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A Lightweight CNN-based Mechanism to Efficiently Identify Electric Meters under Stealthy False data Injection Attacks in Smart Power Grid 29

A Lightweight CNN-based Mechanism to Efficiently Identify El...

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29th ieee international conference on Parallel and Distributed systems, ICPADS 2023

作者： Ye, Tong Zhou, Boyang Nie, Yinghui Zhu, Zhen Intelligent Networks Institute Zhejiang Lab Hangzhou311100 China Hangzhou Institute for Advanced Study Ucas School of Intelligent Science and Technology Hangzhou310024 China

ISBN: (纸本)9798350330717

In the era of the power grid digitization, the smart electric meters faces the stealthy false data injection attack (SFDI), which compromises the state estimation (SE) in the supervisory control and data acquisition system (SCADA). Especially, the state-of-the-art identification mechanism in the intrusion detection systems (IDSes) cannot meet the timely response demand, despite its high accuracy as it adopts a deep CNN to capture the injections via fully exploiting the wide-area measurement statistics. To solve the above challenge, we propose an efficient and accurate identification mechanism (EACI) designed in the following aspects. To improve the efficiency, EACI adopts a lightweight CNN with reduced neural layers to avoid the unnecessary exploitation. To maintain the accuracy, EACI extracts the major correlation features in the adjacent statistics, and employs the CNN to accurately discriminate the injections. We evaluate EACI under extensive SFDI scenarios, in both the low and medium voltages power grids constructed according to the ieee 30-bus and 57-bus datasets, respectively. The numerical results demonstrate that EACI reduces the response time to hundreds of microseconds, while keeping a high detection accuracy. © 2023 ieee.

关键词： convolutional neural network Intrusion detection system smart power grid stealthy false data injection attack

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intelligent Rail Sentinel a perspective of Indian Railway

Intelligent Rail Sentinel a perspective of Indian Railway

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2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Limgaokar, Arun D. Hinge, Sharvari S. Vinchu, Yash S. Rane, Vivek S. Patel, Prashant B. Vibhute, Anup Department of Instrumentation Engineering Dr. D.Y. Patil Institute of Technology Maharashtra Pimpri Pune India

ISBN: (纸本)9798331529833

The paper presents a PLC and SCADA-based advanced safety system design for railway signaling and interlocking. While the Indian Railway systems are infrequently beset by issues, a solitary mishap over an extended timeframe might profoundly adversely affect various facets of life and property. These issues can be resolved through the implementation of interlocking and railway signaling systems. In railways, railway personnel continue to supervise the signaling and interlocking systems, which remain partially manual. The implementation of PLC and SCADA can effectively address these challenges. The primary objective of this paper is to examine dispatcher telecommunications with crews, telecommunications with signals governing switchgears, signals monitoring track temperature, and the acquisition and storage of real-time video through CCTV for telecommunications in emergencies and automatic railway gates. The proposed solution depends on PLCs that can execute signaling and interlocking functions automatically. Moreover, these processes can be altered, monitored, and corrected through the use of a SCADA system, hence minimizing human participation and alleviating safety issues. © 2025 ieee.

关键词： SCADA systems

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Decision Support for Documentation Control in Project Management on the Basis of Association Rule Mining 27

Decision Support for Documentation Control in Project Manage...

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27th international conference on Soft computing and Measurements, SCM 2024

作者： Latypova, Viktorija A. Ufa University of Science and Technology Department of Automated Management Systems Ufa Russia

ISBN: (纸本)9798350363708

data mining is utilized for intelligent decision support provision in project management to resolve the wide range of problems. But, nevertheless, intelligent support for the project documentation control procedure is given too little attention in ongoing research, despite the fact that the procedure is significant. The quality of the project documentation directly influences on the project outcome. Also, it takes a long time and much effort for project office staff to control the project documentation. To meet this challenge, an approach to decision making support for project office staff for the documentation control in project management on the basis of association rule mining and a corresponding intelligent system are suggested in the paper. The approach was tested in controlling the project documentation at the training course on project workshop at university. © 2024 ieee.

关键词： Association rules

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Research on Big data Analysis and intelligent Decision Support System under Cloud computing Environment 7

Research on Big Data Analysis and Intelligent Decision Suppo...

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7th ieee international conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Wang, Huijuan Wuhan Technology and Business University Hubei Wuhan430065 China

ISBN: (纸本)9798350350760

This paper deeply discusses how to mine valuable information in massive data to assist decision makers to make more accurate decisions. Firstly, this paper designs and implements an intelligent decision support system based on LSSVM regression analysis algorithm, which can effectively process and analyze big data and provide real-time and accurate data analysis results for decision makers. The introduction of LSSVM regression analysis algorithm not only improves the speed of data processing, but also significantly improves the prediction accuracy of DSS. In order to verify the performance of the system, a series of system simulations are carried out in this paper. Through comparative experiments, the decision support ability of the system is significantly better than that of the traditional decision support system when processing data sets of different sizes. The simulation results show that the prediction accuracy of the system is increased by 15% and the processing speed is increased by 20% on average when processing large-scale data sets. In addition, this paper also analyzes the application of the system in different scenarios, including marketing, supply chain management and financial risk prediction, and shows the wide applicability and high efficiency of the system in practical applications. ©2024 ieee.

关键词： data accuracy

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Managing Metadata in data Warehouse for data Quality and data Stewardship in Telecom Industry-A Compact Survey 4

Managing Metadata in Data Warehouse for Data Quality and Dat...

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4th ieee international conference on computing, Communication, and intelligent systems, ICCCIS 2023

作者： Chaudhuri, Bhola Gan Rani, Shalli Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350306118

Present telecom industry database provided poor data quality, specifically metadata quality. As a result, decision making data are missing for analyses. Metadata may contain information about the schema of the data so that the data can be transformed into quarriable data objects when required. The data warehouses (DWH) acceptance has grown hugely for data analysis. The main part of data warehouse includes source of data, software for data acquisition, repository, tools for data analysis, and user interface. So many factors are responsible and be critical for DWH successful implementation, they are type of organization, sufficient technical information, project type, work environment, infrastructure, quality of information, quality of system, quality of Service, and quality of relationship. For data quality and its analysis, metadata, and its roles that metadata plays are very important for organization. Metadata management is a central part of the data analysis process. Metadata carries information about data schema that helps to transformed into quarriable data objects when required. For all projects related to data warehouse, the most important factor is metadata. It catches all types of necessary information to design, simplify, build, and use the data warehouse which contains project related data. Functions of metadata system are to collect and store information in single place, which provides an overview for metadata, such as searches, queries, create, maintain, evaluate and managing access of metadata by establishing role-based security. Stewarding metadata is important for the application of data governance practice for enterprise to provide the value and the context for explaining the data and its components to the end users. With the help of the metadata concepts, the organizations can define a business rule for the specific data attribute. © 2023 ieee.

关键词： Metadata

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Modeling car-following behavior of electric adaptive cruise control vehicles using experimental testbed data 26

Modeling car-following behavior of electric adaptive cruise ...

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ieee 26th international conference on intelligent Transportation systems (ITSC)

作者： Zare, Arian Shang, Mingfeng Kan, Xingan (David) Stern, Raphael Univ Minnesota Dept Civil Environm & Geo Engn Minneapolis MN 55455 USA Florida Atlant Univ Dept Civil Environm & Geomat Engn Boca Raton FL 33431 USA

ISBN: (纸本)9798350399462

Automated vehicles (AVs) have the potential to revolutionize the transportation industry. While extensive research has been conducted to explore the benefits of AVs on traffic flow, commercially available adaptive cruise control (ACC) vehicles with advanced driver assistance features have been shown adverse effects on traffic flow. As vehicle automation advances, electric vehicles (EVs) equipped with ACC are emerging as an alternative to traditional internal combustion engine (ICE) vehicles. However, there is still a limited understanding of the differences in vehicle dynamics between EV-ACC and ICE-ACC vehicles. This study utilizes microscopic car-following models to describe the vehicle dynamics of EV-ACC vehicles. The model parameters are calibrated based on an experiment conducted with commercially available EV-ACC vehicles. The calibration results indicate that the optimal velocity relative velocity (OVRV) model outperforms the intelligent driver model (IDM) in most gap settings by up to 37%, suggesting that the OVRV model can effectively capture the driving behavior of EV-ACC vehicles. However, simulations of a string of vehicles imply that the IDM is more accurate in capturing amplifications due to velocity disturbances. Therefore, the development of higher-fidelity microscopic car-following models specifically for EV-ACC vehicles is necessary.

关键词： Automobile drivers

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DeepSentiment: Unlocking Insights from Twitter data with advanced Deep Learning Analytics 4

DeepSentiment: Unlocking Insights from Twitter Data with Adv...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Sandhi, Mahammad Idrish I. Begum, M. Amina Jadhav, Rahul Jalinder Maroor, Jnaneshwar Pai Ankushavali, M.D. Venkateshmurthy, B.S. Sankalchand Patel College of Engg. Faculty of Computer Science Visnagar India Sri Venkateswaraa College of Technology Department of Ece Chennai India Bharati Vidyapeeth Yashwantrao mohite Institute of management karad India Department of Business Administration Karnataka Mangaluru India School of Science Sr University Telangana Warangal India School of Eee Reva University Bengaluru India

ISBN: (纸本)9798331529635

Sentiment analysis learning systems incorporate both textual tweets and reviews. The hybrid model is a combination of the support-vector-machine (SVM), the Long Short-Term Memory (LSTM), and convolution-neural-network (CNN). Each method was tested for reliability and its computational time was measured. When deep-learning (DL) and SVM are merged, it outperforms both approaches on all datasets. DL algorithms have recently proved to offer significant promise in sentiment analysis. Hyperparameter tuning and monitoring for LSTM takes more time but yields better results than for CNN. The integrated model's efficacy varied from job to job, but in every case it outperformed the alternatives. LSTM networks, CNNs, and SVMs are required for DL-SA strategies. All three methods-SVM, LSTM, and CNN-were evaluated for their accuracy and error rates using a hybrid model. The proposed model has been found to be accurate to within 6.76 percentage points of the true value. © 2024 ieee.

关键词： Tweets

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Detector Plane data Analysis of Scattered X-Rays Distribution: Theoretical Study vs Simulation Result

Detector Plane Data Analysis of Scattered X-Rays Distributio...

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ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Mariya Smahina Andrii Netreba Sergiy Radchenko Faculty of Radiophysics Electronics and Computer Systems National Taras Shevchenko University of Kyiv Kyiv Ukraine

The precise description of the photons' scattering in matter is a complex physical, mathematical and computational task. However, just the scattered radiation is the dominant factor of X-ray images formation. So an approximate description of the scattered quantas distribution in the detector plane of the X-ray system is obtained based on theoretical dependencies. The data analysis and verification are carried out using the results of mathematical Monte Carlo modeling. The dependency simplicity and sufficient accuracy of the photon distribution description are grounds for mathematical correction algorithm developing of initial radiological data and image improvement.

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Reliable ML-Based Image Processing and Compression for an Onboard Imaging Pipeline 15

Reliable ML-Based Image Processing and Compression for an On...

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15th Space computing conference

作者： Garrett, Tyler Wildenstein, Diego Wang, Eileen Gretok, Evan George, Alan Univ Pittsburgh NSF Ctr Space High Performance & Resilient Comp S Pittsburgh PA 15260 USA

ISBN: (纸本)9798350384543;9798350384536

Sensing and onboard-processing capabilities of next-generation spacecraft continue to evolve. Enabled by advances in avionic systems, large amounts of data can be collected and stored on orbit. Nevertheless, loss of signal, communication delays, and limited downlink rates remain a bottleneck for delivering data to ground stations or between satellites. This research investigates a multistage image-processing pipeline and demonstrates rapid collection, detection, and transmission of data using the Space Test Program - Houston 7 - Configurable and Autonomous Sensor Processing Research (STP-H7-CASPR) experiment aboard the international Space Station as a case study. Machine-learning (ML) models are leveraged to perform intelligent processing and compression of data prior to downlink to maximize available bandwidth. Moreover, to ensure accuracy and preserve data integrity, a fault-tolerant ML framework is employed to increase pipeline reliability. This pipeline fuses the fault-tolerant Resilient TensorFlow framework with ML-based tile classification and the CNNJPEG compression algorithm. This research shows that the imaging pipeline is able to alleviate the impact of limited communication bandwidth by using reliable, autonomous data processing and compression techniques to achieve reduced transfer sizes of essential data. The results highlight the benefits provided by resilient classification and compression including minimized storage use and reduced downlink time. Furthermore, the findings of this research are used to assess the feasibility of such a system for future space missions. The combination of these approaches enables the system to achieve up to a 98.67% reduction in data size and downlink time as well as the capacity to capture imagery up to 75.19x longer time period for a given storage size, respectively, while maintaining reconstruction quality and data integrity.

关键词： data Compression Fault-Tolerant computing Resilient computing Image Processing Image Classification Machine Learning Deep Learning Onboard Processing

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Health Monitoring of Solar Photovoltaic Panels and data acquisition System using IoT 3

Health Monitoring of Solar Photovoltaic Panels and Data Acqu...

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3rd international conference on Power, Control and computing Technologies, ICPC2T 2024

作者： Chaudhary, Anupama Agrawal, Purushotam Kumar Choudhary, Niraj Kumar Singh, Nitin Singh, R.K. Motilal Nehru National Institute of Technology Department of Electrical Engineering Prayagraj India

ISBN: (纸本)9798350349207

With depleting non-renewable resources and concern for global warming, efforts are being made to shift to renewable energy sources. Renewable energy resources are freely available into the nature. The efforts are made to convert these resources into electrical energy and use them to fulfil the energy needs, but they face operational challenges and require constant technological innovation. At present, most of the energy produced by renewable sources comes from solar photovoltaic (PV) systems. Monitoring all those PV systems, especially remote ones, is a tough task. Thus, this work is aimed to contribute to develop solar PV health monitoring and data acquisition system using internet of things (IoT) technology. In this paper, there is a provision of monitoring and logging multiple parameters like current, voltage, power, ambient temperature, panel temperature, humidity, solar irradiance etc. of a solar PV system in the real time. Anyone with correct authorization can access the live data feed and visualize all the previous data with the help of multiple plots. To achieve this, the solar PV module is operated in multiple conditions and all the corresponding data are logged and live monitored. The solar PV health monitoring system makes system much more robust and ready to be used in industries. © 2024 ieee.

关键词： data acquisition

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