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IEEE International Conference on industrial technology (ICIT)

作者： Cheng Su Weimin Zhong Dan Yang Linlin Li Xin Peng State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai China Key Laboratory of Smart Manufacturing in Energy Chemical Process East China University of Science and Technology Shanghai China Department of Control Science and Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Domain adaptation (DA) can be used for fault detection under multiple operating conditions. However, the lack of interpretability of DA models prevents potential users from understanding the decisions made by models. To give a solution, a neural additive domain adaptation network (NADAN) is proposed, which is constructed based on a neural additive model (NAM). Benefiting from the structural advantages of NAM, the predictions of NADAN are interpretable. Specifically, NADAN is able to explain how each feature affects the decision of the model. In addition, NADAN consists of two modules, feature learning (FL) and domain adaptation (DA), and achieves domain alignment through the combination of FL and DA. The FL module is pretrained to learn high-order features. The DA module takes learned features as input and achieves domain alignment based on multi-kernel maximum mean discrepancy (MK-MMD). Compared to the raw data, the learned features are more task-relevant, which helps to improve the performance of NADAN on DA. Furthermore, NADAN augments the feature dimensions during DA to measure the inter-domain differences in detail, which in turn achieves better domain alignment. Experiments on datasets generated from wastewater treatment simulation model demonstrate the effectiveness of the proposed NADAN.

关键词： Representation learning Adaptation models Additives Fault detection Computational modeling Feature extraction Computational efficiency

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UKF-Based Multi-sensor Data Processing Algorithm for SLAM 9

UKF-Based Multi-sensor Data Processing Algorithm for SLAM

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9th International Conference on control science and Systems engineering, ICCSSE 2023

作者： Xu, Kun Zhang, Boheng Li, Jiangang Wang, Guizhong Sun, Mingjian School of Information Science and Engineering Harbin Institute of Technology Weihai China Harbin Institute of Technology Department of Control Science and Engineering Harbin China Co. Ltd. Shenzhen China Hebei Key Laboratory of Grid Advanced Measurement Monitor Jinyanhagong Industrial Technology Research Institute Zhangjiakou China

ISBN: (纸本)9798350339055

Simultaneous localization and mapping (SLAM) algorithm is widely used in unmanned and robotic applications. SLAM usually uses sensor data, which are from the LiDAR, the inertial measurement unit (IMU) and the wheeled odometry, to build maps of the surrounding environment. However, the motion distortion of LiDAR data and the error accumulation of wheeled odometry can affect the accuracy of the front-end odometry of the SLAM algorithm, which can lead to serious errors in the maps created. Therefore, in order to solve the problem of poor map construction results of SLAM algorithm due to various types of sensor errors. we propose the traceless Kalman filtering based (UKF-based) multi-sensor data processing algorithm. Firstly, the proposed algorithm uses the UKF to fuse IMU and wheeled odometry data to reduce the cumulative error of wheeled odometry. And then the algorithm uses IMU and corrected wheeled odometry data and combines with the motion states of the robot to solve the motion distortion problem of LiDAR data. Finally, we verified the effectiveness of the proposed algorithm for wheeled odometry data correction in Gazebo simulation environment and real environment experiments, and compared the mapping effectiveness of SLAM by using the original data and the data processed by our algorithm. The experimental results show that the map construction effect of SLAM algorithm is significantly improved when using the sensor data processed by the algorithm. Experimental validation on the robot shows that the proposed algorithm has universal applicability and good compatibility. © 2023 IEEE.

关键词： Sensor data fusion

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Research on Gastritis Endoscopic Images Classification Based on SE-RepLKNet

Research on Gastritis Endoscopic Images Classification Based...

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Computer, Vision and Intelligent technology (ICCVIT), International Conference on

作者： Xinao Jin Yining Zhao Yuqi Cao Gangzhi Zhang Guangxin Zhang Yuehua Han State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Gastroenterology The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Autoimmune gastritis (AIG) is an atrophic gastritis caused by abnormal autoimmune function. Recent studies indicate an increasing prevalence of AIG in China. In advanced stages, AIG can progress to neuroendocrine tumors and gastric adenocarcinoma, posing a life-threatening risk to patients. However, due to the insufficient clinical awareness and nonspecific clinical manifestations of the disease, AIG has a low diagnostic rate and is frequently misdiagnosed as type B gastritis caused by Helicobacter pylori infection, which leads to delayed diagnosis. Gastroscopy serves as a critical tool for diagnosing AIG based on distinct gastroscopic features of AIG and type B gastritis. This study proposes a gastritis classification and diagnostic method based on gastroscopic images using a deep learning model, RepLKNet, with large-kernel convolution. Additionally, a channel attention mechanism, the SE module, is integrated to enhance the model's feature extraction capability. Considering the scarcity of publicly available medical image datasets for pretraining, a two-stage pretraining strategy is adopted to incrementally train the model for extracting hierarchical features. Experimental results demonstrate a classification accuracy of 89.6% on gastroscopic images. This system provides clinicians with more accurate classification suggestions for two frequently misdiagnosed types of gastritis, offering crucial information for subsequent treatment and examination.

关键词： Training Accuracy Convolution Computational modeling Maintenance engineering Feature extraction Kernel Medical diagnostic imaging Gastritis Tumors

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Analysis of the Influence of Decision Module Performance on Transient Events in Steam Generator Level control 1

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6th International Symposium on Software Reliability, industrial Safety, Cyber Security and Physical Protection of Nuclear Power Plant, ISNPP 2021

作者： Liu, Dao-Guang Luan, Zhen-Hua Fang, Zi-Rui Yan, Xin-hong Liu, Shuang-Jin Jun, Liang State Key Lab of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China New Materials and New Energy College Shenzhen Technology University Lantian Road Pingshan District Shenzhen518124 China State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment Guangdong Shenzhen518172 China

ISBN: (数字)9789811911811

ISBN: (纸本)9789811911804

Decision module is widely used in nuclear power plant, participating in important control and protection system. Decision module performance directly affects the response of important control and protection, which is important for the security and stable operation of the nuclear power plant. This paper researches the impact of the decision module used in the steam generator level control system, which causes most of the unexpected reactor trip events. The transient response of two units is used to explain the process of decision module failure. The optimization scheme and configuration strategy of the application effect is provided. The scheme has been used in several units and is an important reference for the commissioning tests of nuclear power plant. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Transient analysis

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A Study of Structured Surface Generation on Tungsten Carbide by Electrical Discharge Machining with Self-assemble Ball Electrodes 38

A Study of Structured Surface Generation on Tungsten Carbide...

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38th Annual Meeting of the American Society for Precision engineering, ASPE 2023

作者： Li, Kangsen Cheung, Chi Fai Wang, Chunjin Gong, Feng State key Laboratory of Ultra-precision Machining Technology Department of Industrial and Systems Engineering Hongkong Polytechnic University Hong Kong Hong Kong Shenzhen Key Laboratory of High Performance Nontraditional Manufacturing College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9781887706599

Electrical discharge machining (EDM) has been extensively utilized in the precision manufacturing of difficult-to-cut material. The preparation of electrodes is of great importance for structured surface generation after EDM. Electrode wear is an inevitable problem, which affects the machining accuracy and efficiency of the workpiece. Therefore, the design and optimization of the electrodes are vital for the EDM process. In this paper, a novel self-assembly ball electrode for EDM of the structured array on the binderless tungsten carbide (WC) is proposed. The shape evolution and surface roughness of the structured surface was investigated. The result indicates that self-assembly ball electrodes have a great advantage in machining the structured surface arrays. © 2023 Proceedings - ASPE 2023 Annual Meeting. All rights reserved.

关键词： Tungsten carbide

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Multi-component quantitative and feed-forward neural network for pattern classification of raw and wine-processed Corni Fructus

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Traditional Medicine Research 2023年第1期8卷 12-19页

作者： Yu Liu Ying-Fang Cui Dan-Dan Shi Shu-Li Man Xia Li Wen-Yuan Gao State Key Laboratory of Food Nutrition and Safety/Key Laboratory of Industrial Microbiology Ministry of Education/Tianjin Key Laboratory of Industry Microbiology/National and Local United Engineering Lab of Metabolic Control Fermentation Technology/China International Science and Technology Cooperation Base of Food NutritionSafety and Medicinal Chemistry/College of BiotechnologyTianjin University of Science&TechnologyTianjin 300457China Tianjin Key Laboratory for Modern Drug Delivery&High-Efficiency School of Pharmaceutical Science and TechnologyTianjin UniversityTianjin 300072China.

Background:To promote the quality evaluation,clarify the processing mechanism and distinguish origins of Corni Fructus(cornus)from different ***:This study developed a high performance liquid chromatography method for simultaneous determination of 5-hydroxymethylfurfural,2 phenolic acids and 4 iridoid glycosides and the reference fingerprint of cornus from different *** addition,the feedforward neural network model provided a pattern classification of sample ***:The content of morroniside and loganin were the highest in all raw cornus samples ranging from 9.45μg/mg to 16.3μg/mg and 6.64μg/mg to 13.7μg/mg,*** level of sweroside in raw cornus from Henan(0.83μg/mg^(-1).39μg/mg)and Zhejiang(0.64μg/mg^(-1).17μg/mg)were greater than other *** wine-processing,the glucose or fructose were dehydrated to increase the levels of *** C-4 position of-COOCH3 of hot-sensitive iridoid glycosides was hydrolyzed to generate-COOH as stable *** derivatives may be degraded to increase the content of phenolic ***,an excellent feedforward neural network model for identification of raw cornus and wine-prepared cornus was established which could distinguish the sample ***:This work provided a trustworthy method to evaluate the quality and distinguish the sources of ***,the clear processing mechanism provided a scientific foundation for controlling the cornus quality during wine-processing.

关键词： Cornus officinalis Sieb.et Zucc. quality evaluation fingerprints processing mechanism feedforward neural network

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Noble and Non-Noble Metal Based Catalysts for Electrochemical Nitrate Reduction to Ammonia:Activity,Selectivity and Stability

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Electrochemical Energy Reviews 2024年第1期7卷 1384-1422页

作者： Israr Masood ul Hasan Nengneng Xu Yuyu Liu Muhammad Zubair Nawaz Haitao Feng Jinli Qiao State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Environmental Science and EngineeringDonghua UniversityShanghai 201620China Institute for Sustainable Energy College of SciencesShanghai UniversityShanghai 200444China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China Laboratory of 2D Optoelectronics and Nanoelectronics(L2DON) Department of Materials Science and EngineeringSouthern University of Science TechnologyShenzhen 518055GuangdongChina Key Laboratory of Green and High-End Utilization of Salt Lake Resources Qinghai Institute of Salt LakesChinese Academy of SciencesXining 810008QinghaiChina

Excessive nitrate(NO_(3)^(-))contamination has emerged as a critical environmental issue owing to the widespread use of nitrogen-based fertilizers,fossil fuel combustion,and the discharge of industrial and domestic ***,electrochemical nitrate reduction(eNO_(3)R)to ammonia(NH3)has emerged as a promising alternative to the traditional Haber-Bosch ***,the industrial implementation ofeNO_(3)R is hindered by low catalytic activity,poor selectiv-ity,and limited stability owing to competing hydrogen evolution *** paper provides a comprehensive overview of recent advancements ineNO_(3)R,particularly evaluating the catalytic activity,selectivity,and stability of both noble and non-noble metal *** review elucidates innovative catalyst design strategies,state-of-the-art developments,and potential directions for future ***,the paper explores the fundamental mechanisms underlyingeNO_(3)R forNH_(3)production,including electrocatalyst development methodologies,electrolyte effects,in situ characterization techniques,theoretical modeling,and cell design ***,factors influencingNH_(3)selectivity and catalyst structural composition are thoroughly ***,this review provides comprehensive insights into optimizingeNO_(3)R processes for synthesizingNH_(3),which can promote further advancements in this field.

关键词： Nitrate reduction Electrocatalysis NH_(3)production Activity Selectivity Stability

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A 3D-Printed Centimeter-Scale Pneumatically Actuated Robotic Manipulator for Micro-Manipulations

A 3D-Printed Centimeter-Scale Pneumatically Actuated Robotic...

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International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

作者： Xingwen Zheng Hao Mo Haoran Yao Fumihito Arai Department of Mechanical Engineering The University of Tokyo Tokyo Japan State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou China Department of Control Science and Engineering Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Bioengineering The University of Tokyo Tokyo Japan

ISBN: (数字)9798350376807

ISBN: (纸本)9798350376814

This paper presents a centimeter-scale robotic manipulator fabricated using 3D printing technology. It consists of three mechanisms: a grasping mechanism, an RCM (remote center of motion) function mechanism, and a rotational mechanism, actuated by bellows-driven soft pneumatic actuators. Thus, the robotic manipulator enables the grasping of tools for micro-manipulations and the 2-DOF orientation adjustment of the tools, including the rotation around the axis of the rotational mechanism (0° - 45°) and the rotation around an RCM (−10° - 10°). Finally, one 5-DOF robotic manipulation system consisting of the 2-DOF rotational centimeter-scale robotic manipulator and one 3-DOF translational external manipulator (moving speed ~ 500 μm/s) was used to conduct micro-manipulations, in which a printed needle was grasped to carve the desired trajectory on the agar (gel strength ~ 650 g/cm 2 ) resembling biological tissues. the external manipulator controlled the displacement of the needle while the robotic manipulator adjusted the orientation of the needle insertion into the agar. Results showed that the 5-DOF robotic manipulation system had mechanical stability while achieving micron-level accuracy (200 μm) in the micro-manipulation task.

关键词： Pneumatic actuators 5-DOF 2-DOF Grasping Pneumatic systems Manipulators Needles

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Improving the activity of electrochemical reduction of CO_(2) to C_(1) products by oxidation derived copper catalyst

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Materials Reports(Energy) 2023年第1期3卷 107-114,I0004页

作者： Lingxue Diao Yingda Liu Feifei Chen Hong Pan David Perez de Lara Hui Liu Yahui Cheng Feng Luo Department of Electronic Science and Engineering and Tianjin Key Laboratory of Process Control and Green Technology for Pharmaceutical Industry Nankai UniversityTianjin300350China Department of Basic Science Tianjin Agricultural UniversityTianjin300392China IMDEA Nanosci Faraday 9Ciudad Univ CantoblancoMadrid28049Spain Tianjin Key Lab for Rare Earth Materials and Applications Center for Rare Earth and Inorganic Functional MaterialsSchool of Materials Science and EngineeringNankai UniversityTianjin300350China

Cu-based electrocatalysts have become the focus in the field of electrochemical CO_(2) reduction reaction(ECO_(2) RR)due to their ability to produce multicarbon ***,the research on generating single carbon products with higher economic feasibility via ECO_(2) RR based on Cu-based electrocatalysts is rather rare,and the roles of the surface architecture and oxides of the electrocatalysts have not been explained *** this work,a two-step method including thermal oxidation and electroreduction is proposed to introduce Cuþinto pure Cu foil to form Cu_(2)O/Cu *** regulating the surface composition and morphology of the electrocatalyst in this way,the activity of ECO 2 RR to C_(1) products has been greatly *** Faradaic efficiency of carbon products of the Cu_(2)O/Cu electrode reaches 84%at?0.7 V *** with good selectivity for HCOOH and *** current density of Cu_(2)O/Cu electrode reaches-12.21 mA cm^(2) at-0.8 V ***,which is much higher than that of the Cu foil electrode(?0.09 mA cm?2).In-situ Raman characterization shows that Cuþin Cu_(2)O/Cu electrode could inhibit hydrogen generation and promote ECO_(2) RR by stabilizing the adsorption of CO_(2).

关键词： Cu-based electrocatalyst Electroreduction of CO_(2) In-situ Raman spectra C_(1)product

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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