检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Zhang, Chao Li, Yongwang Fu, Wencheng Zhiting, Tong 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

In the present study, multi-objective optimization is conducted to search for the best operating condition for a subcritical organic Rankine system with the trade-off between system exergy efficiency and dynamic payback period (PBP). A 150°C wastewater heat source is employed as a typical low-temperature heat source. Five decision variables are considered: turbine inlet temperature and pressure, pinch point temperature differences in evaporator and condenser, and condensing temperature. The multi-objective genetic algorithm combined with the backpropagation neural network method is employed as the optimization approach. In addition, the improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is developed to gain the optimal Pareto front solution for either working fluid and find the optimal working fluid among the candidates investigated. The results show that working fluid R236fa is the optimal solution, despite the minimum PBP for working fluid R123 and the maximum system exergy efficiency for working fluid R1234ze. Under the optimal working condition for working fluid R236fa, the system exergy efficiency and the PBP are 0.465 and 7.377 years, respectively. © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Research on Feedforward control Based on Robot Dynamics Parameters Identification 17

Research on Feedforward Control Based on Robot Dynamics Para...

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17th IEEE International Conference on mechatronics and Automation, ICMA 2020

作者： Shao, Xingmao Wang, Shoujun Yang, Liusong Liu, Nan Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin300384 China Citic Heavy Industries Company Ltd. Luoyang471003 China

ISBN: (纸本)9781728164151

Accurate robot dynamics model can effectively improve the motion control performance of robot, and accurate dynamics parameters are the basis for establishing dynamics model. This paper discusses the parameter identification method of the tandem manipulator and the feedforward control scheme based on dynamics. First, we deduced the basic parameters of the first three joints of the UR5 manipulator, and identified the parameters through experimental sampling and calculation. Secondly, we established a feedforward control mechanism based on dynamics using the Simulink platform, and simulated and verified the feedforward control of the robot three-axis system. The experimental results show that the feedforward control scheme based on robot dynamics can effectively improve the error convergence performance, which proves the effectiveness of the method. © 2020 IEEE.

关键词： Manipulators

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DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing

arXiv

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arXiv 2024年

作者： Zhou, Junjie Wang, Lin Meng, Qiang Wang, Xiaofan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Automation Shanghai Jiao Tong University School of Mechatronic Engineering and Automation Shanghai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai201418 China Department of Civil and Environmental Engineering National University of Singapore Singapore117576 Singapore

Generating realistic and diverse road scenarios is essential for autonomous vehicle testing and validation. Nevertheless, owing to the complexity and variability of real-world road environments, creating authentic and varied scenarios for intelligent driving testing is challenging. In this paper, we propose DiffRoad, a novel diffusion model designed to produce controllable and high-fidelity 3D road scenarios. DiffRoad leverages the generative capabilities of diffusion models to synthesize road layouts from white noise through an inverse denoising process, preserving real-world spatial features. To enhance the quality of generated scenarios, we design the Road-UNet architecture, optimizing the balance between backbone and skip connections for high-realism scenario generation. Furthermore, we introduce a road scenario evaluation module that screens adequate and reasonable scenarios for intelligent driving testing using two critical metrics: road continuity and road reasonableness. Experimental results on multiple real-world datasets demonstrate DiffRoad’s ability to generate realistic and smooth road structures while maintaining the original distribution. Additionally, the generated scenarios can be fully automated into the OpenDRIVE format, facilitating generalized autonomous vehicle simulation testing. DiffRoad provides a rich and diverse scenario library for large-scale autonomous vehicle testing and offers valuable insights for future infrastructure designs that are better suited for autonomous vehicles. Copyright © 2024, The Authors. All rights reserved.

关键词： Automobile testing

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Autoregressive GNN-ODE GRU Model for Network Dynamics

arXiv

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arXiv 2022年

作者： Liang, Bo Wang, Lin Wang, Xiaofan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai China

Revealing the continuous dynamics on the networks is essential for understanding, predicting, and even controlling complex systems, but it is hard to learn and model the continuous network dynamics because of complex and unknown governing equations, high dimensions of complex systems, and unsatisfactory observations. Moreover, in real cases, observed time-series data are usually non-uniform and sparse, which also causes serious challenges. In this paper, we propose an Autoregressive GNN-ODE GRU Model (AGOG) to learn and capture the continuous network dynamics and realize predictions of node states at an arbitrary time in a data-driven manner. The GNN module is used to model complicated and nonlinear network dynamics. The hidden state of node states is specified by the ODE system, and the augmented ODE system is utilized to map the GNN into the continuous time domain. The hidden state is updated through GRUCell by observations. As prior knowledge, the true observations at the same timestamp are combined with the hidden states for the next prediction. We use the autoregressive model to make a one-step ahead prediction based on observation history. The prediction is achieved by solving an initial-value problem for ODE. To verify the performance of our model, we visualize the learned dynamics and test them in three tasks: interpolation reconstruction, extrapolation prediction, and regular sequences prediction. The results demonstrate that our model can capture the continuous dynamic process of complex systems accurately and make precise predictions of node states with minimal error. Our model can consistently outperform other baselines or achieve comparable performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Sound Based Fault Diagnosis for Mechanical Components Based on Iceemdan and Fractional Multiscale Weighted Dispersion Entropy

SSRN

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SSRN 2023年

作者： Yin, Xiaojing He, Qiangqiang Zhang, Hao He, Weidong Zhang, Bangcheng Cao, You Zhou, Zhijie School of Mechatronic Engineering Changchun University of Technology Changchun China Rocket Force University of Engineering Xi’an China Changchun Faway Automobile Mirror System Co. Ltd. Changchun China The Control Technology Teaching and Research Office High-Tech Institute of Qingzhou Qingzhou China Mechanical & Automotive Engineering Changchun Institute of Technology Changchun China

Sound acquisition and feature extraction are critical aspects of the sound fault diagnosis. Unfortunately, the sound acquisition is obviously affected by environmental noise, and it is difficult to extract the features for mechanical component fault diagnosis. To address this challenge, based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and fractional multiscale weighted dispersion entropy (FMWDE), a fault diagnosis method for mechanical components is proposed in this paper. Firstly, in the ICEEMDAN denoising method, the effect of noise is overcome by selecting the signal components processed based on the hybrid criterion of relative energy ratio and relative correlation ratio. Secondly, weighted dispersion entropy (WDE) based on kurtosis and refined composite multiscale root mean square (rcMRMS) is proposed for feature extraction. In addition, the introduction of fractional calculus enhances the feature extraction capability of WDE, and the fractional weighted dispersion entropy (FWDE) is extended to multi-scale domains through coarse-graining for obtaining FMWDE. Finally, integrated decision trees (DT), random forests (RF), and particle swarm optimization support vector machines (PSO-SVM) fault diagnosis models are established. The feasibility and superiority of the proposed method are validated by using two datasets. Specifically, the proposed method achieved average accuracy of 85.27%, 86.13%, and 85.67% for the three models using the data collected from a company, and average accuracy of 99.17%, 98.33%, and 99.35% was achieved in the data collected by the aeroengine fuel pump (AFP) monitoring platform established. © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Prototype Development of Portable Head Feed Harvesting Robot 5

Prototype Development of Portable Head Feed Harvesting Robot

引用

5th International Symposium on Material, mechatronics and Energy 2018, ISMME 2018

作者： Anugrah, Muh Rifai Jeky Muhammad, Abdul Kadir Taufik, Akhmad Syam, Rafiuddin Center for Mechatronics and Control System Mechatronic Engineering Polytechnic of Ujung Pandang Indonesia

This study aims to develop a prototype of portable head feed harvesting robot that can be installed in a tractor so that the users of tractors that initially only function during the farming season can also function during the harvesting season. This machine is divided into 3 main parts. In the first part there is a cutting mechanism that functions to cut rice using a DC electric motor with a crank mechanism connection to the cutting shaft. The second part is a rice lifting mechanism which functions to lift the rice cut into the conveyor belt position. The third part is a connection that connects the rice cutting machine to the used tractor using a bolt connection. The machine has been tested on rice field and work successfully. © Published under licence by IOP Publishing Ltd.

关键词： Harvesting

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Adversarial Learning Network for Recovering Rppg Signals from Low-Resolution Images for Remote Heart Rate Measurement

SSRN

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SSRN 2024年

作者： Zhai, Dezhao Chen, Wei Li, Qinwei Yu, Ming Wu, Hang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Systems Engineering Institute Academy of Military Sciences People's Liberation Army Tianjin300161 China Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin300300 China School of Artificial Intelligence Nankai University Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, which has significant research value and application potential. However, in long-distance vital sign measurement tasks, the low resolution of facial images and insufficient pixels in the facial region severely degrade the quality of the rPPG signals, leading to a sharp decline in HR accuracy. To address this issue, this paper proposes an adversarial learning network for recovering rPPG signals from low-resolution images (ALRSL-HR). First, arbitrary resolution face images are fed into multi-channel network to extract rPPG signal features capturing rich rPPG information, generating rPPG feature blocks, and temporally aggregating for richer feature representation. These features are then inputted into the backbone network to generate adversarial examples of rPPG signals of varying qualities. Finally, a two-stage adversarial loss function is employed to enable the low-quality rPPG signals to learn the periodic characteristics of high-quality rPPG signals, recover the low-quality signals, and narrow the quality gap between rPPG signals from low-resolution images. Extensive experiments were conducted in this paper to validate the effectiveness of our model on the public COHFACE, UBFC-Phys and UBFC-rPPG datasets. The results of the experiments demonstrate that the proposed ALRSL-HR model can effectively recover degraded rPPG signals from low-resolution images. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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Design of wearable squat booster for automobile assembler based on ergonomics 6

Design of wearable squat booster for automobile assembler ba...

引用

2020 6th International Forum on Engineering Materials and Manufacturing Technology, IFEMMT 2020

作者： Chen, Zhewen Qiao, Zhifeng Bao, Tianxu Dou, Fuchao Chen, Xinpu Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin China

Aiming at the problem that automobile assembler need to squat for a long time, a wearable squatting assistance system with low cost, simple control and easy to squat and walk is designed. Based on ergonomics and finite element theory, UG was used to draw a three-dimensional solid model of the wearable squatting power system for automobile assemblers. With the help of ANSYS software, the statics analysis of key components is carried out to check their strength and stiffness. It is concluded that the wearable squatting power assist device for automotive fitters based on ergonomics has reached the design requirements in terms of function use and strength and stiffness. © Published under licence by IOP Publishing Ltd.

关键词： Ergonomics

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controllability of Networked Sampled-data systems

arXiv

引用

arXiv 2022年

作者： Yang, Zixuan Wang, Xiaofan Wang, Lin The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

The controllability of networked sampled-data systems with zero-order holders on the control and transmission channels is explored, where single- and multi-rate sampling patterns are considered, respectively. The effects of sampling on the controllability of networked systems are analyzed, with some necessary and/or sufficient controllability conditions derived. Different from the sampling control of single systems, the pathological sampling of node systems could be eliminated by an appropriate design of network structure and inner couplings. While for singular topology matrices, the pathological sampling of single nodes will cause the entire system to lose controllability. Moreover, any periodic sampling will not affect the controllability of networked systems with specific node dynamics. All the results indicate that whether a networked system is under pathological sampling or not is jointly determined by mutually coupled factors. Copyright © 2022, The Authors. All rights reserved.

关键词： controllability

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The relationship between orthopedic clinical imaging and bone strength prediction

引用

Medicine in Novel Technology and Devices 2021年第1期9卷 9-17页

作者： He Gong Meng Zhang Shaowei Jia Linwei Lv Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beijing Advanced Innovation Center for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijingChina Department of Engineering Mechanics Nanling CampusJilin UniversityChangchun130025China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin300384China

Pathologic fracture is caused by bone diseases,which is common in middle-aged and elderly *** bone strength is the leading contributor to this type of ***,accurately quantifying bone strength could help clinicians assess individualized fracture risk and develop therapeutic *** clinical imaging including dual X-ray absorptiometry(DXA),computed tomography(CT),and magnetic resonance imaging(MRI)has been an important tool to obtain radiology images of bone,which could be further used for noninvasive assessment of bone strength and related fracture *** the development of biomechanics and computer technology,the combination of finite element analysis with orthopedic imaging is the most advanced method for noninvasive prediction of bone strength in *** this review,an overview is provided for the commonly noninvasive methods based on the radiology images(i.e.,DXA,CT,and MRI)to predict bone strength,which may improve the understanding of the relationship between orthopedic clinical imaging and biomechanics.

关键词： Orthopedic clinical imaging Biomechanics Bone strength Finite element analysis

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