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2021 International Conference on Advanced Manufacturing Technology and Electronic Information, AMTEI 2021

作者： Han, Zhonghua Qi, Hongfeng Chang, Daliang Gong, Wei Dai, Shilong School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

According to the characteristics of the painting process of passenger car manufacturing enterprises, by formulating the routing buffer linkage rules based on the total renewal cost, the linkage process of the bus in the routing buffer is controlled, and an improved Q-learning (Q- The routing buffer of learning) algorithm quickly finds the optimal path method. According to the actual production situation, this method improves the dynamic parameters of the algorithm on the basis of the traditional Q-learning algorithm, and improves the optimization speed and accuracy of the algorithm by establishing the correlation between the work-in-process and its neighboring work-in-process in the current state. Through multiple sets of example simulation tests, the effectiveness of the Q-learning algorithm in solving the optimization problem of routing buffer linkage is verified. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Improved Cascade Mask RCNN Single Character Detection Algorithm for Cast Pipe 4

Improved Cascade Mask RCNN Single Character Detection Algori...

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4th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Xu, Fuli Wang, Yu Jiang, Yan Wang, Huaizhen Cui, Yong Tao, Ye Wang, Yisheng Zhang, Qi Shenyang University of Technology School of Software Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China Shanghai Ballsnow Intelligent Technology Co. Ltd Shanghai China Shenyang Zhongke Microcyber Technology Co. Ltd Shenyang China

ISBN: (纸本)9798350329490

Due to the interference of blurry background, character occlusion, and low background contrast in the Cast Pipe character images, the detection accuracy and real-time performance cannot meet the requirements of industrial scenes. To address these issues, this paper presents an improvement based on the Cascade Mask RCNN. Firstly, the Channel Attention Mechanism Module (Squeeze-and-Excitation, SE) is embedded into the ResNet structure. By constructing a bottleneck structure with two fully connected layers, the normalized weights are applied to each channel feature to enhance the network representation ability. Secondly, a bottom-up structure is introduced after the Feature Pyramid Networks (FPN) to transmit the strong localization information from lower-level features to higher-level semantic features. Thirdly, an adaptive pooling layer is added to allocate the candidate regions generated by the Region Proposal Network (RPN) to different scale feature maps. Additionally, skip connections are introduced between the bottom-level features and the highest-level features of each stage to reduce model parameters while retaining global representation capabilities. The improved algorithm is tested on a cast pipe dataset, achieving an average detection accuracy (mAP) of 98.7%, which is 2.7% higher than that of Cascade Mask RCNN. The accuracy indicates that the improved performance is superior to the original algorithm. © 2023 IEEE.

关键词： Semantics

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Research on cloud-side collaborative task migration strategy based on simulated annealing-Bee Colony Algorithm in Power IoT 2

Research on cloud-side collaborative task migration strategy...

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2nd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2022

作者： Zhang, Bowen Zeng, Zeng Miao, Weiwei Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China State Grid Jiangsu Electric Power Co. Ltd. Information Telecommunication Branch Nanjing China

ISBN: (纸本)9781665408868

In the power IoT, in order to make up for the shortcomings of insufficient computing capabilities of edge-side devices, cloud-side collaboration technology has attracted more and more attention. The cloud-side collaboration technology relies on the selection of task migration strategies. However, when the existing migration strategies face the complex edge-side terminal devices in the power IoT, the energy consumption and delay performance are not good enough, which greatly affects the user's power experience. Therefore, it is very valuable to study a cloud-side collaborative task migration strategy under the power IoT environment. This paper proposes a task migration strategy based on simulated annealing-Bee Colony Algorithm in the environment of power IoT, which can solve the problem of task migration. Experimental results show that compared with the existing popular methods, this method has advantages in terms of energy consumption and time delay. This research can meet the real-time requirements of the power IoT, while reducing energy consumption and improving the user's power experience, thereby promoting the further development of the power IoT. © 2022 IEEE.

关键词： Simulated annealing

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SheetCopilot: bringing software productivity to the next level through large language models 23

SheetCopilot: bringing software productivity to the next lev...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Hongxin Li Jingran Su Yuntao Chen Qing Li Zhaoxiang Zhang School of Artificial Intelligence University of Chinese Academy of Sciences (UCAS) and State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences Center for Artificial Intelligence and Robotics HKISI CAS and The Hong Kong Polytechnic University Center for Artificial Intelligence and Robotics HKISI CAS The Hong Kong Polytechnic University School of Artificial Intelligence University of Chinese Academy of Sciences (UCAS) and State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences and Center for Artificial Intelligence and Robotics HKISI CAS and Shanghai Artificial Intelligence Laboratory

Computer end users have spent billions of hours completing daily tasks like tabular data processing and project timeline scheduling. Most of these tasks are repetitive and error-prone, yet most end users lack the skill to automate these burdensome works. With the advent of large language models (LLMs), directing software with natural language user requests become a reachable goal. In this work, we propose a SheetCopilot agent that takes natural language task and control spreadsheet to fulfill the requirements. We propose a set of atomic actions as an abstraction of spreadsheet software functionalities. We further design a state machine-based task planning framework for LLMs to robustly interact with spreadsheets. We curate a representative dataset containing 221 spreadsheet control tasks and establish a fully automated evaluation pipeline for rigorously benchmarking the ability of LLMs in software control tasks. Our SheetCopilot correctly completes 44.3% of tasks for a single generation, outperforming the strong code generation baseline by a wide margin. Our project page: https://***/.

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A Charge Density Model of Silicon-nanowire GAA MOSFET Incoroperating the Source-drian Tunneling Effect for IC Design

A Charge Density Model of Silicon-nanowire GAA MOSFET Incoro...

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2021 International Symposium on Computer Technology and Information Science, ISCTIS 2021

作者： Cheng, He Zhang, Chao Liu, Tiefeng Xie, Chuang Yang, Zhjia Zhang, Zhipeng Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665414418

One numerical compact model for one-dimensional electron charge density of n-Type silicon ballistic nanowire gate-All-Around metal-oxide-semiconductor field-effect transistors with ultra-short channel incorporating the source-drain tunneling is proposed. When establishing the AC model in practical device applications, compact models describing transient analysis of the carrier behaviors in the device channel are needed. When focusing on the charge density of carriers within GAA MOSFET devices, carriers transporting along the channel direction induced by the source-drain tunneling effect should also be taken into account in the subthreshold region. In this study, transmission coefficients of the source-drain tunneling effect are evaluated through expressing energy level of the lowest subband with bringing in the exact boundary conditions and using the Wentzel-Kramers-Brillouin approximation. Then, the carrier density can be derived from the charge profile in the channel using quantum statistics theory. Finally, comparisons of the calculations of the carrier densities between the source-drain tunneling and the thermionic modes in ballistic transport are demonstrated. © 2021 IEEE.

关键词： MOSFET devices

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Parameter Efficient Merging for Multimodal Large Language Models with Complementary Parameter Adaptation

arXiv

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arXiv 2025年

作者： Zeng, Fanhu Guo, Haiyang Zhu, Fei Shen, Li Tang, Hao State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS Hong Kong School of Cyber Science and Technology Sun Yat-sen University China School of Computer Science Peking University China

Fine-tuning pre-trained models with custom data leads to numerous expert models on specific tasks. Merging models into one universal model to empower multi-task ability refraining from data leakage has gained popularity. With the expansion in data and model size, parameter efficient tuning becomes the common practice for obtaining task-specific models efficiently. However, we observe that existing methods designed for full fine-tuning merging fail under efficient tuning. To address the issues, we analyze from low-rank decomposition and reveal that maintaining direction and compensating for gap between singular values are crucial for efficient model merging. Consequently, we propose CoPA-Merging, a training-free parameter efficient merging method with complementary parameter adaptation. Specifically, we (1) prune parameters and construct scaling coefficients from inter-parameter relation to compensate for performance drop from task interference and (2) perform cross-task normalization to enhance unseen task generalization. We establish a benchmark consisting of diverse multimodal tasks, on which we conduct experiments to certificate the outstanding performance and generalizability of our method. Additional study and extensive analyses further showcase the effectiveness. Copyright © 2025, The Authors. All rights reserved.

关键词： Information leakage

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Human-in-the-loop Optimization for Adaptive Assist-as-Needed Rehabilitation 12

Human-in-the-loop Optimization for Adaptive Assist-as-Needed...

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12th International Conference on CYBER Technology in automation, Control, and Intelligent systems, CYBER 2022

作者： Li, Ning Yang, Yang Yang, Tie Chen, Wenyuan Wang, Yihan Yu, Peng Wang, Wenxue Xi, Ning Liu, Lianqing Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of the Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang10016 China School of Electrical and Electronics Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Emerging Technologies Institute Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Pokfulam999077 Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Active voluntary participation in robot-assisted rehabilitation promote the recovery of stroke hemiplegia. However, different patients have personalized recovery states, which needs the adaptive control strategy to improve the patient's participation. Therefore, in this study, a Mirror Adaptive Assist-As-Needed (MAAN) scheme is proposed to encourage subjects to participate actively and avoid the slack of the subjects including two modules: Functional Capability Estimation (FCE) and Mirror Adaptation (MAD). Specifically, the first MSFE module can obtain joint functional capability based on fused biological and motional signals. The second MAD module acquires the needed assistance via the estimated torques of FCE based on healthy limb. Through the iteration and collaboration of these two modules, a human-in-the-loop adaptive training is achieved to promote the patient participation. The results demonstrate that the MAAN strategy can generate suitable assistance for the patients to correct abnormal muscle activation patterns and promote active voluntary participation. Compared with other methods, the proposed strategy improves motion stability and consistency with the natural physiological state of healthy limbs. This research can be expected to greatly enhance the rehabilitation and power assistance outcome of exoskeleton robot in more practical applications. © 2022 IEEE.

关键词： Mirrors

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International Conference on Computer Vision (ICCV)

作者： Benzhi Wang Yang Yang Jinlin Wu Guo-Jun Qi Zhen Lei State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences OPPO Research

Weakly supervised person search aims to jointly detect and match persons with only bounding box annotations. Existing approaches typically focus on improving the features by exploring the relations of persons. However, scale variation problem is a more severe obstacle and under-studied that a person often owns images with different scales (resolutions). For one thing, small-scale images contain less information of a person, thus affecting the accuracy of the generated pseudo labels. For another, different similarities between cross-scale images of a person increase the difficulty of matching. In this paper, we address it by proposing a novel one-step framework, named Self-similarity driven Scale-invariant Learning (SSL). Scale invariance can be explored based on the self-similarity prior that it shows the same statistical properties of an image at different scales. To this end, we introduce a Multi-scale Exemplar Branch to guide the network in concentrating on the foreground and learning scale-invariant features by hard exemplars mining. To enhance the discriminative power of the learned features, we further introduce a dynamic pseudo label prediction that progressively seeks true labels for training. Experimental results on two standard benchmarks, i.e., PRW and CUHK-SYSU datasets, demonstrate that the proposed method can solve scale variation problem effectively and perform favorably against state-of-the-art methods. Code is available at https://***/Wangbenzhi/***.

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3D Vision Based Lamination Gap Detection of Carbon Fiber Composite

3D Vision Based Lamination Gap Detection of Carbon Fiber Com...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yuanye Xu Libiao Yuan Wei Liang Yutuo Yang Shuai Liu Ziming Zeng Yinlong Zhang Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China College of Information Engineering Shenyang University of Chemical Technology Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Automotive and Transportation Engineering Shenzhen Polytechnic Shenzhen China

The paper presents a machine learning approach to detect laminate gaps and to classify them as open or closed. Compared with the traditional detection methods, which suffer from low precision of point cloud segmentation and poor effect of gap extraction in traditional gap detection, this paper proposes a gap detection algorithm based on 3D Vision. First, an improved undirected graph point cloud segmentation algorithm is used to overcome the under-segmentation of the target region. Second, an adaptive vector for angle discriminant model is proposed to solve the problem of poor extraction effect of the gap edge points. Finally, the B-spline curve-fitting model is used to describe the 3D points on the lamination edges. The proposed algorithm has been evaluated in a visual quality detection platform for carbon fiber composite laminates. The results show that the proposed approach is able to accurately detect and classify laminate gaps in carbon fiber composite structures, providing a reliable tool for quality control in industrial manufacturing processes.

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Fault Diagnostic Opportunities for Robot Servo Motors Using the Physics of Failure Analysis

Fault Diagnostic Opportunities for Robot Servo Motors Using ...

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International Conference on Reliability, Maintainability and Safety,ICRMS

作者： Bo Zhang Shan Liang Kai Wang Key Laboratory of Networked Control Systems Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China College of Computer Science and Technology University of Chemical Technology Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350329988

ISBN: (纸本)9798350329995

Joint robots are widely used in various industries, such as aviation, aerospace, and automotive manufacturing. The performance degradation of servo motors can significantly affect the overall performance of the robots because of the core component of joint robots, and therefore, it is critical to detect and address servo motor failures promptly. In this study, we analyze some common causes and mechanisms of performance degradation in robot servo motors using the physics of failure analysis. We also discuss strategies for detecting and diagnosing faults using these mechanisms before the servo motors fail completely.

关键词： Degradation Industries Service robots Failure analysis Safety Manufacturing Reliability Servomotors Physics Automotive engineering

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