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Research Progress of Bionic Water Strider Robot

Recent Patents on Mechanical Engineering 2022年第2期15卷 122-148页

作者： Jiang, Jingang Tan, Qiyun Yu, Xiaoyang Wu, Dianhao Yao, Liang Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology Harbin150080 China Intelligent Machine Institute Harbin University of Science and Technology Harbin150080 China School of Measurement-Control Tech and Communications Engineering Harbin University of Science and Technology Harbin150080 China

Background: The bionic water strider robot can achieve sliding, jumping, and other movements on the water surface, having advantages of small size, light weight, flexible movements, and other characteristics. It can detect the quality of water, investigate and search the water surface, and perform some other operations. It has a very broad range of applications and development prospects. Therefore, the trend of biomimetic water strider robots is attracting more and more attention. Objective: This study aimed to review the bionic water strider robot and introduce its classification, characteristics, and development. Methods: This paper reviews various productions and patents related to the bionic water strider robot from 2003 to the present. The sources of the papers include CNKI, Wanfang, Patent publication an-nouncement in China, Web of Science, IEEE, Elsevier, Springer-Verlag, Espacenet, and FPO IP Research & Communities. To obtain the results, an endnote was used for documentation, and citeSapce was used for visual analysis. Results: The mechanical structure of existing bionic water strider robots has been analyzed and com-pared. Furthermore, the typical characteristics are concluded. The main problems in its development are analyzed, and the development trend is foreseen. Furthermore, the current and future research prospects of the productions and patents on the bionic water strider robot are discussed. Conclusion: The optimization and development of the structure of the bionic water strider robot and the development of associated components help to improve the simulation of the water strider's motion and perform a better task in a complex water surface environment. In the future, with the improvement in the research, the bionic water strider robot will develop into miniaturization, intelligence, and integration. © 2022 Bentham Science Publishers.

关键词： Bionics

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Preclinical Assessment of Upper Limb Tremor in Parkinson's Disease with Deep Learning and Wearable technology

Preclinical Assessment of Upper Limb Tremor in Parkinson's D...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Fang Lin Zhelong Wang Sen Qiu Hongyu Zhao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian Liaoning China

Tremors are typically experienced by patients at the beginning of Parkinson's disease (PD). Clinicians evaluate clinical symptoms based on scale and experience, but mild tremors do not have significant characteristics and are difficult to observe with the naked eye. Implementing an intelligent and objective method to identify PD patients with early tremor symptoms and healthy controls (HC) is necessary. This study used wearable sensors to collect 9-axis inertial signals and 2-channel sEMG signals at the wrists of 13 HC and 24 PD patients from Dalian Municipal Central Hospital. Based on a Long Short-Term Memory Network (LSTM), an attention mechanism, and a Fully Convolutional Network (FCN), we develop a model to classify and recognize data from PD patients. Compared the proposed method with several classification methods, the results showed that the proposed method achieved higher classification accuracy (91.78%), precision (100%), recall (87.50%), and F1-score (93.33%) of PD class than Support Vector Machine, FCN, and LSTM. The computing time of the proposed method is approximately 1 second. The proposed method identifies early PD patients by pre-clinical assessment of mild upper limb tremors, which is valuable for early treatment and rehabilitation.

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Efficient Load Identification Method Based on Time-Image Features and Dsdcn Model

SSRN

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SSRN 2023年

作者： Jiang, Jiahao Wang, Zhelong Qiu, Sen Zhao, Hongyu Liu, Ruichen Shi, Xin Huang, Zhaowu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

HighlightsEfficient Load Identification Method based on Time-Image Features and DSDCN ModelJiahao Jiang,Zhelong Wang,Sen Qiu,Hongyu Zhao,Ruichen Liu,Xin Shi,Zhaowu Huang• A new image feature named color AP was proposed, which captures the overall patterns of the load characteristics.• A new time series feature called DCT-AF was introduced to capture the temporal dependencies and periodicities of the signal in an efficient representation.• A lightweight model called DSDCN was proposed, which can simultaneously process both image features and time series features.• The proposed model was evaluated on three dataset and was deployed on a low-power device. © 2023, The Authors. All rights reserved.

关键词： Discrete cosine transforms

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A Gender Recognition Method for Chinese Mitten Crab in Low Light Environment Based on Machine Vision

A Gender Recognition Method for Chinese Mitten Crab in Low L...

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2024 China Automation Congress, CAC 2024

作者： Tian, Junlong Guo, Ya Liu, Yingfei Zhang, Jianguo Jiang, Yongnian International Joint Research Center for Intelligent Optical Sensing and Applications Jiangnan University Wuxi China Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China Aquatic and Livestock Station of Yixing Agriculture and Rural Bureau Yixing China Wuxi Agricultural Equipment Technology Extension Center Wuxi China Jiangsu Zhongnong IoT Technology Co. LTD Yixing China

ISBN: (纸本)9798350368604

The acquisition of gender information of Chinese mitten crabs is crucial for survival rate assessment and biomass estimation. Machine vision is commonly used for the automatic gender recognition of Chinese mitten crabs, but the low light conditions caused by deeper containers reduce the accuracy of existing algorithms. To improve the accuracy of gender recognition of Chinese mitten crabs in low light environments, this study incorporates the GAM into the backbone of YOLOv7, improving the capability to distinguish gender-specific features. A lightweight upsampling operator, CARAFE, replaces the original upsampling module, expanding the model’s receptive field and better aggregating global information. Using Focal-CIoU to mitigate the impact of low-quality prediction boxes, enabling the model to better optimize high-quality ones. By constructing an image dataset of Chinese mitten crabs in low-light environments to validate the model’s performance, experimental results demonstrate that the average precision of the model established in this study is 0.870, 10.6% higher than the most update algorithm GMNet-YOLOv4, effectively performing the classification task of Chinese mitten crabs in low-light environments. © 2024 IEEE.

关键词： Machine vision

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PDE Discovery for Soft Sensors Using Coupled Physics-Informed Neural Network with Akaike's Information Criterion

arXiv

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arXiv 2023年

作者： Wang, Aina Qin, Pan Sun, Xi-Ming The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

Soft sensors have been extensively used to monitor key variables using easy-to-measure variables and mathematical models. Partial differential equations (PDEs) are model candidates for soft sensors in industrial processes with spatiotemporal dependence. However, gaps often exist between idealized PDEs and practical situations. Discovering proper structures of PDEs, including the differential operators and source terms, can remedy the gaps. To this end, a coupled physics-informed neural network with Akaike's criterion information (CPINN-AIC) is proposed for PDE discovery of soft sensors. First, CPINN is adopted for obtaining solutions and source terms satisfying PDEs. Then, we propose a data-physics-hybrid loss function for training CPINN, in which undetermined combinations of differential operators are involved. Consequently, AIC is used to discover the proper combination of differential operators. Finally, the artificial and practical datasets are used to verify the feasibility and effectiveness of CPINN-AIC for soft sensors. The proposed CPINN-AIC is a data-driven method to discover proper PDE structures and neural network-based solutions for soft sensors. Copyright © 2023, The Authors. All rights reserved.

关键词： Partial differential equations

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Recent Patents on a Discharge State Detection of EDM

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Recent Patents on Engineering 2021年第6期15卷 3-15页

作者： Xie, Baocheng Liu, Le Ji, Xuhui Wang, Yi Zeng, Zhaoqi Gou, Haoliang Zhang, Bing Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology Harbin150080 China Beijing Institute of Control Engineering Beijing200085 China

Background:Electrical Discharge Machining (EDM) has been widely applied in manufacturing high strength and high hardness of mental material due to no mechanical forces in the EDM process. The discharge state, an important process parameter of EDM, directly affects the machining quality and machining efficiency. So, it is necessary to detect the real-time discharge state of EDM. Therefore, discharge state detection of EDM at present has been paid more and more attention. Objective: To meet the increasing requirement of machining quality and machining efficiency in the EDM process, the methods for discharge state detection of EDM are being improved continuously. Methods: This paper reviews various representative patents related to the methods for discharge state detection of EDM. Results: By summarizing numerous patents about the methods for discharge state detection of EDM, the main problems such as low detection accuracy and long delay time of discharge state detection of EDM are summarized and analyzed. In addition, the further development of discharge state detection method of EDM is discussed. Conclusion The optimization of methods for discharge state detection is conducive to improving machining quality and machining efficiency in the EDM process. Moreover, more relevant patents will be invented in the future. © 2021 Bentham Science Publishers.

关键词： Efficiency

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Recent patents on overflow valve for pressure control

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Recent Patents on Mechanical Engineering 2021年第4期14卷 481-492页

作者： Xie, Baocheng Wang, Yi Zeng, Zhaoqi Gou, Haoliang Zhang, Bing Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology Harbin150080 China Beijing Institute of Control Engineering Beijing200085 China

Background: The overflow valve, as an essential pressure control element, is widely applied in various hydraulic transmission systems because of its characteristics of constant pressure overflow, stable pressure, system’s unloading and safety protection. The research on the structure and working principle of the overflow valve is beneficial to improve the working efficiency and operating stability of pressure control of the hydraulic transmission. Therefore, the development trend of the overflow valve for pressure control has been paid more and more attention. Objective: To meet the growing demands for the working efficiency and operating stability of hydraulic transmission, the structure of the overflow valve for pressure control such as direct-acting overflow valve and pilot-operated overflow valve is being improved continuously. Methods: This paper retraces various current representative patents relative to the structural device of direct-acting overflow valve and pilot-operated overflow valve for pressure control. Results: Through investigating a large number of patents on the overflow valve for pressure control, the main existing problems, such as low working efficiency and poor operating stability are summarized and analyzed. Moreover, the development tendency of the overflow valve for pressure control in the future is also discussed. Conclusion: The optimization of the structure of the overflow valve for pressure control is conducive to improve working efficiency and operating stability. More correlative patents will be invented in the future. © 2021 Bentham Science Publishers.

关键词： Efficiency

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Solving PDEs with Unmeasurable Source Terms Using Coupled Physics-Informed Neural Network with Recurrent Prediction for Soft Sensors

arXiv

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arXiv 2023年

作者： Wang, Aina Qin, Pan Sun, Xi-Ming The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

Partial differential equations (PDEs) are a model candidate for soft sensors in industrial processes with spatiotemporal dependence. Although physics-informed neural networks (PINNs) are a promising machine learning method for solving PDEs, they are infeasible for the nonhomogeneous PDEs with unmeasurable source terms. To this end, a coupled PINN (CPINN) with a recurrent prediction (RP) learning strategy (CPINN-RP) is proposed. First, CPINN composed of NetU and NetG is proposed. NetU is for approximating PDEs solutions and NetG is for regularizing the training of NetU. The two networks are integrated into a data-physics-hybrid loss function. Then, we theoretically prove that the proposed CPINN has a satisfying approximation capability for solutions to nonhomogeneous PDEs with unmeasurable source terms. Besides the theoretical aspects, we propose a hierarchical training strategy to optimize and couple NetU and NetG. Secondly, NetU-RP is proposed for compensating information loss in data sampling to improve the prediction performance, in which RP is the recurrently delayed outputs of well-trained CPINN and hard sensors. Finally, the artificial and practical datasets are used to verify the feasibility and effectiveness of CPINN-RP for soft sensors. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Distributed convex optimization for nonlinear multi-agent systems disturbed by a second-order stationary process over a digraph

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Science China(Information Sciences) 2022年第3期65卷 110-122页

作者： Dong WANG Zhu WANG Zhaojing WU Wei WANG Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology School of Control Science and Engineering Dalian University of Technology School of Mathematics and Informational Science Yantai University

In this paper, we investigate the distributed convex optimization problem for a class of nonlinear multi-agent systems disturbed by random noise over a directed graph. The target problem involves designing a continuous-time algorithm to minimize the sum of all local cost functions associated with each agent. The target noise is considered as a second-order stationary process under mild assumptions. The noise-to-state exponential stability for the multi-agent system based on random differential equations is analyzed using a random field method. Sufficient conditions corresponding to the second moment relative to the optimal solution in the form of matrix inequalities are established. Then, the grid search method is employed to determine the best system parameters such that the second moment of the estimation error has the minimum value. In addition, the obtained results are applied to solve the average consensus problem in the presence of a stationary process. Finally, a numerical example is presented to verify the effectiveness of the proposed algorithm.

关键词： distributed optimization random differential equations stationary process noise-to-state exponential stability

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A distributed fixed-time optimal consensus algorithm over second-order multi-agent systems

A distributed fixed-time optimal consensus algorithm over se...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Xiasheng Shi Tianju Sui The School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment (Dalian University of Technology) Ministry of Education Dalian China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

With the rapid development of artificial intelligence technology and communication technology, the distributed optimization problem for distributed second-order multi-agent systems has received extensive attention. In this paper, a specified time convergence optimization algorithm is designed for the distributed optimization problem of multi-agent systems under undirected networks. Firstly, the sliding mode theory is used to achieve fixed-time convergence of the agent speed to the target value, transforming the second-order agent-distributed optimization problem into a first-order agent-distributed optimization problem. Secondly, with the help of the sliding mode theory, the agent realizes that the gradient sum is zero at a fixed time. Then, this paper uses the second derivative information of the cost function to design an initialization-free fixed-time convergent algorithm. Finally, the effectiveness of the algorithm designed in this paper is illustrated by case simulation.

关键词： Consensus algorithm Cost function Communications technology Artificial intelligence Multi-agent systems Convergence

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