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A novel strategy to solve communication constraints for formation control of multi-AUVs

Science China(Information Sciences) 2021年第7期64卷 253-255页

作者： Zhenyu GAO Ge GUO Department of Automation Dalian Maritime University School of Control Engineering Northeastern University Qinhuangdao Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Dear editor,Interest in terms of research on formation control of multiple autonomous underwater vehicles (multi-AUVs) has gained considerable attention in recent years [1–5], owing to its extensive applications in d... 详细信息

关键词： Information Systems and Communication Service

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Data-Based Efficient Off-Policy Stabilizing Optimal Control Algorithms for Discrete-Time Linear Systems via Damping Coefficients

arXiv

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arXiv 2024年

作者： Li, Dongdong Dong, Jiuxiang College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation of Process Industries Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Northeastern University Shenyang110819 China

Policy iteration is one of the classical frameworks of reinforcement learning, which requires a known initial stabilizing control. However, finding the initial stabilizing control depends on the known system model. To relax this requirement and achieve model-free optimal control, in this paper, two different reinforcement learning algorithms based on policy iteration and variable damping coefficients are designed for unknown discrete-time linear systems. First, a stable artificial system is designed, and this system is gradually iterated to the original system by varying the damping coefficients. This allows the initial stabilizing control to be obtained in a finite number of iteration steps. Then, an off-policy iteration algorithm and an off-policy Q-learning algorithm are designed to select the appropriate damping coefficients and realize data-driven. In these two algorithms, the current estimates of optimal control gain are not applied to the system to re-collect data. Moreover, they are characterized by the fast convergence of the traditional policy iteration. Finally, the proposed algorithms are validated by simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Damping

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Group-Mix SAM: Lightweight Solution for Industrial Assembly Line Applications

arXiv

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arXiv 2024年

作者： Wu, Liang Ma, X.-G. Faculty of Robot Science and Engineering Northeastern University Shenyang China Foshan Graduate School Northeastern University Foshan China College of Information Science and Engineering Northeastern University Shenyang China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Since the advent of the Segment Anything Model(SAM) approximately one year ago, it has engendered significant academic interest and has spawned a large number of investigations and publications from various perspectives. However, the deployment of SAM in practical assembly line scenarios has yet to materialize due to its large image encoder, which weighs in at an imposing 632M. In this study, we have replaced the heavyweight image encoder with a lightweight one, thereby enabling the deployment of SAM in practical assembly line scenarios. Specifically, we have employed decoupled distillation to train the encoder of MobileSAM in a resource-limited setting. The entire knowledge distillation experiment can be completed in a single day on a single RTX 4090. The resulting lightweight SAM, referred to as Group-Mix SAM, had 37.63% (2.16M) fewer parameters and 42.5% (15614.7M) fewer floating-point operations compared to MobileSAM. However, on our constructed industrial dataset, MALSD, its mIoU was only marginally lower than that of MobileSAM, at 0.615. Finally, we conducted a comprehensive comparative experiment to demonstrate the superiority of Group-Mix SAM in the industrial domain. With its exceptional performance, our Group-Mix SAM is more suitable for practical assembly line applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Assembly

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Fixed-time Sliding Mode Formation Control of AUVs Based on a Disturbance Observer

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 539-545页

作者： Zhenyu Gao Ge Guo School of Control Engineering Northeastern University at QinhuangdaoQinhuangdao 066004China Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819 School of Control Engineering Northeastern University at QinhuangdaoQinhuangdao 066004China(

In this paper,we investigate formation tracking control of autonomous underwater vehicles(AUVs)with model parameter uncertainties and external *** external disturbances due to the wind,waves,and ocean currents are combined with the model parameter uncertainties as a compound *** a disturbance observer(DO)is introduced to estimate the compound disturbance,which can be achieved within a finite time independent of the initial estimation *** on a DO,a novel fixed-time sliding control scheme is developed,by which the follower vehicle can track the leader vehicle with all the states globally stabilized within a given settling *** effectiveness and performance of the method are demonstrated by numerical simulations.

关键词： Autonomous underwater vehicles(AUVs) disturbance observer(DO) sliding mode control formation control

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A Soft Sensor Model Based on HGA-LSTM for the Feed Density in a Thickener

A Soft Sensor Model Based on HGA-LSTM for the Feed Density i...

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第33届中国过程控制会议

作者： Kang Li Lianyu Wang Guobin Zou BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy Research College of Information Science and Engineering Northeastern University

This study focused on the issue of accurately measuring the feed density in *** problem is common in the mineral processing ***,owing to the limitations of existing instruments and devices,the feed density in the thickener is difficult to measure online *** sensors are extensively utilized as an effective *** long short-term memory(LSTM) network has been proposed for the soft sensor model development and has shown significant modeling ability considering time ***,it is challenging to develop an accurate prediction model as it involves optimizing the hyperparameters,such as the time window size and structure of the LSTM ***,aiming at the problem of accurately measuring the feed density,we proposed a new soft sensor modeling method based on the LSTM and hybrid genetic algorithm(HGA).Specifically,the HGA refers to a novel optimization search algorithm model that can optimize the hyperparameters and improve the modeling performance of the ***,the proposed soft sensor modeling method was successfully applied to a real thickener case in *** soft sensor prediction results demonstrated that the hybrid model outperformed other models and satisfied the measurement accuracy requirements in the factory well.

关键词： soft sensor thickening process feed density long short-term memory hybrid genetic algorithm

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Nonlinear industrial process fault diagnosis with latent label consistency and sparse Gaussian feature learning

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Journal of Central South University 2022年第12期29卷 3956-3973页

作者： LI Xian-ling ZHANG Jian-feng ZHAO Chun-hui DING Jin-liang SUN You-xian Science and Technology on Thermal Energy and Power Laboratory Wuhan 2nd Ship Design and Research InstituteWuhan 430205China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China State Key Laboratory of Synthetical Automation for Process Industries(Northeastern University) Shenyang 110819China

With the increasing complexity of industrial processes, the high-dimensional industrial data exhibit a strong nonlinearity, bringing considerable challenges to the fault diagnosis of industrial processes. To efficiently extract deep meaningful features that are crucial for fault diagnosis, a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label dimension. The feature space is described by the one-hot encoding of the fault category label as an orthogonal basis. In this way, the deep sparse Gaussian features related to fault categories can be gradually learned from the raw data by SGFE. In the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian distributions. The sparse Gaussian features are linearly separable in the feature space, which is conducive to improving the accuracy of the downstream fault classification task. The feasibility and practical utility of the proposed SGFE are verified by the handwritten digits MNIST benchmark and Tennessee-Eastman(TE) benchmark process,respectively.

关键词： nonlinear fault diagnosis multiple multivariate Gaussian distributions sparse Gaussian feature learning Gaussian feature extractor

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A Novel Surface Plasmon Resonance Sensor Sensitivity Enhancement Method for Temperature Measurement by Depositing Tungsten Disulfide Nanosheets on the Silver Film

SSRN

引用

SSRN 2023年

作者： Zhao, Yang Cheng, Tonglei Fu, Rao Hu, Taotao Wei, Luo Li, Haihui Yan, Xin State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

Surface plasmon resonance (SPR) sensors are extensively used with refractive index and temperature sensing. A novel SPR sensor for temperature measurement is proposed in this paper. A novel two-dimensional material Tungsten disulfide is used to improve the sensitivity of this temperature sensor. Polydimethylsiloxane covers the sensing area to better sense changes in external temperature. The experimental results show that the SPR sensor can measure the temperature range of 30 °C -100 °C with a temperature sensitivity of 2.03 nm/°C. This sensor has the merits of a simple structure, low manufacturing process cost, a broad range of tests, and high sensitivity compared to other temperature sensors. Therefore, the sensor has great potential for future applications in environmental monitoring, biomedicine, the food industry, and other production activities. © 2023, The Authors. All rights reserved.

关键词： Refractive index

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Leveraging Foundation Model Automatic Data Augmentation Strategies and Skeletal Points for Hands Action Recognition in Industrial Assembly Lines

arXiv

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arXiv 2024年

On modern industrial assembly lines, many intelligent algorithms have been developed to replace or supervise workers. However, we found that there were bottlenecks in both training datasets and real-time performance when deploying algorithms on actual assembly line. Therefore, we developed a promising strategy for expanding industrial datasets, which utilized large models with strong generalization abilities to achieve efficient, high-quality, and large-scale dataset expansion, solving the problem of insufficient and low-quality industrial datasets. We also applied this strategy to video action recognition. We proposed a method of converting hand action recognition problems into hand skeletal trajectory classification problems, which solved the real-time performance problem of industrial algorithms. In the "hand movements during wire insertion" scenarios on the actual assembly line, the accuracy of hand action recognition reached 98.8%. We conducted detailed experimental analysis to demonstrate the effectiveness and superiority of the method, and deployed the entire process on Midea’s actual assembly line. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Automated machine learning-assisted enhanced product carbon footprint tracking and analysis in refinery industry: A graph-based life cycle assessment framework

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Journal of Cleaner Production 2025年 514卷

作者： Zhang, Tingwei Peng, Xin Zhong, Weimin Shen, Feifei Ding, Jinliang State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai200237 China State Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai200237 China School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang110819 China

Product carbon footprint assessment and tracking in the refinery industry mainly rely on the expertise of carbon management professionals, which leads to high service costs and involves significant manual intervention, reducing the accuracy of carbon footprint analysis. Accordingly, a data-driven automated product carbon footprint analysis framework based on life cycle assessment is developed in this work. Specifically, an integrated material-energy-carbon multi-scale emission accounting model is established to quantify CO2 emissions accurately. Subsequently, considering that traditional carbon emission allocation methods are generally based on fixed assumptions or simplified proportional relationships, which fail to fully account for the dynamic variations in complex production processes, the allocation results lack accuracy and reliability. Additionally, these methods lack sufficient flexibility and are unable to effectively adjust to the actual carbon emission characteristics of different production stages and products. A data-driven contribution-based carbon emission allocation method is proposed from the carbon flow supply–demand balance perspective to improve allocation transparency. Moreover, a graph-based automated recognition framework of product carbon footprint chains is developed to achieve process operation automation and eliminate manual operations. The proposed models and methods are applied to a real refinery production site for a case study. The results show that the developed product carbon footprint analysis framework can highlight high-energy consumption and high-emission links in the production process and pay more attention to the environmental costs of high-value products. Moreover, CO2 can act as a carbon flow regulation hub, effectively managing emissions through the balance of carbon flow supply and demand. Light oil has a higher contribution rate to CO2 emissions than heavy oil. The carbon footprint of petroleum coke is 3.8677 tCO2eq/t, ma

关键词： Carbon footprint

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Research on Joint Optimization of Single Train's Delay Reduction and Energy-Saving Operation Trajectory 6

Research on Joint Optimization of Single Train's Delay Reduc...

引用

6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Zhang, Yunjia Cui, Dongliang Li, Jianming Gao, Feng Zang, Haijun Dai, Xuewu Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China China Railway Shenyang Group Co. Ltd Tongliao Logistics Center Shenyang110000 China China Railway Shenyang Group Co. Ltd Dispatching Office Shenyang110000 China Northumbria University Department of Mathematics Physics and Electrical Engineering Newcastle upon TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350356618

This study focuses on optimizing urban rail transit operations during emergencies, aiming to reduce train traction energy consumption and expedite delay recovery by jointly optimizing arrival/departure times at each station and the operational trajectory between stations. A 3D STS (Space-Time-Speed) network is created to merge macroscopic train timetable scheduling with microscopic train operation trajectory optimization. By appropriately setting dimension resolutions, the system approximates the train's position, speed, and acceleration, transforming the complex train operation problem into a more manageable path planning challenge. Propose dual objectives: minimize net traction energy and delay time, aiming to reduce overall train operating costs and delays. Weighted summation combines objectives into a single nonlinear optimization. Dynamic programming then tackles the transformed path planning problem. The simulation results of Beijing Yizhuang Line show that while delay increases energy consumption, judicious selection of train operation trajectory can significantly reduce the energy consumption of the train. Factoring in energy consumption yields a 15.43% reduction compared to disregarding it, ensuring more efficient energy use without disrupting normal train operations. © 2024 IEEE.

关键词： Dynamic programming

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