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42nd Chinese Control Conference, CCC 2023

作者： Yang, Guanci Wang, Xiaoyuan Zhu, Dongying Li, Yang Guizhou University Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guiyang550025 China School of Mechanical Engineering Guizhou University Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China

ISBN: (纸本)9789887581543

Focusing on the poor real-time performance of 3D semantic mapping using RGB-D cameras in indoor environments, this paper investigates the Real-time Dense 3D Semantic Mapping (RD3DSM) using RGB-D camera. Firstly, a lightweight semantic point cloud acquisition algorithm based on keyframe is designed, which is used to segment scene images and obtain the semantic point cloud real-timely. Secondly, to address the loss of inter-frame tracking in the semantic mapping process, ORB-SLAM3 system is employed as the underlying architecture of the visual 3D semantic mapping system to provide stable camera pose. Thirdly, an information fusion mechanism of the visual 3D semantic mapping system is designed, and the OctoMap 3D scene reconstruction module is adopted to implement dense 3D map representation with semantic information, and then a visual 3D semantic mapping algorithm based on ORB-SLAM3 is proposed. Finally, the designed RD3DSM system is integrated into a robot platform. The tests were conducted on the TUM dataset and natural indoor scenes. The results show the effectiveness and real-time performance of the RD3DSM system. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Cameras

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Structure-and Logic-Aware Heterogeneous Graph Learning for Recommendation 40

Structure-and Logic-Aware Heterogeneous Graph Learning for R...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Li, Anchen Yang, Bo Huo, Huan Hussain, Farookh Khadeer Xu, Guandong College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Aalto University Espoo Finland School of Computer Science University of Technology Sydney Sydney Australia Education University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798350317152

Recently, there has been a surge in recommendations based on heterogeneous information networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite this advancement, most HIN-based recommenders overlook two critical aspects. First, they often fail to consider HIN's heterophily nature, hindering the capture of non-local structures in HINs. Second, most methods lack the capability for logical reasoning. In this paper, we propose a novel structure-and logic-aware heterogeneous graph learning framework for recommender systems (SLHRec). Our SLHRec contains a structure-aware module and a logic-aware module. The former uses network geometry to construct non-local neighborhoods for nodes in HINs, and then introduces a graph neural network to integrate constructed neighbors for modeling the heterophily of HINs. The logic-aware module uses the Markov logic network (MLN) to infuse logic rules into heterogeneous graph learning, thereby boosting logic reasoning in recommendations. Furthermore, we utilize contrastive learning to model cooperative signals between modules, enabling them to complement each other. In the prediction stage, both modules contribute to generating recommendations. Compared with several strong recommender baselines, our SLHRec achieves superior performance on four real-world datasets. © 2024 IEEE.

关键词： Semantics

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Adaptive Gaussian Process Regression Based Remaining Useful Life Prediction of PEMFC Incorporating An Improved Health Indicator 11

Adaptive Gaussian Process Regression Based Remaining Useful ...

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11th IEEE data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Tang, Lin Yang, Xu Gao, Jing Jing Huang, Jian Cui, Jia Rui School of Automation and Electrical Engineering University of Science and Technology Beijing Ministry of Education Key Laboratory of Knowledge Automation for Industrial Processes Beijing100083 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Considering the importance of the proton exchange membrane fuel cells(PEMFC) to daily life and industry, this paper makes the remaining useful life(RUL) prediction of the PEMFC based on two different environments. To this end, the improved health indicator is proposed to describe the health state of PEMFC. On this basis, a data-driven method, namely the adaptive Gaussian process regression(GPR) method, is proposed to predict the RUL of PEMFC. The effectiveness of the proposed life prediction method is demonstrated in the aging data set of PEMFC provided by the prognostic and health management(PHM) challenge by a case study, the artificial neural network(ANN) method, and the adaptive GPR method are used to predict the PEMFC's RUL. Results show that the adaptive GPR method achieves better prediction results and provides the probability distribution of the results compared with the ANN method. © 2022 IEEE.

关键词： Proton exchange membrane fuel cells (PEMFC)

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In-context decision transformer: reinforcement learning via hierarchical chain-of-thought 24

In-context decision transformer: reinforcement learning via ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Sili Huang Jifeng Hu Hechang Chen Lichao Sun Bo Yang School of Artificial Intelligence and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Artificial Intelligence Jilin University China Lehigh University Bethlehem Pennsylvania Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-and-error manner when treating RL tasks as an across-episodic sequential prediction problem. Despite the self-improvement not requiring gradient updates, current works still suffer from high computational costs when the across-episodic sequence increases with task horizons. To this end, we propose an In-context Decision Transformer (IDT) to achieve self-improvement in a high-level trial-and-error manner. Specifically, IDT is inspired by the efficient hierarchical structure of human decision-making and thus reconstructs the sequence to consist of high-level decisions instead of low-level actions that interact with environments. As one high-level decision can guide multi-step low-level actions, IDT naturally avoids excessively long sequences and solves online tasks more efficiently. Experimental results show that IDT achieves state-of-the-art in long-horizon tasks over current in-context RL methods. In particular, the online evaluation time of our IDT is 36× times faster than baselines in the D4RL benchmark and 27× times faster in the Grid World benchmark.

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Physical Review Fluids 2024年第5期9卷 054608-054608页

作者： Xue-Lu Xiong (熊雪露) Shujin Laima (赖马树金) Hui Li (李惠) Yi Zhou (周毅) School of Energy and Power Engineering Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications

This paper presents a quadrant analysis of turbulent rotor wakes, placing a particular emphasis on the self-similarity in single-point turbulent statistics. The self-similarity analysis reveals that within the wake self-similar region, the distribution of the Reynolds shear stress −u′v′¯ and the turbulent kinetic energy k in different quadrants can also attain a state of self-similarity. The length scaling is the same for −u′v′¯, k, and their different quadrant contributions. However, the velocity scaling of the ejection contribution differs from that of −u′v′¯ and k. Additionally, the analysis of hole filtering and spectrum filtering effects on the self-similarity and scaling of the ejection contribution to −u′v′¯ reveals a strong connection between ejection events and large-scale coherent structures, as well as deceleration extreme events. This finding is further confirmed through quadrant analysis of the time evolution of velocity fluctuations. The present study can be helpful in improving our understanding of the turbulent wake self-similarity.

关键词： Shear flows Wakes & jets

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Deep Learning for Semantic Segmentation of Football Match Image 2

Deep Learning for Semantic Segmentation of Football Match Im...

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2nd International Conference on Innovations and Development of Information Technologies and Robotics, IDITR 2023

作者： Wu, Yutian Zhao, Wuqi Huang, Chen Xi, Yaming Li, Qing Wang, Heng University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing China Beijing International Studies University Department of Physical Education Beijing China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing China

ISBN: (纸本)9798350345575

As one of the most popular sports, football has been a subject to growth and advancements in technology. The combination of football and artificial intelligence is expected to be used for intelligent football analysis. Image semantic segmentation is an important basis for image analysis and understanding. This paper proposes a deep learning-based image segmentation model for pixel-level classification of the video recordings frames of football matches. Every pixel of football video frame is classified into one of the 10 classes, e.g., players, ball, goal bar and several background scenes. In this paper, we first test a variety of CNN architectures and pre-Trained models and select the MobileNet-UNet architecture as our baseline. We note the severe unbalanced data distribution in football scene segmentation. To solve this problem, the weighted multi-class cross-entropy loss is adopted in training of MobileNet-UNet to redistribute the weights of classification loss, focusing on smaller foreground object classes and improving segmentation accuracy. We also propose to use image transformations and a random mixture sampling technique for training data augmentation to reduce model overfitting. The model is trained and validated in the well-Annotated Football Semantic Segmentation Open dataset. The proposed best model achieves 0.96 frequency weighted IoU and 0.90 mean IoU segmentation accuracy on validation set. © 2023 IEEE.

关键词： Semantic Segmentation

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： data mining

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Bumpless Transfer Control of Switched Time-Delay Linear Systems with State-dependent Switching 41

Bumpless Transfer Control of Switched Time-Delay Linear Syst...

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第41届中国控制会议

作者： Jinghan Li Jun Zhao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education College of Information Science and Engineering Northeastern University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper deals with the bumpless transfer control for switched time-delay *** time-delay bumpless transfer performance for switched linear system is provided,which can suppress control bumps caused by the current state and the time-delay state at the switching ***,by utilizing the multiple Lyapunov functions,under the co-design of the state-dependent switching law and the time-delay controller,a sufficient condition guaranteeing bumpless transfer control of switched time-delay systems is *** effectiveness of the result is validated by an *** result shows that our method reduces the bumpy amplitude while ensuring the stability of the switched time-delay system.

关键词： Switched Systems Time-delay State-dependent Bumpless Transfer

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Dissipative Tracking Control of T-S Fuzzy Switched Systems under Bumpless Transfer Performance 41

Dissipative Tracking Control of T-S Fuzzy Switched Systems u...

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第41届中国控制会议

作者： Guangxu He Jun Zhao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Collee of Information Science and Engineering Northeastern University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper studies the tracking control problem of the T-S fuzzy switched systems with the α-dissipativity and the bumpless transfer *** on the reference system,the fuzzy tracking controller will be designed by using the parallel distributed compensation *** sufficient conditions in terms of linear matrix inequalities are obtained to guarantee the adissipative tracking bumpless transfer performance via the co-design of the bumpless transfer controller and the state-dependent switching law,even if the tracking problem of each fuzzy subsystems is not ***,an example is given to validate the effectiveness of the results.

关键词： T-S fuzzy switched systems α-dissipative tracking bumpless transfer control

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： knowledge graph

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