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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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Prompt Distillation for Emotion Analysis 16

Prompt Distillation for Emotion Analysis

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16th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2024

作者： Mackey, Andrew L. Gauch, Susan Cuevas, Israel Department of Electrical Engineering and Computer Science University of Arkansas FayettevilleAR United States

ISBN: (纸本)9789897587160

Emotion Analysis (EA) is a field of study closely aligned with sentiment analysis whereby a discrete set of emotions are extracted from a given document. Existing methods of EA have traditionally explored both lexicon and machine learning techniques for this task. Recent advancements in large language models have achieved success in a wide range of tasks, including language, images, speech, and videos. In this work, we construct a model that applies knowledge distillation techniques to extract information from a large language model which instructs a lightweight student model to improve its performance with the EA task. Specifically, the teacher model, which is much larger in terms of parameters and training inputs, performs an analysis of the document and shares this information with the student model to predict the target emotions for a given document. Experimental results demonstrate the efficacy of our proposed prompt-based knowledge distillation approach for EA. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Students

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A Regularization Approach to Maximize Common Sub-Expressions in Neural Network Weights 30

A Regularization Approach to Maximize Common Sub-Expressions...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Kavvousanos, E. Kouretas, I. Paliouras, V. Stouraitis, T. University of Patras Department of Electrical and Computer Engineering Greece Khalifa University Department of Electrical Engineering and Computer Science United Arab Emirates

ISBN: (纸本)9798350326499

This paper modifies the training of artificial neural networks in order to derive weights the binary expression of which is composed of a limited set of sub-expressions only, placed in specified positions. The benefit of the proposed technique is the substantial complexity reduction achieved. The proposed method can be applied as a post-processing step on pre-trained models, further expanding its impact. The main concept is the use of an introduced regularization function that promotes specific sub-expressions which, in turn, are shown to improve the performance of common sub-expression sharing techniques, reducing area, time and power requirements for inference. Synthesis results reported here, quantify the impact of the proposed method onto hardware implementations and demonstrate substantial area, delay and power improvements, over prior art. In certain cases a ×4 area reduction is achieved, combined with corresponding reduction in delay and power dissipation. © 2023 IEEE.

关键词： Deep neural networks

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A SiC MOSFET Based Multi-Port Solid State Circuit Breaker for DC Protection

A SiC MOSFET Based Multi-Port Solid State Circuit Breaker fo...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Song, Xiaoqing Nawafleh, Yannal Rahman, Mohammad Dehan University of Arkansas Department of Electrical Engineering and Computer Science FayettevilleAR United States

ISBN: (纸本)9798350376067

Direct current (DC) systems require fast fault protection speed due to ultrafast transients with high currents in short circuit faults, where solid state circuit breakers (SSCBs) can perfectly fit. Compared to the traditional mechanical circuit breakers (MCBs), SSCBs demonstrate 10 to 100 orders of magnitude faster fault breaking speed and enhanced safety with arc-less fault current interruption. One remaining challenge of the SSCB is large conduction losses, resulting in heavy and bulky cooling systems. In this paper, a novel high efficiency multi-port SSCB is proposed, which reduces >50% conduction losses compared to conventional SSCB topology. The power loss reduction benefits and operation principles of the proposed multiport SSCB are analyzed. A SiC MOSFET based SSCB hardware prototype is built, and experimental results validate the feasibility and effectiveness of the proposed SSCB concept. © 2024 IEEE.

关键词： MOSFET devices

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A Comparative Study on Prosumers Load Demand and Production Forecasting Using Machine Learning and Time Series Techniques 16

A Comparative Study on Prosumers Load Demand and Production ...

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Solea, Claudiu Hera, Corina Florea, Adrian Morariu, Daniel Stanescu, Dorel Vintan, Maria Lucian Blaga University of Sibiu Computer Science and Electrical Engineering Department Sibiu Romania

ISBN: (纸本)9798350388107

A data-driven and realistic policy could overcome the current turmoil in the intricate energy landscape. With solar and wind expanding faster than liquified natural gas or nuclear ever have, local, bottom-up solutions, such as organising collaboratively residential prosumers into electricity trading communities and the efficient use of aggregators, are one way to resilience and control over these resources. This paper analyses the features and characteristics of some prosumers and facilitates the identification of relevant algorithms for the specific tasks of both load demand and production forecasting using machine learning and time series techniques, thus enabling them to operate on a level playing field in the market. Load demand and production forecast simulations on two data sets, one collected in 2019, corresponding to 50 residential households and a public building from Portugal, and the other from 2023, that aggregates values from 29 household prosumers from Romania, show that both simple and double exponential smoothing techniques are more accurate than Markov predictors. Provided that the external contingencies influencing either the consumption or the production are considered, the latter gives a better prediction accuracy for energy production, whereas the former provides a better forecast for the consumption. © 2024 IEEE.

关键词： Demand response

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Investigation Emotion Dynamics in Bangla Social Media Text for Improved Emotion Detection

Investigation Emotion Dynamics in Bangla Social Media Text f...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Ahmed, Mostak Sobhan, Abdus Hossen Sajib, Md. Sahadat Masud, Nasrullah Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Varendra University Department of Computer Science and Engineering Rajshahi Bangladesh Varendra University Department of Electrical and Electronic Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331529765

Recognizing emotion from text is fundamental to machine learning and influences our understanding of human interaction. While English sentiment analysis has been well-researched, Bengali (Bangla) is still under-researched. Our study bridges this gap by investigating the emotional dynamics in Bengali social media posts using extensive user-generated content. We use a modified dataset defined by seven emotions: Happy, Surprise, Fearful, Angry, Neutral, Disgust, and Sad. Using machine learning techniques such as support vector classifiers, decision trees, logistic regression, and random forest, we found that the Random Forest model achieved the highest accuracy of 84%. This study advances our understanding of Bengali emotional expression and demonstrates its application in social media sentiment analysis, psychoanalysis, and communication technologies. By addressing the complexities of Bengali language and culture, our work contributes to a broader definition of emotion and lays the foundation for future research in different contexts. © 2024 IEEE.

关键词： Adversarial machine learning

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Securing Energy Equity With Multilayer Market Clearing

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第3期2卷 301-312页

作者： Zhang, Qiwei Li, Fangxing The University of Tennessee Min H. Kao Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States

Energy equity has been quickly gaining attention as policymakers seek to place 'Clean Energy for ALL' at the center of the low-carbon energy transition. Power system researchers have started to build the groundwork for incorporating energy equity into grid modernization. However, existing research has mostly focused on energy equity from a policy perspective, such as concept clarifications and tariff designs for residential customers. Little effort has been devoted to discussing energy equity in wholesale markets and to reflecting energy equity in electricity price with technical models. To the best of our knowledge, this paper presents the first study attempting to understand and implement energy equity consideration within a physically constrained electricity market-clearing model. First, we identify and discuss several concerns regarding energy equity in the existing locational marginal pricing (LMP) model. Then, we propose a multilayer framework reflecting energy equity in LMPs. Finally, the proposed framework is elaborated via a modified PJM 5-bus system and demonstrated with the WECC 179-bus system. © 2023 IEEE.

关键词： Power markets

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A Large Model's Ability to Identify 3D Objects as a Function of Viewing Angle 6

A Large Model's Ability to Identify 3D Objects as a Function...

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6th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2024

作者： Rubinstein, Jacob Ferraro, Francis Matuszek, Cynthia Engel, Don University of Maryland Department of Computer Science and Electrical Engineering BaltimoreMD United States

ISBN: (纸本)9798350372021

Virtual reality is progressively more widely used to support embodied AI agents, such as robots, which frequently engage in 'sim-to-real' based learning approaches. At the same time, tools such as large vision-and-language models offer new capabilities that tie into a wide variety of tasks and capabilities. In order to understand how such agents can learn from simulated environments, we explore a language model's ability to recover the type of object represented by a photorealistic 3D model as a function of the 3D perspective from which the model is viewed. We used photogrammetry to create 3D models of commonplace objects and rendered 2D images of these models from an fixed set of 420 virtual camera perspectives. A well-studied image and language model (CLIP) was used to generate text (i.e., prompts) corresponding to these images. Using multiple instances of various object classes, we studied which camera perspectives were most likely to return accurate text categorizations for each class of object. © 2024 IEEE.

关键词： Virtual reality

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Command, Control and Communications for Unmanned Aereal Systems in C-Band

Command, Control and Communications for Unmanned Aereal Syst...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Alghamdi, Mubark Kostanic, Ivica Florida Institute of Technology Department of Electrical Engineering and Computer Science Melbourne United States

ISBN: (纸本)9798350364910

C band spectrum 5030-5091 MHz is allocated for command-and-control communication services with unmanned aircraft systems. This paper evaluates the possibility of using 3GPP 5G standards for provisioning of such services. Channelization of the spectrum and major system parameters are proposed. The performance of the proposed system in channel fading environment is evaluated using MATLAB based simulations. The evaluations examine SINR, and average throughput of the proposed system at different altitudes of the unmanned aircraft system. © 2024 IEEE.

关键词： MATLAB

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A Hybrid Rectifier Mode Control for Communication-Free Wireless Power Transfer

A Hybrid Rectifier Mode Control for Communication-Free Wirel...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Zhu, Gangwei Dong, Jianning Bauer, Pavol Delft University of Technology Department of Electrical Engineering Mathematics and Computer Science Delft Netherlands

ISBN: (纸本)9798350376067

This paper presents a hybrid rectifier mode control for broad-range output power regulation in wireless power transfer (WPT) systems. The proposed control method employs a secondary-side active rectifier for output tuning, thereby eliminating the necessity for communication links. Furthermore, leveraging the pulse-skipping technique, two hybrid modes are introduced in the proposed approach. These hybrid modes reduce circulating reactive power within the resonant circuits, thereby optimizing the transmission efficiency of the WPT systems. To validate the effectiveness of the proposed method, experiments were conducted using a WPT prototype. Experimental results demonstrate that the proposed approach achieves higher efficiency than the conventional phase-shift control strategy, with a maximum efficiency improvement of up to 3.7%. © 2024 IEEE.

关键词： Inductive power transmission

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