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IEEE International Conference on Robotics and Automation (ICRA)

作者： Shaoping Huang Anzhu Gao Zicong Wu Chuqian Lou Yanjun Wang Guang-Zhong Yang The Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Jiao Tong University The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Imperial College London and Empa-Swiss Federal Laboratories for Materials Science and Technology

MRI-guided robotic systems are emerging platforms for minimally invasive intervention because of high positioning accuracy and excellent tissue contrast. MR safe encoders are critical components for closed-loop robotic control. This paper develops an MR safe absolute rotary encoder based on eccentric sheave and FBG sensors. The eccentric sheave transforms the rotational motion of the shaft to the bending deflection of the beam on which FBG sensors are integrated. A model is built by establishing the relationship of the kinematics of the sheave, the mechanical properties of the beam with unknown length, and the strain model of two Fiber Bragg Grating (FBG) sensors. A Pseudo-Rigid Body (PRB) 3R model is used to solve a set of constrained equations for accurate rotary encoding. A prototype is built to calibrate the parameters and validate the accuracy of the encoder and its MR compatibility. Results show that the maximum angular error is 1.6°, and the RMS error is 0.46°. MRI shows that no noticeable artifacts are observed, and the Signal to Noise Ratio (SNR) is not affected. The results demonstrate the potential of the proposed method for it to be integrated with MR safe robots with easy fabrication, compact structures, and continuous measurement.

关键词： Mechanical sensors Shafts Analytical models Kinematics Transforms Fiber gratings Robot sensing systems

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Role of strategy update rules in the spatial memory-based mixed strategy games

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The European Physical Journal B 2021年第1期94卷 1-11页

作者： Zhang, Fan Wang, Juan Gao, Hongyu Li, Xiaopeng Xia, Chengyi Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology Tianjin People’s Republic of China School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin People’s Republic of China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin People’s Republic of China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin People’s Republic of China

Based on a continuous mixed strategy game model considering the memory mechanism and the uncertainty of individual decision-making, we explore the influence of several different update rules on the spatial cooperation behavior. Here, we mainly investigate the role of the Moran-like process, Fermi rule and replicator dynamics in the evolution of cooperation. Meanwhile, we further compare the evolution of cooperation when the memory mechanism and individual uncertainty are taken into account. In particular, memory length M and strategy adjustment factor $$\delta $$ can promote the cooperation behavior in different ways. Extensive Monte Carlo simulations indicate that the Moran-like process can generally drive the expansion of cooperative clusters faster and finally achieve the highest frequency of cooperation although the Fermi update rule performs better under the same condition. Moreover, the replicator dynamics create the worst scenario as far as the evolution of cooperation is concerned. Therefore, the level of cooperation strongly depends on the strategy update rules; these findings may be helpful to understand and analyze the evolutionary process of cooperation in many real-world natural and social systems.

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Advancing energy absorption in additively manufactured meta-plates through machine learning-driven inverse design

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Thin-Walled Structures 2025年 215卷

作者： Qingyuan Liu Liang Mou Lan Zhang Shuai Yuan Haiyang Peng Jinguo Ge Yunze Yang Yuhong Long Hualin Fan Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Jinji road Guilin Guangxi 541004 China Research Center of Lightweight Structures and Intelligent Manufacturing State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

Transportation safety systems (e.g., automotive crash boxes, train front-end energy absorption structures) require energy absorbers to simultaneously control energy absorption ( EA ), peak crushing force or stress ( PCF , PCS ), mean crushing force or stress ( MCF , MCS ), and undulation of load carrying ( ULC ). This study proposed meta-plates, characterized by stacking layers, twisting angles, and wall thicknesses. An additive manufacturing constrained machine learning and genetic algorithm (ML-GA) co-design framework was designed to optimize the crashworthiness performances. Finite element (FE) simulations incorporating nonlinear material models and contact algorithms, combined with Latin hypercube sampling, efficiently explored 10.7% of the design space, capturing nonlinear large deformations under quasi-static compression up to 50% strain. 600 independent samples were calculated to evaluate the performance of the ML model, which achieved 96.0% prediction accuracy ( R 2 score). At a given target relative density ( ρ ∗ ) ranging from 4% to 14%, the optimized meta-plates demonstrated 80%–200% higher MCS compared to benchmark shell/plate lattices at equivalent ρ ∗ . The meta-plates also exhibited directional crushing behavior with crushing force efficiency ( CFE ) around 1.0. Experimental validations show < 6% deviation from predictions also witnessed the high MCS , low PCS , and nearly free from stress fluctuation for meta-plates with 8%, 10%, and 14% ρ ∗ values. This methodology enables rapid design of lightweight energy absorbers with tunable deformation modes and extremely high material efficiency, providing a paradigm shift from trial-and-error approaches to intelligent crashworthy structure development.

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Transformer-LSTM-KOA: A novel approach for prediction of chlorophyll in the South China Sea

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Environmental Modelling & Software 2025年 192卷

作者： Gang Chen Zhenzhong Liu Runfeng Zhang Guobin Zhang Jutao Wang Tongrui Zhang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin 300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) Tianjin China School of Environmental Science and Safety Engineering Tianjin University of Technology Tianjin 300384 China Tianjin Hanhailanfan Marine Technology Co. Ltd. Tianjin 300457 China

Chlorophyll is a critical indicator of marine primary productivity and is essential in assessing aquatic ecosystem health. To address the limitations of current prediction models regarding accuracy, mobility, and reliability, this study proposes a novel Transformer-LSTM-KOA model for Chlorophyll prediction. Leveraging remote sensing data from the South China Sea, the model synergistically integrates Transformer's global analytical capabilities via self-attention and multi-head attention mechanisms with LSTM's proficiency in capturing long-term dependencies through gated mechanisms. The Kepler Optimization Algorithm (KOA) is subsequently employed to systematically optimize both global and local model parameters, thereby enhancing adaptability and robustness across diverse prediction datasets. Experimental results demonstrate superior performance with MSE and MAE values of 0.00011 and 0.00685, respectively, outperforming benchmark models. This enhanced modeling framework provides crucial technical support for marine ecological forecasting and early warning systems.

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Multi-objective vehicle path planning method for recycling process of used electrical and electronic products

Multi-objective vehicle path planning method for recycling p...

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2020 IEEE International Symposium on Product Compliance engineering-Asia, ISPCE-CN 2020

作者： Yang, Cuili Wu, Zhanhong Han, Honggui Bi, Xiaowen Beijing University of Technology Faculty of Information Technology Beijing China City University of Hong Kong Department of Electrical Engineering Hong Kong Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligence System Eng. Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Department of Electrical Engineering City University of Hong Kong. Hong Kong

ISBN: (纸本)9781665414616

During the recycling process of used electrical and electronic products from recycling places to sorting center, the vehicle path planning is an urgent problem to be solved which needs to minimize the number of vehicles and vehicle transportation distance simultaneously. To solve this problem, a multi-objective optimization algorithm is proposed which combines the genetic algorithm (GA) and multi-objective evolutionary algorithm based on decomposition (MOEA/D). Firstly, the vehicle path planning model of the sorting center is established according to characteristics of product transportation process. Secondly, the GA is used to produce some vehicle path planning schemes. Then, the MOEA/D is introduced to deal with the trade-off relationship between two objectives. Simulation results demonstrate that the proposed algorithm is able to generate a set of non-dominated solutions. © 2020 IEEE.

关键词： Genetic algorithms

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Model-Predictive-control-based Simultaneous Trajectory Tracking and Speed control for intelligent Vehicles*

Model-Predictive-Control-based Simultaneous Trajectory Track...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Hanyang Zhuang Yikai Hu Chunxiang Wang Ming Yang University of Michigan - Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China Guangxi Key Laboratory of Automobile Components and Vehicle Technology Guangxi University of Science and Technology Liuzhou China Shanghai Jiao Tong University Shanghai Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665431545

Trajectory tracking and speed control for intelligent vehicles are put forward higher requirements on accuracy by the high traffic density scenario. A standard Model Predictive control (MPC) algorithm focuses on the trajectory tracking but ignores the speed control. This paper proposes a simultaneous trajectory tracking and speed control algorithm based on MPC approach to address fine control in both space and time dimensions for intelligent vehicles. A threefold cost function including lateral and longitudinal speed as well as comfort has been proposed to improve the control algorithm. The simulations and comparisons demonstrate that the proposed method has a better tracking and speed control performance than the existing method.

关键词： Space vehicles Costs Mechatronics Trajectory tracking intelligent vehicles Velocity control Prediction algorithms

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Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing

arXiv

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arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tuğfe Ai, Bo The School of Electronic and Information Engineering The Frontiers Science Center for Smart Highspeed Railway System Beijing Jiaotong University Beijing100044 China Purple Mountain Laboratories Nanjing211111 China The Department of Computer Science KTH Royal Institute of Technology Kista164 40 Sweden The Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Study on Rotating Speed control of Dissimilar Metal Friction Stir Welding based on Fuzzy PID

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Journal of Physics: Conference Series 2022年第1期2206卷

作者： Yongning Liu Zhifeng Qiao Qingjian Liu Jiahao Huang Kang Fu Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin 300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology)

The demand for lightweight vehicles that achieve energy conservation and emission reduction continues to grow. Aluminum alloys were commonly applied to the automotive industry to reduce the structural quality of the entire vehicle. However, the cost of aluminum alloy was higher than that of steel, so the industry considered the use of aluminum in combination with low-cost materials such as steel. Traditional welding technology was limited by the great physical difference between aluminum alloy and steel. Friction Stir Welding, a solid phase welding technology, can overcome the connection problem of aluminum and its alloys with steel, but the process parameters of FSW directly affect the welding quality. This paper established the FSW process of two different metals of DC05 and AL6061. Aimed to get a good welding joint, the error of tool speed is reduced by fuzzy PID system established in this paper, and the tool speed is controlled stably. Simulink simulation was established, and compared with the simulation results, fuzzy PID control can control the precision and stability of the tool speed of FSW, improve the welding quality, and provide a certain theoretical basis for the tool speed control.

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A New Anomaly Detector of Flare Pilot Based on Siamese Network

A New Anomaly Detector of Flare Pilot Based on Siamese Netwo...

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Chinese Automation Congress (CAC)

作者： Xiaotian Xie Ke Gu Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The flare pilot is the key to ensure that the flare system is ignited at any time. At present, domestic and foreign companies mainly use thermocouples, flame ionization detectors and other sensors to detect the working condition of the flare pilot. These electronic components have the hysteretic and vulnerable problems due to extreme heat, thermal shock and vibration, which may well further lead to the failure to ignite the flare gas with flare pilot in time and cause production accidents. In view of the above problems, this paper proposes an anomaly detector of flare pilot based on deep learning technology. First of all, we made an anomaly detection database for flare pilot, which was shot in a domestic petrochemical company. Then, we designed a specific siamese network based on the triplet loss function for learning the similarity between the samples, in order to apply to the curing scene of the flare pilot. Finally, it is determined whether the flare pilot is abnormal according to the similarity between the input picture and the positive and negative samples. Experimental results demonstrate that the proposed method can be effectively used for the anomaly detection of flare pilot.

关键词： Databases Anomaly detection Petrochemicals Feature extraction Employee welfare Detectors Combustion

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Energy-Constrained Computation Offloading in Space-Air-Ground Integrated Networks using Distributionally Robust Optimization

arXiv

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arXiv 2022年

作者： Chen, Yali Ai, Bo Niu, Yong Zhang, Hongliang Han, Zhu The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

With the rapid development of connecting massive devices to the Internet, especially for remote areas without cellular network infrastructures, space-air-ground integrated networks (SAGINs) emerge and offload computation-intensive tasks. In this paper, we consider a SAGIN, where multiple low-earthorbit (LEO) satellites providing connections to the cloud server, an unmanned aerial vehicle (UAV), and nearby base stations (BSs) providing edge computing services are included. The UAV flies along a fixed trajectory to collect tasks generated by Internet of Things (IoT) devices, and forwards these tasks to a BS or the cloud server for further processing. To facilitate efficient processing, the UAV needs to decide where to offload as well as the proportion of offloaded tasks. However, in practice, due to the variability of environment and actual demand, the amount of arrival tasks is uncertain. If the deterministic optimization is utilized to develop offloading strategy, unnecessary system overhead or higher task drop rate may occur, which severely damages the system robustness. To address this issue, we characterize the uncertainty with a data-driven approach, and formulate a distributionally robust optimization problem to minimize the expected energy-constrained system latency under the worst-case probability distribution. Furthermore, the distributionally robust latency optimization algorithm is proposed to reach the suboptimal solution. Finally, we perform simulations on the realworld data set, and compare with other benchmark schemes to verify the efficiency and robustness of our proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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