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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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A Hybrid Deep Learning-based Person Re-identification Across Multi-Cameras 10

A Hybrid Deep Learning-based Person Re-identification Across...

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10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Aonon, Manaschai Charoenkwan, Phasit Singkhamfu, Phudinun Kaewkamol, Porntida Chiang Mai University Data Science Consortium Faculty of Engineering Chiang Mai Thailand Chiang Mai University Modern Management and Information Technology College of Arts Media and Technology Chiang Mai Thailand Chiang Mai University Software Engineering College of Arts Media and Technology Chiang Mai Thailand

ISBN: (纸本)9798331543273

Surveillance systems play an essential role in enhancing security and ensuring safety within society. Public spaces and critical areas are commonly monitored through multi-camera networks for tracking individuals in real-time to enable quick responses to potential security threats. However, variations in lighting, camera angles, and occlusions pose significant challenges to the process of recognizing individuals across multiple camera views. this research proposes a Person Re-Identification (Re-ID) with deep learning and feature extraction method to improve the accuracy of individual matching and identification despite these challenges. this research encompasses multiple neural network topologies, such as ResNet50, ResNet101, and OSNet architectures with traditional computer vision methodologies such as Scale-Invariant Feature Transform (SIFT) to examine the body part weighting coefficients and feature selection mechanisms. the results demonstrated that integrating dimensionality reduction via principal component analysis (PCA) coupled with semantic segmentation techniques yields statistically significant improvements in re-identification accuracy. the ResNet101 architecture emerges as the superior methodology, achieving performance with a Rank-1 accuracy of 0.88 and mean Average Precision (mAP) of 0.94 on the test dataset. © 2025 ieee.

关键词： Semantic Segmentation

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Multipath TCP for Short Flows Supported by SDN in Heterogeneous Networks

Multipath TCP for Short Flows Supported by SDN in Heterogene...

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international conference on computers, Communications, and systems (ICCCS)

作者： Ahmad Abumihsan Abdalkarim Awad Mohammad Jubran Aziz Qaroush Iyad Tumar Abdullah S. Alshra Faculty of Engineering and Technology Birzeit University Birzeit Palestine Department of Computer Science CS7 Friedrich-Alexander-Universitat Erlangen Nurnberg Germany

Multipath TCP (MPTCP) offers better performance, throughput, and resilience in connection failure cases by dividing the flow into multiple subflows and submitting them over many paths. Generally, MPTCP has a perfect impact on long flows in terms of throughput once transferring data with big sizes. However, MPTCP may degrade the efficiency of short flows, which are sensitive to latency (e.g., web transfer applications). this work investigates the impact of MPTCP on short flows in homogeneous and heterogeneous networks and confirms that a bandwidth gap between paths is a critical factor influencing the performance of the short flows. therefore, the work uses software-Defined Networking (SDN) architecture to address the MPTCP problems and improves its performance for short flows. Our performance results show an improvement in MPTCP performance for short flows compared to the disjoint approach.

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A Digital Twin-Based Production-Maintenance Joint Scheduling Framework with Reinforcement Learning

A Digital Twin-Based Production-Maintenance Joint Scheduling...

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ieee international conference on Control and Robotics engineering (ICCRE)

作者： Qinglong Hao Yaqiong Lv Sourthern Marine Science and Engineering Guangdong Labortory (Zhuhai) School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan China School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan China

the bridge of job scheduling and production equipment maintenance is usually the main joint scheduling task of a production system. However, the predicament of data acquisition in real systems leads to the difficulty of verifying the effectiveness of scheduling algorithms. In order to make joint scheduling work easier to implement in real production systems, this paper presents a joint scheduling framework for production systems based on digital twin and reinforcement learning. Firstly, the virtual mapping of physical production system, namely digital twin system, is established by using AnyLogic software and multi-agent modeling technology. then, a joint scheduling agent is trained by Deep Q Network (DQN) algorithm and the virtual data generated by the twinning system. And the experimental results demonstrate the effectiveness of proposed framework in production systems with uncertainties, and it has higher production efficiency and lower machine failure frequency compared with a scheduling scheme based on common-used heuristic rules.

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IoT based Multifunctional Power Analyzer

IoT based Multifunctional Power Analyzer

引用

international conference for Convergence of Technology (I2CT)

作者： Bhagyashri Baviskar Gargi Deshmukh Dipak Shimpi Amrita Tuteja Electrical Engineering JSPM'S BSIOTR Wagholi Pune India

To ensure power quality, the power system needs to be monitored and analyzed. Often, accidents happen as a result of poor power supply quality. So both the power department and the consumers of electricity seek to raise the quality of the power. Observe and analyses Power quality systems are commonly employed. Using a technique for frequency spectrum analysis based on the capture of time domain data, the research tracked fluctuations in the power quality index and examined their causes. Using LabVIEW, a monitoring and analysis system is created. this study first discussed the significance of monitoring power quality and analyzing its index before introducing the fundamental concept

关键词： Time-frequency analysis Reactive power Power quality Power system harmonics Harmonic analysis software Indexes

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Energy-Efficient and Reliable Federated Learning in Heterogeneous Mobile-Edge Computing 19

Energy-Efficient and Reliable Federated Learning in Heteroge...

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2022 ieee SmartWorld, 19th ieee international conference on Ubiquitous Intelligence and Computing, 2022 ieee international conference on Autonomous and Trusted Vehicles conference, 22nd ieee international conference on Scalable Computing and Communications, 2022 ieee international conference on Digital Twin, 8th ieee international conference on Privacy Computing and 2022 ieee international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xu, Yan Li, Liying Cong, Peijin Zhou, Junlong Nanjing University of Science and Technology School of Computer Science and Engineering China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture China

ISBN: (纸本)9798350346558

As a decentralized machine learning paradigm, Federated Learning (FL) is an emerging technique to protect user data privacy in Mobile Edge Computing (MEC). FL adopts the idea of distributed privacy computing, enabling the terminal devices to train machine learning models required by servers locally and upload model parameters to servers for aggregation. However, in the process of coupling FL with MEC systems, device heterogeneity, malicious use of poisoned data by users and limited system resources will seriously affect FL training accuracy, system reliability and energy efficiency. In this paper, a highly reliable and energy-efficient FL scheme is designed to solve these issues. Specifically, we first propose a calculation method for estimating the reliability of heterogeneous devices. then, we design a highly reliable device selection scheme based on devices' reliability, computing power, and participation times. Finally, we utilize the slack time in FL training to dynamically adjust the voltage and frequency of devices for reducing the energy consumption of training. Experimental results show that our proposed scheme improves the accuracy by up to 36.45% on average and saves energy by up to 48.63% when compared to the baseline and benchmarking methods. © 2022 ieee.

关键词： Reliability

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Dynamic Event-Triggered Load Frequency Control for Multi-Area Power systems Under Switching Cyber-Attacks 8th

Dynamic Event-Triggered Load Frequency Control for Multi-Are...

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： Wang, Yan Jia, Xinchun Lv, Teng Wang, Zhongbao School of Automation and Software Engineering Shanxi University Taiyuan030006 China School of Mathematical Sciences Shanxi University Taiyuan030006 China

ISBN: (纸本)9789819602247

In this paper, the decentralized dynamic event-triggered load frequency control problem for multi-area power systems under switching cyber-attacks is investigated. A new switching cyber-attack model including Denial of Service (DoS) attacks and deception attacks occurring in the sensor-to-controller channel is considered, which is characterized by being composite in both the spatial and temporal domains. the attack model is characterized by compositing in the spatial and temporal domains. To reduce the network communication burden and mitigate the impact of switching cyber-attacks on the closed-loop system, we design a decentralized dynamic event-triggering mechanism (DETM) that dynamically adjusts threshold parameters using hyperbolic tangent functions related to output measurement deviations. Further, a decentralized secure LFC scheme is developed based on periodic sampling, which can reduce the consumption of communication resources under switching network attacks and protect the security and frequency stability of the power system. Finally, the effectiveness of the proposed method is verified by a simulation case of the ieee 39 BUS test system containing 10 generating units. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Frequency stability

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Development of Automatic Cartesian Robot Cannabis Seeder using LabVIEW 10

Development of Automatic Cartesian Robot Cannabis Seeder usi...

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作者： Kosarat, Rujipan Sangwai, Keattipoom Wongjan, Anan Rajamangala University of Technology Lanna Faculty of Engineering Department of Software Engineering Chiangmai Thailand Rajamangala University of Technology Lanna Faculty of Engineering Department of Electronic Engineering and Automatic Control Systems Chiangmai Thailand

ISBN: (纸本)9798331543273

this article describes the invention of autonomous cannabis seeding equipment to reduce contamination and planting time. the automated cannabis seeder employs an NI myRIO control board to operate a y-axis stepper motor attached to a ball screw to move along the breadth of each row of 14 seed trays. Soil drilling and burial Drill all holes at the same depth. this promotes seed germination and lowers human-seed germ contamination. these 7 soil drill heads and 7 seeders use 3D printers to design and shape the workpiece. An 800-watt air pump smokes the cannabis seeds. Linear electric motors function in two rows in the z-axis. the buffer reverses the drowning process from row 14 to the first row, starting from the 3rd row and continuing through the final series of steps until 14 rows in the y-axis are complete. the hardware controls all program activities in LabVIEW. We tested the automated cannabis seeding machine 100 times on average, sowing cannabis seeds into 98 seed trays and dropping 93 seeds at 94.80% efficiency in 10.38 minutes. A 16 percent faster machine than human work is available. © 2025 ieee.

关键词： Stepping motors

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Analysis of Traffic Sign Recognition for Automated Transportation systems Using Neural Networks

Analysis of Traffic Sign Recognition for Automated Transport...

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international conference for Convergence of Technology (I2CT)

作者： Arindam Baruah Rakesh Kumar Meenu Gupta Computer Science Engineering Chandigarh University Punjab

Recently, numerous applications of Intelligent Transportation systems (ITS) have gained increasingly more focus. One of the most crucial functions of ITS is Traffic Signs Detection and Recognition (TSDR) by notifying drivers of the status of road signs and providing helpful information regarding safety procedures which helps to improve safety. this paper analyzes the different deep learning algorithms implemented for TSDR along with discrete optimization functions used, Image conditions of the datasets, and accuracy attained during the training and testing of Traffic Sign Detection algorithms (TSD) in recent research. A comparison of all the approaches is presented in the study with the help of tables. In the paper, a list of publicly accessible datasets is also mentioned, along with a discussion that can be very useful for future TSDR research work.

关键词： Training Deep learning Roads Neural networks Benchmark testing Safety Detection algorithms

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Network Intrusion Detection Using CNN-based Classification Method

Network Intrusion Detection Using CNN-based Classification M...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Tzu-En Peng I-Hsien Liu Jung-Shian Li Chuan-Kang Liu Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan Department of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology Taichung Taiwan

With the rapid advancement of Artificial Intelligence (AI) in recent years, there has been a growing body of AI-related research in the field of Network Intrusion Detection systems (NIDSs). In this paper, a network intrusion detection method based on Convolutional Neural Network (CNN) was tested using the KDD Cup 99 dataset. Along with Sigmoid-weighted Linear Unit (SiLU) as the activation function and Stochastic Gradient Descent (SGD) as the optimizer, Batch Normalization (BN) was applied to mitigate internal covariate shift. the evaluation was carried out using performance metrics for the attack classes, which yielded good results for intrusion detection and demonstrated an impressive average accuracy rate of 99.26% after ten epochs, showcasing its promising results in intrusion detection.

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System for Enhancing Accuracy of Noisy Text using Deep Network Language Models

System for Enhancing Accuracy of Noisy Text using Deep Netwo...

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international conference for Convergence of Technology (I2CT)

作者： R Rohit SA Gandheesh KS Suriya Peeta Basa Pati Department of Computer Science & Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

Text from image documents must be recognized for its usage. Various tasks such as plagiarism & error check, language analysis, information capture rely on the accuracy of this text conversion. OCR systems convert the document images to their text equivalent. these OCR systems are prone to introducing errors during the recognition *** work reports a system developed to ingest image documents which is converted to text using available OCR technologies. the recognized text, subsequently, is processed with deep network language models to enhance the accuracy of text. the system consists of a client server architecture with user interface available from web application as well as from mobile app. For the language models, encoder-decoder based BART & MarianMT are used. the results obtained demonstrate a 35% reduction in WER using the BART language model.

关键词： Image recognition Text recognition Plagiarism Optical character recognition Switches User interfaces Service-oriented architecture

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