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2024 international conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Mahdi, Ali Jafer Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq AI-Zahraa University for Women Department of Scientific Affairs Bangi43600 Malaysia College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Innovative grid technology leverages Information and Communication Technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a 'novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)' ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces. © 2024 ieee.

关键词： Smart homes

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Intelligent Operation and Inspection Software Based on edge computing Concept and Artificial Intelligence Technology 3

Intelligent Operation and Inspection Software Based on Edge ...

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3rd international conference on energy engineering and Power Systems, eePS 2023

作者： Ren, Jiaxuan Wang, Shaorong Yang, Youhang Zhang, Yanyan Wei, Zheng Zheng, Chao Li, Jingsong Chen, Yunan Huazhong University of Science and Technology School of Electrical and Electronic Engineering Wuhan China Dongguan Power Supply Bureau of Guangdong Power Grid Co. Ltd Dongguan China Wuhan Qiyi Electric Co. Ltd Wuhan China Yunnan Power Dispatch Control Center Kunming China

ISBN: (纸本)9798350313857

A new intelligent operation and inspection software based on the concept of edge computing and artificial intelligence technology is proposed for the physical and operational architecture of substation electrical equipment intelligent inspection, and a human-computer interaction interface is designed for the convenience of operation and maintenance personnel, which effectively avoids the problems of poor real-time and monitoring blind areas of off-line inspection and conventional energized inspection, and solves the defects of cloud computing and manual inspection. The key technologies included in the realization of intelligent operation and inspection software functions are: the design of physical and operational architecture, and the analysis of structured and unstructured charged inspection data. The intelligent operation and inspection software is stable and reliable, which can greatly improve the efficiency of inspection and the intelligence level of the substation. © 2023 ieee.

关键词： edge computing

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Uncertain power flow calculation and global sensitivity analysis considering parametric probability-boxes

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eNeRGY RePORTS 2023年 9卷 80-87页

作者： Wang, Chenxu Peng, Yan Ma, Junchao Shen, Yifei State Grid Zhejiang Elect Power Corp Elect Power Res Inst Hangzhou 310014 Peoples R China

Probabilistic power flow is one of the fundamental tools for assessing the impacts of uncertainties on the operating states of power systems. However, this analysis requires sufficient historical data to obtain precise probability distributions of input variables, which may not be met in practical engineering problems. In this paper, input variables with insufficient data are represented by parametric probability boxes (p-boxes), i.e., probability distributions with imprecise parameters. In order to facilitate the uncertain power flow calculation with p-boxes, a polynomial chaos expansion-based method is developed. Moreover, the interval-valued Borgonovo index is proposed for global sensitivity analysis and to identify the input variables that have critical impacts on systems. The simulations in ieee 14-bus and 118-bus systems verify the accuracy and efficiency of the proposed method by comparing it with the conventional double-loop sampling method. (c) 2023 The Author(s). Published by elsevier Ltd. This is an open access article under the CCBY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Uncertain power flow P-boxes PCe surrogate Global sensitivity Borgonovo index

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ML-driven emotion Identification for Feedback Analysis in e-learning Platforms

ML-driven Emotion Identification for Feedback Analysis in E-...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chitteti, Chengamma Kopparam, Runvika Ganesh, B.V.S.S. Sutraya, Sukerthi Kamakshi, V. Jangam, Shiridinath School of Computing Dept. of Data Science Tirupati India Dept. of Csse Tirupati India G. Narayanamma Institute of Technology and Science for Women Dept. of Data Science Hyderabad India

ISBN: (纸本)9798350373783

With an emphasis on the cognitive and emotional effects of e-learning platforms, this study fills a research gap concerning user experience studies. The study suggests a nonintrusive technique to evaluate the psychological impacts of e-learning platforms by considering feedback data of users in the form of videos and analyzes emotions from video and decides the user feedback level. This process automates the understanding of feedback in a short amount of time for anyone, without requiring specific knowledge of the system. Feedback analysis for e-Learning platforms using deep learning methods for facial expression analysis is proposed in this work. Convolutional Neural Networks (CNNs), a category of deep learning, has become increasingly popular for its ability to precisely analyze and comprehend emotions from facial photos. These models may efficiently learn and extract essential features by being trained on large datasets of tagged facial expression photos. This application takes video feedback as input for various e-learning platforms and analyzes emotions and displays the predicted emotions on video. A publicly accessible dataset is used to test the proposed technique, and the results show that it achieves 90.3% accuracy. This indicates that the approach is highly effective in accurately recognizing facial emotions. © 2024 ieee.

关键词： Contrastive Learning

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Optimal Scheduling of Power System with Demand Response: Cloud-edge Cooperation Approach 4

Optimal Scheduling of Power System with Demand Response: Clo...

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4th international conference on Smart grid and energy engineering, SGee 2023

作者： Lu, Peng Feng, Chunxian Wu, Weiming Wang, Tieqiang Tong, Chenjie Hebei Electric Power Co. LTD. Shijiazhuang China North China Electric Power University School of Electrical and Electronic Engineering Beijing China

ISBN: (纸本)9798350359619

Price-based demand response (PBDR) is an effective way to reduce load peak-valley difference and promote the accommodation of renewable energy generation. To mitigate the limitations of traditional PBDR modeling methods in complexity and adaptability, a newly data-driven PBDR modeling methods based on deep learning is proposed in this paper. For purpose of reducing the computational burden of cloud-based calculation mode after applying deep learning technique, a cloud-edge cooperation calculation framework is proposed for power system optimal scheduling considering PBDR. Then, the optimal scheduling model of cloud center and PBDR learning models of edge nodes are constructed respectively. Finally, the proposed optimal scheduling approach is tested on ieee 30 bus system and the results show that the optimal scheduling approach proposed in this paper has better adaptability to consumer behavioral variance. © 2023 ieee.

关键词： edge computing

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Trusted Dynamic Threshold Caculation Method in Power IoT 14

Trusted Dynamic Threshold Caculation Method in Power IoT

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14th international conference on Communication Software and Networks (ICCSN)

作者： Dang, Fangfang Yan, Lijing Li, Shuai Li, Dingding State Grid Henan Informat Telecommun Co Data Ctr Beijing Peoples R China

ISBN: (纸本)9781665453288

Smart grid is a new generation of grid that inte-grates traditional grid and grid information system, and infor-mation security of smart grid is an extremely important part of the whole grid. The research of trusted computing technology provides new ideas to protect the information security of the power grid. To address the problem of large deviations in the calculation of credible dynamic thresholds due to the existence of characteristics such as self-similarity and traffic bursts in smart grid information collection, a traffic prediction model based on ARMA and Poisson process is proposed. And the Hurst coefficient is determined more accurately using R/S analysis, which finally improves the efficiency and accuracy of the trusted dynamic threshold calculation.

关键词： smart grid trusted dynamic threshold ARMA Poisson process R/S analysis method

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A System to Track the Behaviour or Pattern of Mobile Robot Through RNN Technique 4

A System to Track the Behaviour or Pattern of Mobile Robot T...

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4th ieee international conference on Advance computing and Innovative Technologies in engineering, ICACITe 2024

作者： Banu, e.Afreen Chidambaranathan, Senthilnathan Jose, Naduvathezhath Nessariose Kadiri, Padmaja Abed, Riyad e. Al-Hilali, Aqeel Saveetha School of Engineering Chennai India Architecture and Design Virtusa NJ United States Regent Strategic Solutions Rochester United States Mohan Babu University School of Computing Department of Aiml Andhra Pradesh Tirupati India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

The agricultural business has seen a rise in the use of mobile robots because of their ease of navigation in crop fields. Numerous significant issues pertaining to agricultural robot navigation are covered in this paper, such as obstacle detection, route planning, mapping, localization, and controlled guiding. The proposed hybrid recurrent neural network (RNN) model seeks to optimise mobile autonomous robot tracking performance using installed laser-based weeding equipment. The system can handle a range of trajectories, including curves and straight lines, that are observed in agricultural settings by integrating spiral, lateral, and linear speed controls. A comprehensive field test is carried out on a tracked mobile platform to validate the controllers' functionality in a variety of scenarios, including loose dirt, stones, and humidity. © 2024 ieee.

关键词： Recurrent neural networks

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Proceedings 2023 ieee 19th international conference on e-science (e-science)

Proceedings 2023 IEEE 19th International Conference on e-Sci...

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ieee international conference on e-science and grid computing

Proceedings 2023 ieee 19th international conference on e-science (e-science).

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Optimal Noise Reduction Smooth Model for Power Quality Disturbance Signals Based on VMD 5

Optimal Noise Reduction Smooth Model for Power Quality Distu...

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5th ieee international conference on Power, Intelligent computing and Systems, ICPICS 2023

作者： Chen, Wu Zhong, Jianwei Yang, Yongchao Liang, Huijun Tian, Shidong Chen, Wentao The College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi China Enshi Power Supply Company State Grid Hubei Electric Power Co. Ltd. Enshi China

ISBN: (纸本)9798350333442

To overcome the drawback of variational mode decomposition (VMD) noise suppressing in power quality disturbance (PQD) signals, an optimal noise reduction smooth model based on VMD is proposed. Firstly, a novel energy loss factor method is proposed to determine the decomposition parameter of VMD. Secondly, a novel optimization model based on envelope entropy and Pearson correlation coefficient is constructed for determining penalty factor. Furthermore, the optimization is executed by the sparrow search algorithm (SSA). Finally, the band-pass filtering algorithm is established by the intrinsic mode functions (IMFs) decomposed by the VMD to obtain several noise reduction models. Ulteriorly, the similarity and smoothness indexes are built to determine the optimal noise reduction smooth model for PQD signal de-noising. The proposed method can effectively remove noise, and the signal-to-noise ratio is significantly improved. Further, the singular points of the PQD signal can keep well. Simulation and comparison experiments of seven common kinds of PQD signals are performed to illustrate the effectiveness of the proposed noise reduction method. © 2023 ieee.

关键词： Variational mode decomposition

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Data-driven Industrial enterprise Order Scheduling and Optimization 6

Data-driven Industrial Enterprise Order Scheduling and Optim...

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6th international conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Xu, Sen Lin, Rongheng Wang, Baigen Wang, Ou Zhao, Qian Liu, Huizhou School of Computer Science Beijing University of Posts and Telecommunications State Key l Lab of Networking and Switching Technology Beijing China Anqing Power Supply Company State Grid Anhui Electric Power Co. Ltd. Anqing China State Grid Anhui Electric Power Co. Ltd. Hefei China

ISBN: (纸本)9798331541750

As China's industrial enterprises continue to grow and competition intensifies, efficient order scheduling and management have become essential to operational success. Many enterprises face challenges such as dispersed and unstandardized order data, reliance on manual scheduling, and limited data analysis capabilities. This study leverages priority-based and genetic algorithms, supported by extensive original fixed data (e.g., reservation order data, raw material supply data, production process data) and system-generated production data (e.g., sub-order data, raw material demand data), to design an optimized order scheduling method. The proposed algorithm not only meets order acceptance and scheduling requirements but also improves the rationality of order scheduling and the effectiveness of resource allocation. By reducing the rate of incomplete orders caused by raw material supply constraints and tight delivery times, this algorithm offers industrial enterprises a standardized production process, thereby enhancing overall operational efficiency and competitiveness. © 2024 ieee.

关键词： genetic algorithm order scheduling priority

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