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Distributed Computing and data processing Technology under the Power data Middleware Architecture 3

Distributed Computing and Data Processing Technology under t...

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2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Zou, Yuelin Shen, Jia Yin, Rui Guo, Jiangtao Guo, Yi State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi830002 China State Grid Xinjiang Electric Power Co. Ltd. Urumqi830002 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fuzhou350001 China

ISBN: (纸本)9798350341546

With the establishment and development of integrated monitoring platforms and communication information platforms for intelligent substations, the data volume of the power system is showing explosive growth. However, existing data centers have poor processing capabilities for massive amounts of data, making it difficult to meet the multiple service quality requirements of users. How to effectively manage massive and complex data, fully utilize the value of data, improve service quality, reduce operational costs, and achieve rapid response to users is an urgent problem to be solved. Therefore, based on the actual needs of power enterprises, this article proposed a cloud computing power data middleware architecture based on distributed technology, which effectively managed, cleaned, converted, and evaluated power data sources through cloud computing platforms. This article mainly analyzed the shortcomings of traditional power data processing platforms and the feasibility of cloud computing based power data platforms. Finally, through load balancing bias comparison and pressure testing experiments under different task numbers, it was proven that cloud computing systems outperformed traditional power data processing systems in terms of utility value, completion time, and load balancing. Moreover, in the pressure testing experiment, under the condition of 10 concurrent users and 673 system request transactions, 91.3% of the interface response time in the cloud computing system was within 50ms. Therefore, cloud computing systems have certain advantages in processing power data. © 2023 IEEE.

关键词： Cloud Computing data Center System data processing Distributed Computing Electricity data

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White Rabbit Technology Evaluation for the SPD Experiment 3

White Rabbit Technology Evaluation for the SPD Experiment

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Kozyrev, Daniil V. Kotov, Kirill A. Ryabikov, Dmitry S. Boykov, Alexey V. Mamoutova, Olga V. Bolsunovskaya, Marina V. Peter the Great St.Petersburg Polytechnic University Lab. of Industrial Systems for Streaming Data Processing St. Petersburg Russia

ISBN: (纸本)9798331516321

The high accuracy profile of the IEEE1588-2019 and its open-source implementation known as White Rabbit provide a standard technology for sub-nanosecond synchronization. The Spin Physics Detector experiment will rely on White Rabbit to perform triggerless data acquisition and event reconstruction. An overview of reports on White Rabbit accuracy and precision performance, including effects of temperature variation, shows the need for experimental evaluation of White Rabbit technology in specific test setups using particular White Rabbit devices. The presented results include measurements and analysis of a White Rabbit network topology with three cascaded WRS-18A switches and two CuteWR-A7 end nodes from Sync Technology. The results cover synchronization signal skew and relative jitter histogram analysis, the temperature effects of extreme cooling and heating on a switch and an end node, as well as the effects of ambient temperature changes. Obtained results demonstrate that both synchronization signal skew and rms jitter remain within the required limits of sub-nanosecond accuracy and 50 ps precision. © 2024 IEEE.

关键词： Timing jitter

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Design and Implementation Realization of Gas Detection System Based on ZigBee 3

Design and Implementation Realization of Gas Detection Syste...

引用

3rd international conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Su, Zhihua Yan, Lihong Yang, Tianqi Huan, Jiayu Xi 'An Eurasia University School of Information Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350316674

ZigBee technology as a mature short-range communications technology, has been widely used in all walks of life. In this paper, a gas detection system with the network structure is designed and implemented based on ZigBee technology. The system mainly realizes the real-time monitoring and alarming of the household gas safety. The system includes wireless local area network, server and detection terminal. The system uses MFC and development technology for mobile devices to carry out application development of system, the server carries on the real-time data acquisition, the analysis and the early warning to the detected terminal contained in the system. After running the test and application, the system meets the function of safety detection for gas. © 2023 IEEE.

关键词： detection terminal gas detection system real-time data processing ZigBee technology

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Improved Algorithm for Solar Power Systems for Efficient Tracking System 3

Improved Algorithm for Solar Power Systems for Efficient Tra...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Kalele, Girish Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350399264

Current evaluation of a pilot concentrated solar energy plant found that the monitoring technology was unable to precisely align the complex heliostats with the sun and direct the sunlight onto the goal. The inability of concentrating solar energy monitoring systems to both increase precision & lower operating costs was shown by a subsequent examination of solar tracking devices. A computers vision solar tracking systems is presented and put into practice in this work. It consists of vision controllers, camera, light sensors, Arduino microprocessor, two stepping motor, sticks, and a transparency plate. The light sensors determine whether sunlight is present, the webcam records a picture of the stick's shadows on the plate, & the sight processor interprets this information in order to derive the sun inclinations. The microprocessor receives the angle and uses them to properly position the heliostat using stepper motor. The suggested system may be put in a single heliostat or a collection of heliostats & is site independently. It follows the sun correctly and costs little to operate, according to initial testing. © 2023 IEEE.

关键词： signal processing

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Preprocessing Methods to Improve Performance of Imbalanced data in Healthcare 3

Preprocessing Methods to Improve Performance of Imbalanced D...

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3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Fathima Begum, M. Narayan, Subhashini School of Information Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350303360

Background: Machine learning is gaining enormous significance and has developed into an essential technology as the reliability of healthcare information improves rapidly. However, the complex, incomplete, and multifaceted nature of healthcare data makes the timely and precise identification of disease a continuing problem. processing the data is an important phase whose main objective is to give processed information in order to increase forecast accuracy. Methods: The imbalanced dataset namely WiDS (Women in data Science) Challenge data is used in our work. Missing values are imputed with advanced multiple imputations by chained equations methods. Dimensionality reduction is accomplished using the wrapper-based forward feature selection method. Various sampling techniques combined with a neural network algorithm are used to address the imbalance problem. Results: Our proposed model got higher accuracy of 92.45% using deep neural network with SMOTH sampling compared to other preprocessing methods. Conclusion: This study compares the model performance with different combination of preprocessing methods and significantly improves the model performance. © 2023 IEEE.

关键词： Deep neural networks

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Challenges in Implementing Real-Time Stream data in Bioinformatics 3

Challenges in Implementing Real-Time Stream Data in Bioinfor...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Acharjya, Kalyan Swetha, G. Narmadha, T. Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Karnataka Bangalore India Karnataka 562112 India

ISBN: (纸本)9798350343274

Creating efficient and adaptable methods for data analysis is crucial in bioinformatics as the amount of real-time stream data continues to grow. We describe an integrated approach that combines complex event processing (CEP), online machine learning (ML), and data stream clustering to solve the problems associated with real-time stream data processing in bioinformatics. This method brings together online machine learning, complex event processing (CEP), and clustering in data streams. Flexible, all-encompassing, and real-time research are just a few of the ways in which this innovative approach excels above more conventional ones. We use several key performance indicators to show how our approach improves upon the status quo. In this study, we looked at ten different strategies and compared their performance over a range of metrics. One of these features was the flexibility to grow or shrink without sacrificing data security, performance, latency, accuracy, or any combination thereof. According to the findings, the novel approach performs better than the conventional methods in almost all respects. Better scalability, resource usage, data integrity, throughput, latency, accuracy, and scalability were all benefits. The proposed method was proven to be a more efficient choice for real-time data processing in bioinformatics due to its increased flexibility and comprehensiveness. This allows us to surmount the obstacles given by the volatile character of biological data. Bio informaticists and other medical professionals may use its flexibility and real-time analytical abilities in a variety of contexts, from analyzing genetic data to monitoring patients' health. © 2023 IEEE.

关键词： Scalability

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An Educational Supply Chain Management Model as Basis for a Big data-Driven Decision Support Framework for the University 3

An Educational Supply Chain Management Model as Basis for a ...

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3rd international conference on Theoretical and Applicative Aspects of Computer Science, ICTAACS 2023

作者： Boughouas, Mohamed Lamine Kissoum, Yacine Mazouzi, Smaine Faculty of Sciences Computer Science Department Université 20 Août 1955 Skikda Skikda Algeria

ISBN: (纸本)9798350385854

The development of supply chain management (SCM) technics in the manufacturing and service industries has led to a well-developed and highly defined supply chain process. The application of SCM best practices to higher education is strongly encouraged and beneficial for high-quality graduates and better research outcomes for society. This paper provides an educational supply chain management (ESCM) model for higher education institutions. Through our study, we noted the importance of decision-making at all levels of the educational supply chain (ESC). To support decision-makers in the ESC, we explored the potential of involving big data analytics (BDA) in the decision-making process. Inspired by the manufacturing sector's experience in applying big data for decision-making, we present a conceptual framework for a BDA-based decision support solution's proper implementation in universities. A data analysis case study is presented to demonstrate the data processing potential in the university. © 2023 IEEE.

关键词： Decision making

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A CEEMDAN-QPSO-BiLSTM Based Hybrid Model for Water Quality Parameter Forecasting 3

A CEEMDAN-QPSO-BiLSTM Based Hybrid Model for Water Quality P...

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3rd international conference on Electronic Information Engineering and Computer, EIECT 2023

作者： Guo, Yongwen Chen, Hongming Huang, Juntao Lin, Yilei Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen China

ISBN: (纸本)9798350357707

Water contamination is a major concern worldwide due to its impact on the global ecology. Accurate forecasting of water quality parameters is crucial for effective water contamination control. However, existing models face challenges in accurately predicting water quality series due to their nonlinearity and non-stationarity. Additionally, further investigation is needed to find out the performance of the Bidirectional Long Short-Term Memory network (BiLSTM) when combined with optimization of signal processing and adjustment of hyperparameters. In this article, we introduce a novel hybrid approach named CEEMDAN-QPSO-BiLSTM, which combines three techniques-Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Quantum behaved Particle Swarm optimization (QPSO), and BiLSTM-for predicting water quality parameters. To evaluate the effectiveness of this hybrid model, we developed four benchmark models, namely LSTM, BiLSTM, PSO-BiLSTM, and QPSO-BiLSTM, and compared their predictive accuracy with that of our hybrid approach. The training and testing of these models utilized actual water quality data from the Chinese National Marine Environmental Monitoring Center's official system, specifically focusing on Dissolved Oxygen (DO), pH value, and Chemical Oxygen Demand (COD) parameters. The experimental results show that the CEEMDAN-QPSO-BiLSTM surpasses the baseline models, providing better error performance and prediction accuracy. © 2023 IEEE.

关键词： Empirical mode decomposition

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Current signal Enhancement of High Voltage Circuit Breaker Based on W-CGAN-GP Algorithm 3

Current Signal Enhancement of High Voltage Circuit Breaker B...

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3rd international conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Tang, Li Sheng, Yifa School of Electrical Engineering University of South China Hengyang421001 China

ISBN: (纸本)9798350372052

The availability of extensive sample data ensures the training effectiveness, although deep learning could significantly enhance the accuracy of the fault diagnosis, potentially. For fault diagnosis, the application of deep learning is hampered in the case of High-Voltage Circuit Breakers (HCVBs) as a result of the insufficient fault signals. To address this limitation, this paper introduces a variant of the generative adversarial network (W-CGAN-GP) tailored for enhancing fault electrical signals. The proposed method generates electrical signals corresponding to various fault conditions, thereby expanding the training dataset. To validate the efficacy of the approach in enhancing the fault diagnosis accuracy of HVCB, a comparative analysis of fault diagnosis accuracy was conducted before and after data generation. The experimental findings demonstrate that the fault signals generated by the method exhibit a high degree of similarity, leading to an improvement in fault diagnosis accuracy. © 2023 IEEE.

关键词： Failure analysis

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Exploring Reinforcement Learning in Large-Scale data processing 3

Exploring Reinforcement Learning in Large-Scale Data Process...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Bhambu, Pawan Kumar, Ritesh Sharmila, P. Patil, Vivek D. Khurana, Shivam Vivek, V. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

Reinforcement mastering (RL) is vital for big-scale fast processing. RL utilizes the availability of large datasets and dealers skilled in this information to remedy a wide variety of obligations, from automated diagnostics and self-reliant using structures to clinical information analysis. Using exploiting the traits of the modern-day significant information era, RL allows efficient and correct decision-making in massive-scale information processing and answers complicated troubles. This paper explores the use of reinforcement learning (RL) in large-scale data processing. RL is a subfield of machine learning that focuses on decision making in dynamic environments. With the increasing demand for processing large amounts of data, traditional techniques such as rule-based systems and batch processing are becoming inadequate. This has led to the emergence of RL as a potential solution due to its ability to learn and adapt to changing environments. Through a comprehensive review of existing literature, this paper discusses the applications of RL in large-scale data processing and its potential benefits. Furthermore, it examines various challenges and their potential solutions in implementing RL for data processing tasks. The paper concludes by highlighting the future research directions in this domain and the potential impact of RL on improving data processing efficiency and accuracy. It similarly examines some of the demanding situations and possibilities related to using RL for huge-scale records processing and the capability programs of RL in the future. © 2023 IEEE.

关键词： applications challenges opportunities potential processing

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