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SSRN 2024年

作者： Shao, Yuqing Shen, Junjie Ren, He Zhao, Liwei Xue, Ziyu Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

The microstructure evolution effects performance degradation of solid oxide fuel cells (SOFCs) under elevated temperatures. The microstructural evolution, including elemental diffusion, phase changes, and Ni agglomeration, is studied via X-ray diffraction (XRD) and scanning electron microscopy energy dispersive spectrometer (SEM-EDS) techniques. The ohmic resistance and polarization impedance, including activation polarization impedance and concentration resistance, causing the performance degradation, are estimated using electrochemical impedance spectroscopy (EIS) and distribution of relaxation time (DRT) techniques. The voltage gradually decreases from 0.9 V, at the beginning of the load, to 0.75 V after 240 h of service. The formation of pores at the (La0.6Sr0.4Co0.2Fe0.8O3-δ) LSCF/(Gd0.1Ce0.9O2-δ) GDC interface and cracks at the (8 mol% Y2O3-stabilized ZrO2) YSZ/NiO-YSZ interface induce the increase in ohmic resistance. The increase in activation polarization resistance is attributed to the formation of the SrZrO3 phase and Gd2(Zr1-xCex)2O7, at the YSZ/GDC interface, and the redistribution and coarsening of Ni. The formation of Co- or Fe-based oxides in the LSCF layer slightly increases the concentration polarization impedance. © 2024, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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Simulation Study on the Influence of Drilling Parameters on the Temperature of Skull Drilling

Simulation Study on the Influence of Drilling Parameters on ...

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2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2021

作者： Jiang, Wenyu Liu, Zhenzhong Tianjin University of Technology National Demonstration Center For Experimental Mechanical and Electrical Engineering Education School of Mechanical Engineering Tianjin Key Laboratory For Advanced Mechatronic System Design and Intelligent Control Tianjin China

ISBN: (纸本)9781665441285

Skull drilling is an important and difficult process in neurosurgical craniotomy, which will inevitably produce a lot of heat. Correct understanding of the heat production law of skull drilling has important guiding significance for craniotomy robot. In this study, cortical bone and cancellous bone were layered to establish models according to the 3D reconstruction data of skull. The finite element method based on ABAQUS is used to establish the skull drilling model and realize the simulation of bone tissue surgical instrument interaction state for surgical robot. The results show that when the drill rotation speed and feed speed increase, the drilling temperature will also increase, but the drill rotation speed will have a more significant effect on bone drilling temperature, and when the drill rotation speed is low (300r/min, 600r/min), the drill bit diameter has little effect on the drilling temperature, but when the drill rotation speed is higher (900r/min, 1200r/min), the larger diameter drill bit(11mm) will produce more drilling heat. © 2021 IEEE.

关键词： Finite element method

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Optimization of Robot Hopping Mechanism Based on Parametric Model Method

Optimization of Robot Hopping Mechanism Based on Parametric ...

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2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2021

作者： Shi, Yanan Yang, Yuwei Liu, Zhenzhong Zhang, Haiming Tianjin University of Technology National Demonstration Center For Experimental Mechanical and Electrical Engineering Education School of Mechanical Engineering Tianjin Key Laboratory For Advanced Mechatronic System Design and Intelligent Control Tianjin China

ISBN: (纸本)9781665441285

In order to solve the problem of hopping mechanism design of robot, we proposed a method of optimization of transmission mechanism based on parametric model. In this paper, the optimization of energy transfer efficiency of robot jump mechanism is completed by parametric model method through the mechanism simplification, parametric modeling and parameter optimization. The results show that the method can greatly optimize the energy transfer efficiency and reduce the driving torque required by 74.6%. It provides a feasible optimization method for the design of the robot hopping structure. © 2021 IEEE.

关键词： Energy transfer

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Mechanism design and Motion Simulation of Mobile Robot with Compound Motion Mode

Mechanism Design and Motion Simulation of Mobile Robot with ...

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2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2021

作者： Shi, Yanan Yang, Yuwei Liu, Zhenzhong Zhang, Haiming Tianjin University of Technology National Demonstration Center For Experimental Mechanical and Electrical Engineering Education Tianjin Key Laboratory For Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin China

ISBN: (纸本)9781665441285

Aiming at the problem of poor trafficability and difficulty in dealing with complex environment of traditional mobile robot, we proposed a climbing and hopping hybrid mobile robot. In this paper, we took the spring four-bar linkage mechanism as the hopping mechanism and the C-shaped wheel foot mechanism as the walking mechanism. Completed the structure design of the robot. Realized the assembly and disassembly of the virtual prototype, and completed the motion simulation and analysis. The results show that the robot is stable in the aspects of structure design and its jumping height can reach 988mm, and has a great potential in reconnaissance, monitoring, survey etc. © 2021 IEEE.

关键词： Mobile robots

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A Study of Soil Modelling Methods Based on Line-Structured Light——Preparing for the Subsoiling Digital Twin

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SSRN 2024年

作者： Li, Xia You, Birong Wang, Xuhui Zhao, Zhipeng Qi, Tianyu Xu, Jinyou Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Centre for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

The virtual model serves as the foundation for constructing a digital twin system. In order to provide technical guidance for the construction of a digital twin system for subsoiling, this study employed the line structured light method for soil model generation. Field experiments and indoor experiments were conducted separately, using the extreme value method, grayscale centroid method, and Steger algorithm to extract the laser centrelines in the experiments. The results show that the extreme value method and grayscale centroid method have almost the same processing time when dealing with two types of images, which are 1.9ms and 18ms respectively. The Steger algorithm has a significant difference in processing time when dealing with indoor and field images, with the processing time being longer for indoor images, approximately 300ms. Regarding memory usage, there is a large difference when the extreme value method processes two sets of images. Therefore, the grayscale centroid method is the most suitable method for centreline extraction in a digital twin system for subsoiling. When performing 3D reconstruction, the camera shooting frequency is positively correlated with the reconstruction effect, while the moving speed is negatively correlated with the reconstruction effect. Of course, the reconstruction effect is also related to the performance of the binocular camera used. In addition, the processing time for each image during reconstruction is within 1ms, indicating that the method of line laser 3D reconstruction can solve the problem of soil modelling in subsoiling digital twin. © 2024, The Authors. All rights reserved.

关键词： Soils

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Learning a Coordinated Network for Detail-Refinement Multiexposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video Technology 2023年第2期33卷 713-727页

作者： Li, Jiawei Liu, Jinyuan Zhou, Shihua Zhang, Qiang Kasabov, Nikola K. Ministry of Education School of Software Engineering Dalian University Key Laboratory of Advanced Design and Intelligent Computing Dalian116622 China Dalian University of Technology School of Software Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Nowadays, deep learning has made rapid progress in the field of multi-exposure image fusion. However, it is still challenging to extract available features while retaining texture details and color. To address this difficult issue, in this paper, we propose a coordinated learning network for detail-refinement in an end-to-end manner. Firstly, we obtain shallow feature maps from extreme over/under-exposed source images by a collaborative extraction module. Secondly, smooth attention weight maps are generated under the guidance of a self-attention module, which can draw a global connection to correlate patches in different locations. With the cooperation of the two aforementioned used modules, our proposed network can obtain a coarse fused image. Moreover, by assisting with an edge revision module, edge details of fused results are refined and noise is suppressed effectively. We conduct subjective qualitative and objective quantitative comparisons between the proposed method and twelve state-of-the-art methods on two available public datasets, respectively. The results show that our fused images significantly outperform others in visual effects and evaluation metrics. In addition, we also perform ablation experiments to verify the function and effectiveness of each module in our proposed method. The source code can be achieved at https://***/lok-18/LCNDR. © 1991-2012 IEEE.

关键词： Extraction

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A Multiscale Dynamic Temporal Convolution Network For Continuous Dimensional Emotion Recognition

A Multiscale Dynamic Temporal Convolution Network For Contin...

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International Joint Conference on Neural Networks (IJCNN)

作者： Min Hu Jialu Sun Xiaohua Wang Ning An Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Video-based dimensional emotion recognition, especially long sequence modeling, is a challenging task. This paper proposes a Multiscale Dynamic Temporal Convolution Network(MDTCN) for long sequence modeling of dimensional emotion recognition. Although Temporal Convolution Network(TCN) has performed well in sequence modeling, there are still some limitations that the feature scale of each layer is single and the receptive field of the upper layer is too large to capture the short-term dependence. Therefore, Dynamic Dilated Block(DDB) is constructed to better capture the temporal dependence by using dilated convolutions of different kernel size. In order to obtain both long-term and short-term temporal features, Multiscale TCN Structure is proposed. Extensive experiments are conducted on the AFFWILD2 and SEWA Dataset and the competitive result is shown compared to state-of-the-art methods.

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Finite-Time Synchronization of Flux-Controlled Complex-Valued Memristive Neural Networks

Finite-Time Synchronization of Flux-Controlled Complex-Value...

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Neuromorphic computing (ICNC), International Conference on

作者： Yonghuan Wang Leimin Wang Genping Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

In this paper, by using the flux-controlled memristor model, the finite-time synchronization problem of delayed complex-valued memristive neural networks (MCNNs) is studied. Firstly, according to the proposed memristor model, we model the MCNNs as continuous systems on voltage-flux-time $(\wp, \varpi, t)$ domain. Then, we design a class of approaches to realize state synchronization between the drive and response systems, and the corresponding synchronization conditions are achieved. Finally, the effectiveness of results is illustrated with the simulations.

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A Novel Ins/Usbl/Dvl Integrated Navigation Scheme Against Complex Underwater Environment

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SSRN 2023年

作者： Qin, Hongmao Wang, Xi Wang, Guangcai Hu, Manjiang Bian, Yougang Qin, Xiaohui Ding, Rongjun State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Wuxi Intelligent Control Research Institute of Hunan University Wuxi214115 China

In view of challenges of complex underwater environments with a high occurrence of sensor outliers, traditional Kalman-filter-based localization methods are difficult to achieve precise positioning. To address this issue, a factor graph optimization (FGO)-based tightly-coupled INS/USBL/DVL scheme and core fusion algorithm are proposed. The FGO method is used to construct a tightly-coupled INS/USBL/DVL factor graph model, and a weight function is added to adjust the weights of each factor, thereby improving the utilization of underwater sensor information and enhancing the accuracy and reliability of underwater navigation. The feasibility of the proposed scheme is verified through simulations and field test. The results show that the proposed method outperforms KF-LC, KF-TC, and FGO-LC methods, with horizontal position accuracy improvements of 29.2%, 6.6%, and 13.2%, respectively. Moreover, the proposed method exhibits smooth navigation error performance in the presence of abnormal sensor information. © 2023, The Authors. All rights reserved.

关键词： Navigation

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Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1752-1762页

作者： Xianglei Zhu Wen Hu Zejian Deng Jinwei Zhang Fengqing Hu Rui Zhou Keqiu Li Fei-Yue Wang the College of Intelligence and Computing Tianjin UniversityTianjin 300350 the China Automotive Technology and Research Center Co.Ltd. Tianjin 300300China the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterlooON N2L3G1Canada the School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China the Macao University of Science and Technology MacaoChina the Waytous Inc. Qingdao 266000China IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by *** improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic *** integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and *** dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference *** on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax ***,the comparative analysis is performed to validate the proposed method using the naturalistic driving *** results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

关键词： Cut-in behavior interaction-aware mixed traffic risk assessment trajectory prediction

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