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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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Simulation of the Health Monitoring and Disease Warning System on the Pig Herd with a Cloud-based GAMA Platform 8

Simulation of the Health Monitoring and Disease Warning Syst...

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8th ieee/ACIS international conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Nguyen, Xuan-Truong Pham, Linh Manh Bui, Quang Hung VNU University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798350373615

More and more Internet of things devices are being used in precision farming to measure parameters related to animal growth. Sensors from precision feeding systems are used to collect and analyze data on feed intake of group-housed pigs helping farmers to make valuable decisions that help in pig growth. Besides, African Swine Fever (ASF) is a disease occurring in pigs that kills millions of them. the outbreak comes from the lack of management by localities, connection between farms, and the ability to improve productivity. It is a fact that effects of disease on animals can be observed early through data of voluntary feed intake which can be used to detect dangerous outbreaks of ASF over a wide graphical area in real time. this will contribute to reducing economic losses due to epidemics and improving the quality of animal products. In this paper, we first introduce a cloud-based simulation of the precision feeding system to quantify the cumulative feed intake of growing pigs, thanks to which it is possible to detect early signs of disease in the daily decrease in food intake. Secondly, output statistical data on the disease of pig herds from the first simulation are used for the second simulation of ASF transmission in Hanoi, which is an essential tool for policymakers to inform decision-making people and help them understand better the impact of the disease outbreaks. through these simulations on GAMA platform, we wish to introduce a cost-effective research model on disease transmission in pig herds using cloud-based simulation and illustration instead of investing heavily in expensive actual equipment. © 2023 ieee.

关键词： Losses

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NM-LUD: A NLOS Mitigation Method with Low-cost UWB Devices for Indoor Positioning 8

NM-LUD: A NLOS Mitigation Method with Low-cost UWB Devices f...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Wu, Tong Li, Han Gao, Bo Xiong, Ke Fan, Pingyi Ben Letaief, Khaled Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China Beijing Jiaotong University National Engineering Research Center of Advanced Network Technologies Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665456128

Ultra-wideband (UWB) is a popular technology for indoor positioning. However, non-line-of-sight (NLOS) propagation caused by human occlusion dramatically affects the accuracy of UWB-based indoor positioning. Most of the existing works focused on mitigating NLOS errors in the scenarios with building structure occlusion, but the NLOS mitigation under human occlusion has not been well addressed. To this end, this paper aims to mitigate the NLOS impact of human occlusion, and then proposes a method of NLOS mitigation to reduce NLOS ranging errors under human occlusion with low-cost UWB devices (i.e., NM-LUD). It takes a set of signal features extracted from low-cost devices as input, calculates their weights and mitigates the ranging error based on the features' weights. Experiment results on the two data sets collected by LinkTrack S and UML1 UWB devices show that the method reduces ranging error from 0.9009 m to 0.1435 m (about by 84.07%) and from 0.7466 m to 0.0792 m (about by 89.39%) respectively, which greatly mitigates the influence of NLOS caused by human occlusion. © 2023 ieee.

关键词： Costs

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Deep Unified Model for Face Recognition based on Convolution Neural Network and Edge Computing 8

Deep Unified Model for Face Recognition based on Convolution...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Senthilkumar, V. Saranya, P. Rani, B Kezia Srinivasan, P. Kuchipudi, Ramu Walid, Md. Abul Ala Kumaraguru College of Technology Department of Cse Tamilnadu Coimbatore India Dr. N.G.P. Institute of Technology Department of Ece Tamilnadu Coimbatore India Vasavi College of Engineering Department of Information Technology Telangana Hyderabad India Sona College of Technology Department of Ece Tamilnadu Salem India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Bangladesh

ISBN: (纸本)9798350396638

CCTV, communication, and alarm systems use face recognition technologies. Face detection in photos is a popular topic in science for practical reasons and because it challenges computer-generated vision systems. the variety of shooting situations (position, lighting, hairdo, emotion, backdrop, etc.) and face traits requires versatility. Deep learning-based image identification methods beat machine learning methods in efficiency and information processing. Modern computer systems have major authentication issues. Internet-connected smart devices are producing more data every day. A new model is needed to handle its vast data output. Deep learning and edge computing process vast volumes of data with high precision. Many trust facial recognition systems. SIFT and accelerated robust features are used in traditional facial recognition algorithms (SURF). this paper presents a convolutional neural network-based face identification and recognition solution that outperforms established methods. Tagged photographs of people taken in the outdoors teach the face-recognition algorithm (LFW). the suggested system had 99.1% accuracy on test data. © 2023 ieee.

关键词： Face recognition

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Hybrid Decision Based Multi-Agent Deep Reinforcement learning for Task Offloading in Collaborative Edge-Cloud Computing 24

Hybrid Decision Based Multi-Agent Deep Reinforcement learnin...

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24th ieee international conference on High Performance Computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Chen, Juan Wu, Zongling Wu, Lei Xia, Yunni Wang, Yang Xiong, Ling Shi, Canghong School of Computer and Software Engineering Xihua University Chengdu China School of Information Science and Technology Southwest Jiaotong University Chengdu China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798350319934

To support computing-intensive and latency-sensitive Internet-of-things (IoT) applications, we establish a three-layer collaborative edge-cloud computing network, in which multiple IoT devices, edge servers and cloud servers are allocated at IoT layer, edge layer and cloud layer, ***, it is still challenging to satisfy the service requests with inevitable resource competition among task offloading. To solve the joint computation offloading and resource allocation problem, we formulate it as a multi-agent Markov decision process (MAMDP), where action space of each agent contains discrete-continuous hybrid decision. Firstly, we relax the discrete action to a continuous set by developing a probabilistic method. then, by taking advantage of a continuous action based centralized training and distributed execution strategy, a cooperative multi-agent deep reinforcement learning (MADRL) based framework with each IoT device acts as an agent is established. the objective function is to minimize the total system cost in terms of the energy consumption of IoT device and the renting charge of edge/cloud servers. the simulation results demonstrate the advantageous of the proposed MADRL over the three state-of-the-art DRL based agents in reducing the system cost. © 2022 ieee.

关键词： Reinforcement learning

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A Structure-Based Method for Prediction of Protein-Protein Interaction Sites through Combining Local and Global Features 8th

A Structure-Based Method for Prediction of Protein-Protein...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhang, Hongyang Zhao, Yanpeng Liu, Ziyi He, Song Bo, Xiaochen Zan, Peng School of Mechatronics Engineering and Automation Shanghai Key Laboratory of Power Station Automation Technology Shanghai University Shanghai200444 China Department of Biotechnology Institute of Health Service and Transfusion Medicine Beijing100850 China

ISBN: (纸本)9789819601875

the identification of protein-protein interaction(PPI) sites is critical for drug discovery and analysis of protein function. So far, numerous computational methods based on artificial intelligence have been developed to accelerate the identification of PPI sites. However, those methods learned only sequential features or local structural features, leading to low predictive performance. therefore, it is urgently necessary to develop a new method to improve the performance of predicting PPI sites. Herein, a new structure-based method for predicting the protein-protein interaction sites through combing the local and global encoder, SLGPPIS, was therefore proposed. Specifically, SLGPPIS utilizes the local and global encoder to extract the residue feature respectively and concatenating them to predict the PPI sites. Compared with other existing methods, SLGPPIS exhibited superior performance on prediction of PPI sites. Moreover, a visual interpretative experiment was conducted to systematically investigate the prediction process of SLGPPIS. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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An Optimized H6-Type Single-Phase PV Inverter with Bi-Directional Quasi-Diode Clamping 8

An Optimized H6-Type Single-Phase PV Inverter with Bi-Direct...

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8th international conference on Technology and Energy Management, ICTEM 2023

作者： Farahani, Mohammad Shamsi-Nejad, Mohammad-Ali University of Birjand Faculty of Engineering Birjand Iran

ISBN: (纸本)9781665452854

Transformerless inverters (TLIs) have become increasingly popular in single-phase distributed photovoltaic power generation systems due to the superiorities of their high efficiency, slighter volume, moreover lower cost than the one with the transformer. However, in the absence of a transformer, high-frequency common-mode voltage (CMV) brings about hazardous ground leakage current flow. To reduce the leakage current, plenty of TLI structures have been published in the works, and among them, H5, H6, and Heric topologies are the three topology's very popular mainstays. the third has the highest efficiency but the worst CM behavior owning to the switches' junction capacitances. Consequently, the CM leakage current may exceed the recommended limit. In this paper, an optimized HERIC inverter with a quasi-diode clamp structure, namely OHERIC-BQDC topology, is suggested, in that a bi-directional clamping based upon MOSFETs, but like a diode clamping without dead time, is added on the basic HERIC inverter to eradicate the common-mode voltage fluctuation in freewheeling periods. A Matlab/Simulink pattern is constructed to investigate the feasibility of the suggested approach. In addition, PSIM's software package is used to confirm the effectiveness of the suggested HERIC-based inverter. © 2023 ieee.

关键词： Diodes

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Protection of the Blinding Area in Active Distribution Network by Multi-Function Relays 8

Protection of the Blinding Area in Active Distribution Netwo...

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8th international conference on Technology and Energy Management, ICTEM 2023

作者： Zareein, Mohammad Farkhani, Jalal Sahebkar K.N. Toosi University Department of Electrical Engineering Tehran Iran Aalborg University Department of Energy Aalborg Denmark

ISBN: (纸本)9781665452854

the overcurrent relay (OCR) is the major device to protect the distribution network feeder. the OCR issues increase with distributed generation (DG) embedded in active distribution networks (ADN). In addition, high impedance fault (HIF) has been challenging to properly detect the fault in the power systems. the blinding areas lead to a delay or non-tripping in the OCR. the typical method to cover the blinding area is the increasing sensitivity of relay settings. Changes in the settings of the protective relays may cause challenges to protect ADN. Most of the modern protection relays are digital multifunction. the relays can activate multiple protections, simultaneously. this paper presents a simple and economical solution to protect the fault in blinding areas by the OCR and thermal overload relay. the blind possibility points are determined in a case study by changing the fault impedances and DG presence. the case study is based on a real distribution network that includes two CHP generators with 4 MW capacity. the simulations are carried out on a realistic case study using DIg SILENT software. the results show the proposed method can cover the blinding zones with the thermal overload and OCRs. © 2023 ieee.

关键词： Relay protection

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A Robust and Accurate Multivariate Time Series Anomaly Detection in Fluctuating Cloud-Edge Computing systems 24

A Robust and Accurate Multivariate Time Series Anomaly Detec...

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作者： Song, Yujia Xin, Ruyue Zhang, Rui Chen, Juan Zhao, Zhiming School of Computer and Software Engineering Xihua University Chengdu China Amsterdam Netherlands

ISBN: (纸本)9798350319934

Cloud-edge computing uses edge infrastructure to extend cloud computing further away from the data source, compensating for some of the limitations of conventional cloud computing. Multivariate time series anomaly detection can reflect the operating status of edge cloud computing systems. Many anomaly detection methods have been developed but still face challenges, especially irregular fluctuations in data leading to false positive detection. In addition, good detection robustness is necessary to capture complex data patterns in cloud-edge computing systems, but few existing methods focus on it. Furthermore, monitoring data usually lack labels, while manual data labeling is ineffective. To address these issues, we propose an unsupervised multivariate time series anomaly detection method: Correlative-GNN with Multi-head Self-attention and Auto-Regression Ensemble Method (CGNN-MSAR). Our approach provides parallel Graph Neural Networks(GNNs) to learn inter-dependencies in multivariate time series from feature and time dimensions to achieve fewer false positives without any prior knowledge. Moreover, Our method integrates a multi-head self-attention mechanism and AR models to improve the robustness of the model. Extensive experiments have been conducted on five publicly available datasets. Specifically, CGNN-MSAR outperforms its competitors with an F1-Score of 0.86 on average and is 18.2% better than state-of-the-art baseline methods. © 2022 ieee.

关键词： Anomaly detection

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A Two-Stage Stochastic Optimization Scheduling Approach for Integrating Renewable Energy Sources and Deferrable Demand in the Spinning Reserve Market 8

A Two-Stage Stochastic Optimization Scheduling Approach for ...

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8th international conference on Technology and Energy Management, ICTEM 2023

作者： Hayati, Mohammad Mohsen Aminlou, Ali Zare, Kazem Abapour, Mehdi University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9781665452854

An ideal way to reap the benefits of demand flexibility is to use real-Time pricing at the retail level. However, due to political situations and the current state of the industry, it is unlikely that this procedure will happen quickly in the future. An alternative method to achieve this goal is the use of deferrable loads and an aggregator to introduce these loads in retail markets. An alternative method for applying load flexibility in existing markets and operation protocols is the direct connection of renewable energy sources, energy storage systems, and deferrable load aggregators. Based on the type of coupling proposed in this paper, deferrable load aggregators can be coupled with renewable generators and large-scale energy storages, which, as a result, allocate their production to these loads. Also, the considered load aggregator is the type of plug-in electric vehicle connected to the grid. In this paper, a two-stage stochastic model is presented to evaluate the effects of integrating renewable energy sources and energy storage systems with large-scale deferrable load aggregators. Also, to examine the benefits of demand flexibility, a demand response method is analyzed. In addition, to coordinate the deferrable load aggregator with energy-producing sources, a dynamic planning algorithm is proposed. this research study is covered by probabilistic modeling. For the validation of the models and the proposed algorithm, the Western Electricity Coordinating Council (WECC) of the U.S. has been considered. © 2023 ieee.

关键词： Stochastic systems

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Application and Implementation of Convolutional Neural Network Accelerator Based on FPGA in Environmental Sound Classification 8

Application and Implementation of Convolutional Neural Netwo...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Zhang, Jianwei Yan, Haonan School of Biomedical Engineering School of Artificial Intelligence Dalian University of Technology Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System Dalian China School of Biomedical Engineering Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Dalian China

ISBN: (纸本)9781665456128

With the development of artificial intelligence, convolutional neural network (CNN) has achieved great success in various learning tasks, and has been applied to environmental sound classification tasks, achieving a high classification accuracy. With the development of convolutional neural network in the application of environmental sound classification, the design of hardware accelerator for convolutional neural network has become a hot topic. Compared with CPU, GPU and ASIC, FPGA has the unique advantages of low power consumption and high energy efficiency in accelerating deep learning, and has become the preferred hardware platform for neural network hardware implementation. In this paper, a convolution neural network accelerator based on FPGA is proposed, which can realize convolution, tanh nonlinear activation, average pooling and other operations. the hardware accelerator can realize the inference process of environmental sound classification, and achieve high energy efficiency and low resource consumption while having a high classification accuracy. the network was tested on Xilinx Virtex VC-707 FPGA board. the experimental results show that its energy efficiency is 547.60 and 377.36 times higher than that of CPU and GPU respectively. © 2023 ieee.

关键词： Field programmable gate arrays (FPGA)

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