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Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-constrained Model

Journal of Modern Power systems and Clean Energy 2022年第2期10卷 471-481页

作者： Shengyuan Liu Tianhan Zhang Zhenzhi Lin Yilu Liu Yi Ding Li Yang the School of Electrical Engineering Zhejiang UniversityHangzhou 310027China the Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA the School of Electrical Engineering Shandong UniversityJinan 250061China Electrical and Electronics Systems Research Division Oak Ridge National LaboratoryOak RidgeTN 37830USA

Controlled islanding plays an essential role in preventing the blackout of power *** there are several studies on this topic in the past,no enough attention is paid to the uncertainty brought by renewable energy sources(RESs)that may cause unpredictable unbalanced power and the observabilit>T of power systems after islanding that is essential for back-up black-start ***,a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming(MISOCCP)is proposed to address these ***,the uncertainty of RESs is characterized by their possibility distribution models with chance constraints,and the requirements,e.g.,system observability,for rapid back-up black-start measures are also ***,a law of large numbers(LLN)based method is em-ployed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex ***,case studies on the revised IEEE 39-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed *** results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

关键词： Controlled islanding second-order cone chance-constrained programming renewable energy source(RES) black-start observability.

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Using Random Forest and Linear SVM for Dyslexia Classification on Hebb-Williams Mazes 59

Using Random Forest and Linear SVM for Dyslexia Classificati...

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59th Annual Conference on Information Sciences and systems, CISS 2025

作者： Kaushik, Padmanabh Yu, Yih-Choung Gabel, Lisa Lafayette College Department of Electrical and Computer Engineering EastonPA18042 United States Lafayette College Department of Psychology and Neuroscience Program EastonPA18042 United States

ISBN: (纸本)9798331513269

Dyslexia is a learning disability that negatively impacts an individual's ability to read, write, spell, and sometimes speak. It results in difficulties in recognizing and decoding words and patterns, despite normal level of education and intelligence. Studies have shown that early detection of dyslexia is vital for improving learning abilities in young children. Many virtual platforms exist for diagnosing and rehabilitating dyslexia;however, most require tests that measure reading skills. Since developing reading capabilities can delay the detection of dyslexia, a gaming platform based on Hebb-Williams mazes has been developed. This platform does not rely on reading skills and can diagnose dyslexia in young children. This paper presents a machine learning driven approach using two algorithms - Random Forest and Linear SVM - to classify reading abilities based on data from participants performing virtual maze tasks, which are indicative of symptoms of dyslexia. Results from this study indicate that it is possible to predict dyslexia with up to 95% accuracy based on a participant's performance in virtual gaming environments. © 2025 IEEE.

关键词： Adversarial machine learning

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration 31

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Chen, Yuzong Abouelhamayed, Ahmed F. Dai, Xilai Wang, Yang Andronic, Marta Constantinides, George A. Abdelfattah, Mohamed S. Cornell University Computer Systems Lab United States Systems and Networking Research Group Microsoft Research United States Imperial College London Department of Electrical and Electronic Engineering United Kingdom

ISBN: (纸本)9798331506476

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD1, an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight precision. On the algorithm side, BitMoD introduces fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights. Through the careful design of these new data types, BitMoD is able to quantize LLM weights to very low precision (e.g., 4 bits and 3 bits) while maintaining high accuracy. On the hardware side, BitMoD employs a bitserial processing element to easily support multiple numerical precisions and data types;our hardware design includes two key innovations: First, it employs a unified representation to process different weight data types, thus reducing the hardware cost. Second, it adopts a bit-serial dequantization unit to rescale the per-group partial sum with minimal hardware overhead. Our evaluation on six representative LLMs demonstrates that BitMoD significantly outperforms state-of-the-art LLM quantization and acceleration methods. For discriminative tasks, BitMoD can quantize LLM weights to 4 -bit with © 2025 IEEE.

关键词： Integrated circuit design

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Frequency Dependent RSSI Behavior of Rotating Polarization Waves in Diverse Environmental Conditions 13

Frequency Dependent RSSI Behavior of Rotating Polarization W...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Ali, Muhammad Moazzam Hashim, Shaiful Jahari Ahmad, Zaid Ferré, Guillaume Rokhani, Fakhrul Zaman Chaudhary, Muhammad Akmal Universiti of Putra Malaysia Department of Computer and Communication Systems Engineering Malaysia Inspem Universiti of Putra Malaysia Department of Computer and Communication Systems Engineering Malaysia Islamabad Pakistan Univ. Bordeaux Cnrs Bordeaux Inp Ims Umr 5218 TalenceF-33400 France Ajman University Department of Electrical and Computer Engineering Ajman346 United Arab Emirates

ISBN: (纸本)9798350368086

This study examines the effectiveness of Rotating Polarization Waves (RPW) in Low Power Wide Area Networks (LPWAN) under various frequencies and environmental conditions. MATLAB simulations quantified the Received Signal Strength Indicator (RSSI) across frequencies of 169 MHz, 433 MHz, 868 MHz, 915 MHz, and 2.4 GHz, and 2.4 GHz, covering distances from 10 to 10,000 meters in both rural and urban settings. The simulations incorporated transmitter power, antenna gains, system losses, and polarization mismatch loss, employing tailored path loss exponents for each scenario. The findings indicated that lower frequencies yielded enhanced coverage and a more robust RSSI, particularly in rural regions where signal propagation faced fewer obstructions. In urban environments, the received signal strength indicator showed a more pronounced decrease with distance, influenced by barriers such as buildings. Graphical representations of RSSI versus distance and frequency illustrated the variations in propagation, affirming that lower frequencies exhibit superior performance in both environments. The findings provide important insights for enhancing LPWAN deployment, highlighting the significance of frequency selection and environmental factors to achieve better coverage and reliability. © 2024 IEEE.

关键词： MATLAB

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Foundry Manufactured 6-bit Resolution, 150μm Long Slow-Light Electro-Optic Modulator for On-Chip Photonic Tensor Computing

Foundry Manufactured 6-bit Resolution, 150μm Long Slow-Ligh...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Zhang, Meng Begović, Amir Yin, Dennis Gangi, Nicholas Gu, Jiaqi Huang, Rena Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering 110 8th Street TroyNY12180 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States

ISBN: (纸本)9781957171395

We demonstrate 6-bit DAC resolution with an ultra-compact slow-light electro-optic modulator. The 10× modulation length reduction enables 31× compute density, 1.17× energy efficiency, and 36.1× energy efficiency per unit area for photonic tensor computing. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Light modulation

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A Novel Rotor Position Synchronization Method for Central Converter Multi-Machine Drives 4

A Novel Rotor Position Synchronization Method for Central Co...

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4th International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： De Andrade Lima, Claudio Cale, James State University Electrical and Computer Engineering Departament Colorado Fort Collins United States Colorado State University Systems Engineering Department Fort Collins United States

ISBN: (纸本)9798350391183

The aerospace industry is undergoing a paradigm shift towards More Electric Aircraft. Safe and reliable electromechanical drives are a key component of this transition. Previous research has shown a potential rotor position synchronization control for central-converter, multi-machine systems applied to aerospace actuators. This paper proposes a novel rotor position synchronization method referred to as the 'voltage-boost' method. The method is based on in-line induced voltages through compact split-core three-phase transformers and low-power auxiliary converters. The paper describes the principle of operation of the method along with practical installation advantages, such as a non-invasive deployment. Simulation results show that the method is capable of achieving position synchronization under different load torque distributions, maintaining reasonable position error, and achieving a fast response. Additionally, a real aerospace typical load profile is utilized to verify the method's robustness under realistic operating conditions. © 2024 IEEE.

关键词： Aerospace industry

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Automated Warehouse 5G Infrastructure Modeling Using Variational Autoencoders

Automated Warehouse 5G Infrastructure Modeling Using Variati...

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

作者： Gulia, Rahul Ganguly, Amlan Kwasinski, Andres Kuhl, Michael E. Rashedi, Ehsan Hochgraf, Clark Rochester Institute of Technology Department of Computer Engineering RochesterNY United States Rochester Institute of Technology Department of Industrial and Systems Engineering RochesterNY United States Rochester Institute of Technology Department of Electrical and Computer Engineering Technology RochesterNY United States

ISBN: (纸本)9798350364910

The next decade is poised for a transformative shift in wireless communication technologies, driven by the increasing demand for data-intensive applications. Innovations in signal processing, network architecture estimation and design, and spectrum utilization will be critical in realizing the potential of 5G and beyond. These advancements will enable the seamless integration of emerging technologies and empower the digital transformation of industries and society. In this paper, we introduce a Variational Autoencoder (VAE) based model for indoor radio propagation modeling within an automated Industry4.0 warehouse or factory floor in the 5G wireless bands. We detail the creation of training data tensors, the architecture of our proposed VAE model, and its training using tensors that capture the impact of various interacting objects within a 5G-enabled smart warehouse infrastructure. The model is trained on multiple warehouse configurations to predict the signal-to-interference-plus-noise ratio (SINR) for both the validation dataset and unknown warehouse configurations. We present reconstruction error heatmaps to demonstrate the accuracy of our model and analyze its performance in different complex warehouse environments. © 2024 IEEE.

关键词： Signal to noise ratio

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of systems Science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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Compression with Unlabeled Graph Side Information

Compression with Unlabeled Graph Side Information

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Nikpey, Hesam Sarkar, Saswati Bidokhti, Shirin Saeedi University of Pennsylvania Department of Computer and Information Science United States University of Pennsylvania Department of Electrical and Systems Engineering United States

ISBN: (纸本)9781665475549

With the growth of big data in the past few decades, compression has become inseparable from data generation. The data generated daily across different platforms are correlated: friend networks on Facebook and Instagram, contact networks in subsequent days, and many more. This raises the question of compressing a dataset using another correlated dataset. For instance, can we compress the Facebook graph of friends when we know Instagram's graph? This can be cast as the classical problem of source coding with side information, and the answer is known to be positive when the graphs are "labeled"and/or aligned, meaning we need to know the node corresponding to Jon Doe in both Facebook and Instagram graphs. The classical idea is to utilize joint typicality to decide whether two graphs are correlated or not. In practice, graphs are often not aligned and/or the labels are concealed to keep the identity of the users private. In these scenarios, classical ideas are no longer applicable as joint typicality highly depends on the ordering of sequences. In this work, we prove for the first time the existence of lossless graph compression schemes that utilize unlabeled side information and improve the compression rate. In order to do that, we design binning along with a novel testing criterion that relies on graph matching, the closely related quadratic assignment problem and its asymptotic properties. © 2023 IEEE.

关键词： Combinatorial optimization

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Foundry Manufactured 6-bit Resolution, 150µm Long Slow-Light Electro-Optic Modulator for On-Chip Photonic Tensor Computing

Foundry Manufactured 6-bit Resolution, 150µm Long Slow-Ligh...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Zhang, Meng Begovic, Amir Yin, Dennis Gangi, Nicholas Gu, Jiaqi Huang, Rena Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering 110 8th Street TroyNY12180 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States

关键词： Light modulation

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