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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Du, Can Hua, Haochen Wu, Xingchen Su, Yuxin Jia, Yunsi Chen, Xingying Naidoo, Pathmanathan Tavares, Maria Cristina College Of Energy And Electrical Engineering Hohai University Nanjing China University Of Johannesburg Department Of Mechanical Engineering Science Johannesburg South Africa School Of Electrical And Computer Engineering University Of Campinas São Paulo Campinas Brazil

ISBN: (纸本)9798350340266

The interconnection of park-level integrated energy systems (PLIES) can effectively realize the integration of various energy sources and improve the efficiency of energy utilization. However, due to the diversity of PLIES's types and the complex coupling characteristics among multiple energy sources within each PLIES, it is rather challenging to collaborate the energy interaction between each PLIES to achieve the overall optimal scheduling. To tackle with this issue, a distributed optimal scheduling strategy for multiple PLIESs is proposed in this paper. Firstly, the PLIESs operation architecture considering demand response and energy interaction is constructed. Then, with the aim of minimizing the operation cost, the economic dispatch model is established. Finally, the dispatch process has been migrated to the proposed distributed framework, where only a limited set of information is shared among participants, and the inexact generalized alternating direction method of multipliers with indefinite proximal term (IGADMM-IPT) is adopted to obtain the optimal energy dispatch strategy. The simulation results show that the proposed method can reduce the operating cost compared with the PLIESs without interconnection, and the solution time can be reduced by 20.14% compared with the traditional ADMM, so as to obtain the optimal strategy. © 2024 IEEE.

关键词： Parks

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Data-driven Lithium-ion Battery Degradation Estimation using Electrochemical Impedance Spectroscopy and Throughput 2

Data-driven Lithium-ion Battery Degradation Estimation using...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Li, Yichun Maleki, Mina Banitaana, Shadi Samadib, M. Foad University of Detroit Mercy Dept. of Electrical and Computer Engineering and Computer Science DetroitMI United States Ford Motor Company DearbornMI United States

ISBN: (纸本)9798350373363

Accurately estimating the state of health (SOH) of Lithium-ion batteries not only can reflect the degradation level of batteries but also improve the reliability and efficiency of electric vehicles (EVs). Due to the dynamic operation scenarios of the EVs, accurately estimating the degradation of Li-ion batteries remains a challenge in the battery management system. This work incorporates Electrochemical Impedance Spectroscopy (EIS), battery cycle number, and battery throughput, which provide detailed insights into battery degradation mechanisms, with machine learning models for estimating the battery state of health (SOH). The effectiveness of each combination of EIS measurement data, battery cycle number, and battery throughout has been examined in order to determine the efficacy of each feature in predicting battery degradation. The results on one of the largest EIS measurement datasets indicate that using the proposed battery cycle number and throughput as additional features in a Gaussian process regression model (GPR) to estimate the degradation of lithium-ion batteries improves prediction accuracy up to 0.0105 for mean squared error (MSE) and 0.0655 for mean absolute error (MAE), compared with relying solely on EIS measurement data. Moreover, in order to demonstrate the effectiveness of the GPR model, the deep neural network model (DNN) and support vector machine (SVM) have been used to compare the performance with the GPR model for the estimation of SOH. Results show that the GPR model outperformed the DNN and SVM models when the EIS measurement data were combined with cycle number and battery throughput to describe the battery degradation. © 2023 IEEE.

关键词： Electrochemical impedance spectroscopy

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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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Optimal phase-based control of strongly perturbed limit cycle oscillators using phase reduction techniques

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Physical Review E 2024年第2期109卷 024223-024223页

作者： Adharaa Neelim Dewanjee Dan Wilson Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA

Phase reduction is a well-established technique for analysis and control of weakly perturbed limit cycle oscillators. However, its accuracy is diminished in a strongly perturbed setting where information about the amplitude dynamics must also be considered. In this paper, we consider phase-based control of general limit cycle oscillators in both weakly and strongly perturbed regimes. For use at the strongly perturbed end of the continuum, we propose a strategy for optimal phase control of general limit cycle oscillators that uses an adaptive phase-amplitude reduced order model in conjunction with dynamic programming. This strategy can accommodate large magnitude inputs at the expense of requiring additional dimensions in the reduced order equations, thereby increasing the computational complexity. We apply this strategy to two biologically motivated prototype problems and provide direct comparisons to two related phase-based control algorithms. In situations where other commonly used strategies fail due to the application of large magnitude inputs, the adaptive phase-amplitude reduction provides a viable reduced order model while still yielding a computationally tractable control problem. These results highlight the need for discernment in reduced order model selection for limit cycle oscillators to balance the trade-off between accuracy and dimensionality.

关键词： Circadian rhythms Collective behavior Neuronal dynamics Population dynamics Phase oscillator models Phase space methods

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A Single-Weight Neural Network for MIMO Signal Detection: Algorithm and Implementation 10

A Single-Weight Neural Network for MIMO Signal Detection: Al...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ho, Yi-Cheng Yen, Mao-Hsu Lu, Hoang-Yang National Taiwan Ocean University Dept. of Computer Science and Engineering Keelung Taiwan National Taiwan Ocean University Dept. of Electrical and Engineering Keelung Taiwan

ISBN: (纸本)9798331532895

Existing deep learning-based MIMO detectors often face substantial memory demands due to the need to store neural network weights, posing a significant challenge to practical implementation. To address this issue, we propose a single-weight neural network, namely SW-Net. By reducing each layer's weight vector to a scalar, the required hardware storage is significantly reduced. Simulation results demonstrate that hardware requirements can be reduced while maintaining bit error rate (BER) performance. Additionally, we present the corresponding VLSI hardware architecture, implemented using TSMC's 90-nm CMOS technology, operating at 707 MHz. This design requires 1109 kGEs and achieves a throughput of 707 Mbps. © 2024 IEEE.

关键词： Deep neural networks

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A Comparison of the Performance of ResNet and DenseNet for Obstacle Avoidance and Path Following on a Mobile Robot Using Embedded GPUs

A Comparison of the Performance of ResNet and DenseNet for O...

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2024 IEEE International Conference on Agents, ICA 2024

作者： Ma, Shengjun Karkal, Pawan Sudhir Ma, Jing Alam, Fakhrul Auckland University of Technology Electrical and Electronic Engineering Department Auckland New Zealand Auckland University of Technology Computer Science and Software Engineering Department Auckland New Zealand

ISBN: (纸本)9798331539917

In this study, we evaluated the performance of deep learning-based navigation models on an novel platform that combines the Jetson Xavier NX with an RC four-wheel-drive car for autonomous driving. Focusing on data collection and model training using a single-camera setup, obstacle avoidance and path navigation were prioritised. The study compares the effectiveness of ResNet18, ResNet34, and DenseNet121 models by analysing training time, accuracy, obstacle avoidance efficiency, and path navigation capabilities. The results highlight the computational advantages of the Jetson Xavier NX over the Jetson Nano, demonstrating enhanced real-time processing and decision-making capabilities. This integration of the Jetson Xavier NX with an RC vehicle not only improves overall performance but also introduces a novel approach to autonomous vehicle development. The findings offer valuable insights into optimising deep learning models and hardware configurations for improved autonomous navigation. © 2024 IEEE.

关键词： Mobile robots

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Repairing Reed-Solomon Codes over Prime Fields via Exponential Sums

Repairing Reed-Solomon Codes over Prime Fields via Exponenti...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Con, Roni Shutty, Noah Tamo, Itzhak Wootters, Mary Tel Aviv University Blavatnik School of Computer Science Israel Stanford University Stanford Institute for Theoretical Physics United States Tel Aviv University Department of Electrical Engineering-Systems Israel Stanford University Department of Computer Science and Electrical Engineering United States

ISBN: (纸本)9781665475549

This paper presents several repair schemes for lowrate Reed Solomon (RS) codes over prime fields that can repair any node by downloading a constant number of bits from each surviving node. The resulting total bandwidth is higher than the bandwidth incurred during the trivial repair;however, this is still interesting in the context of leakage-resilient secret sharing. In that language, our results give attacks that show that k-out-of-n Shamir's Secret Sharing over prime fields for small k is not leakage resilient, even if the parties only leak a constant number of bits. To the best of our knowledge, these are the first such *** another application, we provide decoding schemes for RS codes over prime fields, where the entire RS codeword is recovered by transmitting a constant number of bits from each *** results follow from a novel connection between exponential sums and repair of RS codes. In particular, we show that nontrivial bounds on certain exponential sums imply the existence of efficient nonlinear repair schemes for RS codes over prime fields. © 2023 IEEE.

关键词： Reed-Solomon codes

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Testing for the Bradley-Terry-Luce Model

Testing for the Bradley-Terry-Luce Model

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Makur, Anuran Singh, Japneet Purdue University Department of Computer Science West LafayetteIN47907 United States Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States

ISBN: (纸本)9781665475549

The Bradley-Terry-Luce (BTL) model is one of the most widely used models for ranking a set of items given data about pairwise comparisons among them. While several studies in the literature have attempted to empirically test how accurately a BTL model can model some given pairwise comparison data, this work aims to develop a formal, computationally efficient hypothesis test to determine whether the BTL model accurately represents the data. Specifically, we first propose such a formal hypothesis test, establish an upper bound on the critical radius of the proposed test, and then provide a complementary lower bound on the critical radius. Our bounds prove the minimax optimality of the scaling of the critical radius with respect to the number of items (up to constant factors). Finally, we also take the first step towards characterizing the stability of rankings under the BTL model when there is a small model mismatch. © 2023 IEEE.

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Distributionally Robust Domain Adaptation 33

Distributionally Robust Domain Adaptation

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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Awad, Akram S. Atia, George K. University of Central Florida Department of Electrical and Computer Engineering OrlandoFL United States University of Central Florida Department of Computer Science OrlandoFL United States

ISBN: (纸本)9798350324112

Domain Adaptation (DA) has recently received significant attention due to its potential to adapt a learning model across source and target domains with mismatched distributions. Since DA methods rely exclusively on the given source and target domain samples, they generally yield models that are vulnerable to noise and unable to adapt to unseen samples from the target domain, which calls for DA methods that guarantee the robustness and generalization of the learned models. In this paper, we propose DRDA, a distributionally robust domain adaptation method. DRDA leverages a distributionally robust optimization (DRO) framework to learn a robust decision function that minimizes the worst-case target domain risk by transferring knowledge from a given labeled source domain sample. We utilize the Maximum Mean Discrepancy (MMD) metric to construct an ambiguity set of distributions that provably contains the source and target domain distributions with high probability. Hence, the risk is shown to upper bound the out-of-sample target domain loss. Our experimental results demonstrate that our formulation outperforms existing robust learning approaches. © 2023 IEEE.

关键词： Probability distributions

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A Heterogeneous Network Model Based Analysis and Detection of RNA Enabling Convolutional Neural Network 4

A Heterogeneous Network Model Based Analysis and Detection o...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Priyatharsini, G. Soniya Aruna, M. Myneni, Madhu Bala Kumar, P. Kiran Anil Kumar, D.B. Singh, Vijay Dr. M.G.R. Educational and Research Institute Department of Computer Science and Engineering Tamil Nadu Chennai600095 India Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bangalore India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Cvr College of Engineering Department of Computer Science and Engineering Telangana Ibrahimpatnam India School of Electrical and Electronic Engineering Reva University Karnataka Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350300888

RNA is an important target in the research of genetic diseases and disorders since it is essential for the process of gene expression and control. Convolutional neural networks (CNNs) and other machine learning-based methods for analyzing RNA sequences have recently attracted more attention. But reliable analysis and detection are significantly hampered by the enormous variability of RNA sequences. In this research, a brand-new heterogeneous network model for CNN-based RNA analysis and detection is provided. This model accounts for the wide range of RNA sequence properties, such as length fluctuation, secondary structure, and chemical changes. A unified model that incorporates these features is created by combining 1D and 2D convolutional layers. Multiple datasets, including synthetic and real-world datasets, were used to assess proposed model's performance. The outcomes show that the model performs better in terms of accuracy, sensitivity, and specificity than current state-of-the-art approaches. Furthermore, the model's performance is greatly enhanced with the addition of 2D convolutional layers and attention processes, according to rigorous studies to assess the contributions of each component of the model. Overall, the heterogeneous network model offers a reliable and precise method for analyzing and identifying RNA sequences, which has significant ramifications for comprehending hereditary illnesses and creating targeted therapies. © 2023 IEEE.

关键词： Machine learning

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