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检索条件"任意字段=2015 2nd International Conference on Engineering Technology and Application"

共 27583 条记录，以下是131-140 订阅

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Blockchain Based E-Voting System 2

Blockchain Based E-Voting System

引用

2nd international conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Singathala, Harshith Narayansetty, Swarag Kata, Harsha Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Online voting has been a subject of study for many years. The credibility of the technology, or where the solution rests in counting, is where the validity of the votes is at great risk. Additionally, the transactions need to be secure. This study was conducted to determine how Blockchain technology will affect the security of online voting systems. Blockchain is a cutting-edge computing technology that is little known to the general public. The influence of Bitcoin has nevertheless demonstrated the legitimacy of Blockchain as a Blockchain application, which has revolutionized Fintech. The decentralized nature of the Blockchain is its key characteristic. To safeguard the data, this prevents it from being stored in a single location and instead distributes it throughout the network. There is no wrong in a decentralized system. By doing so, a programmer is prevented from attacking the frame even if they only target a single block in the structure that has no bearing on the frame. Stream The US-based application My Vote is utilized for a trusted trust voting design that relies on regulatory advances to track the voting process continually. Blondes' innovation is employed in the alternative electoral system known as Election Runner. Security is the most well-known risk that all e-administrations face. By utilizing blockchain technology, the web-based democratic platform can find a solution to this problem. The advancement of blockchain technology has the potential to shift power and trust away from the main entertainers. © 2024 IEEE.

关键词： Blockchain

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Biologically Inspired Network Design to Train Only Through Forward Pass 2

Biologically Inspired Network Design to Train Only Through F...

引用

2nd international conference on Information and Communication technology, ICICT 2024

作者： Hossain, Md Shahariar Hassan, Md Tasneemul Hossain, Md Safaiat Department of Electrical and Electronic Engineering BRAC University Dhaka1212 Bangladesh Department of Electrical and Electronic Engineering Bangladesh Army University of Engineering and Technology Natore6431 Bangladesh

ISBN: (纸本)9798331508227

Commonly used neural network architectures in the industry almost entirely rely on backpropagation techniques like SGD to minimize the error of the network. It is not only computationally expensive but needs to be done in a separate phase before inference time. To address these issues, this paper introduces a perceptron architecture that can train neural networks without backpropagation, a design inspired by the recent discovery of Brain Criticality Theory in neuroscience and the Hebbian Learning process of the biological brain. These neural computation units, called "Biceptrons" use a binary system to indicate either an active or inactive state in the network, analogous to the spike potentials of cortical neurons. The weights are adjusted with a special training algorithm only through a forward pass during inference time, where the weights gradually reach a steady state while learning the feature representation of the dataset. The authors present some toy problems of neural networks built using these "Biceptrons" units to demonstrate the application of this architecture. ©2024 IEEE.

关键词： Integrated circuit design

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Research on the application Effects of AI technology in Data Center Energy Saving 24

Research on the Application Effects of AI Technology in Data...

引用

2nd international conference on Electronics, Computers and Communication technology, CECCT 2024

作者： Chen, Yaojia Ruan, Pan Wang, Guanghui Wuhan Vocational College of Software and Engineering Hubei Wuhan China Fiberhome Telecommunication Technologies Co. Ltd Hubei Wuhan China Hubei University of Automotive Technology Hubei Shiyan China

ISBN: (纸本)9798400710193

Current data centers face urgent demands for energy conservation and efficiency improvements. Integrating AI energy-saving systems into the data center infrastructure allows for the prediction of operational trends and enables second-level adjustments to the return air temperature setpoints of the air conditioning units. This proactive regulation of the AC equipment’s operational state results in real-time alignment of cooling capacity with the IT power consumption trends. The study selected an existing air-cooled micro-module data center and a chilled water terminal air-conditioned data center. After over six months of application, a substantial amount of data related to IT power consumption, air conditioning power consumption, and data center temperatures were collected. Comparative analyses were conducted on the energy consumption and PUE under conditions with and without AI technology during the operation of the data centers. The results indicate that AI technology can effectively reduce the energy consumption and PUE of the data *** energy-saving technology not only demonstrates efficacy in existing data centers but also holds significant potential for energy savings in newly constructed communication and data centers, offering high cost-effectiveness and considerable promotion value. © 2024 Copyright held by the owner/author(s).

关键词： Energy saving

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Research on Early Warning and Monitoring technology for Severe Weather Conditions on Highways 2

Research on Early Warning and Monitoring Technology for Seve...

引用

2nd IEEE international conference on Electrical, Automation and Computer engineering, ICEACE 2024

作者： Huaqing, Zhang Wenning, Wang Jinbin, Wang Xue, Sheng Bingqian, Li Lili, Bi Mengqi, Liu Wenjun, Liu Deqin, Ran Crcc Shandong G2 Beijing-Shanghai Expressway Jile Co. Ltd. Shandong250013 China Shandong Transportation Institute Shandong250031 China

ISBN: (纸本)9798350368208

Severe weather is a major hidden danger to safe driving on highways, and how to ensure smooth traffic and traffic safety on highways under adverse weather conditions has become a social security issue that every country attaches great importance to. This article analyzes the warning and monitoring strategies and systems established by various countries, summarizes the research of domestic and foreign scholars on severe weather warning and monitoring technology. We introduce severe weather warning and monitoring technology, and hope to help strengthen the management of highway driving safety under severe weather conditions, reduce the incidence of traffic accidents caused by severe weather, and ensure the safety of people's property. © 2024 IEEE.

关键词： highways inclement weather traffic safety warning and monitoring

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application Research of Wireless Sensor Network in Structural Health Monitoring of Civil engineering 2

Application Research of Wireless Sensor Network in Structura...

引用

2nd IEEE international conference on Electrical, Automation and Computer engineering, ICEACE 2024

作者： Li, Songtao Gao, Yanlong Wang, Baolin Tian, Ruxue Zhengzhou Railway Vocational & Technical College College of Railway Engineering Zhengzhou China Henan Jiaoyuan Engineering Technology Group Co. Ltd Zhengzhou China Zhengzhou Road Inspection Engineering Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9798350368208

Aiming at the needs of structural health monitoring in civil engineering, this paper deeply studies the application of wireless sensor networks in it, and focuses on the design and optimization of intelligent sensor networks. First, this paper outlines the important role of wireless sensor networks in civil structural health monitoring, including real- time data acquisition, remote monitoring and fault warning. Subsequently, a structural health monitoring method based on modal recognition technology is introduced in detail, which evaluates the health status of the structure by analyzing its dynamic response. This paper introduces a particle swarm optimization algorithm to optimize the layout and parameter settings of the sensor network to improve the accuracy and reliability of the monitoring system. The effectiveness of the proposed method is verified through system simulation. The simulation results show that the optimized sensor network can more accurately capture the abnormal behavior of the structure and maintain stable performance under various working conditions. The research results of this paper provide an efficient technical solution for the structural health monitoring of civil engineering, which has important theoretical significance and practical application value. © 2024 IEEE.

关键词： Civil structure health monitoring intelligent sensor network modal recognition particle swarm optimization algorithm

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An Overview of Event Extraction Methods based on Semantic Disambiguation 24

An Overview of Event Extraction Methods based on Semantic Di...

引用

2nd international conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Li, Haili Wang, Xiaodong Zhou, Yunyan Liu, Weijie Pan, Shilong College of Computer Science and Technology National University of Defense Technology Hunan Changsha China Unit 63893 of PLA Henan Luoyang China Unit 63891 of PLA Henan Luoyang China

ISBN: (纸本)9798400711237

Event extraction is a fundamental and complex task in information extraction, aiming at automatically identifying and extracting structured event information from unstructured text. However, the same word may have different meanings in different contexts, necessitating semantic disambiguation during the classification process. The polysemy of triggers presents a significant challenge to accurate EE. Existing surveys primarily focus on different domains, fields, or technical paradigms, but none provide a systematic summary of the semantic disambiguation techniques employed. This study classifies these techniques into three categories based on the underlying technical frameworks and offers a comprehensive overview of the most advanced methods in each category. Finally, we summarize the application scenarios, strengths, and limitations of existing approaches, and discuss potential directions for future research. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Wavelet Transform based Enhanced Statistical Voice Conversion of Non-Audible Murmur (NAM) for Improved Speech Intelligibility 2

Wavelet Transform based Enhanced Statistical Voice Conversio...

引用

2nd IEEE international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Veera Boopathy, E. Ramjan Begam, A. Anandhakumar, P. Vignesh, M. Mydhili, K.S. Rajeshwaran, K. Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Akshaya College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India VSB Engineering College Department of Electronics and Communication Engineering India Pollachi Institute of Engineering and Technology Department of Electronics and Communication Engineering Pollachi India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331534950

This article offers a novel wavelet transformation based statistical approach for transforming Non-Audible Murmur (NAM) signals into speech sounds in order to overcome the problems arising from the low energy and low-frequency content of NAMs. The wavelet transform of the NAM signals safely partitions the signals into detailed time-frequency features, enabling both noise reduction and feature enhancement. Some of the proposed system design steps involve pre-processing, framing, wavelet analysis of the underlying signal, and reconstruction of the enhanced signal. The simulation results show that this method can effectively reduce the standard deviation of the converted speech to 98% of the word accuracy rate. This shows that the wavelet transform is beneficial in enhancing the quality of the speech by filtering out noise and making it easier to implement in any application where clear speech reproduction is crucial. © 2024 IEEE.

关键词： Noise abatement

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Machine Learning Technique Enabled Learning Methodology for Human Depression Prediction 2

Machine Learning Technique Enabled Learning Methodology for ...

引用

2nd IEEE international conference on Advances in Information technology, ICAIT 2024

作者： Shanthi, J. Maria Sharma, Himanshu Rao, P. Srinivasa Thomas, G. Arun Sampaul Gupta, Amit Department of AI and ML J. B. Institute of Engineering and Technology Telangana Hyderabad India Department of ECE J. B. Institute of Engineering & Technology Telangana Hyderabad India Department of CSE J. B. Institute of Engineering &Technology Telangana Hyderabad India Department of AI and ML J. B. Institute of Engineering &Technology Telangana Hyderabad India Department of AI and ML J. B. Institute of Engineering & Technology Telangana Hyderabad India

ISBN: (纸本)9798350383867

Human Depression Prediction is important for a number of reasons, the most important of which are that it can help with faster interventions and better mental health outcomes. First and foremost, early identification of depression facilitates timely and focused interventions, allowing patients to obtain the right care and support prior to the worsening of their illness. By combining the deep learning powers of the LeNet architecture with the optimization skills of the Grey Wolf Optimization (GWO) algorithm, the Human Depression Prediction Scheme (HDPS) presents a novel approach to depression prediction. The proposed technique shows promise in identifying depression tendencies with a noteworthy 95% accuracy ***, Google Colab uses the HDPS, highlighting its usability and accessibility. By combining cutting-edge technologies and optimization strategies, HDPS is positioned as a potentially useful tool for depression early diagnosis, providing high accuracy and useful application in a computing environment that is widely available. ©2024 IEEE.

关键词： Adversarial machine learning

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application of GBDT algorithm and data fusion in power engineering big data analysis 2

Application of GBDT algorithm and data fusion in power engin...

引用

2nd IEEE international conference on Electrical, Automation and Computer engineering, ICEACE 2024

作者： Guo, Shaodan Yin, Yilin Tianjin University of Technology China

ISBN: (纸本)9798350368208

The field of electric power engineering generates vast amounts of complex data, encompassing various aspects such as power generation, transmission, distribution, and consumption. With the rapid advancement of big data technologies, effectively managing and analyzing this data has become a critical challenge. Traditional methods of data processing often struggle to handle the scale and intricacy of power system data. This paper proposes a novel power engineering data analysis and prediction method based on the Gradient Boosting Decision Tree (GBDT) algorithm, which is particularly well-suited for large, high-dimensional datasets. By leveraging the power of GBDT, this approach aims to enhance the accuracy of predictions related to power system performance, equipment maintenance, and load forecasting. Furthermore, it highlights the potential of big data technologies to optimize operations, reduce costs, and improve the efficiency of power grids. The goal is to contribute to the digital transformation of the power industry and foster smarter, more resilient energy systems. © 2024 IEEE.

关键词： Data accuracy

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Research on Object Recognition Detection Based on Image Processing and Machine Learning 2

Research on Object Recognition Detection Based on Image Proc...

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2nd IEEE international conference on Control, Electronics and Computer technology, ICCECT 2024

作者： Zou, Jiahui Liu, Zhenwen Jiang, Shuhang Li, Xu Dong, Yingjie Wang, Chenxi Computer Science Institute of Technology Changchun University of Science and Technology Changchun China School of Mathematics and Statistics Changchun University of Science and Technology Changchun China Optoelectronic Engineering Institute Changchun University of Science and Technology Changchun China College of Medical Instruments Shenyang Pharmaceutical University Shenyang China

ISBN: (纸本)9798350380958

In this paper, the application of image processing and machine learning in object recognition is studied. Firstly, a calculation method based on color, size and leaf area ratio is proposed. Through image preprocessing, RGB color extraction, edge detection and other steps, the classification model is trained and predicted. Secondly, a method based on image preprocessing, two-dimensional area calculation and leaf area ratio conversion is proposed to estimate the target quality by calculating the mass distribution histogram. In object recognition, a feature extraction method based on image preprocessing, standardization and convolutional neural network is proposed to train and evaluate deep learning models and realize object recognition. © 2024 IEEE.

关键词： Object recognition

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