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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Zadehgol-Mohammadi, Asef Shadmand, Mohammad B. Dept. of Electrical & Computer Engineering University of Illinois ChicagoIL United States

ISBN: (纸本)9781665464543

This work presents a novel approach to tackle the problem of real-time detection of cyber intrusions at power electronics dominated grids (PEDG) inverters by establishing an analogy between electrical and medical fields. The methodology leverages the significant similarities between electric grid and human body and posits the feasibility of utilizing the large body of knowledge available in medicine, particularly in the area of disease diagnosis, for the benefit of detecting intrusions occurring to the grid, considering the tools introduced by tremendous advances in artificial intelligence (AI). The theoretical foundation of the analogy is discussed and as a first validation step, the grid inverter voltage is classified into three types of normal, disturbed and anomalous by associating it with three classes of heart rate electrocardiogram (ECG) signals of normal sinus rhythm, mild arrhythmia (supraventricular), and severe arrhythmia (ventricular), respectively. This is done via AI-based time series analyzers which establish a mapping between the two signal categories and enable classifying grid voltage based on ECG using the trained analyzer block. Achieved results demonstrate validity of the approach and promise addressing one of the main challenges in developing intrusion detection systems, that is, data scarcity, by tapping at the enormous knowledge base in the medical field. © 2024 IEEE.

关键词： Knowledge based systems

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Multi-Objective Phase Shift Control Optimization for Dual Active Bridge Converters

Multi-Objective Phase Shift Control Optimization for Dual Ac...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Davila, Antonella Haro Hasan, Syed Imam Uzum, Alper Elrayyah, Ali Sozer, Yilmaz The University of Akron Dept. of Electrical and Computer Engineering AkronOH United States

ISBN: (纸本)9798350376067

This comprehensive analysis evaluates multiobjective optimization of the phase shift modulation techniques to evaluate the optimized phase shift at various operating points for a Dual Active Bridge converter. By simulating scenarios to understand the behavior of a system under various conditions for different modulation techniques, this study provides insights into different outcomes to optimize the system and identify the most effective phase-shift combinations. The system's performance is assessed based on two key metrics: the inductor RMS current and Zero Voltage Switching (ZVS) utilization. The analysis focuses on predicting the RMS inductor current for various phase-shift modulations, including Single-Phase Shift, Dual Phase Shift, Extended Phase Shift, and Triple Phase Shift modulation. Besides, peak current is crucial for assessing ZVS conditions, facilitating the optimization of the final set of phase-shift modulations. © 2024 IEEE.

关键词： Zero voltage switching

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Ontology-Driven Reinforcement Learning for Personalized Student Support

Ontology-Driven Reinforcement Learning for Personalized Stud...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hare, Ryan Tang, Ying Rowan University Dept. of Electrical and Computer Engineering GlassboroNJ United States

ISBN: (纸本)9781665410205

In the search for more effective education, there is a widespread effort to develop better approaches to personalize student education. Unassisted, educators often do not have time or resources to personally support every student in a given classroom. Motivated by this issue, and by recent advancements in artificial intelligence, this paper presents a general-purpose framework for personalized student support, applicable to any virtual educational system such as a serious game or an intelligent tutoring system. To fit any educational situation, we apply ontologies for their semantic organization, combining them with data collection considerations and multi-agent reinforcement learning. The result is a modular system that can be adapted to any virtual educational software to provide useful personalized assistance to students. © 2024 IEEE.

关键词： Adversarial machine learning

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Kinematic Cycle Consistency Using Wearables and RF Data for Improved Human Skeleton Estimation 58

Kinematic Cycle Consistency Using Wearables and RF Data for ...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Salehin, Sultanus Kearney, Sean Gurbuz, Z. The University of Alabama Dept. of Electrical & Computer Engineering TuscaloosaAL United States

ISBN: (纸本)9798350354058

Radar-based health monitoring has the potential to enable 24/7 in-home ambient sensing to increase accessibility to care in a privacy-protective, low-cost fashion. This paper focuses on the assessment of mobility and gait for early warning, post-treatment monitoring, and emergency response to health conditions affecting human movement. Current technology has focused on wearables for monitoring personalized health, but wearables are not effective if not worn. In this case radar can provide a backup source of health information, that can be utilized standalone or in conjunction with the information provided by a wearable. This paper explores the estimation of gait parameters and human pose (i.e. skeleton) using RF sensing alone as well as how wearables can be utilized with RF to improve estimation accuracy. In particular, we propose the concept of kinematic cycle consistency using RF and wearable-estimated kinematics to improve skeleton estimation accuracy. Our results are validated using a gold standard Vicon optical tracking system. © 2024 IEEE.

关键词： Musculoskeletal system

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Lightweight Federated Learning based White Space Detector for Cognitive Radios

Lightweight Federated Learning based White Space Detector fo...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Galib, Md Mehedi Hassan Younis, Mohamed University of Maryland Baltimore County Dept. of Computer Science and Electrical and Engineering BaltimoreMD21250 United States

ISBN: (纸本)9798350351255

In cognitive communication, detecting white space is critical for preventing interference with primary user's transmissions. Rather than modifying the radio transceivers, several machine learning (ML)-based detection techniques have been proposed, where a model is trained offline and then employed to infer white spaces in real-time. Despite their viability for spectrum sensing, these techniques are computationally complex, especially when deep neural network (DNN) models are pursued. Moreover, training a centralized model requires transmitting massive data to the fusion center (FC), which ultimately results in congestion on the transmission channel rather than flagging opportunities for cognitive transmissions. This paper opts to fill the technical gap by proposing: (1) a federated learning model that enables effective design of spectrum monitors in a distributed manner, and (2) a spiking neural network (SNN)-based lightweight white space detector that solves the issue of computationally expensive DNN models and is suitable for resource-constrained devices. The SNN-based federated learning (SFL) model employs secondary users to train their respective SNN models using local data (spatial locality) and sends the gradient of SNN to FC. FC combines the individual SNN models and sends the aggregated model back to each edge node. Validation using live LTE data has demonstrated the effectiveness of SFL with a detection accuracy of 91.16%. © 2024 IEEE.

关键词： Radio transceivers

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IMU-Based Model for Precise Toothbrush Stroke and Orientation Tracking

IMU-Based Model for Precise Toothbrush Stroke and Orientatio...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Islam Bhuyan, Mohammad Ariful Uddin, Md Mayen Kim, Hyeon Cheol University of Ulsan Dept. of Electrical Electronic and Computer Engineering Ulsan Korea Republic of

ISBN: (纸本)9798350350821

This paper presents a model designed for precise orientation estimation and stroke distance tracking of toothbrush activity. The model leverages Inertial Measurement Unit (IMU) technology, which includes accelerometers and gyroscopes, to capture detailed motion data during the activity. The proposed Corrective Fusion Filter Technique (CFFT) helps integrate accelerometer and gyroscope data with several corrective steps to estimate orientation precisely, and the combined sensor data accurately measures stroke distance. This filtering method also minimizes sensor noise and drifting issues, enhancing the model's reliability and performance. © 2024 IEEE.

关键词： Accelerometers

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A Second-Order Noise Shaping SAR ADC with 8x Fully Passive Gain Circuit Technique

A Second-Order Noise Shaping SAR ADC with 8x Fully Passive G...

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2024 IET International Conference on engineering Technologies and Applications, ICETA 2024

作者： Chueh, Chuang-Hsuan Chang, Yun-Chieh Chen, Hsin-Liang Chiang, Jen-Shiun Dept. of Electrical and Computer Engineering Tamkang University New Taipei City Taiwan

ISBN: (纸本)9781837242672

This paper presents a 14-bit second-order fully passive noise-shaping successive approximation register analog-to-digital converter using capacitors and switches to perform a passive integrator. It employs two prior circuit techniques: embedded passive gain and passive signal residue summation. A circuit technique of signal charge redistribution is proposed to reduce the noise and area costs of the comparator and capacitor. Therefore, the zero of the noise transfer function can be improved with an efficient solution. This work achieves SNDR of 87.3dB with a bandwidth of 260kHz at an oversampling ratio of 16. It consumes only 96.6μW of power under a supply voltage of 1V using the 90-nm CMOS technology. The Schreier FoM would reach 182 dB. © The Institution of engineering & Technology 2024.

关键词： CMOS integrated circuits

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Analysis on the Contribution and Economic Feasibility of Pumped Storage Hydropower in Korea Power System Considering the Penetration of Renewable Energy Generation

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Transactions of the Korean Institute of electrical Engineers 2023年第6期72卷 709-716页

作者： Kim, Hyun Joong Oh, Jae Young Yoon, Yong Tae Yoon, Yong Beum Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Dept. KEPCO International Nuclear Graduate School Korea Republic of

The government of South Korea proclaimed Energy Transition 3020 and the details thereupon with the generation mix. According to the Basic Plan for Long-term Electricity, additional Pump Storage Hydropower (PSH) were planned to be installed as measure for the variability from the Renewables. However, despite of the astronomical hard cost required for the PSHs, there has been a lack of assessments on the economic feasibility and effectiveness of the PSHs. According to the simulation, there were total three scenarios with high load variability due to the Renewables. The monitoring factors in each scenario were changes, in the generation cost, generation by fuel type, reserve capability by the PSHs, and emission of product gas. Furthermore, the economic feasibility was assessed from the perspectives of the PSHs. Based on the simulation, it was concluded that the additional PSHs decrease the daily generation cost and the ramping of the thermal generation, the greater reserve capabilities procured. On the other hand, emission of the product gas increases due to greater pumping load on the thermal generation. Moreover, it is economically infeasible for the PSHs to salvage their initial investment cost. Thus, this paper implicates the regulatory improvements for the formulation of the just market environment. Copyright © The Korean Institute of electrical Engineers.

关键词： Power markets

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SFNet: Synergistic Fusion Network for Text Localization in Natural Scene Images 3

SFNet: Synergistic Fusion Network for Text Localization in N...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Suman, Suman Champa, H.N. University of Visvesvaraya College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350381931

Prior methodologies for the detection of scene text have demonstrated promising outcomes across many evaluation metrics. Nevertheless, despite the utilization of deep neural network models, their effectiveness is sometimes limited in complicated situations due to the intricate interplay of several stages and components within the pipelines, which ultimately governs overall performance. This study presents a straightforward yet robust methodology for identifying textual content in practical environments. Phrases or text lines may be predicted in images of varying orientations and quadrilateral forms using the suggested pipeline's single neural network. This eliminates the need for superfluous intermediary steps such as candidate aggregation and word division. Due to the streamlined nature of our SFNet(Synergistic Fusion Network), our primary emphasis is directed at the development of loss functions and neural network design. The suggested approach has been evaluated using a well-established dataset including ICDAR 2015. The results of these experiments demonstrate that the new technique outperforms existing techniques in terms of both accuracy and efficiency. The proposed methodology demonstrates a performance metric, namely an F-score of 0.89 while operating at a frame rate of 13.2 frames per second and a resolution of 720p using the ICDAR 2015 dataset. © 2024 IEEE.

关键词： Pipelines

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DC Fault Detection Using Time Derivative of High-Pass Filter Response in MTDC Systems 50

DC Fault Detection Using Time Derivative of High-Pass Filter...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Khan, Saad Ahmed Ukil, Abhisek Nair, Nirmal-Kumar C. Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (纸本)9781665464543

In the DC fault event, DC link capacitors immediately discharge a colossal current of high frequency. These large currents of high frequencies can instantly damage the components attached to the DC transmission system. Therefore, a rapid and robust DC fault detection topology is necessary, facilitating protection coordination in forthcoming Multi-Terminal Direct Current (MTDC) systems. This study introduces a novel fault detection technique using the time derivative of the High Pass Filter (HPF) response to identify fault currents in just a few microseconds (µs). This topology enables protection coordination for modern MTDC systems. Furthermore, this technique can identify different types of DC faults and remains unaffected by AC faults and load variation. © 2024 IEEE.

关键词： Chebyshev filters

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