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Digital Twins and Parallel intelligence (DTPI), IEEE International Conference on

作者： Long Fu Zhen Shen Xisong Dong Yunjun Han Shuguang Yang Gang Xiong Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 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 Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Schoool of Rail Transportation Shandong Jiaotong University Jinan China State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In this study, unmanned ships test platform based on the parallel systems framework is proposed to improve the test efficiency and accuracy of unmanned ships in complex ocean environment. The parallel intelligence theory and ACP approach, the efficient interaction between virtual and real environment is achieved, which provides more comprehensive and accurate data for unmanned ships testing. Future research will further expand the scope of virtual environment simulation and improve systems accuracy and response speed to a wider range of unmanned ships test requirements.

关键词： Accuracy Oceans Virtual environments Digital twins Marine vehicles Testing

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Air Pollutants NO2 Concentration Prediction Based on LSTM Neural Network method

Air Pollutants NO2 Concentration Prediction Based on LSTM Ne...

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Chinese intelligent systems Conference, CISC 2020

作者： Li, Jihan Li, Xiaoli Liu, Jian Wang, Kang Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Advanced Innovation Center for Future Internet Technology Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9789811584572

In recent years, the Chinas economy has developed rapidly. The peoples living standard has been improved. The number of cars has been increasing, and the pollutant NO has been produced continuously, which leads to the formation of NO2. These harmful particles have an impact on human health. Thus, the effective and accurate NO2 concentration prediction model plays an effective role in peoples health and prevention. For this problem, this paper presents a prediction model based on the long short-term memory (LSTM) method to predict NO2 concentration. Firstly, the PM10, SO2, NO2, CO, O3, temperature in a campus monitoring point in beijing is collected as the research object in this paper. Then, the LSTM prediction model and BP (back propagation) neural network prediction model are established respectively. Finally, the accuracy of the two prediction models for the prediction of NO2 concentration is compared. The results show that the prediction model based on LSTM method is superior to BP neural network model, and the prediction accuracy is more accurate. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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A Feature Extraction Method Based on Two-branch Sparsity and Manifold Regularized Temporal Convolutional Network for Chiller Fault Diagnosis

A Feature Extraction Method Based on Two-branch Sparsity and...

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Chinese Automation Congress (CAC)

作者： Huihui Gao Bo Zhao Xuejin Gao Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Digital Community Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

Fault diagnosis of chiller is of great significance for saving energy and maintaining indoor comfort. Deep neural network (DNN) has been extensively studied for fault diagnosis of chiller due to the excellent feature extraction ability from fault data. However, the neglect of redundancy among the fault features of chiller degrades performances of these DNN in fault diagnosis of chiller. In this paper, a two-branch sparsity and manifold regularized temporal convolutional neural network (TBSMRTCN) is used for fault diagnosis of chiller. Firstly, a two-branch architecture is applied to TBSMRTCN to improve the result of fault diagnosis. Secondly, a sparsity regularization method is applied to the TBSMRTCN that it ensures TBSMRTCN to filter out some features to reduce the redundancy of extracted fault features. Finally, a manifold regularization method is applied to the TBSMRTCN, which enhances the generalization performance of TBSMRTCN. According to the dataset of American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), the results show that the feature extraction method proposed in this paper improve the fault diagnosis rate (FDR) of chiller. The overall fault diagnosis rate of TBSMRTCN reaches 97%.

关键词： Fault diagnosis Manifolds Deep learning Automation Redundancy Neural networks Heat engines

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Optimizing Model-Driven Federated Learning for Rational and Data-Efficient in Social Mobile Network

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IEEE Network 2024年

作者： Lu, Jianfeng Zhang, Ying Cao, Shuqin Wang, Wei Tang, Changbing Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China

With the rise of data-intensive services in mobile networks, the demand for intelligent and efficient learning frameworks has grown exponentially. Federated Learning (FL), as an emerging paradigm, enables decentralized edge devices to collaboratively train large models without sharing raw data, ensuring privacy preservation. However, deploying FL in resource-constrained mobile environments faces significant challenges, such as free-riding and malicious behavior. These challenges hinder model performance and raise concerns about data integrity and inter-user cooperation. To address these issues, we propose a layered dynamic framework for social mobile networks, called FedSMN, which aims to enhance the generalization ability of collaboratively trained large models in FL environments. FedSMN leverages Bayesian inference to evaluate user behavior and employs evolutionary game theory to simulate dynamic interactions, thereby optimizing cooperation strategies and reducing communication overhead. Unlike traditional approaches that assume fully rational actors, FedSMN accounts for irrational behavior and fluctuating resources, designing an efficient incentive mechanism to improve data utilization. Extensive experiments on large-scale datasets demonstrate that FedSMN increases model accuracy by 21% and reduces communication overhead by 49%, paving the way for scalable, intelligent mobile networks powered by large models. © 1986-2012 IEEE.

关键词： Collaborative learning

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A model of information circulation and transmission network of grid cells in entorhinal cortex and place cells in the hippocampus of rat 9

A model of information circulation and transmission network ...

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9th IEEE Joint International Information Technology and Artificial intelligence Conference, ITAIC 2020

作者： Yu, Naigong Yu, Hejie Wang, Lin Liao, Yishen Yin, Chunlei Beijing University of Technology Department of Information Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System China

Biological studies have found that the grid cells in the entorhinal cortex of the rat brain structure and the place cells in the hippocampus structure have a mutual input and output cyclic relationship in the process of information transmission, from entorhinal cortex-hippocampus information transfer loop. The traditional neural network model from grid cells to place cells is not enough to satisfy the information transmission relationships, ignores the information transfer from place cells to grid cells, to solve this problem, a neural network model based on grid cells in entorhinal cortex and place cells in hippocampus information transfer loop is proposed. The simulation results show that the model can simulate the firing characteristics of place cells and grid cells. © 2020 IEEE.

关键词： Neural network models

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Design and Implementation of a Web-based Collaborative Manufacturing system Based on Knowledge Graph

Design and Implementation of a Web-based Collaborative Manuf...

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Digital Twins and Parallel intelligence (DTPI), IEEE International Conference on

作者： Meng Yuan Huaiyu Wu Zhen Shen Yunjun Han Jiehan Zhou Zhijian Yue Gang Xiong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences 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 Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

Collaborative manufacturing is central to agile, intelligent, and cloud-based manufacturing paradigms. This paper presents the design and implementation of a web-based collaborative manufacturing system based on a knowledge graph (KG). The system achieves the full spectrum of knowledge extraction, fusion, and reasoning across the industrial chain, thereby enhancing efficiency and reducing operational costs. The proposed system constructs an industrial chain KG and multi-agent systems, offering comprehensive digital services across the manufacturing value chain. It supports the integration of business processes, data, and standard information for enterprise groups, presenting collaborative manufacturing data of the industrial chain in real-time visualization. The paper further discusses a method for real-time perception of anomalies in the collaborative manufacturing process, utilizing expert knowledge and data-driven insights to construct an operational risk KG. The system’s architecture, workflow, and functional design are detailed, along with application cases in visual dynamic management. The conclusion underscores the system’s potential to improve supply-demand chain management and operational efficiency in collaborative manufacturing.

关键词： Knowledge engineering Visualization Manufacturing processes Collaboration Data visualization Knowledge graphs Real-time systems Digital twins Standards Multi-agent systems

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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Continuous Monitoring of Tissue Oxygen Metabolism Using LSTM-Based RNN Model Based on Multi-Wavelength Diffuse Correlation Spectroscopy

SSRN

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SSRN 2023年

作者： Li, Zhe Jiang, Minnan Feng, Jinchao Chen, Xing Wei, Ran Jia, Kebin Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beihang University School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beijing China Tiangong University School of Life Sciences Tianjin China

Background and objective: Growing incidences of cardiovascular diseases have driven demand for continuous tissue oxygen metabolism monitoring. Diffuse correlation spectroscopy (DCS) has been employed for noninvasive acquisition of blood flow information in deep tissues. Traditionally, for monitoring oxygen metabolism, DCS is often deployed together with diffuse optical spectroscopy (DOS). While combined DCS and DOS approaches need a complex and high-cost system architecture with high time-consuming and data noise drawbacks. To continuously measure tissue oxygen metabolism changes, in this work we propose and evaluate the utility of a deep learning approach, i.e., long short-term memory (LSTM) based recurrent neural network (RNN), based on multi-wavelength diffuse correlation spectroscopy (mwDCS).Methods: Multi-wavelength DCS measured dynamic and static properties in the tissue by acquiring the normalized intensity autocorrelation functions and multi-wavelength intensities. Simulation and in vivo experiments were performed to generate the data in 25 simulated media and 6 healthy individuals. Further, based on the dataset acquired by multi-wavelength DCS, a LSTM-based RNN model was developed to directly assess changes of blood flow and oxygen ***: Compared to conventional methods, both simulation and in vivo experiments in this study demonstrated a faster quantifying and more portable system with promising results. For simulation prediction of blood flow and oxygen saturation, the Pearsons correlation coefficients of the proposed model reach 0.92 and 0.99, respectively. Meanwhile, for in vivo prediction of blood flow and oxygen saturation, the Pearsons correlation coefficients of the proposed model reach 0.82 and 0.64, ***: To conclude, our method generates the direct prediction of blood flow and oxygen saturation, and importantly decreased the instrumentation cost. This study shows that multi-wavelength DCS using LSTM-based RNN mode

关键词： Tissue

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A Multi-objective Evolutionary Algorithm Based on Cone Heart Point Adjustment Vectors to Solve Concave-convex Problems

A Multi-objective Evolutionary Algorithm Based on Cone Heart...

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Chinese Control and Decision Conference, CCDC

作者： Xin Li Xiaoli Li Kang Wang Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing China

ISBN: (纸本)9781665478977

Decomposition-based multi-objective optimization algorithms have been shown to be successful in solving multi-objective optimization problems (MOPs). Since the shape is unknown before the search, the classical decomposition method relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. However, the the distribution of reference vectors produced by the Das and Dennis systematic approach is difficult to maintain the diversity of (extremely) concave and (extremely) convex problems. To remedy this issue, a multi-objective evolutionary algorithm based on cone heart point adjustment vectors is proposed to solve concave and convex problems (CHP-MOEA). In this paper, the concavity or convexity is first according to the intermediate objective vector. Then, the distribution of reference vectors are adjusted according to the cone heart point on the basis of the traditional vectors. Finally, experimental results on 31 benchmark problems show that the proposed algorithm exhibits better performance than other state-of-the-art algorithms.

关键词： Heart systematics Shape Sociology Evolutionary computation Benchmark testing Search problems

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Balanced Representation Learning for Long-tailed Skeleton-based Action Recognition

arXiv

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arXiv 2023年

作者： Liu, Hongda Wang, Yunlong Ren, Min Hu, Junxing Luo, Zhengquan Hou, Guangqi Sun, Zhenan The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence Beijing Normal University Beijing100875 China The University of Science and Technology of China Hefei230027 China

Skeleton-based action recognition has recently made significant progress. However, data imbalance is still a great challenge in real-world scenarios. The performance of current action recognition algorithms declines sharply when training data suffers from heavy class imbalance. The imbalanced data actually degrades the representations learned by these methods and becomes the bottleneck for action recognition. How to learn unbiased representations from imbalanced action data is the key to long-tailed action recognition. In this paper, we propose a novel balanced representation learning method to address the long-tailed problem in action recognition. Firstly, a spatial-temporal action exploration strategy is presented to expand the sample space effectively, generating more valuable samples in a rebalanced manner. Secondly, we design a detached action-aware learning schedule to further mitigate the bias in the representation space. The schedule detaches the representation learning of tail classes from training and proposes an action-aware loss to impose more effective constraints. Additionally, a skip-modal representation is proposed to provide complementary structural information. The proposed method is validated on four skeleton datasets, NTU RGB+D 60, NTU RGB+D 120, NW-UCLA, and Kinetics. It not only achieves consistently large improvement compared to the state-of-the-art (SOTA) methods, but also demonstrates a superior generalization capacity through extensive experiments. Our code is available at https://***/firework8/BRL. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

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