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Zero-phase CARIMA Filtering and Application in Wind-storage System Sizing and Power Dispatch Optimization

CSEE Journal of Power and Energy Systems 2024年第6期10卷 2283-2295页

作者： Wei Wang Peng Chen Guorui Ren Jizhen Liu Fang Fang Zhe Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology Aalborg University9220 AalborgDenmark

Hybrid energy storage system(HESS)is an effective way to mitigate wind power fluctuations on multi-time scale,and can improve influence of large-scale grid-connected wind power on stability and reliability of power system.A novel methodology named zero-phase controlled auto-regressive integrated moving-average(CARIMA)filter is proposed to integrate HESS to smooth wind power ***,a design method for zero-phase CARIMA filter is provided,and then used to determine grid-connected power for a wind storage system and size *** reasons,direct current(DC)component caused by energy storage efficiency and grid-connected power delay caused by phase shift,for causing superfluous energy storage configuration are *** addition,a nonlinear programming scheduling strategy considering battery degradation is *** imbalance caused by efficiency difference during dynamic adjustment of energy storage output power is ***,thermostatically controlled loads(TCLs)are integrated in sizing and scheduling HESS to reduce energy storage demand and improve operating conditions of energy ***,effectiveness of the proposed strategy is verified by a case study.

关键词： CARIMA demand response energy storage optimization wind power zero-phase

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An Efficient Hybrid Quantum Variational Classifier With Matrix Product State

An Efficient Hybrid Quantum Variational Classifier With Matr...

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

作者： Sun, Wanqi Xu, Jungang Duan, Chenghua School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Matrix Product States have been extensively explored as a powerful tool for simulating quantum states in image classification task. However, most research has focused on classical simulations or computations involving high-dimensional unitaries, and significant challenges still exist in the practical preparation of Matrix Product States on quantum computers. This paper proposes a novel and practically feasible quantum variational algorithm based on Matrix Product States for image classification task. We design a hardware-efficient quantum circuit with several adjustable entangling operators to prepare the local tensors in Matrix Product States and integrate minimal residuals to ensure computational stability. We demonstrate that our algorithm can reduce the parameter complexity from growing exponentially with the system size to a linear scale. To validate the effectiveness of this quantum variational algorithm, we conducted experiments on the MNIST dataset, achieving accuracies of 99.95% and 95.96% for binary and ten-class classification tasks, which outperforms other related quantum algorithms. This work advances the practical application of quantum machine learning in resource-constrained environments of quantum computing. © 2025 IEEE.

关键词： Image classification Matrix product states Quantum circuit Variational quantum algorithm

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A Segmentation-Based Scheme to Expedite Block Propagation in Blockchain networks 21

A Segmentation-Based Scheme to Expedite Block Propagation in...

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21st International Conference on networking, Sensing and Control, ICNSC 2024

作者： Qiu, Zhihan Zhao, Qinglin Feng, Li Zhou, MengChu Shen, Meng Liu, Yi Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology Taipa China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science Guangdong University of Technology Guangzhou510006 China Institute of Computing Technology Chinese Academy of Sciences Beijing100045 China

ISBN: (纸本)9798350365221

Blockchain technology, the foundation of cryptocurrencies like Bitcoin, has utility beyond finance due to its decentralized and secure transactional nature. However, today's blockchain networks face the challenge of low transaction throughput due to block propagation delays. In this study, we propose a novel approach that divides large blocks into segments and leverages multi-origin pipelining for segment dissemination, thereby expediting block propagation. The scheme also integrates the use of a Bloom filter for lightweight segment integrity verification to prevent the reception of tampered segments. Simulation results reveal that our scheme outperforms Bitcoin's legacy approach, demonstrating significant improvement in propagation speed, particularly for large blocks, while maintaining a low fork rate. © 2024 IEEE.

关键词： Blockchain

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Transferable Deep Reinforce Learning With Limited Target Data 21

Transferable Deep Reinforce Learning With Limited Target Dat...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Xiaoqin, Liang Xingyu, Chen Zheng, Wang Wenyi, Xu Shijiao, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chengdu Aircraft Design and Research Institude Chengdu610091 China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

While there has been significant progress in the application of transfer learning within reinforcement learning, most existing research primarily focuses on online reinforcement learning, with few studies addressing the optimization of offline reinforcement learning. This paper proposes a novel transferable deep reinforcement learning method designed to enhance limited offline samples in the target domain by utilizing online interactive samples from a similar source domain. The method is versatile and can be integrated with any offline reinforcement learning algorithm. To validate its effectiveness, extensive evaluations were conducted across multiple Mujoco environments and D4RL datasets. The results indicate that by employing the proposed method, the algorithm's performance can approach that of using 100% of the target domain data, even when only 10% of the data is available. © 2024 IEEE.

关键词： Adversarial machine learning

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What It Takes to Be a Great Mentor [Educator’s Desk]

IEEE Electron Devices Magazine

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IEEE Electron Devices Magazine 2024年第1期2卷 46-47页

作者： John D. Cressler Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA

Some thoughts on what it takes to be a great mentor.

关键词： Electron devices Mentoring engineering profession Professional aspects

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Peer-to-Peer Learning Dynamics of Wide Neural networks

Peer-to-Peer Learning Dynamics of Wide Neural Networks

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shreyas Chaudhari Srinivasa Pranav Emile Anand José M. F. Moura Electrical and Computer Engineering Carnegie Mellon University School of Computer Science Georgia Institute of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Peer-to-peer learning is an increasingly popular framework that enables beyond-5G distributed edge devices to collaboratively train deep neural networks in a privacy-preserving manner without the aid of a central server. Neural network training algorithms for emerging environments, e.g., smart cities, have many design considerations that are difficult to tune in deployment settings – such as neural network architectures and hyperparameters. This presents a critical need for characterizing the training dynamics of distributed optimization algorithms used to train highly nonconvex neural networks in peer-to-peer learning environments. In this work, we provide an explicit characterization of the learning dynamics of wide neural networks trained using popular distributed gradient descent (DGD) algorithms. Our results leverage both recent advancements in neural tangent kernel (NTK) theory and extensive previous work on distributed learning and consensus. We validate our analytical results by accurately predicting the parameter and error dynamics of wide neural networks trained for classification tasks.

关键词： Training computer aided instruction Distance learning Heuristic algorithms Neural networks Signal processing algorithms Prediction algorithms Peer-to-peer computing Classification algorithms Optimization

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Enhancing the Prediction of Underwater Wireless Communication using Machine Learning Techniques and Related Issues

Enhancing the Prediction of Underwater Wireless Communicatio...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Saravanan, M.S. Naveen, V. Kumar, S.Selvin Pradeep Dilli Ganesh, V. Sivashankar, S. Bhaskar, K. Vijaya Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electrical and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348910

This research aims to enhance the prediction of underwater wireless communication for underwater wireless sensor networks using Logistic Regression compared with Linear Regression Algorithms. To forecast the functionality of underwater wireless sensor networks Linear Regression and Novel Logistic Regression algorithms were used with different training and testing splits with Underwater Acoustic Wireless Sensor. A sample size of 20 (Each 10 from Group 1 and Group 2) is calculated by fixing a G-power of 0.8, alpha and beta values of 0.05 and 0.2, and a confidence interval of 95%. With a statistical significance value of 0.001 (p © 2023 IEEE.

关键词： Wireless sensor networks

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Throughput Maximization for Multiedge Multiuser Edge Computing Systems

Throughput Maximization for Multiedge Multiuser Edge Computi...

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作者： Deng, Yiqin Chen, Zhigang Chen, Xianhao Fang, Yuguang School of Computer Science and Engineering Central South University Changsha China Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

The multiaccess edge computing/mobile-edge computing (MEC) is becoming a key technology toward 'full 5G.' However, as it gets widely used, a fundamental problem is how to support as many service requests as possible under stringent Quality-of-Service (QoS) requirements and limited communications and computing resources. In this article, we study the long-term throughput maximization problem for multicell multiuser MEC systems. Different from most of the existing works that focus on energy or latency minimization problem for a single-edge system, a novel design is proposed from the service provider's perspective to maximize the system-wide throughput under latency bounds by jointly taking user association and resource allocation for both communications and computing into account. To capture the stochastic nature of MEC environments, a Markov decision process (MDP) is employed to model the queuing states for both mobile devices and MEC servers. By combining MDP and matching theory, a joint user association and resource allocation algorithm is given, where the resource allocation policy under given user-server association is solved. Extensive numerical results demonstrate the superiority of the proposed scheme in comparison with several existing approaches. © 2014 IEEE.

关键词： Markov processes

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Open-Circuit Switch Fault-Tolerant Operation of Five-Level Nested Neutral Point Clamped Converter

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Hassanifar, Mahyar Shamouei-Milan, Milad Monfared, Kourosh Khalaj Neyshabouri, Yousef Iman-Eini, Hossein Kiel University Power Electronics Kiel24143 Germany Shahid Beheshti University Faculty of Electrical Engineering Tehran*** Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran Urmia University Faculty of Electrical and Computer Engineering Urmia*** Iran

Five-level nested neutral point clamped (5L-NNPC) converter is a compelling topology to use in renewable energy conversion, grid-connected facilities, and motor drives. However, an open circuit fault (OCF) in any of the switches in the NNPC results in the inverter shutdown. Therefore, to tackle this issue, this paper presents a comprehensive fault-tolerant strategy including OCF detection, diagnosis, and post-fault operation in 5L-NNPC. As an IGBT switch fails, the failed switch is diagnosed through a model-based two-layer strategy. Furthermore, taking advantage of model predictive control (MPC) and reconfiguring the switching states, a post-fault operation strategy with the ability to provide partial power is developed. Due to the OCF, some switching states are removed and the phase voltages are unbalanced in post-fault conditions. Therefore, the weighting factors of the multi-objective cost function are tuned automatically to improve the functionality of MPC, especially, in terms of balancing the voltages of flying capacitors. Accordingly, the introduced method exploits the remaining capacity of the converter, while the voltages of the capacitors are maintained. Simulation and experimental results verify the accuracy of the detection and diagnosis method and also, the capability of the converter to supply partial power to the load after an OCF occurrence. © 2013 IEEE.

关键词： Predictive control systems

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Model-Based Analysis of Single-Event Upset (SEU) Vulnerability of 6T SRAM Using FinFET Technologies 35

Model-Based Analysis of Single-Event Upset (SEU) Vulnerabili...

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35th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2022

作者： Olowogemo, Semiu A. Qiu, Hao Lin, Bor-Tyng Robinson, William H. Limbrick, Daniel B. Vanderbilt University Department of Electrical and Computer Engineering NashvilleTN United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States North Carolina AandT State University Department of Electrical and Computer Engineering GreensboroNC United States

ISBN: (数字)9781665459389

ISBN: (纸本)9781665459389

The modeling process, when validated with the experimental data, can be used for additional analysis of similar technologies without the expense of laser beam or heavy ion testing. This paper evaluates the vulnerability of SRAM cells to single-event upsets (SEUs) using the NCSU FreePDK 15-nm and the ASAP 7-nm Predictive PDK models. Due to scaling, SRAM cells designed and fabricated in advanced technologies have reduced critical node capacitances that make them more vulnerable to the sources of radiation because of the corresponding critical charge of the cell. The maximum threshold linear energy transfer (LET) without an upset in the 15-nm technology is approximately 6.3 times the maximum of the 7-nm technology. In addition, reducing the critical charge affects the soft error rate (SER). The estimated results were compared with previously published experimental data for validation. © 2022 IEEE.

关键词： FinFET

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