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Security control analysis of distribution network scheduling data based on multi-source information fusion 4

Security control analysis of distribution network scheduling...

引用

4th international conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Chen, Huan Zhang, Yu Liang, Jiyun Xu, Bozhi Liu, Yanping Guangdong Power Grid Guangzhou Power Supply Bureau Co. Ltd Guangdong Guangzhou510620 China

ISBN: (纸本)9798331518066

With the development of smart grids, distribution network dispatch systems are facing increasingly complex challenges in multi-source data processing and security management. This article proposes a secure control method for distribution network scheduling data based on multi-source information fusion, aiming to improve the accuracy, real-time performance, and reliability of scheduling data by integrating information from different data sources. In addition, by studying security protection strategies during the data fusion process, potential security threat risks have been reduced. Through simulation analysis, the effectiveness of this method in data fusion and security control analysis capabilities has been verified. Finally, this article looks forward to the development direction of power grid intelligence, cloud edge collaborative computing, and standardization, providing reference for further improving the intelligence and security of distribution network dispatch systems. © 2024 ieee.

关键词： Analytic Hierarchy Process Multi-Source information fusion Security analysis Security control SM4 encryption algorithm Standard deviation

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data Ownership and Control in IoT: Legal Rights and Responsibilities

Data Ownership and Control in IoT: Legal Rights and Responsi...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kulshrestha, Sony Acharya, Nishtha Pal, Raneeta Vijayvargiya, Lucky Manipal University Jaipur India Icfai Hyderabad India

ISBN: (纸本)9798331543617

The purpose of this exploratory article is to investigate the complex geography of data power and control inside the Internet of Things (IoT) paradigm, with a particular emphasis on the legal rights and responsibilities of stakeholders. As a result of the development of Internet of Things bias, which generates huge quantities of data, the problems that surround power, access, and control have become substantially less complicated. This study elucidates the colourful bounds of data power and control by conducting a complete review of existing literature and legal structures. It takes into consideration the positions of device manufacturers, data collectors, and end-users. In a similar manner, it examines significant legal legislation and precedents, highlighting the difficulties and ambiguity that are associated with defining and administering data power rights. In addition, the study covers recently emerging issues such as data sovereignty and sequestration legislation, both of which have an additional impact on data governance in Internet of Things ecosystems. The purpose of this article is to provide clarity and assistance for governments, businesses, and individuals who are navigating the ever-changing landscape of data power and control in Internet of Things environments. This will be accomplished by outlining the legal geography and agitating implied counteraccusations. © 2024 ieee.

关键词： Control data ownership Governance IoT Legal rights Responsibilities Stakeholders

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Phobia Detection: A Comprehensive Integration of data Science, AR/VR, and Electroencephalogram for Enhanced Diagnosis and Therapeutic Intervention

Phobia Detection: A Comprehensive Integration of Data Scienc...

引用

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

作者： Makote, Harshal Borkar, Avishkar Sapkal, Rushikesh Waghmode, Sourabh Rangdale, Sonali Ranjan, Nihar JSPM's Rajarshi Shahu College of Engineering Information Technology Department Pune India

ISBN: (纸本)9798350373783

This paper advances the field of phobia detection by introducing an innovative algorithm that utilizes the synergies of data Science, Augmented Reality/Virtual Reality (AR/VR), and Electroencephalogram (EEG) technologies. Our system fills in important gaps in existing diagnostic methods, which are brought about by the rising prevalence of phobic disorders and the need for tailored therapeutic approaches. Our suggested system's complex algorithm is based on machine learning models such as Logistic Regression, Support Vector Classifier, Random Forest Classifier, and Extreme Gradient Boosting. By processing EEG data, these models allow for precise and effective phobia detection, severity categorization, and therapeutic intervention. With robust performance and accuracy rates of 96.44% for Support Vector Classifier, 98.24% for Random Forest Classifier, 98.59% for Extreme Gradient Boosting, and 96.48% for Logistic Regression, our algorithm has been trained on a variety of datasets. This demonstrates how well the algorithm can identify subtle patterns linked to phobic reactions. © 2024 ieee.

关键词： data Science

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data-Driven Insights into Automotive Customer Complaints: A Machine Learning Approach to Predictive Analytics

Data-Driven Insights into Automotive Customer Complaints: A ...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Adike, Anil Kumar Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

Automotive customer complaints forecast as a difficult-to-solve and data-driven machine learning classification problem. Key findings from background research include the problems associated with data imbalance, shortcomings of the models in terms of temporal pattern analysis and variability of prediction errors. It incorporates clustering and LSTM models to analyze product complaint characteristics, improving accuracy and flexibility. The proposed model operates based on a diverse dataset of automotive complaints, and the model accuracy is 94.5%, the precision is 92.7%, the recall is 93.1%, the F1 score is 92.9%, and the AUC is 95.3%. Compared to other methods, the advantage of this particular work is that it achieves far better accuracy on complex and imbalanced data sets than previous approaches. The findings proved that the chosen model's approach is rather effective for the prognosis of different kinds of complaints to assist in the usage of the anticipative maintenance approaches. It is useful for automakers, because, knowing that the issues, and indicators of the low degree of customer satisfaction define, manufacturers can come to them before high-risk issues occur. © 2024 ieee.

关键词： Adversarial machine learning

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Optimizing the Strategic Fusion of IoT and AI for Enhanced HR Performance in Stocks 7

Optimizing the Strategic Fusion of IoT and AI for Enhanced H...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Ramya, S.S. Saikrishnan, S. Sumathi, S. Kamalakannan, S. Sri Keerthana, J. Indhumathi, G. PSG College of Arts and Science Department of Commerce Tamil Nadu Coimbatore India PSG College of Arts and Science Department of Corporate Secretaryship Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

Organizations are constantly exploring new technological avenues to enhance their human resources operations. This study explores the potential advantages of incorporating the technologies of artificial intelligence with the Internet of things into HR operations. This paper aims to provide a thorough examination of prior research and case studies, shedding light on the benefits, drawbacks, and optimal approaches related to this merger. Companies are closely examining both their internal human resources (HR) processes and their external recruiting efforts. Utilizing empirical data and qualitative research, this paper adopts a mixed-methods approach to offer practical implementation recommendations. There are several benefits to consider, such as cost savings, improved recruitment, increased employee motivation, and more efficient performance evaluation. Addressing concerns such as data security, complex integration, and a lack of expertise is crucial. When it comes to HR productivity and efficiency, organizations can greatly benefit from the combination of IoT and AI. By adopting a strategic approach that emphasizes collaboration and careful planning, businesses can maximize their gains. © 2024 ieee.

关键词： Collaboration data Security Institutions Internal Operations Optimization Recruitment Technology Integration

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Power Systems Optimization Challenges and Applications: An Overview

Power Systems Optimization Challenges and Applications: An O...

引用

2023 international conference on cognitive computing and complex data, ICCD 2023

作者： Jaber, Aqeel S. Kunna, Mohammed A. Khatir, Sharafeldin K. Independent Researcher Helsinki Finland Universiti Teknologi Mara Institute for Big Data Analytics & Artificial Intelligence Shah Alam Malaysia Universiti Tun Hussein Onn Batu Fakulti Sains Komputer Dan Teknologi Maklumat Pahat Malaysia

ISBN: (纸本)9798350306293

As the complexity of power systems and the challenges posed by uncertainty continue to evolve, identifying promising areas for further investigation becomes essential to improve decision-making processes and enhance the resilience and efficiency of power systems. The objective of this paper is to offer a comprehensive overview of the existing methods used for modeling and optimization of problems that are affected by uncertainty, with a specific focus on the context of power systems. Furthermore, this paper delves into the examination of significant challenges and applications where optimization plays a crucial role in power systems. By exploring real-world use cases, it sheds light on the practical implications of these methods and their potential benefits for power system operation and planning. Additionally, the paper provides a glimpse into the future by outlining potential research directions in this field. © 2023 ieee.

关键词： Identification (control systems)

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Privacy and Security Concerns in Big data 2

Privacy and Security Concerns in Big Data

引用

2nd international conference on Cyber Resilience, ICCR 2024

作者： Asasfeh, Afnan Tarawneh, Leen Pradhan, Manas Ranjan Al-Dmour, Nidal A. Mutah University Department Of Computer Engineering Al-Karak Jordan School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering Mutah University Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

In the Big data age, where huge volumes of information are leveraged for analysis and innovation, the paramount importance of protecting privacy and enhancing security has become a critical imperative. The complex landscape of privacy and security concerns is explored in this paper, which outlines the importance of the topic and explores the challenges. It defines privacy and security, delves into their meaning, examines technologies and security measures that protect privacy, and identifies gaps in current research. This paper provides insight into potential areas for future exploration and provides a comprehensive guide to addressing the ever-changing landscape of privacy and security in the big data era. © 2024 ieee.

关键词： Big data

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Scheduled Sampling for Recursive Multi-Step GPU Temperature Forecasting 23

Scheduled Sampling for Recursive Multi-Step GPU Temperature ...

引用

23rd ieee international conference on Machine Learning and Applications, ICMLA 2024

作者： Wang, Harold Jiang, Xunfei Ebrahimi, Mahdi University of California San Diego Department of Cognitive Science San DiegoCA92092 United States California State University Department of Computer Science NorthridgeCA91330 United States

ISBN: (纸本)9798350374889

Energy-efficient workload management is increasingly important in data centers as demand for cloud computing continues to grow. Recent findings in workload management point to thermal-aware load balancers as an effective approach to reduce energy consumption via cooling costs. Due to the high energy cost of data Centers (DCs) and possible risk of disrupting critical services, simulations can be used to evaluate temperature-aware load-balancing algorithms. In this paper, we leverage an existing DC simulator's environmental parameters for integrating a temperature prediction model to construct a temperature-aware workload distribution simulation. Using scheduled sampling, we implemented a custom recursive multi-step forecasting model that reduced the discrepancy between training error and simulation predictions. Compared with a baseline GPU temperature prediction model, our hybrid CNN-LSTM model achieved a reduction in RMSE from 2.54 to 0.73. © 2024 ieee.

关键词： Prediction models

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Research on Service Scanning Supervision Technology Based on Improved Association Algorithm 3

Research on Service Scanning Supervision Technology Based on...

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3rd ieee international conference on Electrical Engineering, Big data and Algorithms, EEBDA 2024

作者： Shang, Qingqing Liu, Chao Li, Jiwei Ren, Liwei Zhang, Xinyi Jia, Zhen Jiang, Yuchen Liu, Haitao State Grid Information & Telecommunication Branch Beijing China State Grid Zhejiang Electric Power Co. Ltd. Lishui Power Supply Company Zhejiang Lishui China

ISBN: (纸本)9798350380989

The scale of data in the Internet environment is becoming increasingly large and complex. Its heterogeneous, dynamic and distributed information characteristics have caused the existing business scanning and monitoring models to be unable to meet its application needs. This project proposes an improved iterative algorithm based on multiple servers. By introducing the idea of local operation of memory nodes, a service scanning and monitoring model that can support concurrency is constructed. A cluster analysis method based on genetic algorithm based on cluster analysis is proposed. It is then synthesized to correlate the largest set of recurring items to achieve data association, extend the iterative service scanning supervision algorithm, and obtain user-interested implicit patterns from server logs. At the same time, personalized service purposes are achieved by recommending implicit interest collections to users. Simulation results show that this method can effectively improve the performance of business search monitoring, and has the characteristics of enhancement as the data size increases. © 2024 ieee.

关键词： iterative algorithm local computing parallel algorithm Web data association

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Planning and Interconnection of State Grid Power Dispatching data Network Based on Cloud computing

Planning and Interconnection of State Grid Power Dispatching...

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2024 international conference on Electrical Drives, Power Electronics and Engineering, EDPEE 2024

作者： Huang, Jinglei Xiao, Guolei He, Feicheng Shi, Pengyu Jiao, Dongdong State Grid Zhejiang Electric Power Co. Ltd. Yunhe County Power Supply Company Zhejiang Lishui323600 China

ISBN: (纸本)9798350395631

The emergence of cloud computing provides convenient conditions for fast and reliable command of power grid operation, which can improve the system's ability to perform a large number of rapid calculations and improve the efficiency of dispatching process. This paper studies the planning and interconnection of state grid power dispatching data network based on cloud computing. Aiming at the complex analysis and calculation problems existing in the processing of massive data information in the power dispatching data network center, this paper applies cloud computing to it, proposes an improved GA (genetic algorithm) cloud computing scheme, and designs the service access scheme of state grid dispatching data network. The network management center consists of LAN system of network management center, hardware platform and operating system of network management host, network management platform software, network management application software and peripheral devices. The main parts of the power dispatching data network management system and the power supply are configured redundantly to ensure the reliability of the power dispatching data network management system. The core router at the dispatching end is cross-connected with the switch through twisted pair, and each business system is connected to different switches through twisted pair at the same time. The planning and interconnection technology of state grid power dispatching data network based on cloud computing designed in this paper fundamentally changes the traditional point-to-point information transmission mode, saves a lot of time and cost to further improve the efficiency of power grid management and operation, and is an important technical measure to ensure the safe, stable and economic operation of state grid. © 2024 ieee.

关键词： Cloud computing

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