain state information. Next, by formulating the formation tracking task as an optimization problem — with a constraint-augmented Lagrangian subject to dynamic constraints — we solve for the control law that leads to an optimal solution in the control and trajectory error cost-minimizing sense. Results from numerical simulations show that for the planar quadrotor model considered — with uncertainty in sensor measurements modeled as Gaussian noise — the resulting optimal control is able to drive each agent to achieve the desired global objective: leader trajectory tracking with formation maintenance. Finally, we evaluate the performance of the control law using the tracking and formation errors of the multiagent system. © 2023, CC BY.

关键词： Multi agent systems

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Spatial Temporal Transpose Convolutional Networks for Action Recognition

Spatial Temporal Transpose Convolutional Networks for Action...

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International Conference on computer and Communications (ICCC)

作者： Ying Bai Dongsheng Yang Hongliang Wang Changyi Deng Wei Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In recent years, the use of skeleton data for human action recognition has received increasing attention in fields such as intelligent monitoring, human-object interaction, and video retrieval. Among them, Modeling Human Skeleton as Spatial-Temporal Graph Convolutional Network (ST-GCN) has made significant progress. However, methods based on Graph Convolution (GCN) generally face two issues: on one hand, the receptive fields of the spatial and temporal graphs are not flexible enough, limiting their feature representation capabilities. On the other hand, the model is overly parameterized, leading to high computational costs. In this paper, we first propose a spatial-temporal transpose convolution module to overcome the inflexibility of the spatial-temporal graph receptive field. Secondly, we introduce 1 *1 convolutions and depth-wise separable convolution layers into the network model, effectively reducing the computational complexity of the model. On the two subsets of the large-scale skeleton action recognition dataset NTU RGB-D, experimental results show that the proposed method achieved Top1 accuracy metrics of 84.6% and 92.5% respectively, while the model size was reduced by 13.48 times compared to the classic ST-GCN method, and the computational complexity was reduced by approximately 17.18 times.

关键词： Hands Accuracy Convolution Computational modeling Benchmark testing Approximation algorithms Skeleton Partitioning algorithms Computational complexity Monitoring

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Identification of Clinical Features Associated with Mortality in COVID-19 Patients

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Operations Research Forum 2023年第1期4卷 16页

作者： Eskandarian, Rahimeh Alizadehsani, Roohallah Behjati, Mohaddeseh Zahmatkesh, Mehrdad Sani, Zahra Alizadeh Haddadi, Azadeh Kakhi, Kourosh Roshanzamir, Mohamad Shoeibi, Afshin Hussain, Sadiq Khozeimeh, Fahime Darbandy, Mohammad Tayarani Joloudari, Javad Hassannataj Lashgari, Reza Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Internal Medicine Research Center Semnan University of Medical Sciences Semnan Iran Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Rajaei Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Biology Faculty of Basic Sciences Shahrekord Branch Islamic Azad University Shahrekord Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa 74617-81189 Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain System Administrator Dibrugarh University Assam 786004 India School of Architecture Islamic Azad University Taft Taft *** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Computer Engineering Amol Institute of Higher Education Amol Iran Institute of Medical Science and Technology Shahid Beheshti University Tehran Iran School of Engineering and Applied Sciences Harvard Paulson Harvard University Allston 02134 MA United States Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University Geelong 3220 VIC Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings. © 2023, The Author(s).

关键词： COVID‐19 Machine learning Mortality Risk factors Symptoms

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Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures 22

Realizing Emotional Interactions to Learn User Experience an...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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Biofunctionalization-Optimized MoS2-Based Fet Biosensors for the Detection of Tau Protein

SSRN

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SSRN 2024年

作者： Wen, Xiaohong Zhao, Xuefeng Shan, Xinzhi Lu, Hongliang Gao, Xiumin Zhuang, Songlin Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China State Key Laboratory of High-Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai200050 China State Key Laboratory of ASIC System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Fudan University Shanghai200433 China

Field effect transistor (FET)-based biosensors can accurately quantify changes in weak signals and show excellent performance advantages in the detection of trace biological disease markers. Biological functionalization (biofunctionalization) is an important factor in the fabrication of FET-based biosensors, which directly affects the performance of the biosensor. However, little attention from researchers has been paid to biofunctionalization, which results in uneven performance of biosensors. Herein, MoS2-based FET biosensors are designed and fabricated using four different biofunctionalization methods. The most suitable biofunctionalization method is obtained by comparing the sensing performances of different functionalized MoS2based FET biosensors. Moreover, the working mechanism of the optimized biofunctionalization method is explored. Besides, the biofunctionalization-optimized MoS2-based FET biosensor exhibits excellent sensing performance, achieving sensitive detection of Tau protein in the concentration range of 10-2~103 pg/mL. This study provides a universal and effective strategy for the biofunctionalization optimization of two-dimensional materials-based FET biosensors. © 2024, The Authors. All rights reserved.

关键词： Molybdenum compounds

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Biofunctionalization-Optimized Mos2-Based Fet Biosensors for the Detection of Tau Protein

SSRN

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SSRN 2024年

Field effect transistor (FET)-based biosensors can accurately quantify changes in weak signals and show excellent performance advantages in the detection of trace biological disease markers. Biological functionalization (biofunctionalization) is an important factor in the fabrication of FET-based biosensors, which directly affects the performance of the biosensor. However, little attention from researchers has been paid to biofunctionalization, which results in uneven performance of biosensors. Herein, MoS2-based FET biosensors are designed and fabricated using four different biofunctionalization methods. The most suitable biofunctionalization method is obtained by comparing the sensing performances of different functionalized MoS2-based FET biosensors. Moreover, the working mechanism of the optimized biofunctionalization method is explored. Besides, the biofunctionalization-optimized MoS2-based FET biosensor exhibits excellent sensing performance, achieving sensitive detection of Tau protein in the concentration range of 10-2~103 pg/mL. This study provides a universal and effective strategy for the biofunctionalization optimization of two-dimensional materials-based FET biosensors. © 2024, The Authors. All rights reserved.

关键词： Molybdenum compounds

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