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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Sato, Yuji Kumagai, Watatu Yasuda, Yusuke Tamura, Kenichi Yasuda, Keiichiro Tokyo Metropolitan University Department of Electrical Engineering and Computer Science 1-1 Minamiosawa Tokyo Hachioji-shi192-0397 Japan Innovation Center Marketing Headquarters Yokogawa Electric Corporation 2-9-32 Naka-cho Tokyo Musashino-shi180-0006 Japan

ISBN: (纸本)9798350337020

Differential Evolution (DE) is one of the effective metaheuristics for solving unconstrained optimization problems. Constraint Handling Technique (CHT) is needed to extend DE to constrained optimization. Feasibility Rule (FR) is one of the typical CHT. FR addresses constraints by using a simple rule that considers the objective function and constraint violation when comparing search individuals. However, since the solutions with small constraint violation, i.e., feasible solutions, are preferentially selected, the set of search individuals may be biased toward feasible regions and the improvement of the objective function value may stagnate. This paper overcomes this challenge by proposing a DE that uses a superior infeasible solution in an external archive. The external archive in the proposed DE stores search individuals that are superior in both objective function value and constraint violation and utilize them to generate mutant individuals. Finally, we verify the effectiveness of the proposed method using a benchmark problem where the feasible region is a convex set. © 2023 IEEE.

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Zinc anode-based electrochromic devices for dynamic light modulation 14

Zinc anode-based electrochromic devices for dynamic light mo...

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Oxide-based Materials and Devices XIV 2023

作者： Zhang, Wu Li, Haizeng Elezzabi, Abdulhakem Y. Ultrafast Optics and Nanophotonics Laboratory Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2V4 Canada Optics and Thermal Radiation Research Center Institute of Frontier & Interdisciplinary Science Shandong University Qingdao266237 China

ISBN: (纸本)9781510659490

As an emerging energy conversion technology, electrochromism has attracted immense interest due to its various applications including variable optical filters, bistable displays and energy-efficient smart windows. Although the current electrochromic devices do not consume energy while maintaining their colored or colorless states, their bistable operation requires external electrical energy to be consumed during switching. As such, a novel design of Zn anode-based electrochromic devices was recently developed as a key strategy to partially retrieve the consumed electrical energy. Compared to conventional electrochromic devices, Zn anode-based electrochromic devices exhibit a rapid spontaneous switching behavior due to the high value of redox potential difference between a metal anode and an electrochromic cathode, thus providing the most energy-efficient consumption during operation. Moreover, Zn-based electrochromic devices enable independent operation of top and bottom electrochromic electrodes, thus providing additional configuration flexibility of the devices through the utilization of dual electrochromic layers under the same or different color states. As such, the color overlay effect can greatly broaden the color palettes via altering the coordinate of the 2D CIE color space. These key issues of Zn anode-based electrochromic devices make them very promising for the future development of electrochromic devices. © 2023 SPIE.

关键词： Redox reactions

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Reduced biquaternion tensors and applications in color video processing

arXiv

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arXiv 2024年

作者： Yu, Cui-E Liu, Xin Luo, Hui Zhang, Yang Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China Department of Mathematics University of Manitoba WinnipegMBR3T 2N2 Canada

In this paper, we introduce the applications of third-order reduced biquaternion tensors in color video processing. We first develop an algorithm for computing the singular value decomposition (SVD) of a third-order reduced biquaternion tensor via a new Ht-product. As theoretical applications, we define the Moore-Penrose inverse of a third-order reduced biquaternion tensor and consider its characterizations via its SVD. Using Moore-Penrose inverses, we mainly discuss the general (or Hermitian) solutions to reduced biquaternion tensor equation A ∗Ht X = B as well as its least-squares solutions. Finally, we develop two algorithms and apply them in color video compression and deblurring, both of which perform better than the compared algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Ultrafast Negative Diffusion in gold films

Ultrafast Negative Diffusion in gold films

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Block, A. Yu, R. Un, I.W. Varghese, S. Liebel, M. van Hulst, N.F. Fan, S. Tielrooij, K.J. Sivan, Y. BIST and CSIC Bellaterra Spain Department of Electrical Engineering Ginzton Laboratory Stanford University StanfordCA94305 United States School of Electrical and Computer Engineering Ben-Gurion University of the Negev Be'er Sheva8410501 Israel ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain Department of Applied Physics TU Eindhoven Den Dolech 2 Eindhoven5612 AZ Netherlands

Using ultrafast thermo-modulation microscopy, we show that the spatio-temporal heat diffusion in gold films has an initial ps-scale, electron-dominated diffusion, followed by an unexpected negative diffusion stage, an... 详细信息

ISBN: (纸本)9781957171395

关键词： Heat flux

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PSCodec: A Series of High-Fidelity Low-bitrate Neural Speech Codecs Leveraging Prompt Encoders

arXiv

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arXiv 2024年

作者： Pan, Yu Zhang, Xiang Yang, Yuguang Yao, Jixun Hu, Yanni Ye, Jianhao Zhou, Hongbin Ma, Lei Zhao, Jianjun The School of Information Science and Electrical Engineering Kyushu University Fukuoka8190385 Japan The Shanghai Ximalaya Technology Co Ltd. Shanghai201203 China The Department of Computer Science The University of Tokyo Tokyo1138656 Japan The Department of Electrical and Computer Engineering University of Alberta EdmontonT2G 1S6 Canada

Neural speech codecs have recently emerged as a focal point in the fields of speech compression and generation. Despite this progress, achieving high-quality speech reconstruction under low-bitrate scenarios remains a significant challenge. In this paper, we propose PSCodec, a series of neural speech codecs integrated effective prompt encoders, including PSCodec-Base, PSCodec-DRL-ICT, and PSCodec-CasAN, which excel in delivering high-performance speech reconstruction with low bandwidths. Specifically, we first introduce PSCodec-Base, which leverages a pretrained speaker verification model-based prompt encoder (VPP-Enc) and a learnable Mel-spectrogram-based prompt encoder (MelP-Enc) to effectively disentangle and integrate voiceprint and Mel-related features in utterances. To further enhance feature utilization efficiency, we propose PSCodec-DRL-ICT, incorporating a structural similarity (SSIM) based disentangled representation loss (DRL) and an incremental continuous training (ICT) strategy. While PSCodec-DRL-ICT achieves impressive performance, its reliance on extensive hyperparameter tuning and multi-stage training introduces complexity. To circumvent these limitations, we propose PSCodec-CasAN, utilizing an advanced cascaded attention network (CasAN) to enhance representational capacity of the entire system. Extensive experiments indicate that our proposed PSCodec-Base, PSCodec-DRL-ICT, and PSCodec-CasAN all significantly outperform several state-of-the-art neural codecs, offering substantial improvements in speech reconstruction quality and speaker similarity under low-bitrate conditions. © 2024, CC BY-NC-ND.

关键词： Speech enhancement

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Image classification and discrimination of COVID pneumonia based on convolutional neural network 7

Image classification and discrimination of COVID pneumonia b...

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7th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Qin, Chuan Liu, Xuesong Che, Yuanxi Yao, Enjie School of Computing Newcastle University Newcastle Upon TyneNE1 7RU United Kingdom Department of Electronic and Electrical Engineering The University of Sheffield SheffieldS10 2TN United Kingdom Department of Computer Science and Technology Northwest University Xi an710127 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

To address the negative quality of RT-PCR and the time taken to obtain results, this paper reports on the classification and discrimination of COVID pneumonia images by developing a neural network adaptively. Given the limited number of published COVID-19 CT data currently available, we suggest using the proposed negative process to refine the data to obtain more CT data to reduce matching risk;Experimental results show that compared to network models such as AlexNet and GoogleNet, the proposed BUF-Net network model has the best performance with an accuracy of 93%. Visualization of systemic findings using Grad-CAM techniques may further elucidate the critical role of CT imaging in the diagnosis of ***: The use of deep learning tools in clinical practice can help radiologists make better diagnoses. © 2023 SPIE. All rights reserved.

关键词： Coronavirus

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LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition

arXiv

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arXiv 2024年

作者： Gasmi, Houssem Bouras, Abdelaziz Laval, Jannik Computer Science Department College of Engineering Qatar University Doha Qatar Université Lumière Lyon 2 Lyon France DISP Laboratory Université Lumière Lyon 2 Lyon France

The automated and timely conversion of cybersecurity information from unstructured online sources, such as blogs and articles to more formal representations has become a necessity for many applications in the domain nowadays. Named Entity Recognition (NER) is one of the early phases towards this goal. It involves the detection of the relevant domain entities, such as product, version, attack name, etc. in technical documents. Although generally considered a simple task in the information extraction field, it is quite challenging in some domains like cybersecurity because of the complex structure of its entities. The state of the art methods require time-consuming and labor intensive feature engineering that describes the properties of the entities, their context, domain knowledge, and linguistic characteristics. The model demonstrated in this paper is domain independent and does not rely on any features specific to the entities in the cybersecurity domain, hence does not require expert knowledge to perform feature engineering. The method used relies on a type of recurrent neural networks called Long Short-Term Memory (LSTM) and the Conditional Random Fields (CRFs) method. The results we obtained showed that this method outperforms the state of the art methods given an annotated corpus of a decent size. Copyright © 2024, The Authors. All rights reserved.

关键词： Cybersecurity

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Towards a Rigorous Evaluation of Explainability for Multivariate Time Series

SSRN

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SSRN 2023年

作者： Saluja, Rohit Malhi, Avleen Knapič, Samanta Främling, Kary Cavdar, Cicek Department of Computer Science Aalto University Konemiehentie 2 Espoo02150 Finland Department of Computing Science Umeå University Mit-huset Umeå90187 Sweden University of Warwick United Kingdom Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Machine learning-based systems are rapidly gaining popularity and in-line with that, there has been a huge research surge in the field of explainability to ensure that machine learning models are reliable, fair, and can be held liable for the decision-making process. Explainable Artificial Intelligence (XAI) methods are typically deployed to debug black-box machine learning models but in comparison to tabular, text, and image data, explainability in time series is still relatively unexplored. The aim of this study was to achieve and evaluate model agnostic explainability in a time series forecasting problem. This work focused on proving a solution for a digital consultancy company aiming to find a data-driven approach in order to understand the effect of their sales related activities on the sales deals closed. The solution involved framing the problem as a time series forecasting problem to predict the sales deals and the explainability was achieved using two novel model agnostic explainability techniques, Local explainable model- agnostic explanations (LIME) and Shapley additive explanations (SHAP) which were evaluated using human evaluations. The results clearly indicate that the explanations produced by LIME and SHAP greatly helped lay humans in understanding the predictions made by the machine learning model. The presented work can easily be extended to any time series forecasting or classification scenario for achieving explainability and evaluating the produced explanations. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Short-term solar eruptive activity prediction models based on machine learning approaches:A review

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science China Earth sciences 2024年第12期67卷 3727-3764页

作者： Xin HUANG Zhongrui ZHAO Yufeng ZHONG Long XU Marianna B.KORSÓS R.ERDÉLYI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China Beijing Cellular Exploration Science and Technology Center Beijing 100190China Dipartimento di Fisica e Astronomia“Ettore Majorana” Universitàdi CataniaCatania I 95123Italy Department of Astronomy Eötvös Loránd UniversityBudapest H-1112Hungary Hungarian Solar Physics Foundation Gyula H-5700Hungary Solar Physics&Space Plasma Research Center(SP2RC) School of Mathematics and StatisticsUniversity of SheffieldSheffield S37RHUK

Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our *** short-term solar eruptive activity prediction is an active field of research in the space weather ***,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive *** the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant *** this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.

关键词： Solar flare Coronal mass ejection Solar proton event Machine learning Prediction model

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Digital Twin Empowered Wireless Positioning: Prospects, Architecture, and Challenges

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IEEE Network 2025年

作者： Shi, Xiufang Xuan, Xinlu Huang, Xinyu Xue, Jianzhe Shen, Xuemin Zhejiang University of Technology College of Information Engineering Hangzhou310023 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Nanjing University School of Electronic Science and Engineering Nanjing210023 China

Achieving precise and reliable positioning in complex and dynamic environments is very challenging due to multipath propagation, environmental changes, and system limitations. This article explores the role of digital twin (DT) in wireless positioning and provides comprehensive study on the prospects of DT in enhancing positioning performance with respect to accuracy, coverage, latency, and reliability. Particularly, a general architecture for DT-empowered wireless positioning is proposed. The powerful simulation and prediction capabilities of DT are leveraged for fine-grained multi-path feature extraction, dynamic model update, seamless-transition, and system optimization. A case study based on fingerprinting positioning is presented to demonstrate the enhancement due to DT in feature extraction and model update. Some open research issues in DT-empowered wireless positioning are also discussed. © 1986-2012 IEEE.

关键词： Digital elevation model

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