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5th International Conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Chen, Siwen Zheng, Xiujuan Shanghai United Imaging Healthcare Co. Ltd. Shanghai China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology Ministry of Education Wuhan China

ISBN: (纸本)9798331530334

To enhance the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, this study proposes a novel RUL prediction model, termed FDNet, which integrates Discrete Fourier Series (DFS) and Densely Connected Convolutional Networks (DenseNet). In contrast to traditional methods that rely on one-dimensional (1D) numerical representations of time-series data, this model utilizes a two-dimensional (2D) visual representation for direct RUL prediction. First, the DFS is employed to convert 1D time-series signals into a 2D time-frequency domain, enabling the extraction of high-dimensional features from the original data. Then, these 2D images are subsequently input into DenseNet, which learns the mapping between the image features and the RUL targets. Finally, the proposed model is validated using the NASA lithium-ion battery aging dataset, and the results indicate that FDNet achieves superior prediction accuracy and exhibits notable robustness. © 2024 IEEE.

关键词： Prediction models

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Approximating Principal-Agent Problem Under Bayesian 18th

Approximating Principal-Agent Problem Under Bayesian

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18th International Joint Conference on Theoretical Computer Science-Frontier of Algorithmic Wisdom, IJTCS-FAW 2024

作者： Gong, Qinqin Gai, Ling Wang, Yijing Xu, Dachuan Yang, Ruiqi Institute of Operations Research and Information Engineering Beijing University of Technology Beijing100124 China Business School University of Shanghai for Science and Technology Shanghai200093 China School of Intelligent Emergency Management University of Shanghai for Science and Technology Shanghai200093 China Consulting Center for Strategic Assessment Academy of Military Science Beijing100091 China

ISBN: (纸本)9789819777518

In the classical principal-agent model, the principal delegates a task to an agent who takes an action that is unobservable to the principal and that is costly. We examine this issue within a Bayesian framework where the agent’s privacy type, representing the unit cost of effort, is single-dimensional and follows a publicly known probability distribution. Each action necessitates distinct levels of effort. This conception of effort and cost per unit underpins the model’s objective to assess the approximation guarantee of linear contracts compared to optimal contracts within a Bayesian framework. Linear contracts encompass allocation rules mapping types to actions and payment rules associating types with random outcomes for a selected set of actions. We approach this problem through a computational lens. Our primary focus lies on cases where the agent determines an action set to fulfill a complex task delegated by the principal, with success being uncertain. We demonstrate that if the probability of success behaves as a submodular function of the action set, linear contracts approximate optimality under sufficient uncertainty in the principal-agent relationship. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Costs

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A 7DOF redundant manipulator inverse kinematic solution algorithm based on bald eagle search optimization algorithm

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Soft Computing 2024年第23期28卷 13681-13699页

作者： Zhao, Guojun Sun, Ying Jiang, Du Liu, Xin Tao, Bo Jiang, Guozhang Kong, Jianyi Yun, Juntong Liu, Ying Li, Gongfa Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China

Solving inverse kinematics (IK) is an excellent challenge for redundant manipulators due to their highly non-linear and complex coupling, which is not amenable to analytical solutions by conventional methods. In this paper, the conventional solution was replaced by the optimization process of a fitness function. The fitness function aims to obtain a set of solutions for the desired position of the end effector (EE). The function is based on forward kinematics;hence, this method can avoid singularities and be suitable for solving redundant manipulators. For solving this optimization problem, the bald eagle search (BES) optimization algorithm was chosen due to its easy principle and excellent performance. BES integrates the advantages of evolutionary and swarm techniques. On the one hand, BES maximizes the advantages offered by swarm intelligence (SI) in terms of searching within the best position in the swarm. On the other hand, BES capitalizes on the capability of evolutionary techniques to explore the search space frequently and avoid the local optimum. In this study, two 7-degree of freedom (DOF) manipulators were used to test the performance of BES. Meanwhile, several evolutionary and SI optimization algorithms were selected to compare with BES. The experimental results demonstrate that the BES optimization algorithm can efficiently and accurately solve the IK problem of 7DOF redundant manipulators. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Inverse kinematics

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Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 722-736页

作者： Da Yu Mingyi Zhang Mantian Li Fusheng Zha Junge Zhang Lining Sun Kaiqi Huang the State Key Laboratory of Robotics and System Harbin Institute of Technology(HIT)Harbin 150080China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190the School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing 100049 the CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China IEEE

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter *** this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new ***,prior knowledge is unavailable during training and *** works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory *** better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous ***,besides the previous raw samples,we store the corresponding logits and features in the ***,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the *** the same number of samples for replay,our method can use more past knowledge to prevent *** conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic *** further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

关键词： Catastrophic forgetting class-incremental learning continual learning(CL) experience replay

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SAF: Local Shape-aware Face-based Garment Collision Handling via Neural SDFs

SAF: Local Shape-aware Face-based Garment Collision Handling...

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

作者： Tang, Minzhe Yuan, Ruisheng Kou, Dongliang Sun, Mingyang Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Institute of Metaverse & Intelligent Medicine Fudan University Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (纸本)9798350368741

Learning-based garment prediction presents an appealing alternative to physics-based methods owing to its high efficiency. However, the predicted garments can exhibit noticeable penetrations into the body. Many collision-handling methods operate in the point domain, which has an inherent limitation in addressing the penetration issues with the mesh faces. In light of this, we propose a local Shape-Aware, Face-based collision handling approach (SAF) that can be applied to garment prediction networks to achieve real-time collision handling. Considering the nature of garments, we design a compact formulation to model the continuity of the garment surface, and utilize neural Signed Distance Fields (SDFs) to accomplish penetration resolution. Recognizing the significant impact of the body's local shape on collision handling, we further propose using the angles between SDF gradients to characterize the sharpness of the body. Our approach can compensate for the inaccuracy of neural SDFs and preserve local smoothness and details. Extensive experiments demonstrate the outstanding performance and generalizability of our method. © 2025 IEEE.

关键词： collision handling computer graphics deep learning garment simulation

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Application research of Landmarks in Path Planning Algorithms of Mobile robots 2

Application Research of Landmarks in Path Planning Algorithm...

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2nd International Conference on Digital Society and intelligent Systems, DSInS 2022

作者： Fan, Linghao Liu, Xiangde Zhang, Yi Chongqing University of Posts and Telecommunications Intelligent Systems and Robotics Laboratory Chongqing China Chongqing Information Accessibility and Service Robot Engineering Technology Research Center Chongqing China

ISBN: (纸本)9781510663244

A way of incorporating landmark information matrix QR to affect the global path planning algorithm is proposed for the use of landmarks in robot localization and navigation. It is also investigated how this method affects the pace at which the original algorithm solves problems. The reward and punishment items in this technique include dangerous locations with lots of obstacles around them, landmarks with location information connected to the ground, and the two combined. Three representative common robot path planning algorithms are also imposed as influences. Through MATLAB simulation experiment, find the optimal path planning algorithm that is appropriate for applying reward and punishment by comparing the quality of the robot's driving path and the algorithm solution speed before and after the reward and punishment. According to the simulation results, the reward and punishment approach is practical and efficient. The heuristic search algorithm is better suited for reward and punishment, and both its path quality and speed of solution have greatly increased. © 2023 SPIE.

关键词： Genetic algorithms

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Universal and accent-discriminative encoders for conformer-based accent-invariant speech recognition

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International Journal of Speech technology 2022年第4期25卷 987-995页

作者： Wang, Xuefei Long, Yanhua Xu, Dongxing Key Innovation Group of Digital Humanities Resource and Research and Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai200234 China Unisound AI Technology Co. Ltd. Beijing China

Accent-variation is a challenging issue, either for traditional hybrid or current end-to-end (E2E) automatic speech recognition (ASR). Building an accent-invariant and high quality ASR system is very important for most real applications. In this study, we propose a Conformer-based architecture with accent-discriminative encoders, to leverage the accent attributes of input speech for enhancing an accent-invariant E2E ASR system. In this architecture, the encoders are composed of one universal, and two dominant accent-specific encoders. These encoders are first pre-trained and then jointly adapted with a single attention-based decoder in an end-to-end manner. Furthermore, different weighting methods and a multi-encoder-decoder architecture is also investigated and compared. Our experiments are performed on the public Common Voice with five different English-accents, results show that our proposed architecture outperforms the strong baseline in both in-domain and out-of-domain accented-ASR tasks, with a relative 2.9–3.8% word error rate reduction. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Speech recognition

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Prompt-based Generation of Natural Language Explanations of Synthetic Lethality for Cancer Drug Discovery 30

Prompt-based Generation of Natural Language Explanations of ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Ke Feng, Yimiao Zheng, Jie School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China Lingang Laboratory Shanghai China Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University Shanghai China

ISBN: (纸本)9782493814104

Synthetic lethality (SL) offers a promising approach for targeted anti-cancer therapy. Deeply understanding SL gene pair mechanisms is vital for anti-cancer drug discovery. However, current wet-lab and machine learning-based SL prediction methods lack user-friendly and quantitatively evaluable explanations. To address these problems, we propose a prompt-based pipeline for generating natural language explanations. We first construct a natural language dataset named NexLeth. This dataset is derived from New Bing through prompt-based queries and expert annotations and contains 707 instances. NexLeth enhances the understanding of SL mechanisms and it is a benchmark for evaluating SL explanation methods. For the task of natural language generation for SL explanations, we combine subgraph explanations from an SL knowledge graph (KG) with instructions to construct novel personalized prompts, so as to inject the domain knowledge into the generation process. We then leverage the prompts to fine-tune pre-trained biomedical language models on our dataset. Experimental results show that the fine-tuned model equipped with designed prompts performs better than existing biomedical language models in terms of text quality and explainability, suggesting the potential of our dataset and the fine-tuned model for generating understandable and reliable explanations of SL mechanisms. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys

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Science China(Technological Sciences) 2023年第6期66卷 1815-1830页

作者： LIU Yue DING Lin YANG ZhengWei GE XianYuan LIU DaHui LIU Wei YU Tao AVDEEV Maxim SHI SiQi School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444China State Key Laboratory of Advanced Special Steel&Shanghai Key Laboratory of Advanced Ferrometallurgy&School of Materials Science and Engineering Shanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Institute of Functional Materials Central Iron&Steel Research InstituteBeijing 100081China Australian Nuclear Science and Technology Organisation Locked Bag 2001Kirrawee DC NSW 2232Australia School of Chemistry The University of SydneySydney 2006Australia Zhejiang Laboratory Hangzhou 311100China

Despite the huge accumulation of scientific literature,it is inefficient and laborious to manually search it for useful information to investigate structure-activity ***,we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal ***,the credibility of abstracts is quantified in terms of source timeliness,publication authority and author’s academic ***,eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts,achieving an accuracy of 85.10%.Thirdly,by formulating 12 naming rules for the alloy brands derived from the recognized entities,we extract the target entities and refine them as domain knowledge through the credibility *** this,we also map out the academic cooperative“Author-Literature-Institute”network,characterize the generations of Nickel-based single crystal superalloys,as well as obtain the fractions of the most important chemical elements in *** extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys.

关键词： Nickel-based single crystal superalloys text mining named entity recognition credibility analysis domain knowledge

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Effect of alumina fibers on microstructure and mechanical properties of composite enamel coatings

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Ceramics International 2025年第14期51卷 18865-18876页

作者： Chen, Xueying Guo, Yan Gao, Xinrui Shi, Shiyan Sun, Dongguang Jia, Xiaoyun Jiang, Weizhong Luo, Lida Wang, Qingwei State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and Engineering Donghua University Shanghai201620 China Engineering Research Center of Advanced Glass Manufacturing Technology Ministry of Education Donghua University Shanghai201620 China Key Laboratory of Special Glass and Enamel for China Light Industry Donghua University Shanghai201620 China Qingdao Haier Intelligent Cooking Appliances Co. Ltd China

Glass enamel coatings are widely used to protect metal against corrosion and weathering due to their exceptional chemical stability. However, the application potential of enamel coatings will be further enhanced if their mechanical properties, particularly impact resistance and toughness, can be improved. In this study, we investigated the influence of varying amounts of alumina fibers on the microstructure, phase composition, and mechanical properties of enamel coatings by fabricating composite enamel coatings. The impact strength, hardness and modulus of the resulting materials were evaluated by falling ball impact tests and nanoindentation tests. The results showed that the alumina fibers were well bonded to the enamel matrix, and the coating surface roughness and contact angle increased with fiber addition. The coating had the lowest porosity and best impact resistance at 0.5 wt% fiber addition. At this point, the modulus and hardness of the composite enamel coating demonstrated improved stability, with the overall hardness reaching its maximum value, and excessive addition of alumina fibers can lead to a reduction in coating performance. As a result, the alumina fibers appear feasible to use as reinforcing material to produce high performance composite enamel coatings. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Rockwell hardness

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