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第43届中国控制会议

作者： Yihua Jiang Qi Liu Yanjie Li Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics and School of Mechanical Engineering and Automation the Harbin Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Most traditional recommendation systems(RS) improve the model structure to obtain valid user behavior representation and make better recommendations. However, limited by the scale of the model, the model cannot accommodate an extended range of user historical data and cannot effectively extract long-term preferences. Reinforcement learning(RL) aims to maximize the cumulative return. Introducing RL can smoothly solve the long-term and short-term profit balance. However, there are specific problems with the direct combination of the two. Training RS that relies on offline user data will cause the strategy to suffer from an overestimation of action and distribution shift. Unlike the traditional RL environment, the ample space of the RS will also cause problems of insufficient exploration and sparse rewards, making the policy more difficult to learn. In response to the above issues, this work proposes an offline RL two-stage optimization framework. The framework consists of an offline RL method based on unknown behavior to alleviate the problem of action overestimation and a two-stage feature optimization training method to reduce the impact of large spaces. Experimental results prove that the method proposed in this article can effectively improve the long-term recommendation ability of the model and more effectively improve the model performance than the general offline RL algorithm.

关键词： Recommendation Systems Overestimation of Actions Distribution Shift Deep Reinforcement Learning Two-stage Feature Optimization

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A ResNet-based approach for accurate radiographic diagnosis of knee osteoarthritis

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CAAI Transactions on Intelligence Technology 2022年第3期7卷 512-521页

作者： Yu Wang Shibo Li Baoliang Zhao Jianwei Zhang Yuanyuan Yang Bing Li School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhenChina Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina TAMS Department of InformaticsUniversity of HamburgHamburgGermany

Currently X-ray images are clinically graded by experienced clinicians using the Kellgren and Lawrence(KL)scoring ***,individual scoring is subjective and error *** study proposes an approach for automated knee osteoarthritis classification based on deep neural *** knee X-ray images are first preprocessed with frequency-domain filtering and histogram normalisation,making the trabecular bone texture more obvious and benefiting the subsequent classification ***,a two-step classification strategy is proposed by extracting the joint centre based on the VGG network and classifying osteoarthritis grades based on the ResNet-50 *** addition,a rebalance operation is proposed to deal with the dataset unbalance problem,and a quick search technique is proposed to improve the iterative search efficiency for the joint *** all of these techniques,a classification accuracy of 81.41%is obtained,which is higher compared to the state-of-the-art approaches.

关键词： AMSGrad knee osteoarthritis recognition ResNet VGG

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A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1778-1791页

作者： Ning Tan Peng Yu Zhiyan Zhong Fenglei Ni IEEE the School of Computer Science and Engineering&Key Laboratory of Machine Intelligence and Advanced Computing Ministry of EducationSun Yat-sen UniversityGuangzhou 510006China the School of Automation Guangdong Polytechnic Normal UniversityGuangzhou 510665China the State Key Laboratory of Robotics and Systems Harbin Institute of Technology(HIT)Harbin 150080China

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks *** a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated ***,robust model-free control of robotic arms in the presence of noise interference remains a problem worth *** this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant ***,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic *** finite-time convergence and robustness of the proposed control scheme are proven by theoretical ***,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

关键词： Dual zeroing neural networks(ZNN) finite-time convergence model-free robot control robustness analysis

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Preliminary Design of Robotic System for Closing Incision by Rotating Bandage Method∗ 5

Preliminary Design of Robotic System for Closing Incision by...

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5th World Robot Conference Symposium on Advanced robotics and automation, WRC SARA 2023

作者： Chen, Anqi Ma, Xudong Sun, Yixin Liu, Wenyong Wang, Junchen Li, Changsheng Hu, Yida David Su, Bai-Quan Beijing University of Posts and Telecommunications Medical Robotics Laboratory School of Automation Beijing100876 China Beihang University Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beijing100083 China Beihang University School of Mechanical Engineering and Automation Beijing100083 China Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Brigham and Women's Hospital Harvard Medical School BostonMA02115 United States

ISBN: (纸本)9798350307320

Wound treatment is routine work in hospital and bandaging of wound happens numerous even in a hospital scale. However, manual bandaging is accompanied with potential infection and it is tedious and manpower-wasting besides. This paper proposed a customized bandaging mechatronic system for avoiding bacteria from hand contact. The design of the propose bandaging system is developed and the working procedure of the by the system is given step by step. To the best knowledge of the authors, the customized mechatronic system is the first bandaging robotic system for bandaging purpose. After loading a customize bandage package, the proposed system lays a solid foundation for automatous bandaging method without human intervention during bandaging. © 2023 IEEE.

关键词： Hospitals

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Magnetically Driven Capsule Robot for Multi-Targeted Biopsy and Drug Delivery in Stomach

Magnetically Driven Capsule Robot for Multi-Targeted Biopsy ...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xu, Zhihui Wei, Tanyong Li, Zongze Huang, Ding Liu, Shoubin Hu, Chengzhi Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen China Southern University of Science and Technology Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen China

ISBN: (纸本)9798350325706

Capsule robots have emerged as a promising solution for non-invasive and painless screening of gastrointestinal diseases, offering significant advantages in patient comfort and reduced risk of cross-infection. Despite these advantages, existing capsule robots fall short in their ability to conduct targeted biopsies and deliver drugs to specific gastric lesions. To address this limitation, this paper presents the design and development of a magnetically driven capsule robot that leverages visual feedback to achieve accurate targeting for biopsy and drug administration in gastric lesions. Internal magnets, responsive to an external gradient magnetic field, enable the controlled propulsion of the capsule robot. A kinetic model of the capsule robot is analyzed, allowing it to recognize lesion contours through the endoscopic camera. This recognition determines the rotation angle or small adjustments required for the capsule robot's attitude and positioning. Experimental results in a simplified in vitro stomach model showcase the capability of the capsule robot to achieve targeted biopsy and drug delivery. These results highlight the potential feasibility of the proposed capsule robot in enhancing diagnostic accuracy and treatment efficacy with broader applications within the realm of gastric healthcare. © 2023 IEEE.

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A Conceptual Framework Towards the Realization of In situ Monitoring and Control of End-to-End Additive Manufacturing Process

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Micro and Nanosystems 2023年第2期15卷 92-101页

作者： Karadgi, Sachin Bhovi, Prabhakar M. Patil, Arun Y. Ramaiah, Keshavamurthy Venka-Teswarlu, K. Langdon, Terence G. Department of Automation & Robotics KLE Technological University Karnataka Hubballi580031 India School of Mechanical Engineering KLE Technological University Karnataka Hubballi580031 India B.V.B. College of Engineering and Technology Mechanical Engineering KLE Technological University Karnataka Hubballi580031 India Department of Mechanical Engineering Dayananda Sagar College of Engineering and Technology Karnataka Bengaluru560078 India Benga-luru Karnataka India Materials Research Group School of Engineering Sciences University of Southampton SouthamptonSO17 1BJ United Kingdom

Additive Manufacturing (AM) is considered one of the key technologies for realizing Industry 4.0. There are numerous stages in the end-to-end AM process, including component design, material design, build, and so on. An enormous amount of data is generated along the end-to-end AM process that can be acquired from the 3D printer in real-time, micro-characterization studies, and process plan details, among others. For instance, these data can be employed to predict the printed compo-nents’ quality and, at the same time, proactively adapt the 3D printer parameters to achieve better qual-ity. This end-to-end AM process can be mapped onto the digital thread. The current article elaborates on a conceptual framework to acquire the data from various sources associated with the end-to-end AM process and realize monitoring and control of the end-to-end AM process, leading to an intelligent AM process. © 2023, Bentham Science Publishers. All rights reserved.

关键词： 3D printing

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Lumbar spine localisation method based on feature fusion

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 931-945页

作者： Yonghong Zhang Ning Hu Zhuofu Li Xuquan Ji Shanshan Liu Youyang Sha Xiongkang Song Jian Zhang Lei Hu Weishi Li Robotics Institute School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina Beijing Zhuzheng Robot Co. LTDBeijingChina Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKnoxvilleTennesseeUSA Department of Orthopaedics Peking University Third HospitalBeijingChina Engineering Research Center of Bone and Joint Precision Medicine Ministry of EducationBeijingChina Beijing Key Laboratory of Spinal Disease Research BeijingChina School of Biological Science and Medical Engineering Beihang UniversityBeijingChina Department of Computer Science University of WarwickCoventryUK

To eliminate unnecessary background information,such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning,a two‐stage approach for localising lumbar segments is ***,based on the multi‐scale feature fusion technology,a non‐linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine,effectively eliminating useless background information,such as soft *** the second stage,we directly realised the precise positioning of each segment in the lumbar spine space region based on the non‐linear regression method,thus effectively eliminating the interference caused by the adjacent *** 3D Intersection over Union(3D_IOU)is used as the main evaluation indicator for the positioning *** an open dataset,3D_IOU values of 0.8339�0.0990 and 0.8559�0.0332 in the first and second stages,respectively is *** addition,the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s ***,the proposed method performs very well in terms of both pre-cision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.

关键词： CT image lumbar spatial orientation multi‐scale information fusion

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Design and experimental study of a passive power-source-free stiffness-self-adjustable mechanism

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Frontiers of Mechanical engineering 2021年第1期16卷 32-45页

作者： Yuwang LIU Dongqi WANG Shangkui YANG Jinguo LIU Guangbo HAO State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Engineering-Electrical and Electronic Engineering University College CorkCorkIreland

Passive variable stiffness joints have unique advantages over active variable stiffness joints and are currently eliciting increased *** passive variable stiffness joints rely mainly on sensors and special control algorithms,resulting in a bandwidth-limited response speed of the *** propose a new passive power-source-free stiffness-self-adjustable mechanism that can be used as the elbow joint of a robot *** new mechanism does not require special stiffness regulating motors or sensors and can realize large-range self-adaptive adjustment of stiffness in a purely mechanical *** variable stiffness mechanism can automatically adjust joint stiffness in accordance with the magnitude of the payload,and this adjustment is a successful imitation of the stiffness adjustment characteristics of the human *** response speed is high because sensors and control algorithms are not *** variable stiffness principle is explained,and the design of the variable stiffness mechanism is analyzed.A prototype is fabricated,and the associated hardware is set up to validate the analytical stiffness model and design experimentally.

关键词： variable stiffness mechanism stiffness self-regulation bionic robot modeling

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Machine Learning Algorithm Review for the Detection of Formalin in Fish 7

Machine Learning Algorithm Review for the Detection of Forma...

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7th IET Smart Cities Symposium, SCS 2023

作者： Benny, Adheena Emilin Renitta, R. Anitha Mary, X. Dutta, P.K. Karthik, C. El-Kenawy, El-Sayed M. Department of Food Processing Karunya Institute of Technology and Sciences India School of Engineering and Technology Amity University West Bengal Kolkata India Department of Robotics and Automation Jyothi Engineering College Kerala India Department of Communication and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt

ISBN: (纸本)9781839539831

Formalin is a toxic chemical that is commonly used to preserve fish and prevent spoilage, but its use in food products is illegal in many countries. The detection of Formalin in fish is critical to ensure food safety and prevent health hazards for consumers. Artificial Intelligence (AI) can be used to detect Formalin in fish samples by analyzing various parameters such as color, texture, and chemical composition. AI-based detection methods can provide fast and accurate results compared to traditional laboratory methods, which can take several hours or days. Machine learning algorithms can be trained to recognize patterns and identify the presence of Formalin in fish samples with high accuracy. AI-based detection methods can also help identify other types of adulterants and contaminants in food products, improving overall food safety. Spectroscopy techniques, such as Fourier Transform Infrared Spectroscopy (FTIR) and Raman Spectroscopy, can be combined with AI algorithms to enhance the accuracy of Formalin detection in fish. Mobile-based applications that use AI algorithms can be developed to provide on- the-spot detection of Formalin in fish samples, making it easier for food inspectors and consumers to detect adulteration. AI-based detection methods can be scaled up to handle large volumes of fish samples, making it easier to monitor the quality of fish products in the market. The use of AI-based detection methods for Formalin in fish can help promote transparency and accountability in the food industry, ensuring that consumers have access to safe and healthy food products. © The Institution of engineering & Technology 2023.

关键词： Formaldehyde

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Neural network equivalent model for highly efficient massive data classification

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Science China(Information Sciences) 2023年第3期66卷 287-288页

作者： Siquan YU Zhi HAN Yandong TANG Chengdong WU School of Information Science and Engineering Northeastern University Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Faculty of Robot Science and Engineering Northeastern University

Dear editor, Recently, neural networks(NNs) have been successfully applied to massive data classification tasks [1, 2]. However,there is an inconsistent problem in neural network structure and convergence [3]. On one hand, under the overparameterize assumption, it requires to design a complex network to guarantee the convergence [4].

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