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

共 11084 条记录，以下是731-740 订阅

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Design of Airborne Bus Communication Platform based on Isomorphic Dual Redundancy Channels 7

Design of Airborne Bus Communication Platform based on Isomo...

引用

7th international conference on Electrical, Mechanical and Computer engineering, ICEMCE 2023

作者： Wang, Xiaohang Xu, Kangfa Xu, Yaxing Xi'an Aeronautics Computing Technique Research Institute Avic Aviation Key Laboratory of Science and Technology on Airborne and Missle-borne Computer Xi'an China

ISBN: (纸本)9798350382877

In order to solve the problem of low reliability of airborne bus communication platforms under limited volume, weight, power consumption, and computing power in airborne environments, the design scheme of the airborne bus communication platform was completed by demonstrating the software and hardware architecture of isomorphic dual redundancy channels. this design scheme first analyzes the usage scenarios and requirements indicators of the airborne bus communication platform, standardizes the design process based on the analysis indicators, and then designs a hardware architecture based on isomorphic dual redundancy channels according to the requirements and application environment of the design scheme. Finally, it focuses on the data interaction characteristics between the airborne product and the upper computer, Complete the design of dual redundancy data intelligent control software from the perspective of dual channel data flow and channel fault switching. Simulation examples show that this solution effectively solves the problems of high fault reporting rate and excessive resource redundancy in traditional equipment, and significantly improves the reliability of massive data transmission and reception in airborne environments. © 2023 IEEE.

关键词： Redundancy

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Colposcopy Image Classification using Fuzzy Min-Max Neural Network 7

Colposcopy Image Classification using Fuzzy Min-Max Neural N...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Malhari Kalbhor, Madhura Vijay Shinde, Swati Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Cervical cancer is ranked as fourth most prevalent cancer worldwide among women. Effective treatment can lead to complete cure for early-stage cervical cancer. Colposcopy is a widely used diagnostic tool for cervical cancer;its accuracy depends on the experience of examiner. A fuzzy min-max neural network(FMMN) is used to classify images in medical image analysis. Neuro-fuzzy architecture combines fuzzy logic and deep learning techniques to handle overlapping classes and create precise classification models. the proposed system introduces a novel approach that uses deep learning-based feature extraction in conjunction with an FMMN to accurately classify colposcopy images. Performance of proposed system is evaluated by testing various pre-trained models on the IARC dataset. the best accuracy achieved by GoogleNet is 100%. © 2023 IEEE.

关键词： CNN Colposcopy images classification Deep learning fuzzy min-max neural network pre-trained models

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IoT based Smart ID Card for Working Woman Safety 7

IoT based Smart ID Card for Working Woman Safety

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Gowrishankar, V. Prabhakaran, G. Tamilselvan, K.S. Judgi, T. Parimala Devi, M. Murugesan, A. Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Nandha Engineering College Department of Electronics and Communication Engineering Erode India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering India K S R Institute for Engineering and Technology Department of Electrical and Electronics Engineering Tiruchengode India

ISBN: (纸本)9798350397253

Working women must be protected from assault in many parts of the world. A smart ID card based on the Internet of things (IoT) could make female workers safer and be one solution to this problem. Because of the built-in sensors and connectivity components, this ID card will be able to sense and respond to its surroundings. If the sensors detect something unusual, such as a sudden movement or a loud noise, they will send a signal to the system. Following the completion of the data analysis, the system takes any necessary actions, such as sending alerts to security personnel or notifying on-call personnel. the proposed system will provide the user with access to an emergency panic button. the system's GPS component will track the user's location and send alerts to the appropriate parties. there will also be a cloud-based monitoring system with the proposed approach. this allows the employer or loved ones of the worker to track the worker's whereabouts and activities in real time. the system will analyze the data using machine learning techniques, and the results will reveal the worker's behavior and level of safety. A smart ID card linked to the Internet of things (IoT) could help women work in safer environments. the proposed technology would enable real-time monitoring, predictive alarms, and data analysis. All of these precautions will help ensure female employees' safety and avoid unpleasant situations. © 2023 IEEE.

关键词： Personnel

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VLSI Implementation of Triple-DES Block Cipher 7

VLSI Implementation of Triple-DES Block Cipher

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Subaselvi, S. Mytheesh, C. Sanjay, R. Parithi Malavan, G. Ragunath, S.D. Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering Tamil Nadu Karur India

ISBN: (纸本)9781665464086

More sensitive information is being exchanged over computer networks as they grow. Securing information from unauthorized parties has emerged as a critical issue in the field of information technology security. Records safety may be vital in the course of transmission or storage, and the Triple Data Encryption Standard (TDES) method with DNA Encoding and Decoding and its implementation are both designated at first, this study has developed a smart device for converting statistics into a cryptographic cipher and return the cipher to its original form, both of which are completely described with the help of various algorithms. Verilog HDL is used to implement the DNA based Triple DES design, and Modelsim 6.4c are used for analyzing it. When compared to non-pipeline methods, the proposed pipeline method is intended to increase execution speed. the Xilinx tool synthesis process is used to assess the performance of the suggested methodology. By adding a DNA encoder and decoder to the DES design, the suggested system's security is increased. © 2023 IEEE.

关键词： DNA

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FedHiT: Privacy Protection for Federated Learning via Hierarchical Training 7

FedHiT: Privacy Protection for Federated Learning via Hierar...

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7th international conference on Data Science and Information technology, DSIT 2024

作者： Shen, Wenjie Sheng, Guojun Pan, Xiaohua Chen, Jinyin Wang, Xiaojuan Zheng, Haibin Zhejiang University of Technology College of Information Engineering Hangzhou China COSMOPlat IoT Technology Co. Ltd. Qingdao China Binjiang Institute of Zhejiang University Hangzhou China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China

ISBN: (纸本)9798350384093

Federated learning (FL), collaborating with thousands of participants in a distributed manner, greatly protects the privacy of local data. However, recent research reveals that FL is at risk of privacy leakage attacks. Consequently, a variety of techniques have been applied to address the issue of privacy protection and effective distributed training of FL, such as differential privacy (DP), gradient compression (GC), and homomorphic encryption (HE). However, these techniques are still limited in three aspects, i.e., communication overhead, fidelity, and effectiveness. To address these challenges, we propose FedHiT, a novel privacy protection for FL via hierarchical training. It minimizes the information the curious-but-honest server acquires by employing a hierarchical upload mechanism. FedHiT differs from previous work in the three key aspects: (1) communication - it has low communication overhead since it only uploads partial model parameters to the server iteratively;(2) effectiveness - it can effectively defense various privacy leakage attacks and adaptive attacks;(3) fidelity - it ensures effectiveness while maintaining the model's accuracy without compromise. Extensive experiments are conducted on three datasets under three attacks, compared with three state-of-the-art baselines, and the results testify that FedHiT outperforms other methods in model accuracy, communication overhead, and privacy protection capacity. For instance, the average model accuracy with FedHiT is only 1.59% lower than that of without any defense (i.e., the fidelity of using FedHiT only lost 1.59%), but its privacy protection ability is 1.4∼2 times that of DP and GC methods with the same fidelity. © 2024 IEEE.

关键词： Federated learning

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Drivers Drowsiness Detection using Image Processing and I-Ear Techniques 7

Drivers Drowsiness Detection using Image Processing and I-Ea...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Ananthi, S. Sathya, R. Vaidehi, K. Vijaya, G. Sri Eshwar College of Engineering Department of CSE Tamil Nadu Coimbatore India KongunaduCollege of Engineering and Technology Department of IT Tamil Nadu Trichy India Stanley College of Engineering and Technology for Women Department of Artificial Intelligence and Data Science Telangana Hyderabad India Sri Krishna College of Engineering & Technology Dept of CSE Tamil Nadu Coimbatore India

ISBN: (纸本)9798350397253

Fatigue and micro-sleep at the wheel are often the cause of serious accidents. Consequently, the initial signs of micro-sleep can be detected before a critical situation arises. Drowsiness detection technique helps to prevent accidents caused by the drowsy drivers. Driver drowsiness detection involves three methodologies namely face detection, eye detection, drowsiness detection. Face detection technique is used to detect the driver's face by extracting the facial features using Haar Cascade Classifier Algorithm also it detects the person's eye region. this paper proposed to Identifying Eye Aspect Ratio (I-EAR) method to identify the eye closure of vehicle's driver. If the driving person's eye is closed for minimum of 40 frames or eye blink ratio is less than 8 per minute, then the proposed system identifies that the vehicle driving person is drowsy. Consequently, it began to alert sound intimation that vehicle driver is drowsy. this alert may help the drivers as a wakeup call to take an immediate action by taking rest/refreshment and continue to drive safely. © 2023 IEEE.

关键词： Aspect ratio

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Data-Driven Economic analysis of hydrogen-power coupled integrated energy system 7

Data-Driven Economic analysis of hydrogen-power coupled inte...

引用

7th international conference on Data Science and Information technology, DSIT 2024

作者： Yan, Zhang Zi, Yuan Zhentao, Zhu Zhenqiang, Jin Mulan, Wang Hao, Xu Hairong, Wang Wenyi, Tan Nanjing Institute of Technology School of Economics and Management Nanjing China Nanjing Institute of Technology School of Electric Power Engineering Nanjing China Jiangsu Power Transmisson & Transformation Co. Ltd. Ministry of Industry and Technology Nanjing China Nanjing Institute of Technology School of Management Engineering Nanjing China Nanjing Institute of Technology School of Environmental Engineering Nanjing China

ISBN: (纸本)9798350384093

Energy system choices for zero-carbon parks are challenged by the drive towards a low-carbon transition. Traditional wind-power-gas integrated energy systems centred on natural gas and combined heat and power (CHP) are limited by high carbon capture and storage costs. the aim of this study is to assess the economic viability of an innovative hydrogen-electricity coupled integrated energy system centred on hydrogen production from renewable energy sources and to conduct a comparative analysis with conventional systems. the study begins with the collection of historical data on wind speed and solar radiation in the park over a number of years, which is used to analyse the output characteristics of the wind and photovoltaic systems in depth, providing a solid data base for the optimal design of the hydrogen-electric coupled system. Next, a techno-economic model was constructed to meet the park's demand for electricity, heat and transport energy. the capacity of each component in both systems was optimally configured to reduce the annualised cost of the system through a mixed integer linear programming (MILP) approach. Based on this, a comprehensive techno-economic analysis was conducted to compare the economic indicators of the two systems. the results of the analyses show that the hydrogen-electric coupling system has significant economic advantages, including a higher net present value (improved by 70.79%), a shorter payback period (reduced by 6.61%), and a higher internal rate of return. Sensitivity analyses further revealed the main factors affecting the economics of the hydrogen-electric coupling system, including the demand for hydrogen from vehicles in the park, electrolyser costs and wind turbine costs. the findings of this study provide an important reference for planning and decision-making on the energy system of a zero-carbon park and highlight the potential of hydrogen in the future energy supply of the park. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Energy-Proficient Adaptive Queuing Model-Based Clustering Scheme for Improving Network Lifetime in Wireless Sensor Networks 7th

Energy-Proficient Adaptive Queuing Model-Based Clustering Sc...

引用

7th international conference on Advanced computing and Intelligent engineering, ICACIE 2022

作者： Sengathir, Janakiraman Deva Priya, M. Christy Jeba Malar, A. Jacob, Suma Sira Department of Information Technology CVR College of Engineering Mangalpally Vastunagar Telangana Hyderabad India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India

ISBN: (纸本)9789819950140

Energy preservation in wireless sensor network (WSN) is vital for prolonging the network lifetime as sensors have stringent energy constraints. Reducing energy consumption is an uphill task, and process of clustering increases network lifetime to an expected level. In this context, efficient selection of Cluster Heads (CHs) facilitates effective data distribution. In this paper, Adaptive Queuing Model-based Clustering Scheme (AQMCS) is proposed for supporting maximized energy in the network with high-energy reduction in clusters through dynamic selection that mainly explores the energy incurred during the process of data packet transmission. It specifically determines the time at which packets would arrive for the objective of minimizing transmission delay. Moreover, the proposed AQMCS approach outperforms the benchmarked mechanisms by offering 19.32% improved Packet Delivery Ratio (PDR), 17.84% enhanced throughput, 16.54% minimized delay, 15.98% reduced total energy consumption, and 17.62% minimized Packet Loss Ratio (PLR) for varying number of nodes in the network. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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A Contrastive Learning Framework for ECG Anomaly Detection 7

A Contrastive Learning Framework for ECG Anomaly Detection

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

作者： Li, Fangyu Chang, Hui Jiang, Min Su, Yihuan Wuhan University of Technology School of Information Engineering Wuhan China

ISBN: (纸本)9781665478571

ECG is important for the recognition and diagnosis of cardiac arrhythmias as a physiological signal characterizing the condition of the heart. A lot of studies have started to experiment with statistical and traditional machine learning methods to analyze and detect ECG data, thus to the heart and other organs of intelligent auxiliary treatment. Although a lot of work has been done in ECG signal processing, the existing work still suffers from the following deficiencies:i) Since the number of various types of signals is unbalanced when classifying ECG signals, and end-to-end deep learning models are very sensitive to unbalanced data, which can affect the automatic detection and classification tasks. ii) the models lack robustness due to inconsistent ECG data representation. For this reason, in this paper, we first design a data augmentation-based contrast learning module to alleviate the data imbalance and robustness problems of the model. thus, a new contrast learning ECG abnormality detection framework is designed by capturing the underlying patterns of ECG signals. Many experiments show that our abnormality detection framework outperforms the baseline methods, which provides a new view for cardiovascular disease prevention and automatic diagnosis. © 2022 IEEE.

关键词： Electrocardiography

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A Novel Emotion based Music Recommendation System using CNN 7

A Novel Emotion based Music Recommendation System using CNN

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Tejaswini Priyanka, V. Reshma Reddy, Y. Vajja, Dharani Ramesh, G. Gomathy, S. Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397253

Music has a unique emotional connection with humans. It is a means of connecting individuals from all over the world. On the other hand, it is a highly difficult task to generalize music and claim that everyone would prefer and enjoy the same type. Emotion-based music selection is important because it can assist humans in reducing stress. Its major purpose is to accurately predict the user's emotions, and play songs depending on the user's preferences. Using Human Computer Interaction (HCI), the proposed bot recognizes human emotions from facial emotions. Another significant challenge is the extraction of facial features from the user's face. the proposed CNN Algorithm is utilized in the proposed model to properly capture and recognize the user's face from the live webcam stream and to detect emotions based on facial factors such as lips and eyes. Also, an additional option will be provided for people to make a good choice manually. © 2023 IEEE.

关键词： Emotion Recognition

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