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Investigating explainable transfer learning for battery lifetime prediction under state transitions

eScience 2024年第5期4卷 132-142页

作者： Tianze Lin Sihui Chen Stephen JHarris Tianshou Zhao Yang Liu Jiayu Wan Global Institute of Future Technology Shanghai Jiao Tong UniversityNo.800 Dongchuan RoadShanghai 200240China Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhen 518055China SUSTech Energy Institute for Carbon Neutrality Southern University of Science and TechnologyShenzhen 518055China Energy Storage and Distributed Resources Division Lawrence Berkeley National LabBerkeleyCA 94720USA Data Science Research CenterDuke Kunshan UniversityNo.8 Duke AvenueKunshan 215316China Center for Advanced Energy System and Reliability Shanghai Jiao Tong UniversityNo.800 Dongchuan RoadShanghai 200240China

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology *** learning has been widely utilized to construct data-driven solutions for high-accuracy ***,the internal mechanisms of batteries are sensitive to many factors,such as charging/discharging protocols,manufacturing/storage conditions,and usage *** factors will induce state transitions,thereby decreasing the prediction accuracy of data-driven *** learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various ***,we develop two transfer learning methods,Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit,to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction *** our results,our transfer learning methods reduce root-mean-squared error by 41%through adapting to the target ***,the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data *** findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

关键词： Battery life prediction Transfer learning State transitions Explainability Degradation mechanism

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A Reversible data Hiding Algorithm Based on Image Camouflage and Bit-Plane Compression

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Computers, Materials & Continua 2021年第8期68卷 2634-2650页

作者： Jianyi Liu Ru Zhang Jing Li Lei Guan Cheng Jie Jiaping Gui School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China Network Security Monitoring Center State Grid Information&Telecommunication BranchBeijing100761China School of Electrical Engineering Tsinghua UniversityBeijing100091China Data Science and System Security Department NEC Labs AmericaPrinceton08540America

Reversible data hiding in encrypted image(RDHEI)is a widely used technique for privacy protection,which has been developed in many applications that require high confidentiality,authentication and *** RDHEI methods do not allow high embedding rate while ensuring losslessly recover the original ***,the ciphertext form of encrypted image in RDHEI framework is easy to cause the attention of *** paper proposes a reversible data hiding algorithm based on image camouflage encryption and bit plane compression.A camouflage encryption algorithm is used to transform a secret image into another meaningful target image,which can cover both secret image and encryption behavior based on“plaintext to plaintext”*** edge optimization method based on prediction algorithm is designed to improve the image camouflage encryption *** reversible data hiding based bit-plane level compression,which can improve the redundancy of the bit plane by Gray coding,is used to embed watermark in the camouflage *** experimental results also show the superior performance of the method in terms of embedding capacity and image quality.

关键词： Reversible data hiding image camouflage bit plane compression encryption edge optimization

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Performance Optimization for Multicast Mm Wave MIMO Networks with Mobile Users

Performance Optimization for Multicast Mm Wave MIMO Networks...

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IEEE International Conference on Communications Workshops, ICC

作者： Songling Zhang Mingzhe Chen Wenjing Zhang Zhaohui Yang Danpu Liu Zhilong Zhang Kai-Kit Wong Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Zhejiang Lab Hangzhou China Zhejiang University Hangzhou China Department of Electronic and Electrical Engineering University College London London UK

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, the problem of maximizing the sum rate of mobile users in a multi-base station (BS) cooperative millimeter-wave (mmWave) multicast communication system is studied. In the considered model, due to the real-time mobility of users, the users being served by a given BS and beamforming of BSs and users are dynamic. Multiple BSs must cooperate to serve dynamic requests of multiple mobile users. This problem is posed as an optimization framework whose goal is to maximize the sum rate of all mobile users by jointly optimizing the number of users served by all BSs and beamforming matrices of both BSs and users. To solve this non-convex optimization problem, we first introduce a value decomposition based reinforcement learning (VD- RL) algorithm to determine the users to be served by each BS. Then, we use the block diagonalization method to obtain the fully digital transmit beamforming matrices of all BSs as well as the receive beamforming matrices of the users. Finally, a fast optimization algorithm is used to optimize the hybrid beamforming matrices of both BSs and users. Simulation results show that, the proposed algorithm can achieve up to 51 % gain in terms of the sum rate of all mobile users compared to baseline multi-agent algorithms.

关键词： Multicast algorithms Array signal processing Heuristic algorithms Simulation Conferences Multicast communication Real-time systems

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Adhesive-less bonding of incompatible thermosetting materials

RSC Applied Polymers

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RSC Applied Polymers 2024年第1期3卷 247-256页

作者： Khairkkar, Shhyam Pansare, Amol V. Pansare, Shubham V. Chhatre, Shraddha Y. Sakamoto, Junji Barbezat, Michel Terrasi, Giovanni P. Patil, Vishwanath R. Nagarkar, Amit A. Naito, Masanobu Data-Driven Polymer Design Group Research and Services Division of Materials Data and Integrated System (MaDIS) National Institute for Materials Science (NIMS) 1-2-1 Sengen Ibaraki Tsukuba 305-0047 Japan Composites Group Mechanical Systems Engineering Swiss Federal Laboratories for Materials Science and Technology-Empa ETH Domain Dübendorf 8600 Switzerland Department of Chemistry University of Mumbai Santacruz (E) Mumbai 400098 India National Chemical Laboratory (NCL) Dr Homi Bhabha Road Pune 411008 India Advanced Material Technology Center Technology Division Panasonic Holdings Corporation 1006 Kadoma Kadoma City Osaka 571-8508 Japan Datacule Inc. 529 Main St. Boston 02129 MA United States

We show that dynamic covalent exchange at the interface of two thermosetting polymers results in strong bonding between the materials via creation of a new material at the interface. Thus, polymers of significantly different polarities can be bonded without the use of adhesives. We also show that such dynamic covalent exchange is not only limited to the interface but also penetrates into the bulk material (ca. 20 microns), thereby creating a strong bond. The creation of a new material at the interface was confirmed by Energy Dispersive X-ray (EDX) elemental mapping as well as a new glass transition temperature at the interface. Using this phenomenon, we show that hydrophobic, compliant polymers can also be used as adhesives for polar, stiff materials. We also show that such dynamic exchange also takes place in the presence of fillers like nano-silica. Lastly, using this technique, we demonstrate the adhesive-less fabrication of layered materials where each layer has vastly different polarities and mechanical properties, thereby tuning the failures modes of the resulting composite material. © 2025 RSC.

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features

Evolutionary Multiobjective Feature Selection Assisted by Un...

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Congress on Evolutionary Computation

作者： Xuan Duan Songbai Liu Junkai Ji Lingjie Li Qiuzhen Lin Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets.

关键词： Accuracy Evolutionary computation Multitasking Feature extraction Classification algorithms Task analysis Knowledge transfer

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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

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IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization

arXiv

引用

arXiv 2024年

作者： Ye, Yuyang Tang, Lu-An Wang, Haoyu Yu, Runlong Yu, Wenchao He, Erhu Chen, Haifeng Xiong, Hui Department of Management Science and Information Systems Rutgers Business School Rutgers University NewarkNJ United States Department of Data Science and System Security NEC Laboratories PrincetonNJ United States Department of Computer Science University of Pittsburgh PittsburghPA United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou China

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Single-shot super-resolution imaging via discernibility in the high-dimensional light-field space based on ghost imaging

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Photonics Research 2025年第6期13卷 1709-1725页

作者： Zhishen Tong Chenyu Hu Jian Wang Youheng Zhu Xia Shen Zhentao Liu Shensheng Han Aerospace Laser Technology and System Department Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 China Wangzhijiang Innovation Center for Laser Aerospace Laser Technology and System Department Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 China School of Physics and Optoelectronic Engineering Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou 310024 China School of Data Science Fudan University Shanghai 200433 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049 China e-mail: ztliu@*** e-mail: sshan@***

Super-resolution (SR) imaging has been widely used in several fields like remote sensing and microscopy. However, it is challenging for existing SR approaches to capture SR images in a single shot, especially in dynamic imaging scenarios. In this study, we present a single-shot SR imaging scheme that leverages discernibility in the high-dimensional (H-D) light-field space based on a ghost imaging camera via sparsity constraints (GISC camera), which is capable of encoding H-D imaging information into a two-dimensional speckle pattern detected in a single shot. We demonstrate both theoretically and experimentally that while the resolution in the H-D light-field space, characterized by the second-order light-field correlation, remains limited by light-field diffraction, the single-shot spatial resolution is greatly improved beyond classical Rayleigh’s criterion by utilizing the discernibility in the H-D light-field space. We further quantify the effects of the sampling number, signal-to-noise ratio, and object sparsity on the resolution. Our results offer significant potential for the SR observation of high-speed dynamic processes.

关键词： Ghost imaging Imaging systems Imaging techniques Near field scanning optical microscopy Optical systems Remote sensing

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Candidate Evaluation with Multimodal data-Driven for Recruitment 27th

Candidate Evaluation with Multimodal Data-Driven for Recru...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Liu, Kehong Wang, Jianjia Yao, Junfeng Deng, Bin Lv, Rongqi Song, Jun School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou215412 China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Science and Technology on Near-Surface Detection Laboratory Jangsu Wuxi214035 China Department of Geography Hong Kong Baptist University 999077 Hong Kong

ISBN: (纸本)9783031781858

In the field of intelligent recruitment, automated resume matching and non-contact interviews have significantly improved the efficiency of companies in finding suitable candidates. This corresponds to the techniques of person-job matching and AI interviews. However, current person-job matching methods lack substantial data support, while AI interview methods struggle to integrate deep information from multimodal data and provide comprehensive evaluations of candidates’ responses. To address these challenges, we propose a multimodal data-driven person-job evaluation model, comprising two key stages: a person-job matching method based on graph attention and a multimodal AI interview method. Using a dual-perspective graph neural network approach, we accomplish the screening of candidates and positions. In the second stage, we conduct a comprehensive evaluation of candidates’ interview performance based on text, audio, and image modalities, providing a more objective, consistent, and efficient interview assessment method. Experimental results demonstrate that our person-job matching method surpasses current popular techniques and effectively transfers features to the next stage. In our multimodal AI interview method, we achieve accurate scoring of candidate responses, assessment of intonation stress levels, and inference of their Big Five personality traits, comprehensively evaluating candidates from multiple perspectives. This confirms the superiority and efficiency of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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