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第33届中国控制与决策会议

作者： Liang Wang Jianshu Li Deqiao Fan Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Point cloud data classification has been widely used in autonomous driving,robot perception,and virtual/augmented *** to its irregularity and disorder,the classification task of point clouds needs to transform the point cloud into a multi-view or voxel grid,and then use the traditional convolution neural network ***,this process is not only complex in operation but also low in classification *** solve this problem,a new point cloud classification method based on the graphical convolutional neural network（GCN） is ***,based on PointNet,KNN graph is introduced to obtain global deep *** the 3 D point cloud is represented as a directed graph,local features are extracted by edge ***,the extracted global and local features are aggregated to represent and classify point *** proposed network is evaluated on the open dataset ModelNet40 and 3 *** results show that the proposed network can achieve on par or better performance than state-of-the-art,such as PointNet,PointNet++,DGCNN,and PointCNN,for point cloud classification.

关键词： Point Cloud Classification Graph CNN Deep Learning

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Online monitoring approach for batch process based on dynamic fuzzy broad learning system

Online monitoring approach for batch process based on dynami...

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第32届中国过程控制会议(CPCC2021)

作者： Ding Chunhao Chang Peng Meng Fanchao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Affected by the operation environment and uncertainties,batch processes have complex dynamic characteristics,presenting autocorrelation and mutual correlation among process *** conventional methods tend to ignore this property,resulting in unsatisfactory monitoring *** address the abovementioned issue,a novel monitoring method named Dynamic Fuzzy Broad Learning system(DFBLS) is proposed for batch processes in this *** feature analysis is first used to extract the slowly changing features from the original *** Takagi-Sugeno(TS) fuzzy system is then adopted to identify the subtle difference between normal and abnormal *** that,the incremental learning algorithm is employed to reconstruct network model quickly without the need of retraining the entire network,which contributes to lower computation ***,the DFBLS monitoring model is constructed,which can not only identify the dynamics anomalies,but also quickly update monitoring ***,this method is evaluated by conducting experiments on the penicillin fermentation *** with some state-of-the-art methods involved,the monitoring results indicate that the DFBLS outperforms them.

关键词： Batch process Dynamic characteristic Dynamic fuzzy broad learning system Incremental learning

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PSA-GRU:Modeling Person-Social Twin-Attention Based on GRU for Pedestrian Trajectory Prediction

PSA-GRU:Modeling Person-Social Twin-Attention Based on GRU f...

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第40届中国控制会议

作者： Xue Yan Jinfu Yang Lin Song Yubin Liu Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

This paper addresses the path prediction problem of multiple pedestrians interacting in dynamic scene, which is a great challenge because of the complexity and subjectivity of pedestrian movements. To forecast the future trajectory of a pedestrian, it is necessary to consider pedestrian subjective intention and social interaction information. Previous methods ignore that important position nodes of historical trajectories can reflect the subjective intention of pedestrians in complex *** this work, we present Person-Social Twin-Attention network based on Gated Recurrent Unit（PSA-GRU）, which can fully utilize important position nodes of personal historical trajectory and social interaction information between pedestrians. In our approach, the person-attention encoding module extracts the most salient parts of personal history trajectory and helps model learn where and how pedestrians will go. Meanwhile, the social-attention pool module contributes to keep distance among different pedestrians and simulates the dynamic interaction of all pedestrians in real scenes. PSA-GRU also takes advantage of Gated Recurrent Unit（GRU） to enable the model to improve the computational efficiency. Experimental results demonstrate that the prediction accuracy and efficiency of our proposed model are both greatly improved on UCY and ETH datasets.

关键词： Computer Vision Trajectory Prediction Attention Mechanism Gated Recurrent Neural Network

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Deep forest regression prediction model for dioxin emission concentration by using new representation strategy

Deep forest regression prediction model for dioxin emission ...

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第33届中国控制与决策会议

作者： Jian Tang Heng Xia Wen Xu Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

The dioxin(DXN) is a key environmental indicator for municipal solid waste incineration(MSWI) ***,the concentration of DXN's emission prediction model always relies on long-term and expensive offline experimental *** address this issue,some researchers employed deep neural networks(DNN) to construct a soft measurement model,but it has poor training efficiency,inflexible model size,and weak ***,the deep forest regression(DFR) algorithm has achieved success in the field of modeling ***,it is used for DXN emission prediction in this *** encourage the diversity of features between layers,this paper endeavors to implement DFR for DXN emission concentration soft measurement by a new representation ***,the dioxin emissions and data characteristics of the MSWI process are ***,the structure of DFR is analyzed and the problems of representation strategy are stated ***,we explore the representation learning inside the DFR structure based on stacked *** effectiveness of the proposed method is verified by DXN data of the MSWI process.

关键词： Dioxin Municipal Solid Waste Incineration Deep Forest Regression Soft Measurement

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Feature extraction

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Path Planning of Mobile Robot Based on an Improved Genetic Algorithm 2

Path Planning of Mobile Robot Based on an Improved Genetic A...

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2nd IEEE International Conference on Digital Twins and Parallel intelligence, DTPI 2022

作者： Chen, Zenghua Xiong, Gang Liu, Sheng Shen, Zhen Li, Yue Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of 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 Chang'an University School of Automobile Xi'an China

ISBN: (纸本)9781665492270

In order to solve the problem of premature convergence of the basic genetic algorithm when planning the robot running path, the basic genetic algorithm is improved and optimized. Different population initialization methods are used to initialize multiple populations randomly, so as to improve the diversity of populations;Improve the adaptive strategy and elite strategy of crossover and mutation operators to improve the convergence speed of the algorithm;Add the path tortuosity as the planning index in the fitness function to make the planned path smoother, and add constraints to the model to avoid obstacles;Finally, through the transformation of the coding paradigm of the above improved genetic algorithm, it can run on Flink distributed cluster to obtain faster solution speed, so as to meet the efficiency requirements of path planning in large-scale robot cluster system. The optimized algorithm is compared with the basic genetic algorithm. The simulation results show that the improved algorithm is efficient in robot path planning. © 2022 IEEE.

关键词： Genetic algorithms

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Modeling and monitoring of a multivariate spatio-temporal network system

A I I E Transactions

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A I I E Transactions 2023年第4期55卷 331-347页

作者： Di Wang Fangyu Li Kaibo Liu a Department of Industrial Engineering and Management School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China b Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education and Beijing Laboratory for Urban Mass Transit Beijing University of Technology Beijing China c Department of Industrial and Systems Engineering University of Wisconsin-Madison Madison WI USA

With the development of information technology, various network systems are created to connect physical objects and people by sensor nodes or smart devices, providing unprecedented opportunities to realize automated interconnected systems and revolutionize people’s lives. However, network systems are vulnerable to attacks, due to the integration of physical objects and human behaviors as well as the complex spatio-temporal correlated structures of the network systems. Therefore, how to accurately and effectively model and monitor a network system is critical to ensure information security and support system automation. To address this issue, this article develops a multivariate spatio-temporal modeling and monitoring methodology for a network system by using multiple types of sensor signals collected from the network system. We first propose a Multivariate Spatio-Temporal Autoregressive (MSTA) model by integrating a Gaussian Markov Random Field and a vector autoregressive model structure to characterize the spatio-temporal correlation of the network system. In particular, we develop an iterative model learning algorithm that integrates the Bayesian inference, least squares, and a sum square error-based optimization method to learn the network structure and estimate parameters in the MSTA model. Then, we propose two spatio-temporal control schemes to monitor the network system based on the MSTA model. Numerical experiments and a real case study of an IoT network system are presented to validate the performance of the proposed method.

关键词： Multivariate spatio-temporal autoregressive model spatio-temporal control schemes network structure learning IoT network system

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Steel Coil Recognition Neural Networks for intelligent Cranes

Steel Coil Recognition Neural Networks for Intelligent Crane...

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Control Conference (ANZCC), Australian and New Zealand

作者： Shiqi Wang Enguang Hou Hongyao Ma Jiarui Tang Zhen Shen Gang Xiong Shool of Railway Transportation Shandong Jiaotong University Jinan China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology 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

Steel coil recognition in factories is important for an intelligent crane. In recent years, the deep neural network has become a popular tool in industry. The YOLO family of object detection methods has been widely applied. However, the original YOLOv8 has certain limitations in dealing with light change, occlusive targets, and small long-distance targets. To achieve a better recognition effect and improve the working efficiency, in this paper we make some improvements. Firstly, Space-to-depth (SPD) and Large Selective Kernel Networks (LSK) are added to the network Backbone. Secondly, the BiFormer attention mechanism is added to the neck part of the network, and the self-attention mechanism is used to obtain the global relationship between input to improve the network performance, so that the network can receive information at any time and autonomously adjust its receptive field in the spatial dimension. The improved YOLOv8 has better recognition accuracy for low-resolution occluded objects and small target objects. By experiments, the improved YOLOv8 network has 84.3% mAP on the self-built datasets, which is better than the 82.1% mAP of the original YOLOv8 network.

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Self-Supervised Rotation Learning for 3D Segmentation on Nasopharyngeal Carcinoma MRI Images

Self-Supervised Rotation Learning for 3D Segmentation on Nas...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Changtai Li Ruohui Jiang Shihua Yin Jinzhu Yang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Second Affiliated Hospital of Guangxi Medical University Nanning China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China

Segmenting the tumors of nasopharyngeal carcinoma (NPC) in Magnetic Resonance Imaging (MRI) images is critical for its diagnosis and treatment. In medical image segmentation, it is vital to exploit the rich information contained in 3D sliced images. However, the precise identification and location of lesion regions often require plenty of labels. To alleviate this situation, in this article, we propose 3DRotNPC, a framework based on a tailored Self-Supervised Learning (SSL) strategy, to accurately segment tumor regions for NPC under the circumstance of label limitation. To learn the rich 3D spatial and geometric nature of MRI images in a Self-Supervised way, the proxy task of randomly selecting and rotating images in consecutively sliced data is designed. After the SSL pre-training stage, the learned parameters of the Convolutional Neural Networks (CNNs) based model are transferred to adapt the downstream segmentation task. We verify the capability of 3DRotNPC on the NPC tumor dataset which is collected and curated from the clinical treatment in the representative hospital. Extensive experiments demonstrate that our approach delivers considerable gains in downstream 3D voxel segmentation, especially with a 10% to 50% number of labels.

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STAR-RIS Enabled ISAC systems With RSMA: Joint Rate Splitting and Beamforming Optimization

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Liu, Yuan Zhang, Ruichen Jiang, Ruihong Zhu, Yongdong Hu, Huimin Ni, Qiang Fei, Zesong Niyato, Dusit Zhejiang Laboratory Research Center for Space Computing System Hangzhou311100 China Nanyang Technological University College of Computing and Data Science Singapore 639798 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Cambridge Mechatronics Ltd CambridgeCB1 2PH United Kingdom Xi’an University of Posts and Telecommunications School of Communications and Information Engineering School of Artificial Intelligence Xi’an710121 China Lancaster University School of Computing and Communications LancasterLA1 4WA United Kingdom Beijing Institute of Technology School of Information and Electronics Beijing100081 China

This paper delves into an integrated sensing and communication (ISAC) system bolstered by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Within this system, a base station (BS) is equipped with communication and radar capabilities, enabling it to communicate with ground terminals (GTs) and concurrently probe for echo signals from a target of interest. Moreover, to manage interference and improve communication quality, the rate splitting multiple access (RSMA) scheme is incorporated into the system. The signal-to-interference-plus-noise ratio (SINR) of the received sensing echo signals is a measure of sensing performance. We formulate a joint optimization problem of common rates, transmit beamforming at the BS, and passive beamforming vectors of the STAR-RIS. The objective is to maximize sensing SINR while guaranteeing the communication rate requirements for each GT. We present an iterative algorithm to address the non-convex problem by invoking Dinkelbach’s transform, semidefinite relaxation (SDR), majorization-minimization, and sequential rank-one constraint relaxation (SROCR) theories. Simulation results manifest that the performance of the studied ISAC network enhanced by the STAR-RIS and RSMA surpasses other benchmarks considerably. The results evidently indicate the superior performance improvement of the ISAC system with the proposed RSMA-based transmission strategy design and the dynamic optimization of both transmission and reflection beamforming at STAR-RIS. © 2015 IEEE.

关键词： Signal to noise ratio

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