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Consumer Communications and Networking Conference, CCNC IEEE

作者： Islam Elgarhy Ahmed T. El-Toukhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Maazen Alsabaan Hisham A. Kholidy Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Electrical Engineering College of Engineering Al-Azhar University Cairo Egypt Department of Network and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

In smart power grids, electricity theft causes huge economic losses to electrical utility companies. Machine learning (ML), especially deep neural network (DNN) models hold state-of-the-art performance in detecting electricity theft cyberattacks. However, DNN models are vulnerable to adversarial attacks, i.e., evasion attacks. In this work, we study the vulnerability of the DNN-based electricity theft detectors against evasion attacks and the influence of the model's regularization (generalization) on robustness. We cluster the power consumers and train a detector for each cluster, and compare the performance and robustness of this detector to a global detector that is trained on all the consumers, data. The results indicate that the cluster-based detector is not only more robust against evasion attacks but also enhances normal classification accuracy because its training data has more consumption pattern similarity compared to the training data of the global detector which requires higher level of regularization. Moreover, unlike the existing solutions that sacrifice the normal accuracy of the model to improve the robustness against evasion attacks, the proposed cluster-based detector holds state-of-the-art performance in both robustness and accuracy.

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Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux Motors: A Comparative Analysis

Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux...

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Iranian Conference on electrical engineering (ICEE)

作者： Mohammad Amirkhani Ehsan Farmahini Farahani Alireza Eikani Mojtaba Mirsalim Javad Shokrollahi Moghani Department of Electrical Engineering Electrical Machines and Transformers Research Laboratory Amirkabir University of Technology Tehran Iran Electrical Power Research Group School of Engineering Newcastle University Newcastle upon Tyne U.K Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper analyses three prominent biased-flux permanent magnet (BFPM) motors and compares them with each other. The structures of these BFPMs are different from each other, but they share the same operation principle. These structures are, namely, hybrid-excited multi-tooth slot-PM (HETSPM), multi-tooth variable flux reluctance (TVFR), and inter-modular PM (IMPM). The reason for this selection of structure is because of complementary features that each one possesses. For example, the HETSPM motor places in between the TVFR and IMPM motors in terms of PM consumption and demagnetization. However, the IMPM structure produces high amount torque at high efficiency. The demagnetization of the HETSPM and IMPM structures is also investigated and is shown that, in the similar situation, the HETSPM provides a safer operation environment for the PMs. Two-dimensional finite element method is used to extract the important profiles of the mentioned structures.

关键词： electrical engineering Magnetic flux Torque Motors Permanent magnets Finite element analysis Topology

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The New Frontier in Media: AI-Driven Content Creation for Ad-Supported TV using Generative Adversarial Network

The New Frontier in Media: AI-Driven Content Creation for Ad...

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International Conference of computer and Informatics engineering (IC2IE)

作者： Shashishekhar Ramagundam Niharika Karne Software Development & Engineering in Content Discovery & AI Comcast USA AL. Department of Computer Science Masters in Computer Science Troy University Montgomery Campus USA Sr. Principal Systems Engineer in C& RS Platform Services DT Amtrak USA Department of Electrical Engineering Masters in Electrical Engineering University of South Alabama USA

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

The use of Artificial intelligence (AI) technology in the content creation to produce creative aspects like editing, audience analysis, creating ideas, writing copy, etc. The major aim is to streamline and automate the process of content creation and convert it into a more efficient and effective way. There is a lack of transparency in the AI content production process and hence it does not exactly mimic human activities, which includes fantasizing and picking up new abilities. AI content creation requires more creativity while investigating with a specific goal in mind. Although, AI faces many challenges since it constantly expands its base knowledge. One of the biggest ethical problems is the probability that AI content is used to deceive or influence people. Hence, to overcome these difficulties a machine learning-based Artificial Intelligent content creation framework is generated for content creation in Ad-supported TV. Here, an effective model Generative Adversarial Network (GAN) introduced to deliver enterprise data, customize descriptions, and adjust content based on consumer behaviors. This developed model produces material that is closely related to the preferences of the user. AI content creation increases efficiency and productivity by saving time and money. It also leads to the development of better content. Finally, the experimental analysis is performed to find the effectiveness of the developed deep learning-based AI content creation framework via various metrics.

关键词： Productivity Measurement Deep learning Ethics TV Media Generative adversarial networks Artificial intelligence Informatics Faces

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A Graph-Based Self-Calibration Technique for Cable-Driven Robots with Sagging Cable

A Graph-Based Self-Calibration Technique for Cable-Driven Ro...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： M. R. Dindarloo A. S. Mirjalili S. A. Khalilpour R. Khorrambakht Stephan Weiss H. D. Taghirad Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Tandon School of Engineering Control/Robotics Research Laboratory New York University Brooklyn NY Department of Smart Systems Technologies University of Klagenfurt Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot’s kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.

关键词： Meters Accuracy Perturbation methods Kinematics Robot sensing systems End effectors Software Calibration Sensors Finite element analysis

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Resilience of Data-Driven Cyberattack Detection systems in Smart Power Grids

Resilience of Data-Driven Cyberattack Detection Systems in S...

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European Signal Processing Conference (EUSIPCO)

作者： Abdulrahman Takiddin Rachad Atat Hayat Mbayed Muhammad Ismail Erchin Serpedin Department of Electrical and Computer Engineering Florida State University Tallahassee FL USA Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Center for Advanced Power Systems Florida State University Tallahassee FL USA Department of Computer Science Tennessee Technological University Cookeville TN USA Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Data communications within the smart power grid components are susceptible to cyberattacks due to the inter-connected nature of the grid and reliance on communication networks. Such cyberattacks can exploit the integrity of the exchanged data and result in operational instability. Existing data-driven cyberattack detection systems (CDSs) are proposed in the literature but their effectiveness is only verified against one type of cyberattacks. In reality, a smart grid system could encounter more than one attack type at once. Thus, in this paper, we investigate the resilience of state-of-the-art data-driven CDSs against replay false data injection, adversarial evasion, and adversarial data poisoning attacks on a realistic IEEE 118-bus system model. It turns out that a convolutional recurrent graph autoencoder-based CDS offers an attack detection rate of 96 – 97.5%, which outperforms other machine learning and deep learning-based data-driven CDSs by 16 – 54% since it captures the recurrent and spatial aspects of the data without being trained on attack data.

关键词： Deep learning Europe Feature extraction Graph neural networks Data models Smart grids Reliability computer crime Unsupervised learning Resilience

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Machine Learning for Automated Bladder Event Classification from Single-Channel Vesical Pressure Recordings

Machine Learning for Automated Bladder Event Classification ...

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2022 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2022

作者： Abbaraju, V. Lewis, K. Majerus, S.J.A. Case Western Reserve University Department of Electrical Computer and Systems Engineering ClevelandOH United States Advanced Platform Technology Center Louis Stokes Cleveland Veterans Affairs Medical Center ClevelandOH United States Glickman Urological and Kidney Institute Cleveland Clinic ClevelandOH United States

ISBN: (纸本)9781665470292

Analyzing urodynamic study (UDS) tracings can be prone to error in the presence of artifacts and subjective due to lack of standardization in clinical UDS interpretation. As such, the diagnosis of patients undergoing UDS would greatly benefit from a standardized, automated method to assist clinicians in interpreting UDS tracings. In this work, we evaluated a machine learning framework for automatically classifying bladder events from single-channel vesical pressure recordings (PVES) (N=60) into 4 possible classes: abdominal event (i.e., cough or Valsalva), voiding contraction, detrusor overactivity (DO) and no event. Wavelet multiresolution analysis of PVES was used to extract time-frequency localized wavelet coefficient vectors which were segmented into 0.8 second segments with 55 statistical features per segment. Feature selection was subsequently applied for three classifier architectures: a k-nearest classifier (KNN), an artificial neural network classifier (ANN) and a support vector machine classifier (SVM). Each classifier was trained and evaluated using five-fold cross validation, from which we derived the sensitivity, specificity, F1 score and AUC for all four classes and the overall classification accuracy for each classifier. The KNN, ANN and SVM classifiers labeled 7,861 0.8 second PVES segments with 91.5%, 90.8% and 82.4% accuracy, respectively. We have thus proposed the first framework for automated multi-event bladder classification using single-channel UDS data. © 2022 IEEE.

关键词： Support vector machines

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ZION: A Scalable W3C Web of Things Directory

ZION: A Scalable W3C Web of Things Directory

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Cristiano Aguzzi Lorenzo Gigli Ivan Zyrianoff Luca Roffia Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy Department of Electrical Electronic and Information Engineering “Guglielmo Marconi” University of Bologna Italy

The proliferation of non-interoperable smart objects within the Internet of Things (IoT) has led to a fragmented and heterogeneous landscape. The Web of Things (WoT), particularly the W3C WoT, has emerged as a promising solution to this challenge, enabling seamless integration across IoT platforms and domains by extending known web standards. This paper introduces Zion, an open-source scalable W3C Thing Description Directory (TDD) designed to efficiently address the indexing and querying of Thing Descriptions (TDs) and the associated Web Things (WTs). Zion offers a standard API for performing CRUDL operations while supporting metadata querying through JSONPath. We further demonstrate its practical utility through real-world deployments, applying Zion to Structural Health Monitoring (SHM) and integrating it with IoT devices alongside blockchain technology. Comparative analysis with the other TDD implementations complying with the W3C standards – e.g., WoT Hive and TinyIoT – demonstrates that Zion outperforms both. It exhibits response times approximately ten times lower than those observed in the compared TDDs under high workloads.

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Equivalent Circuit Model and Stability Analysis of Multi-Paralleled Grid-Tied SiC Inverters with Low Voltage Ride-Through Capability

Equivalent Circuit Model and Stability Analysis of Multi-Par...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Xuli Quan Xiaofeng Dong Matthew Bosworth Hui Li Department of Electrical and Computer Engineering Florida State University Tallahassee Florida USA Engineering & Technical Department Tesla San Diego California USA Centre for Advanced Power Systems Florida State University Tallahassee Florida USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The advantages of applying deadbeat control to grid-tied SiC inverters with an L filter include achieving a fast dynamic response and low-voltage-ride-through (LVRT) capability without a phase-locked loop (PLL). However, the inherently low-inertia nature of the SiC inverter makes the system prone to instability. In this paper, the equivalent circuit model of a deadbeat-based grid-tied SiC inverter is developed, and the corresponding Lyapunov-based method is applied to analyze the stability under large signal transients such as LVRT. In addition, a Nyquist diagram is derived for small signal stability analysis of multi-paralleled inverters. Simulation and experimental results are provided to validate the theoretical analysis.

关键词： Analytical models Silicon carbide Power system stability Inverters Stability analysis Circuit stability Integrated circuit modeling Phase locked loops Transient analysis Equivalent circuits

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Securing Smart Grids: Deep Reinforcement Learning Approach for Detecting Cyber-Attacks

Securing Smart Grids: Deep Reinforcement Learning Approach f...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Ahmed T. El-Toukhy Islam Elgarhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Mohamed I. Ibrahem Fathi Amsaad Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Electrical Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Egypt Department of Network and Computer Security: Cybersecurity SUNY Polytechnic Institute Utica NY USA Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Computer Science and Engineering Wright State University Dayton OH USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Smart meters (SMs) are deployed in smart power grids to monitor customer power consumption and facilitate energy management. However, fraudulent customers can compromise these SMs to manipulate power readings and engage in electricity theft cyber-attacks, resulting in reduced electricity bills. While various machine learning approaches have been employed for detecting such attacks, the potential of reinforcement learning (RL) remains unexplored. To bridge this gap, we propose a deep reinforcement learning (DRL) approach that leverages RL's adapt-ability to dynamic cyber-attacks and consumption patterns. This approach integrates exploration and exploitation mechanisms, enabling optimal decision-making. In this study, we present our approach in two scenarios. Firstly, we develop comprehensive detection models using deep Q networks (DQN) and double deep Q networks (DDQN) with various deep neural network architectures. Secondly, we address the challenges of defending against newly launched cyber-attacks. Extensive experimentation provides strong evidence of the effectiveness of our DRL approach in improving the detection of electricity theft cyber-attacks, as well as its capacity to efficiently adapt and defend against newly launched cyber-attacks.

关键词： Adaptation models Power demand Electricity Power system dynamics Detectors Deep reinforcement learning Smart meters

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Design, Dynamic Modeling and Control of a 2-DOF Robotic Wrist Actuated by Twisted and Coiled Actuators

arXiv

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arXiv 2025年

作者： Zhang, Yunsong Zhou, Xinyu Zhang, Feitian Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

Robotic wrists play a pivotal role in the functionality of industrial manipulators and humanoid robots, facilitating manipulation and grasping tasks. In recent years, there has been a growing interest in integrating artificial muscle-driven actuators for robotic wrists, driven by advancements in technology offering high energy density, lightweight construction, and compact designs. However, in the study of robotic wrists driven by artificial muscles, dynamic model-based controllers are often overlooked, despite their critical importance for motion analysis and dynamic control of robots. This paper presents a novel design of a two-degree-of-freedom (2-DOF) robotic wrist driven by twisted and coiled actuators (TCA) utilizing a parallel mechanism with a 3RRRR configuration. The proposed robotic wrist is expected to feature lightweight structures and superior motion performance while mitigating friction issues. The Lagrangian dynamic model of the wrist is established, along with a nonlinear model predictive controller (NMPC) designed for trajectory tracking tasks. A prototype of the robotic wrist is developed, and extensive experiments are conducted to validate its superior motion performance and the proposed dynamic model. Subsequently, extensive comparative experiments between NMPC and PID controller were conducted under various operating conditions. The experimental results demonstrate the effectiveness and robustness of the dynamic model-based controller in the motion control of TCA-driven robotic wrists. Copyright © 2025, The Authors. All rights reserved.

关键词： Industrial manipulators

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