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Cyber-Physical Social intelligence (ICCSI), International Conference on

作者： Jialiang Luo Shichao Chen Yisheng Lv Wenqiao Sun Fan Yang Ruijie Xu The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University Jinan China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong China

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field.

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Artificial Neural Network with Bayes' Rule for Reasoning Task-Oriented Grasp

Artificial Neural Network with Bayes' Rule for Reasoning Tas...

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第33届中国控制与决策会议

作者： Guoyu Zuo Hongxing Liu Jiayuan Tong Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computing Intelligence and Intelligent Systems

For the cognitive process of human grasping,the grasp position for a specific object is not only highly related to its inner structural morphology but also with its subsequent functional purpose of the intended manipulation *** this paper,we propose a Bayesian reasoning improved neural network(BRNN) to identify the task-oriented grasp *** this model,the grasping task is input as a hyperparameter into BRNN to control the feature learning of object.A telescopic translator is learned to transform the task-oriented parameter distribution of the BRNN model to obtain the grasp positions of the current task based on the previous learned task.A task-switching mechanism is realized to switch the BRNN reasoning modes for different *** results show that when there is no task constraint,the model has the grasp detection accuracy of 95.2% on the Cornell Grasp Dataset,a little less than the state-of-the-art performance of 96.0%.However,with the task constraint,the model has the accuracy of 95.1% and 95.6% on the self-built BJUT-Grasp dataset for the reasoning of the non-function-use grasp tasks and function-use grasp tasks,*** the visual 2D images,the BRNN model improves the reasoning ability for grasp detection and shows excellent ability in the task-oriented grasp position reasoning.

关键词： Task-oriented grasp Grasp position reasoning Bayes Artificial neural network

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A Magnetically Steerable and Automatically Propulsion Guidewire Robot system for Vascular Interventional Surgery

A Magnetically Steerable and Automatically Propulsion Guidew...

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2022 IEEE International Conference on Real-Time computing and Robotics, RCAR 2022

作者： Fu, Shixiong Zhang, Shanxiu Yin, Meng Wang, Shu Li, Dong Wu, Xinyu Du, Shiwei Xu, Tiantian Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced Technology CAS Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China South China Hospital of Shenzhen University Department of Neurosurgery Shenzhen518111 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Magnetically soft continuum robot with automatic navigation capability has great potential to improve the treatment conventional vascular interventional surgery, which exists many problems such as long procedure time and high radiation for doctors to manipulate the passive guidewire. This paper proposes a novel guidewire robot system with magnetically steering and automatically propulsion ability. Actuated by an external magnetic field, the system is able to greatly increase the autonomy of conventional guidewires through the efficient coordination of steering and propulsion. We model the kinematics of autonomous propulsion and magnetically actuation steering separately. Then, the steering performance of the guidewire robot is validated under different magnetic field magnitudes and directions. Finally, two scenario application experiments are implemented to demonstrate the automatic navigation capability of the proposed guidewire robot in a simulated clinical environment. The proposed guidewire robot system provides an effective solution for advancing the automation of vascular interventional surgery. © 2022 IEEE.

关键词： Propulsion

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Variational Clustering and Denoising of Spatial Transcriptomics

Variational Clustering and Denoising of Spatial Transcriptom...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Cuiyuan Li Fa Zhang Kai Hu Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China School of Medical Technology Beijing Institute of Technology Beijing China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou China

Spatial transcriptomics data provides a unique opportunity to investigate both gene expression and spatial structure in tissues at the same time. However, incorporating spatial information to accurately identify spatial domains is difficult due to factors such as high-dimensionality, sparsity, noise, and dropout events. To address these issues, we introduce vGraphST, a novel graph-based deep learning approach tailored for spatial transcriptomics data. Our method combines auto-encoder and contrastive learning techniques to process high-dimensional data and generate meaningful low-dimensional embeddings. Additionally, we use continuous distributions instead of discrete values in both the latent space and the denoised gene expression space. Specifically, Gaussian distributions are used to model the latent space, while zero-inflated Poisson distributions are used to model the denoised gene expression space. Experimental results demonstrate the effectiveness of vGraphST in accurately representing and analyzing spatial transcriptomics data. When compared to other methods using the DLPFC dataset, vGraphST achieves an average Adjusted Rand Index (ARI) of 0.58, demonstrating its superiority in segmenting spatial domains and recognizing biologically relevant spatiotemporal patterns.

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Numerical Simulation of Municipal Solid Waste Incineration Process Based on Chemical Process Simulation Software

Numerical Simulation of Municipal Solid Waste Incineration P...

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第40届中国控制会议

作者： Jiabin Zhuang Jian Tang Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Municipal solid waste incineration(MSWI) technology has the advantages of energy recyclability, in-situ incineration,fast processing speed and high capacity reduction rate. However, its inputs have characteristics of diversity and time-varying due to differences in economic development, living habit and natural environment. In addition, it is also influenced by the operating difference, equipment maintenance and seasonal alternation. Thus, it is difficult to maintain MSWI process to run in optimized operating conditions. In order to ensure the safety and optimization of this process, this paper uses chemical process simulation software to simulate a MSWI process based on a grate furnace. First, the research object is described. Then, the simulation strategy and parameter settings of the whole incineration process are given. Then, the whole process is simulated and analyzed. The simulation results show that the O concentration in the G1 flue gas can reach up to 15%, which meets the requirement of full burnout of combustibles. In the flue gas G3, the error between the simulated value of the pollutant and the actual value is within an acceptable range. The simulation results can effectively reflect the status of MSWI. So, it provide effective support for constructing an incineration mechanism model of a near-real MSWI process.

关键词： Grate furnace municipal solid waste incineration（MSWI） numerical simulation chemical process simulation software

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Fault detection and isolation for wastewater treatment plants using interval prediction and Bayesian reasoning

Fault detection and isolation for wastewater treatment plant...

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第40届中国控制会议

作者： Tong Liu Wei Chai Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

The wastewater treatment plant that operates abnormally may lead to poor effluent quality,resulting in the destruction of the environment,and even more serious ***,it is necessary to detect and isolate faults in the wastewater treatment *** paper proposed a method of fault detection and isolation using interval *** basis function(RBF)neural network is utilized to model the wastewater treatment plant,and the linear output weights of the neural network are estimated by the set membership identification *** that,the confidence interval of the predicted effluent variables can be obtained,and then the interval boundary is used as the threshold for fault *** detecting the fault,based on this interval model and the Bayesian reasoning,the posterior probability of the considered faults can be *** the probability in exceed of a certain threshold,the fault can be successfully *** final experimental results verified the method.

关键词： Wastewater treatment Fault isolation Set membership identification Bayesian reasoning

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Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning

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The Innovation 2024年第2期5卷 21-23页

作者： Linlin You Rui Zhu Mei-Po Kwan Min Chen Fan Zhang Bisheng Yang Man Sing Wong Zheng Qin School of Intelligent Systems Engineering Sun Yat-Sen UniversityShenzhen 518107China Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis WaySingapore 138632Republic of Singapore Institute of Space and Earth Information Science Chinese University of Hong KongHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhan 430079China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina

Dear Editor,Electric vehicle(EV)sales have significantly grown over the years to fulfill growing demands for economic travel and greenhouse gas mitigation.1 However,the surge in the number of EVs has led to charging anxiety as users struggle to find an available charging station before running out of electricity,resulting in longer reserve and waiting times.2 Moreover,severe mobility restrictions caused by infectious diseases,such as coronavirus disease 2019(COVID-19),have greatly affected people’s travel behavior3,4 and hindered their willingness to use EVs,given that charging in public spaces consumes time and increases the risk of contracting the virus.5 This implies that in the postpandemic era,in which individuals coexist with the virus,the interplay between the two important trends,namely vehicle electrification and mobility restrictions,can extensively affect people’s daily commuting by using EVs.

关键词： travel behavior charging

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Research on Anti-Swing Strategies for Bridge Cranes

Research on Anti-Swing Strategies for Bridge Cranes

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Chinese Automation Congress (CAC)

作者： Shiqi Wang Enguang Hou Haoyuan Zheng Yuyi Jiang Zhen Shen Gang Xiong The College of Rail Transit of Shandong Jiaotong University Jinan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China State Key Laboratory of Multimodal Artifcial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

A study is conducted on the LH5T-13.5M electric crane, focusing on the swing characteristics of the load during steel coil lifting operations and the displacement issue of the crane. Initially, the system model is formulated using the Lagrange dynamic equations, translating the practical challenges into a solvable mathematical framework and deriving the transfer function of interest. Subsequently, the development of three control strategies is elucidated, formulated on the basis of the established model: a conventional PID control system, a fuzzy PID control system, and an adaptive proportional factor fuzzy neural PID controller. These systems are simulated and validated using Simulink. A comparative experimental setup is employed to evaluate performance. The results show that when the step input signal is 1, the maximum absolute load swing Angle of the traditional PID, fuzzy PID and adaptive scale factor fuzzy neural PID controller are 0.185°, 0.054° and 0.049° respectively for the working condition 1 experiment. The percentage overshoot corresponding to the wheelbarrow is 37.1%, 40.0%, and 30.5%, respectively. The findings indicate that the introduced fuzzy neural PID controller, equipped with an adaptive scaling factor, exhibits superior stability and effectiveness in maintaining the crane's operation within the optimal range of parameters.

关键词： Bridges Fuzzy control Adaptation models Cranes PI control Adaptive systems Fuzzy neural networks Mathematical models PD control Load modeling

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Prediction of Dioxin Emission Concentration from Municipal Solid Waste Incineration Process Based on PSO and Equispaced Interpolation

Prediction of Dioxin Emission Concentration from Municipal S...

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第33届中国控制与决策会议

作者： Dandan Wang Jian Tang Zihao Guo Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory Computational Intelligence and Intelligent System

As currently widely used municipal solid waste resource treatment method,the municipal solid waste incineration(MSWI) process emit dioxins(DXN) compounds with high toxicity and persistent pollution *** is one of the main reasons that cause incineration power plants to have a "Not in my back yard".At present,the long-period,high-cost offline detection method used in industrial sites cannot achieve real-time monitoring of DXN emission ***,the number of samples used to build a DXN emission concentration prediction model is extremely *** at the above problems,a method for predicting DXN emission concentration in MSWI process based on PSO and equispaced interpolation is *** first,the domain of original small sample input and output is expanded based on the improved mega-trend-diffusion(MTD) ***,the equal interval interpolation method is used to generate the virtual sample inputs,which are used to obtain the virtual sample outputs by combining the mapping *** above results are combined with the expansion space to delete the bad virtual ***,the PSO algorithm is used to optimize and select the reduced virtual sample ***,a mixed sample set composed of the optimized selected virtual sample set and the original small sample set is used to construct a DXN emission concentration prediction *** effectiveness of the proposed method is verified with the actual detection data of DXN for many years in a MSWI plant.

关键词： Municipal Solid Waste Incineration(MSWI) Dioxin(DXN) Equispaced Interpolation Sample Selection PSO

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Recognition of Combustion Conditions in MSWI Process Using Convolutional Neural Network

Recognition of Combustion Conditions in MSWI Process Using C...

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第33届中国控制与决策会议

作者： Hao Zhang Xi Meng Jian Tang Zixuan Wang Haoshan Duan Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

The non-linear,time-varying,and uncertain characteristics of municipal solid-waste incineration(MSWI)process cause the difficulty of recognizing the combustion condition of ***,engineers judge the operating conditions in MSWI plants using a video of the combustion flame inside the incinerator,but this method does not help maintain stable operating ***,this paper proposes a convolutional neural network-based recognition method for controlling combustion conditions in MSWI ***,the flame image is pre-processed by resizing,and its features are extracted using the convolutional neural ***,the classification of combustion conditions is realized from the Softmax ***,the proposed method is validated with experimental results based on flame image data of an MSWI plant in beijing.

关键词： Municipal Solid Waste Incineration(MSWI) Feature Extraction Convolutional Neural Network Combustion Condition Recognition

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