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Systematic Common Information Model (CIM) Profile Management for Advanced Distribution Management System

Transactions of the Korean Institute of electrical Engineers 2024年第1期73卷 1-9页

作者： Kim, Hyun Joong Oh, Jae Young Yoon, Yong Tae Jung, Wonwook Shim, Jaeseong Shon, Jin-Man Dept. of Electrical Engineering Hoseo University Korea Republic of Dept. of Energy Engineering Korea Institute of Energy Technology Korea Republic of Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Korea Electric Power Corporation Research Institute Rep. of Korea Korea Republic of

Common Information Model (CIM) is an essential technique for ensuring interoperability in Energy Management Systems (EMS) by defining data profiles for information exchange. Given the changing system environment, CIM undergoes periodic revisions, necessitating updates to the profiles used in EMS. However, comparing and interpreting CIM as defined in the Unified Modeling Language (UML) can be time-consuming and may lead to human errors. In this context, this study proposes systematic approaches for managing data profiles using an automatic tracking function for CIM and a structured process for profile management. The effectiveness of this approach is demonstrated by applying it to profiles defining the key functions of Korea's advanced distribution management system (ADMS), such as operational limits, renewable energy sources, and power transformers. This research can serve as a guideline for addressing practical issues in securing interoperability in ADMS. © 2024 Korean Institute of electrical Engineers. All rights reserved.

关键词： Interoperability

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Denial-of-Service Attack Detection and Mitigation Strategy for Synchrophasor Data Flowing in Power Transmission System 4

Denial-of-Service Attack Detection and Mitigation Strategy f...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Nath, Soumya Ranjan Bhalja, Bhavesh R. Sidhu, Tarlochan S. Indian Institute of Technology Dept. of Electrical Engineering Roorkee India Ontario Tech University Dept. of Electrical and Computer Engineering Oshawa Canada

ISBN: (纸本)9798350383997

The coupling of the legacy power infrastructure with complex computer networks and the integration of communication devices has led to the problem of cyberattacks, which in turn may induce partial/full blackouts. In this regard, an approach based on a Decision Tree for the detection and mitigation of Denial-of-Service (DoS) attacks is proposed in this paper. Based on the patterns and effects observed during DoS attacks on the Synchrophasor data flowing from the Phasor Measurement Unit (PMU) to the Phasor Data Concentrator (PDC), various supervised machine learning models are employed, which classify healthy and attack data and detect DoS attacks. Thereafter, a recurrent deep learning network employing Gated Recurrent Unit (GRU) architecture is utilized to reconstruct the lost data, which in turn is helpful in mitigating the impacts of a DoS attack. The suggested algorithms are validated using a real-world scenario of a communication dataflow between multiple PMUs placed at different buses of a power transmission network and a PDC present at a control center, representing a wide-area network, and are incorporated using Real Time Digital Simulator and openPDC. By comparing various metrics of ML classification, the results indicate that the decision tree based model gives better accuracy along with the least detection time compared to other machine learning-based models. The suggested GRU-based mitigation algorithm can reconstruct the DoS-impacted lost data from the available pre-attack phasors. © 2024 IEEE.

关键词： Phasor measurement units

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Design of a High Voltage Common Mode Resilient FlexRay Receiver in 180nm/5V CMOS Process 6

Design of a High Voltage Common Mode Resilient FlexRay Recei...

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6th Iranian International Conference on Microelectronics, IICM 2024

作者： Mansoury, Hamid Sadat Saeedi, Saeed Atarodi, Mojtaba Sharif University of Technology Dept. of Electrical Engineering Tehran Iran Tarbiat Modares University Dept. of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331511371

A FlexRay receiver that is capable of operating within [-60V, 60V] common mode range using a common mode cancellation method is presented in this paper. The receiver uses 5V MOSFET transistors and incorporates a digital block to reduce the required number of comparators to 2, potentially leading to lower power consumption and die area. Extensive simulations have been conducted to ensure the receiver's functionality within a temperature range of [-40°C, 150°C], which is particularly crucial for automotive applications. Furthermore, corner and mismatch simulations have been performed to ensure the circuit's process tolerance and suitability for mass production. Additionally, Electromagnetic Immunity simulations have been performed to ensure that the receiver, intended for automotive applications, meets the necessary standards. The layout of the receiver occupies an area of 153.6 × 128.7 μm2.. © 2024 IEEE.

关键词： CMOS integrated circuits

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UAV Platform for Real-time Indoor Mapping 2

UAV Platform for Real-time Indoor Mapping

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2nd IEEE International Conference on Innovations in High-Speed Communication and Signal Processing, IHCSP 2024

作者： Kshirsagar, Spruha Nitin Rawat, Abhishek Bhandari, Ravi Visvesvaraya National Institute of Technology Dept. of Computer Science and Engineering Nagpur India Dept. of Electrical and Computer Science Engineering Ahmedabad India

ISBN: (纸本)9798350368949

SLAM enables Robots and UAVs to effectively understand unknown surroundings and estimate their own position within those environments. Recent advancements in 2D mapping have significantly increased its popularity for navigation tasks, primarily because it is easier to generate and requires less computational power compared to 3D mapping. This paper provides a comparison of two widely used ROS-based 2D SLAM libraries: Google Cartographer and Hector SLAM, deployed on a custom built UAV that uses 2D LiDAR, and finds Hector SLAM to be more feature rich as well as computationally efficient. © 2024 IEEE.

关键词： Robot Operating System

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Multi-Agent H-Learning Based Cooperative Spectrum Sensing for Cognitive Radio Networks 32

Multi-Agent H-Learning Based Cooperative Spectrum Sensing fo...

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32nd International Conference on electrical engineering, ICEE 2024

作者： Fard, E. Karimpour Nouri, M. Behroozi, H. Sobhi-Givi, S. Sharif University of Technology Dept. of Electrical Engineering Tehran Iran University of Mohaghegh Ardabili Dept. of Electrical and Computer Engineering Ardabil Iran

ISBN: (纸本)9798350376340

Fast and accurate methods for spectrum sensing (SS) are the key elements in cognitive radio networks (CRNs) that achieve high SS. This paper proposes a reinforcement learning (RL) scheme for secondary users (SUs) in a massive multiple-input multiple-output (MIMO) CRN to determine occupation and emptiness of scanned wideband channels. In the proposed H-learning method, the clustered neighboring SUs collect data to check the status of channels that improve reliability of measured sensing. Then, they share information about different frequency channels with other clusters and the status of channel occupation is determined for all SUs. This model-based learning method increases finding free channels and enhances spectral efficiency (SE) in presence of large probability of missing detection. Furthermore, each SU learns the presence pattern of primary user (PU) and gather a dynamic scan priority list to decrease scan overhead and access delays. Simulation results demonstrate the multi-agent reinforcement modified H-learning (MH-learning) method can achieve better performance in terms of PU usage percent, number of attempts, call block rate, and probability of detection in comparison to H-learning and some state-of-art learning methods. © 2024 IEEE.

关键词： Reinforcement learning

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Improved Object Detection Through a Learning-Based Joint-Initiative Supervised Autonomy (JISA) Framework

Improved Object Detection Through a Learning-Based Joint-Ini...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Tarabay, Andrea Daher, Naseem American University of Beirut Dept. of Electrical and Computer Engineering Beirut Lebanon American University of Beirut Dept. of Electrical and Computer Engineering Dept. of Mechanical Engineering Beirut Lebanon

ISBN: (纸本)9798331529604

Object detection is faced with accuracy and robustness limitations due to unstructured environments with objects of different sizes and occlusions. Despite advancements in object detection models, these models have not reached a point to possess a comparable level of perception to humans, thus limiting their ability to effectively address challenges in diverse environments. Hence, adopting a human-in-the-loop approach in autonomous systems can handle complex perception scenarios. To tackle these issues, the Joint-Initiative Supervised Autonomy (JISA) framework is used to allow a human supervisor to intervene once the agent is uncertain of its predictions based on its self-confidence (SC) metric, or when the human deems the prediction incorrect based on his/her situation awareness (SA). However, the existing JISA framework formally defines the levels of autonomy between the human and the agent, but does not provide a means for learning from human interventions. In this paper, we present an enhanced version of JISA in object detection applications by implementing a learning scheme that is inspired by the Interactive Corrections and Reinforcement for an Epistemic and Aleatoric uncertainty-aware Teaching (ICREATe) algorithm. To address these challenges, we evaluate the results of the proposed framework and demonstrate that the model effectively learns and improves its performance through human feedback. © 2025 IEEE.

关键词： Self-supervised learning

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Deep Variational Sequential Monte Carlo for High-Dimensional Observations

Deep Variational Sequential Monte Carlo for High-Dimensional...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： van Nierop, Wessel L. Shlezinger, Nir van Sloun, Ruud J.G. Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Dept. of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9798350368741

Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a differentiable particle filter that leverages the unsupervised variational SMC objective to parameterize the proposal and transition distributions with a neural network, designed to learn from high-dimensional observations. Experimental results demonstrate that our approach outperforms established baselines in tracking the challenging Lorenz attractor from high-dimensional and partial observations. Furthermore, an evidence lower bound based evaluation indicates that our method offers a more accurate representation of the posterior distribution. © 2025 IEEE.

关键词： Particle Filters Sequential Monte Carlo Variational Inference

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A Study on Simplification of LCC Estimation Through Correlation Estimation Between Cost Elements for Railway Catenary System

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Transactions of the Korean Institute of electrical Engineers 2024年第5期73卷 900-905页

作者： Jeng, Chang-Mo Lee, Jong-Woo Dept. of railway Electrical Signaling Engineering Seoul National University of Science and Technology Korea Republic of Dept. of railway Electrical Signaling Engineering Seoul National University of Science Korea and Technology Korea Republic of

Life cycle cost (LCC) analysis is crucial for estimating total cost of system constructions across various industries, especially defense, aerospace, railways where products are complex and long-lasting. A significant challenge in LCC modeling lies in the limited availability of reliable life-cycle data, leading to uncertainties in system specifications, estimation methods, and scenario analysis. Despite the existence of historical construction data, inherent uncertainties often restrict their direct use. In projects like catenary system construction for railways, LCC is heavily influenced by project length, with material costs being one of the most deterministic elements compared to other cost factors. This paper proposes a novel cost element coefficients method to facilitate the estimation of total construction costs. Our method leverages historical data and addresses uncertainties, providing a practical tool for LCC analysis in railway projects. Copyright © The Korean Institute of electrical Engineers.

关键词： Life cycle

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Active Control for Resonance Mitigation in Electric Drive Applications 15

Active Control for Resonance Mitigation in Electric Drive Ap...

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15th International Symposium on Industrial Electronics and Applications, INDEL 2024

作者： Berglund, Emelie Borg, Johan Johansson, Jonny Luleå University of Technology Dept. of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350352320

The use of inverter-based motor controllers that can generate fast voltage level transitions is increasing in the field of electric drive systems because of their ability to achieve higher efficiency. However, the fast voltage transitions, dv/dt's, are prone to generate bearing currents and cause overvoltages that may harm the electric motor. An LC filter applied between the inverter and the motor terminals can reduce the dv/dt's. However, the LC filter will cause overvoltage due to resonance but this can be overcome by introducing an extra pulse at each pulse-width modulation (PWM) transition. The timing of this extra pulse is crucial for suppressing the resonance, but due to nonlinearities in a real system, the time instants may vary over time and are thus in need of control. In this study, an analytical expression for the optimal pulse location and duration is derived and we propose a control algorithm that continuously calculates the optimal pulse switch time instants based on the measured output voltage. The control algorithm can handle sudden system disturbances and a Monte Carlo simulation is performed to evaluate the performance of the algorithm. This hybrid filter successfully reduces dv/dt's at the motor terminals while simultaneously eliminating overvoltages due to the filter resonance. © 2024 IEEE.

关键词： Control nonlinearities

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A Novel Music Recommendation System Using Filtering Techniques

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第4期48卷 595-610页

作者： Vashishtha, Srishti Varshney, Deepika Sarin, Eva Kaur, Simran Computer Science and Engineering Dept. Bharati Vidyapeeth’s College of Engineering GGSIPU New Delhi India Computer Science and Engineering Dept. Jaypee Institute of Information Technology Noida India

With the growth of the World Wide Web, a large amount of music data is available on the Internet. A large number of people listen to music online rather than downloading and listening offline. But only some sites provide personalized and accurately recommended songs while they listen to an auto-playing playlist. Hence the need for recommendation systems arises. Two approaches can be applied to the recommendation system: content based filtering and collaborative filtering. While in content-based filtering approach, analysis on the songs’ content which has been preferred by the user in history is done and the songs with relative similarity are recommended. While latter suggests songs that certain users of similar listening pattern have preferred. But collaborative filtering-based recommendation systems not only requires time to attain stability but also might recommend unsuitable music because it is not personalized to each user’s preference. Also, the latter requires the songs to be listened by a few users already to recommend it any other user. Hence for overall analysis, answers to few questions need to be implemented in recommendation systems: the very first one is how the properties should be analyzed, next one is how the analysis should be done and last is how the songs related to the user’s preference should be chosen. So, for suggesting the better system which solves these three questions as well as provides better and personalized recommendations, In this paper we build a collaborative filtering as well as content based filtering recommendation system, where various factors determine the essence of the songs, i.e. liveliness, keys used, loudness, pitch, valence, etc., are analyzed for comparison. From the experimental analysis in has been identified that content-based filtering technique performed best on KNN machine learning classifier with accuracy of 85%. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Collaborative filtering

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