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Big Data Аnalysis in Smart Grid Systems 18

18th IMEKO TC10 Conference on Measurement for Diagnostic, Optimisation and Control to Support Sustainability and Resilience 2022

作者： Jun, Yu Hordiichuk-Bublivska, Olena Lingyu, Yan Kyryk, Marian Beshley, Mykola Jiwei, Hu Wuhan Fiberhome Technical Services Co. Ltd. Wuhan China Department of Telecommunications Lviv Polytechnic National University Lviv Ukraine School of Computer Science Hubei University of Technology Wuhan China Department of Information Systems Faculty of Management Comenius University in Bratislava Bratislava Slovakia

ISBN: (纸本)9781713870241

The problem of Big Data processing in large industrial systems requires the use of machine learning methods. A smart grid system is an example of improving the efficiency of traditional data processing systems, which allows much more efficient and flexible distribution of electricity to end-users. However, for a smart grid to work properly, it needs to constantly monitor data from sensors and meters. The Singular Value Decomposition (SVD) algorithm is used to improve the efficiency of Big Data processing and reduce its dimensionality. The paper proposes the use of advanced SVD, which can work in distributed industrial systems and ensure the reliability and speed of data processing. © IMEKO TC10 Conference on Measurement for Diagnostic, Optimisation and Control to Support Sustainability and Resilience *** rights reserved

关键词： Machine learning

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Implementing Machine Learning Techniques to Enhance Real-Time Data Analysis Services

Implementing Machine Learning Techniques to Enhance Real-Tim...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Jaisudha J Dharmesh Dhabliya Narasimhayya B E Akhilendra Pratap Singh Sanjay Kumar Sinha Nitish Vashisht Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

System learning techniques have emerged to automatically extract data from massive volumes of statistics and enable predictive analytics. Gadget-gaining knowledge has, in particular, been applied to records-driven real-time analytics offerings to facilitate accurate, short, and efficient selection-making. This paper discusses how machine studying strategies can be implemented to improve the accuracy and speed of real-time facts analysis. Conventional statistics analysis models are regularly misguided because of latency troubles and the absence of contextual statistics. Machine getting-to-know techniques have been followed to lessen latency issues, contain contextual facts, and enhance the accuracy of predictions. The paper offers a top-level view of some commonly used machine learning algorithms, such as supervised getting to know, unsupervised mastering, decision timber, neural networks, and support vector machines. It also explains how they can beautify actual-time analytics services. It also covers how strategies, including regularization and dimensionality discount, may be applied to enhance the model's overall performance. The paper gives case research of real-time analytics packages in finance, healthcare, and retail to illustrate the efficacy and added cost of gadget learning in facts analytics offerings.

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Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery

arXiv

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arXiv 2023年

作者： Zhang, Xing Zhang, Haiyang Glazer, Nimrod Cohen, Oded Reznitskiy, Eliya Savariego, Shlomi Namer, Moshe Eldar, Yonina C. The School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China The Faculty of Math and Computer Science Weizmann Institute of Science Israel The EE Department Technion Israel Institute of Technology Haifa Israel

Quantization plays a critical role in digital signal processing systems, allowing the representation of continuous-amplitude signals with a finite number of bits. However, accurately representing signals requires a large number of quantization bits, which causes severe cost, power consumption, and memory burden. A promising way to address this issue is task-based quantization. By exploiting the task information for the overall system design, task-based quantization can achieve satisfying performance with low quantization costs. In this work, we apply task-based quantization to multi-user signal recovery and present a hardware prototype implementation. The prototype consists of a tailored configurable combining board, and a software-based processing and demonstration system. Through experiments, we verify that with proper design, the task-based quantization achieves a reduction of 25 fold in memory by reducing from 16 receivers with 16 bits each to 2 receivers with 5 bits each, without compromising signal recovery performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal reconstruction

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Analysis of temporal structure of laser chaos by Allan variance

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Physical Review E 2023年第1期107卷 014211-014211页

作者： Naoki Asuke Nicolas Chauvet André Röhm Kazutaka Kanno Atsushi Uchida Tomoaki Niiyama Satoshi Sunada Ryoichi Horisaki Makoto Naruse Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-Okubo Sakura-ku Saitama city Saitama 338-8570 Japan Faculty of Mechanical Engineering Institute of Science and Engineering Kanazawa University Kakuma Kanazawa city Ishikawa 920-1192 Japan

Allan variance has been widely utilized for evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multiscale examination capability of the Allan variance may also be beneficial in evaluating the chaotic oscillating dynamics of semiconductor lasers— not just for conventional phase stability analysis. In the present study, we demonstrated Allan variance analysis of the complex time series generated by a semiconductor laser with delayed feedback, including low-frequency fluctuations (LFFs), which exhibit both fast and slow dynamics. While the detection of LFFs is difficult with the conventional power spectrum analysis method in the low-frequency regime, the Allan variance approach clearly captured the appearance of multiple time-scale dynamics, such as LFFs. This study demonstrates that Allan variance can help in understanding and characterizing diverse laser dynamics, including LFFs, spanning a wide range of timescales.

关键词： Chaos

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Retain, Blend, and Exchange: A Quality-aware Spatial-Stereo Fusion Approach for Event Stream Recognition

arXiv

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arXiv 2024年

作者： Chen, Lan Li, Dong Wang, Xiao Shao, Pengpeng Zhang, Wei Wang, Yaowei Tian, Yonghong Tang, Jin The School of Electronic and Information Engineering Anhui University Hefei230601 China The School of Computer Science and Technology Anhui University Hefei230601 China The Department of Automation Tsinghua University Beijing China Peng Cheng Laboratory Shenzhen China Shenzhen China Peng Cheng Laboratory China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

Existing event stream-based pattern recognition models usually represent the event stream as the point cloud, voxel, image, etc., and design various deep neural networks to learn their features. Although considerable results can be achieved in simple cases, however, the model performance may be limited by monotonous modality expressions, sub-optimal fusion, and readout mechanisms. In this paper, we propose a novel dual-stream framework for event stream-based pattern recognition via differentiated fusion, termed EFV++. It models two common event representations simultaneously, i.e., event images and event voxels. The spatial and three-dimensional stereo information can be learned separately by utilizing Transformer and Graph Neural Network (GNN). We believe the features of each representation still contain both efficient and redundant features and a suboptimal solution may be obtained if we directly fuse them without differentiation. Thus, we divide each feature into three levels and retain high-quality features, blend medium-quality features, and exchange low-quality features. The enhanced dual features will be fed into the fusion Transformer together with bottleneck features. In addition, we introduce a novel hybrid interaction readout mechanism to enhance the diversity of features as final representations. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art performance on multiple widely used event stream-based classification datasets. Specifically, we achieve new state-of-the-art performance on the Bullying10k dataset, i.e., 90.51%, which exceeds the second place by +2.21%. The source code of this paper has been released on https://***/Event-AHU/EFV_event_classification/tree/ EFVpp. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Efficient Integration Model of MAS and Blockchain for emergence of Self-Organized Smart Grids 3

Efficient Integration Model of MAS and Blockchain for emerge...

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3rd International Conference on Electrical, Communication and computer Engineering, ICECCE 2021

作者： Ostheimer, Ivan Hercog, Mario Bijelic, Bojan Vranjes, Denis Power Engineering Base58 Ltd. Osijek Croatia Information Technology in Osijek Faculty of Electrical Engineering Computer Science Department of Communications Osijek Croatia

ISBN: (纸本)9781665438971

This paper introductory presents a multi agent system model that uses blockchain as the basic technology for successful integration of advanced management solutions into a smart grid environment, realized through the SEGIP project. The paper explains the need for innovative management solutions and provides an overview of energy exchange based on peer-to-peer trading. Additionally, the paper covers the role of a multi-agent system in effective integration and outlines the structure of the proposed project solution. The functioning of such a smart grid is explained through a description of the roles and interactions of different actors, and a smart contract proposal is described through an example of a transaction between different types of network nodes. The conclusion completes everything presented in the paper and confirms the validity of the presented approach. © 2021 IEEE.

关键词： Blockchain

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Optimized Fractional-Order PID Controller for Temperature Control of CSTH: A Comparative Study with PID Cascade Controller

Optimized Fractional-Order PID Controller for Temperature Co...

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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Alaswadi, Abdulaziz F. Alyazidi, Nezar M. AL-Aswadi, Fatima N. Ahmed, Amjed Abbas Department of Control and Instrumentation Engineering King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Center of Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia UCSI University Kuala Lumpur56000 Malaysia Faculty of Computer Science and Engineering Hodeidah University Al Hudaydah Yemen Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Baghdad Iraq

ISBN: (纸本)9781837243235

Controlling the temperature in chemical reactors, heating furnaces, distillation columns, and reboilers for circulating heating fluids is crucial across all industries. This project is a simulation-based study where both PID-cascade and fractional order PID (FO-PID) controllers were used to control the temperature of water in the continuously stirred tank heater system. The simulation was designed by using SIMULINK. Initially, the work reproduces results from a previous study that evaluated the performance of PID cascade controller applied for controlling the temperature of continuous stirred tank heater. Building on this foundation, a novel fractional-order PID (FO-PID) controller was designed and its parameters were optimized using the Whale Optimizer Algorithm (WOA). Results showed that the FO-PID outperforms the PID-cascade controller in terms of settling time and percentage overshoot. This study demonstrates significant advancements in process innovation and optimization, supporting sustainable industrial systems. The FO-PID controller in comparison to the Cascade PID controller reduced the percentage overshoot by approximately 97%, reduced settling time by 87% and maintained comparable steady-state error. © The Institution of Engineering & technology 2025.

关键词： Dynamic models

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Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

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Journal of Healthcare Engineering 2022年 2022卷 2693621页

作者： Arif, Muhammad Ajesh, F. Shamsudheen, Shermin Geman, Oana Izdrui, Diana Vicoveanu, Dragos Department of Computer Science and Information Technology University of Lahore Lahore Pakistan Department of Computer Science and Engineering Sree Buddha College of Engineering Kerala Alappuzha India College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Neuroaesthetics Lab Stefan Cel Mare University of Suceava Suceava Romania Faculty of Electrical Engineering and Computer Science Stefan Cel Mare University of Suceava Suceava Romania

Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, biomedical image processing makes it easier to detect and locate brain tumors. In this study, a segmentation and detection method for brain tumors was developed using images from the MRI sequence as an input image to identify the tumor area. This process is difficult due to the wide variety of tumor tissues in the presence of different patients, and, in most cases, the similarity within normal tissues makes the task difficult. The main goal is to classify the brain in the presence of a brain tumor or a healthy brain. The proposed system has been researched based on Berkeley's wavelet transformation (BWT) and deep learning classifier to improve performance and simplify the process of medical image segmentation. Significant features are extracted from each segmented tissue using the gray-level-co-occurrence matrix (GLCM) method, followed by a feature optimization using a genetic algorithm. The innovative final result of the approach implemented was assessed based on accuracy, sensitivity, specificity, coefficient of dice, Jaccard's coefficient, spatial overlap, AVME, and FoM. © 2022 Muhammad Arif et al.

关键词： Magnetic resonance imaging

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Was Wuhan the early epicenter of the COVID-19 pandemic?——a critique

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National science Review 2023年第4期10卷 19-21页

作者： Yanan Cao Lingling Chen Hua Chen Yupeng Cun Xiaofeng Dai Hongli Du Feng Gao Fengbiao Guo Yalong Guo Pei Hao Shunmin He Shunping He XiongLei He Zheng Hu Boon-Peng Hoh Xin Jin Qian Jiang Qinghua Jiang Asifullah Khan Hong-Zhi Kong Jinchen Li Shuai Cheng Li Ying Li Qiang Lin Jianquan Liu Qi Liu Jian Lu Xuemei Lu Shujin Luo Qinghua Nie Zilong Qiu Tieliu Shi Xiaofeng Song Jianzhong Su Sheng-ce Tao Chaolong Wang Chuan-Chao Wang Guo-Dong Wang Jiguang Wang Qi Wu Shaoyuan Wu Shuhua Xu Yu Xue Wenjun Yang Zhaohui Yang Kai Ye Yuan-Nong Ye Li Yu Fangqing Zhao Yiqiang Zhao Weiwei Zhai Dandan Zhang Liye Zhang Houfeng Zheng Qi Zhou Tianqi Zhu Ya-ping Zhang Ruijin Hospital Shanghai Jiao Tong University College of Life Science and Technology Guangxi University Beijing Institute of Genomics Chinese Academy of Sciences Children's Hospital of Chongqing Medical University Wuxi School of Medicine Jiangnan University School of Biology and Biological Engineering South China University of Technology Department of Physics School of ScienceTianjin University School of Pharmaceutical Sciences Wuhan University Institute of Botany Chinese Academy of Sciences Institut Pasteur of Shanghai Chinese Academy of Sciences Institute of Biophysics Chinese Academy of Sciences Institute of Hydrobiology Chinese Academy of Sciences School of Life Sciences Sun Yat-sen University Institute of Synthetic Biology Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Faculty of Medicine and Health Sciences University College Sedaya International School of Medicine South China University of Technology Department of Medical Genetics Capital Institute of Pediatrics School of Life Science and Technology Harbin Institute of Technology Department of Biochemistry Abdul Wali Khan University Xiangya Hospital Central South University Department of Computer Science City University of Hong Kong College of Life Science and Technology Foshan University South China Sea Institute of Oceanology Chinese Academy of Sciences College of Ecology Lanzhou University School of Life Sciences and Technology Tongji University School of Life Sciences Peking University Kunming Institute of Zoology Chinese Academy of Sciences College of Animal Science South China Agricultural University Institute of Neuroscience Chinese Academy of Sciences School of Life Sciences East China Normal University Nanjing University of Aeronautics and Astronautics Wenzhou Institute University of Chinese Academy of Sciences Institute of Systems Biomedicine Shanghai Jiao Tong University Tongji Medical College Huazhong University of Science and Technology School of Life Sciences Xiamen University Division of Life Science and Department of

Recently, Worobey et al.(2022) published a report entitled ‘The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic' that succinctly summarizes their study [1]. A pre-print version of this study had earlier elicited a series of highprofile media coverages [2,3]. All these reports deliver a social-political message that the Huanan market is the epicenter of COVID-19.

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Event-Aware Precise Dynamic Slicing for Automatic Debugging of Android Applications

SSRN

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SSRN 2022年

作者： Win, Hsu Myat Tan, Shin Hwei Sui, Yulei Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

Dynamic slicing aims to find all program statements that directly or indirectly a ff ect the value of a variable occurrence under agiven program input. It is an enabling technique for many software engineering tasks (e.g., program understanding and debugging). Due to Android’s event-driven nature, dynamic slicing for Android is more challenging than that for traditional Java programs. Its asynchronous events drive the execution of an app through inter-component communications. These non-deterministic user events often yield a large search space when applying existing dynamic slicing techniques, which introduce redundant statements into the resulting slice. We present ESDroid, an Event-aware dynamic Slicing technique for AnDroid applications. The novelty of our approach lies in the combination of segment-based delta debugging and dynamic backward slicing to narrow the search space to produce precise slices for Android. Our experiment across 38 apps shows that ESDroid can help with slicing buggy code from exception program points. We compare the e ff ectiveness of ESDroid with the state-of-the-art dynamic slicing tools (AndroidSlicer and Mandoline). ESDroid outperforms both tools in precision by reporting up to 72% fewer spurious statements than AndroidSlicer, and 50% fewer than Mandoline in the resulting slice (number of instructions to be examined). © 2022, The Authors. All rights reserved.

关键词： Android (operating system)

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