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检索条件"任意字段=7th International Conference on Computing in Engineering and Technology, ICCET 2022"

共 11333 条记录，以下是1271-1280 订阅

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the Fusion of Feature for Person Re-identification Based on Siamese Network 7

The Fusion of Feature for Person Re-identification Based on ...

引用

7th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2022

作者： Du, Tianwen Zhang, Yifeng School of Information Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665487115

this paper proposes Fusion-based Siamese Network (FSN), a new deep learning architecture that targets at learning global and local features convergently for person re-identification (re-ID). this paper demonstrates the advantages of combining global and local features, which is embodied in indicators such as rank-1 and mAP. At the same time, it is noteworthy that FSN is based on siamese network, which leads innovation in image retrieving. Siamese network consists of two identical sub-networks. In the training process, a pair of images are input into the sub-networks at the same time. through training, FSN updates the weights according to the triplet loss. FSN can solve the problem of retrieving pictures under different posture, size and light conditions correctly. On three benchmark datasets, Market-1501, DukeMTMC-reID, and CUHK03, the proposed FSN achieves 94.9%,89.1%,67.7% in rank-1 and 90.1%,86.3%,65.7% in mAP respectively, surpassing most of state-of-the-art methods by a large margin. © 2022 IEEE.

关键词： Training Deep learning Technological innovation Cloud computing Computer architecture Benchmark testing Big Data

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A Human Activity Determination Predicting Abnormality Using SVM Approach for Mining Field Workers 7

A Human Activity Determination Predicting Abnormality Using ...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Nirmala, S. Priya, R.A. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350372816

this paper presents a human activity determination system for predicting abnormalities in mining field workers employing a Support Vector Machine (SVM) approach. this system integrates various sensors including an oxidation sheet sensor, temperature sensor and pulse oximeter sensor to capture the data related to worker activities. the collected data is processed by an ESP32 controller programmed with a classifier, which is capable of learning patterns and predicting the occurrence of abnormal activities based on the sensor readings. the predicted results are then transmitted to a LoRa transceiver for wireless communication. the LoRa receiver receives the transmitted information and relays it to a NodeMCU, which serves as a bridge between the receiver and the mobile application. the mobile application is designed to display the oxidation data, heart rate data, and temperature data obtained from the sensors in real-time. By analysing the sensor data and utilizing the SVM classifier, the system aims to detect and alert mining field workers to potential abnormal activities, thus enhancing safety measures and minimizing the risk of accidents in mining environments. the proposed SVM classifier has a specificity of 89.47%, sensitivity of 89.47 and accuracy of 92.64%. © 2024 IEEE.

关键词： Mining industry

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Robust Multi-agent Federated Reinforcement Learning for Task Offloading 7th

Robust Multi-agent Federated Reinforcement Learning for Task...

引用

7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Yan, Dibao Wang, Yongfeng Hou, Wenjing Song, Huanhuan Wen, Hong Ma, Wendi Sun, Fan School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. this algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. this paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node. © the Author(s) 2025.

关键词： Federated learning

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JOURNEY CRAFT: Crafting a smart traveling experience 7

JOURNEY CRAFT: Crafting a smart traveling experience

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Siddharth Miryalkar, Bola Sai Kalidindi, Hemanth Mashetty, Vishnu Sai Moturi, Ashrith Sanapala, Srinuvasarao B V Raju Institute of Technology Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

the Purpose of travel plan website is to provide the users with an interactive experience for planning the travels with the help of the chatbot that assists users in finding information about destinations and helping with *** the development of travel plan website we require many methods. Qualitative methods such as feedback sessions to understand the user preferences. quantitative methods like surveys , will be employed to gather numerical data on user interactions. Data collection methods will involve user testing, website analysis and feedback mechanism. Additionally, a systematic review of existing travel platforms will inform the design and features of the website. Users can experience more efficient trip planning processes saving time and reducing frustration due to increased user engagement with website due to interactive chatbot and neutral feedback mechanisms. users can get exposed to various destinations based on their convenience and budget planning. In the end we can assume that the travel plan website will be able to fulfill the stated purposes offering a technological user friendly platform for travel planning with respect to the budget and all the facilities for user convenience provided. © 2024 IEEE.

关键词： Budget control

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Adaptive Channel Detection for Full-Duplex Based IAB Systems on Unlicensed Channels 23

Adaptive Channel Detection for Full-Duplex Based IAB Systems...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Xiao, Qiqi Wu, Zheyi Yuan, Jiantao Zhou, Meng Yu, Guanding Yin, Rui School of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665494571

Integrated access and backhaul (IAB) technology in the fifth generation (5G) network is key to facilitating the internet of devices and data sharing in smart cities. To promote the development of smart cities and improve cost-effectiveness, IAB exploits the unlicensed spectrums with full-duplex technology. However, the residual self-interference (RSI) in the full-duplex based IAB system would impact the accuracy of clear channel assessment (CCA), which becomes the main factor reducing system throughput performance. this paper proposes an adaptive channel detection (ACD) scheme, optimizes the sequence of self-interference cancellation and CCA detection with a fixed self-interference channel estimation period, and presents two optimal initial CCA energy detection (ED) threshold schemes. In addition, we dynamically adjust the ED threshold according to the interference power detected and transmission feedback to improve the accuracy of CCA detection. Simulation results are demonstrated to verify that the ACD scheme effectively reduces the impact of RSI on CCA detection and improves throughput performance. © 2021 IEEE.

关键词： Interference cancellation Adaptive systems Smart cities 5G mobile communication Simulation Channel estimation Full-duplex system

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Edge Intelligence-Based UAV Human Target Recognition with Improved YOLOV5 Algorithm 23

Edge Intelligence-Based UAV Human Target Recognition with Im...

引用

作者： Cai, Zeyu Qu, Yuben Dong, Chao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665494571

Edge intelligent computing is often combined with the tasks of Unmanned Aerial Vehicle (UAV). Most applications of UAV require the location and identity information of human targets. Traditional human target detection and face recognition algorithms require a lot of time in edge intelligent computing. In addition, the final recognition result is not accurate enough. To this end, this paper proposes an improved YOLOV5-Cozy target detection algorithm to detect human targets and faces in a fast and accurate way. the algorithm uses MobileNetV3, depthwise separable convolution and shuffleconv to improve the original YOLOV5 (You Only Look Once version 5) algorithm. Compared with the original algorithm, the improved algorithm has an average detection accuracy drop of less than 5%, and its calculation time has been reduced by 20%. In terms of face recognition, we use the FaceNet algorithm with the addition of a face augmentation module. the module enhances the face recognition ability of the FaceNet algorithm in different environments, and the accuracy of face recognition has increased by 20%. We implement the proposed algorithm on the embedded computer Jetson Agx Xavier for UAV edge intelligent computing, and the average FPS achieves at about 23.6. © 2021 IEEE.

关键词： Target recognition Smart cities Convolution Face recognition Image edge detection Computational modeling Object detection

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Smart Staircase Lift for Assisting Elderly People 7

Smart Staircase Lift for Assisting Elderly People

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Badrinath, B. Deepak, K. Kavitha, M. Susitra, D. Nayagam, V. Senthil Jayaprakash, S. Sathyabama Institute of Science and Technology Electrical and Electronic Engineering Chennai India

ISBN: (纸本)9798350372816

this project focuses on the design and implementation of a staircase lift system aimed at enhancing accessibility for individuals with mobility impairments. the system integrates various components, including a 3-phase 0.5HP gear head squirrel cage induction motor, LT control switches, proximity sensors, raspberry pi and other essential stairlift components. the motor serves as the power source for the lift mechanism, providing the necessary torque to drive the lift along the rail system. LT control switches are utilized to regulate the lift's operation, allowing users to initiate forward, reverse, and stop commands with ease. Proximity sensors play a crucial role in enhancing safety by detecting obstacles or individuals in the lift's path and triggering appropriate responses to prevent collisions. Additionally, the project encompasses the design and fabrication of the rail system, platform/chair, and control systems, ensuring seamless integration and efficient operation of the staircase lift. through meticulous design, testing, and validation, the project aims to develop a reliable and cost-effective solution that promotes independence and inclusivity for individuals with mobility challenges. © 2024 IEEE.

关键词： Proximity sensors

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Motor Imagery Tasks EEG Signals Classification Using ResNet with Multi-Time-Frequency Representation 7

Motor Imagery Tasks EEG Signals Classification Using ResNet ...

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Liu, Wuqiao Zeng, Yu Yunnan University School of Information Science and Engineering Kunming China

ISBN: (纸本)9781665478571

this paper presents a ResNet-based multi-feature fusion method for motor imagery task classification in brain-computer interfaces. To address the drawbacks of insufficient information and low signal-to-noise ratio extracted from EEG signal features by a single time-frequency transform. three time-frequency transforms including morlet wavelet transform, multi-window time-frequency transform and stockwell transform are used to extract features from EEG signals. those features are then input to three ResNets for further feature extraction, and finally the high-level feature outputs by the three networks are fused before the fully connected layer of the original ResNet, and then fed into a fully connected layer for classification. the performance of the proposed method is evaluated using the accuracy and kappa value on the IVa dataset of BCI Competition III, and the results show that the ResNet model using feature fusion produces better results with a classification accuracy of 97.86%, which is 39.65% higher than the model using only one time-frequency transform as input. © 2022 IEEE.

关键词： Brain computer interface

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A Novel Fatigue Driving Detection Method Under the Mask-Wearing Condition 23

A Novel Fatigue Driving Detection Method Under the Mask-Wear...

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作者： Lv, Shengyi Wang, Hui Ni, Sirui Li, Jiangpeng Zhao, Zeli Zhang, Yuhan Cheng, Yuqian Zhang, Yunchuang Liu, Bernie Li, Xiulai Xie, Gengquan School of Cyberspace Security Hainan University Haikou China RobAI-Lab Hainan University Haikou China School of Information and Communication Engineering Hainan University Haikou China School of Computer Science and Technology Hainan University Haikou China College of Foreign Language Hainan University Haikou China Hainan Hairui Zhong Chuang Technol Co Ltd China

ISBN: (纸本)9781665494571

Fatigue driving is one of the major contributors to road accidents. Nowadays, COVID-19 is reaching epidemic proportions, which directly leads to the phenomenon of mask-wearing becomes ordinary among drivers. Most of the existing fatigue detection systems are unable to effectively determine the factual fatigue status of a driver that wearing a mask. therefore, we propose a quick-witted fatigue detection system to counteract the obstruction of masks. the system detects faces by means of a pyramidbox-based approach. then a modified PFLD-based method will predict the facial landmarks, from which the eye aspect ratio (EAR) is calculated. Ultimately, our self-made FDUM dataset was tested by using the evaluation method that combined PERCLOS and a method for blink frequency based on Gaussian distribution. Our system can achieve 97.06% accuracy in determining the fatigue status of the driver under the mask, which represents an excellent recognition rate of the system. © 2021 IEEE.

关键词： COVID-19 Epidemics Road accidents Ear Gaussian distribution Fatigue Faces

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Adaptive spatially weighted fuzzy c-means clustering for image segmentation 7

Adaptive spatially weighted fuzzy c-means clustering for ima...

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7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Li, Xiaohe Qu, Zhan Xi'an Shiyou University School of Computer Science Xi'an China Xi'an Shiyou University School of Petroleum Engineering Xi'an China

ISBN: (纸本)9781665478571

Fuzzy c-means (FCM) clustering algorithm is a popular algorithm in the field of image segmentation. In this paper, an adaptive spatially weighted FCM clustering (ASWFCM) is proposed, to improve the performance of image segmentation. through defining the weight of each pixel neighborhood and incorporating the spatial weights into the traditional Fuzzy c-means objective function, the image information which related to local spatial is integrated into the segmentation approach. Because of those spatial weights of neighboring pixels are fully adapted to the local image content and can effectively detect the anisotropy of neighborhood, the proposed algorithm improves the robust to noise. Both synthetic and real image experimental results verify the accuracy and the robustness of the ASWFCM method. © 2022 IEEE.

关键词： Image segmentation

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