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

共 11094 条记录，以下是1091-1100 订阅

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Overhead Power Line Detection Based on Curriculum Learning Strategy 7

Overhead Power Line Detection Based on Curriculum Learning S...

引用

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

作者： Gefei, Zhang Quan, Chen Bingheng, Liu Hao, Ma Yihan, Zhang Zanchen, Cui Fan, Yang Ying, Tong Nanjing Institute of Technology School of Communication and Artificial Intelligence School of Integrated Circuits Nanjing China Nanjing Institute of Technology School of Electrical Power Engineering School of Shen Guorong Nanjing China

ISBN: (纸本)9798350384093

Drone inspection of overhead power lines is a crucial aspect of ensuring electrical safety. Due to the elongated shape of overhead power lines and the complex inspection backgrounds, commonly used detection networks struggle to learn the unique features of these targets. To achieve high-precision detection of overhead power lines, a detection method based on a curriculum learning strategy is proposed. First, multiple target bounding boxes are annotated for individual overhead power lines in aerial images. Next, simulating the human learning process of starting with easier tasks and progressing to more difficult ones, the wavelet-entropy algorithm is used to divide the dataset into simple and complex subsets, allowing the YOLOv5 object detection network to first learn from the simple data and then from the complex data. Finally, a false detection box removal algorithm based on directional gradient statistics is introduced to process the overhead power line images that still contain incorrect identifications after network detection, eliminating the erroneous detection boxes. Experimental results indicate that, under the same network scale, high-precision detection of overhead power lines has been achieved, with an accuracy improvement of 10.8% compared to YOLOv5 based on traditional training strategies, and mAP_0.5 reaching 78.6%. this method effectively detects slender targets like overhead power lines while minimizing background interference, advancing the development of drone-based electrical inspection technology. © 2024 IEEE.

关键词： Overhead lines

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Wireless Sensor Network based Anomaly Detection using SVM-RFE-MRMR 7

Wireless Sensor Network based Anomaly Detection using SVM-RF...

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

作者： Sakthivel, S. Agalya, M. Sudha, R.V. Lathika, V. Selvi, P. Suriyapriya, N. Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Vivekananda College of Technology for Women Tiruchengode Department of Computer Science and Engineering Tamilnadu Namakkal India Vivekananda College of Technology for Women Tiruchengode Department of Information Technology Tamilnadu Namakkal India Computer Science and Engineering Jayam College of Engineering and Technology Tamilnadu Dharmapuri India Vivekananda College of Technology for Women Tiruchengode Department of Information Technology Namakkal India

ISBN: (纸本)9798350397253

there has been a meteoric rise in the use of wireless sensor networks (WSNs) in recent years. WSN's compact size and low cost are enticing many different industries to adopt them for use in a wide range of applications. there are several applications of IoT technology, including environmental monitoring, building security, and precision agriculture. Given that most WSNs are deployed in an unsupervised and potentially hostile environment, security risks are significant. To ensure the privacy of data during transmission from sensors to the base station, a number of methods have been proposed. the work in question is concerned with a crucial part of data and network security: detecting attacks. With the goal of preventing malicious attacks and maintaining the security of wireless sensor networks, anomaly detection has emerged as a significant obstacle. After the input is given, It consist of three phases such as preprocessing, Feature selection and training the model using SVM-RFE-MRMR. the SVMRFE-MRMR methodology is used for classification in the suggested method. While SVM-RFE takes feature-decision correlation into consideration, it does so by neglecting feature-feature relationships, MRMR is able to provide features with little redundancy and maximum relevance. Because of this SVM-RFE-MRMR is used to generate a set of features that is both minimal in redundancy and maximal in relevance. the proposed model produces an accuracy of about 98.6% when compared other models such as SVM and CNN. © 2023 IEEE.

关键词： Support vector machines

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the Ultra-Wideband Communication System: A Human Gesture Recognition Approach 23

The Ultra-Wideband Communication System: A Human Gesture Rec...

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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： Li, Anna Bodanese, Eliane Luo, Fei Hou, Tianwei Wu, Kaishun School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computer Science Shenzhen University Shenzhen China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665494571

In this paper, we propose a cost-effective ultra-wideband (UWB) communication system for gesture recognition in a smart home environment, where the interference issues can be beneficially solved. In the proposed UWB communication system, the gesture trajectories obtained by positioning are employed for recognizing human gestures. To this end, we firstly collect the datasets of four different fine-grained gesture activities. then, we integrate the squeeze-and-excitation block into the convolutional neural network seamlessly for gesture recognition, namely the SE-ConvlD model, which achieves an overall accuracy of 99.48%. © 2021 IEEE.

关键词： Support vector machines Radio frequency Smart cities Ultra wideband communication Gesture recognition Interference Smart homes

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Development of Wireless Pulse Rate Monitoring System 7

Development of Wireless Pulse Rate Monitoring System

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

作者： Tarafder, Md. Mosaddek Tarequzzaman, Md Rahman, Md. Anisur Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

ISBN: (纸本)9781665464086

One of the leading causes of mortality worldwide is cardiovascular disease, which includes coronary artery disease, myocardial infarction, and cardiac arrhythmia. If people are aware of the rate of heartbeat and carefully monitor it, it may be reduced. However, ongoing monitoring can be costly for the private sector, so this study suggests using a Bluetooth-based wireless network to address the issue. A wireless pulse rate monitoring system has been developed in this paper. the Arduino Uno transfers this pulse rate data to a smart phone or mobile device with the help of Bluetooth technology, where it can be more quickly and cheaply studied by the user before being forwarded to a specialist medical officer. © 2023 IEEE.

关键词： Bluetooth

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A New VMP Approach Based on CPU and Memory Using Bin Packing 7

A New VMP Approach Based on CPU and Memory Using Bin Packing

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7th IEEE Pune Section international conference, PuneCon 2024

作者： Tandon, Akanksha Manju, K.M. Patel, Sanjeev Nit Rourkela Department of Computer Science and Engineering Rourkela769008 India

ISBN: (纸本)9798331527822

Cloud computing is one of the demanding technologies that provide services with flexibility in terms of pay as per use, access to the module from anywhere, etc. To meet the growing demand from users, cloud providers are developing extensive data centers. the core technology behind cloud computing is virtualization, which delivers computing services through virtual machines (VMs). In the dynamic cloud computing environment, numerous users request various services, making it essential to implement efficient resource allocation policies to meet user demands. the goal is to maximize resource utilization while minimizing energy consumption by optimizing the use of physical server resources. Simulations conducted using the CloudSim simulator demonstrate that this approach maximizes resource utilization by selecting the minimum number of hosts. the algorithm treats the three parameters as dimensions in a 3D bin packing (BP) model, with the workloads for the data center generated from PlanetLab leftovers. In a scenario with 50 VMs and 40 hosts, the proposed algorithm reduces energy consumption from 0.98 kWh to 0.38 kWh, which shows a significant improvement. the novelty of this paper is the development of a dynamic bin-packing algorithm that optimizes both resource utilization and energy consumption by efficiently managing virtual machine placement in cloud data centers © 2024 IEEE.

关键词： Data centers

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Engagement measurement of a learner during e-learning: A deep learning architecture 7

Engagement measurement of a learner during e-learning: A dee...

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7th international conference on Parallel, Distributed and Grid computing, PDGC 2022

作者： Tanwar, Sanchit Kumar, Vinay Sharma, Shailza Thapar Institute of Engineering and Technology Electronics and Communication Engineering Department Punjab Patiala India

ISBN: (纸本)9781665454018

Teaching in online mode is challenging, but measurement of student engagement is even more challenging and complex but crucial to overall learning of the participants. Although online environment improves the overall learning and removes the barriers of time schedules, it requires a high level of self motivation from the learners. those at the brink of self motivation may perform exceedingly better under online learning if a tool can help them concentrate. these tools require the overall engagement level of the learner. the current manuscript proposes a multi module deep learning based engagement level predictor model. Basically, three modules, a convolutional neural network (CNN) module-for hierarchical feature extraction and other two modules-LSTM and attention based network, for temporal modeling of task are utilized in the proposed architecture. A dataset consisting of 9068 video sequences is used to measure the engagement level of the learners. the proposed model also introduced a novel cost function for prediction. Experimental results validate the usefulness of proposed loss function over traditional loss functions. © 2022 IEEE.

关键词： Cost functions

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A Comparative Study of Time Series Data Forecasting Using Machine Learning Based on Improved Grey Model 23

A Comparative Study of Time Series Data Forecasting Using Ma...

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作者： Li, Yan Zhang, Jiaming Ma, Yanhong Li, Xiaomin Liu, Ziang Zhou, Xiaokang Zhou, Rui School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Company Lanzhou China Shiga University Faculty of Data Science Hikone Japan Riken Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9781665494571

this paper proposes a time-series data prediction hybrid model suitable for groundwater level to further study its impact on new energy power generation through the close relationship between groundwater level and climate. the model is based on the improved grey model (IGM) and combined with other models such as backpropagation neural network (BP) and general vector machine (GVM), which are used to predict time series data. Based on the time series data of groundwater level, the prediction ability of IGM BP, IGM RBF, IGM support vector machine, and IGM-GVM hybrid model is studied, which proved the feasibility of the model. the statistical parameter values finally conclude that the IGM-GVM model outperforms other selected models in the dataset utilized in the paper. In addition, the mixed model has a better effect on the dataset than the corresponding single model. © 2021 IEEE.

关键词： Support vector machines Temperature distribution Smart cities Time series analysis Machine learning Predictive models Data models

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Construction of an Integrated Practice System for Intelligent Manufacturing engineering Based on Digital System 7

Construction of an Integrated Practice System for Intelligen...

引用

7th international conference on Measurement Instrumentation and Electronics, ICMIE 2023

作者： Chen, Shujun Zhang, Ziqiang Li, Jun Liu, Chunqi Beijing University of Technology Faculty of Materials and Manufacturing Beijing China

ISBN: (纸本)9798350326307

Intelligent manufacturing is a new production mode with the functions of self-perception, self-learning, self-decision, self-execution, and self-adaptation. How to highlight the technical characteristics in the practice of intelligent manufacturing engineering and to improve students' understanding of intelligent products, production, and services is a problem to be solved. therefore, an integrated practice system for intelligent manufacturing engineering based on digital system was constructed in this study. the practice content of intelligent manufacturing was divided into four modules, and each module was connected through a unified practice goal. Digital technology was used throughout the practice process, so that students can master the application of digital and information technical means in the manufacturing process. A complete practice process was given, and students' abilities were evaluated on the basis of the competency model. the analysis results showed that teachers in the field of intelligent manufacturing gave a high evaluation to the students' abilities, which proved the feasibility of the practice system. the practice system proposed in this paper has a good reference value for the establishment of similar engineering majors. © 2023 IEEE.

关键词： Students

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Application of Deep Reinforcement Learning in Optimization of Traffic Signal Control 23

Application of Deep Reinforcement Learning in Optimization o...

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作者： Huang, Jian Wang, Wei Wang, Lirong Chen, Hui Deng, Qingshan Fan, Heying Yu, Yaohua School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang China School of Data Science Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665494571

Traffic signal control is the most basic and pivotal task in road traffic problems. the optimization of traffic signal control is for improving vehicle passing efficiency of each intersection in order to improve the traffic capacity of the whole road net. Currently, traditional traffic control methods are not able to satisfy the needs of continuously expanding traffic conditions. thus, with ideal self-adapting and exploring abilities, deep reinforcement learning methods are widely applied in model traffic signal control. However, due to the problems of overestimation and instability during the learning process of Deep Q-learning (DQN), we came up with a model based on Double-Dueling-Deep Q Network (3DQN). this model combines dueling network, double learning method and target network by natural DQN to relieve overestimation and improve data using efficiency. According to the experimental results, compared with traditional traffic control and DQN, the model of 3DQN has better outcomes in traffic signal control. © 2021 IEEE.

关键词： Learning systems Q-learning Smart cities Law enforcement Roads Traffic control Data models

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Developing an Integrated Data Model based on IFC and SensorML for Post-COVID Facility Management 23

Developing an Integrated Data Model based on IFC and SensorM...

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作者： Gan, Vincent J.L. Wang, Tao Hu, Difeng Yin, Chao National University of Singapore Department of the Built Environment Singapore Engineering Hong Kong University of Science and Technology Department of Civil and Environmental Hong Kong

ISBN: (纸本)9781665494571

this paper presents an integrated data model based on IFC and SensorML to facilitate the post-COVID facilities management to ensure indoor thermal comfort and human health. this paper identifies the information with the reference to different industry guidelines, including WELL building Standard and Singapore BCA post-COVID regulations, and extracts necessary information for sensor description and identification. New parameters in the BIM environment are recommended in this paper to support model-based data exchange in the future. the information required is clarified with a process map depicting the information flow, and IFC MVD to provide a structured overview of the sensor requirement. Data mapping between IFC and SensorML is performed, the result indicates that missing entities and attributes can be proposed to enrich the sensor description in the IFC schema. the integration of SensorML and IFC provides better data interoperability between both schemas, improving information standardization and openness of data exchange in sensor description. © 2021 IEEE.

关键词： Unified modeling language Buildings thermal sensors thermal management Data models Regulation Data mining

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