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Consistent Matrix: A Feature Selection Framework for Large-Scale Datasets

IEEE TRANSACTIONS ON FUZZY systems 2023年第11期31卷 4024-4038页

作者： Yang, Tian Li, Yuan-Jiang Qian, Yuhua Wang, Fei-Yue Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language Inf Changsha 410081 Peoples R China Shanxi Univ Inst Big Data Sci & Ind Taiyuan 030006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

Large-scale data processing based on limited computing resources has always been a difficult problem in data mining, where feature selection is often used as an effective data compressing mechanism. For granular computing of Big Data, discernibility matrix and dependency degree are the most representative methods for matrix-based and feature-importance-degree-based feature selection, respectively. However, their temporal and space complexities are high and often lead to poor performance. In this article, a novel feature selection framework for large-scale data processing with linear complexities was proposed for the first time. First, a much more concise fuzzy granule set, called fuzzy arithmetic covering, was introduced to reduce computational costs. Then, a new matrix-based feature selection framework, namely consistent matrix, was proposed for general rough set models. As a result, a heuristic attribute reduction algorithm, i.e., HARCM, was designed accordingly. Compared with six state-of-the-art algorithms for feature selection, the average running time of the newly proposed algorithm was reduced up to 2913 times, with a comparable or even better classification performance.

关键词： Consistent matrix feature selection fuzzy arithmetic covering fuzzy rough sets granular computing

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Deep learning-based automatic scoring models for the disease activity of rheumatoid arthritis based on multimodal ultrasound images

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RHEUMATOLOGY 2024年第3期63卷 866-873页

作者： He, Xuelei Wang, Ming Zhao, Chenyang Wang, Qian Zhang, Rui Liu, Jian Zhang, Yixiu Qi, Zhenhong Su, Na Wei, Yao Gui, Yang Li, Jianchu Tian, Xinping Zeng, Xiaofeng Jiang, Yuxin Wang, Kun Yang, Meng Chinese Acad Med Sci & Peking Union Med Coll Peking Union Med Coll Hosp Dept Ultrasound State Key Lab Complex Severe & Rare Dis Beijing Peoples R China Northwest Univ Sch Informat Sci & Technol Xian Shaanxi Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Peking Union Med Coll Hosp Dept Rheumatol State Key Lab Complex Severe & Rare Dis Beijing Peoples R China Peking Univ Aerosp Aerosp Ctr Hosp Sch Clin Med Dept Rheumatol & Immunol Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Peking Union Med Coll Hosp Dept Ultrasound State Key Lab Complex Severe & Rare Dis Shuaifuyuan 1 Beijing 100730 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Zhongguancun East Rd 95 Beijing 100190 Peoples R China

Objectives We aimed to investigate the value of deep learning (DL) models based on multimodal ultrasonographic (US) images to quantify RA activity. Methods Static greyscale (SGS), dynamic greyscale (DGS), static power Doppler (SPD) and dynamic power Doppler (DPD) US images were collected and evaluated by two expert radiologists according to the EULAR-OMERACT Synovitis Scoring system. Four DL models were developed based on the ResNet-type structure, evaluated on two separate test cohorts, and finally compared with the performance of 12 radiologists with different levels of experience. Results In total, 1244 images were used for the model training, and 152 and 354 for testing (cohort 1 and 2, respectively). The best-performing models for the scores of 0/1/2/3 were the DPD, SGS, DGS and SPD models, respectively (Area Under the receiver operating characteristic Curve [AUC] = 0.87/0.95/0.74/0.95;no significant differences). All the DL models provided results comparable to the experienced radiologists on a per-image basis (intraclass correlation coefficient: 0.239-0.756, P < 0.05). The SPD model performed better than the SGS one on test cohort 1 (score of 0/2/3: AUC = 0.82/0.67/0.95 vs 0.66/0.66/0.75, respectively) and test cohort 2 (score of 0: AUC = 0.89 vs 0.81). The dynamic DL models performed better than the static ones in most of the scoring processes and were more accurate than the most of senior radiologists, especially the DPD model. Conclusion DL models based on multimodal US images allow a quantitative and objective assessment of RA activity. Dynamic DL models in particular have potential value in assisting radiologists to improve the accuracy of RA US-based grading.

关键词： deep learning RA activity scoring multimodal ultrasonography

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Spatiotemporal Feature Enhancement Network for Blur Robust Underwater Object Detection

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2024年第5期16卷 1814-1828页

作者： Zhou, Hao Qi, Lu Huang, Hai Yang, Xu Yang, Jing Harbin Engn Univ Natl Key Lab Sci & Technol Underwater Vehicle Harbin 150000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China Univ Calif Merced Merced CA 95344 USA Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China

Underwater object detection is challenged by the presence of image blur induced by light absorption and scattering, resulting in substantial performance degradation. It is hypothesized that the attenuation of light is directly correlated with the camera-to-object distance, manifesting as variable degrees of image blur across different regions within underwater images. Specifically, regions in close proximity to the camera exhibit less pronounced blur compared to distant regions. Within the same object category, objects situated in clear regions share similar feature embeddings with their counterparts in blurred regions. This observation underscores the potential for leveraging objects in clear regions to aid in the detection of objects within blurred areas, a critical requirement for autonomous agents, such as autonomous underwater vehicles, engaged in continuous underwater object detection. Motivated by this insight, we introduce the spatiotemporal feature enhancement network (STFEN), a novel framework engineered to autonomously extract discriminative features from objects in clear regions. These features are then harnessed to enhance the representations of objects in blurred regions, operating across both spatial and temporal dimensions. Notably, the proposed STFEN seamlessly integrates into two-stage detectors, such as the faster region-based convolutional neural networks (Faster R-CNN) and feature pyramid networks (FPN). Extensive experimentation conducted on two benchmark underwater datasets, URPC 2018 and URPC 2019, conclusively demonstrates the efficacy of the STFEN framework. It delivers substantial enhancements in performance relative to baseline methods, yielding improvements in the mAP evaluation metric ranging from 3.7% to 5.0%.

关键词： Feature extraction Object detection Detectors Proposals Visualization Image color analysis Cameras Blur robust detection feature enhancement spatiotemporal network

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Enhanced Rolling Horizon Evolution Algorithm With Opponent Model Learning: Results for the Fighting Game AI Competition

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IEEE TRANSACTIONS ON GAMES 2023年第1期15卷 5-15页

作者： Tang, Zhentao Zhu, Yuanheng Zhao, Dongbin Lucas, Simon M. Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Queen Mary Univ London Dept Elect Engn & Comp Engn EECS London E1 4NS England

The Fighting Game AI Competition (FTGAIC) provides a challenging benchmark for two-player video game artificial intelligence. The challenge arises from the large action space, diverse styles of characters and abilities, and the real-time nature of the game. In this article, we propose a novel algorithm that combines the rolling horizon evolution algorithm (RHEA) with opponent model learning. The approach is readily applicable to any two-player video game. In contrast to conventional RHEA, an opponent model is proposed and is optimized by supervised learning with cross-entropy and reinforcement learning with policy gradient and Q-learning respectively, based on history observations from opponent. The model is learned during the live gameplay. With the learned opponent model, the extended RHEA is able to make more realistic plans based on what the opponent is likely to do. This tends to lead to better results. We compared our approach directly with the bots from the FTGAIC 2018 competition and found our method to significantly outperform all of them for all three characters. Furthermore, our proposed bot with the policy gradient based opponent model is the only one without using Monte Carlo tree search among the top five bots in the 2019 competition in which it achieved second place, while using much less domain knowledge than the winner.

关键词： Games Real-time systems Artificial intelligence Statistics Sociology Reinforcement learning Frequency modulation Fighting game opponent model reinforcement learning rolling horizon evolution supervised learning

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Parallel Philosophy for MetaOrganizations With MetaOperations: From Leibniz's Monad to HanoiDAO

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第3期9卷 658-666页

作者： WANG, FEI-YUE DING, W. E. N. W. E. N. QIN, R. U. I. HU, B. I. N. Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Taipa 999078 Macao Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Inst Technol Sch Med Technol Beijing 100081 Peoples R China

Welcome to the third issue of IEEE Transactions on Computational Social systems (TCSS) of 2022. According to the latest update of CiteScoreTracker from Elsevier Scopus released on April 6, 2022, the CitesSore of IEEE ...

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Deep Deterministic Policy Gradient for High-Speed Train Trajectory Optimization

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第8期23卷 11562-11574页

作者： Ning, Lingbin Zhou, Min Hou, Zhuopu Goverde, Rob M. P. Wang, Fei-Yue Dong, Hairong Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Delft Univ Technol Dept Transport & Planning NL-2628 CN Delft Netherlands Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper proposes a novel train trajectory optimization approach for high-speed railways. We restrict our attention to single train operation scenarios with different scheduled/rescheduled running times aiming at generating optimal train recommended trajectories in real time, which can ensure punctuality and energy efficiency of train operation. A learning-based approach deep deterministic policy gradient (DDPG) is designed to generate optimal train trajectories based on the offline training from the interaction between the agent and the trajectory simulation environment. An allocating running time and selecting operation modes (ARTSOM) algorithm is proposed to improve train punctuality and give a series of discrete operation modes (full traction, cruising, coasting, full braking), and thus to produce a feasible training set for DDPG, which can speed up the training process. Numerical experiments show that an optimized speed profile can be generated by DDPG within seconds on a realistic railway line. In addition, the results demonstrate the generalization ability of trained DDPG in solving TTO problems with different running times and line conditions.

关键词： Rail transportation Training Heuristic algorithms Resistance Optimal control Trajectory optimization Switches High-speed railway train trajectory optimization deep deterministic policy gradient energy efficiency

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UNMAS: Multiagent Reinforcement Learning for Unshaped Cooperative Scenarios

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2023年第4期34卷 2093-2104页

作者： Chai, Jiajun Li, Weifan Zhu, Yuanheng Zhao, Dongbin Ma, Zhe Sun, Kewu Ding, Jishiyu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Second Acad CASIS X Lab Beijing 100854 Peoples R China

Multiagent reinforcement learning methods, such as VDN, QMIX, and QTRAN, that adopt centralized training with decentralized execution (CTDE) framework have shown promising results in cooperation and competition. However, in some multiagent scenarios, the number of agents and the size of the action set actually vary over time. We call these unshaped scenarios, and the methods mentioned above fail in performing satisfyingly. In this article, we propose a new method, called Unshaped Networks for Multiagent systems (UNMAS), which adapts to the number and size changes in multiagent systems. We propose the self-weighting mixing network to factorize the joint action-value. Its adaption to the change in agent number is attributed to the nonlinear mapping from each-agent Q value to the joint action-value with individual weights. Besides, in order to address the change in an action set, each agent constructs an individual action-value network that is composed of two streams to evaluate the constant environment-oriented subset and the varying unit-oriented subset. We evaluate UNMAS on various StarCraft II micromanagement scenarios and compare the results with several state-of-the-art MARL algorithms. The superiority of UNMAS is demonstrated by its highest winning rates especially on the most difficult scenario 3s5z_vs_3s6z. The agents learn to perform effectively cooperative behaviors, while other MARL algorithms fail. Animated demonstrations and source code are provided in https://***/view/unmas.

关键词： Multi-agent systems Training Task analysis Reinforcement learning Sun Learning systems Semantics Centralized training with decentralized execution (CTDE) multiagent reinforcement learning StarCraft II

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A Novel GSP Auction Mechanism for Dynamic Confirmation Games on Bitcoin Transactions

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第3期15卷 1436-1447页

作者： Li, Juanjuan Ni, Xiaochun Yuan, Yong Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

Bitcoin is gaining ground in recent years. In the Bitcoin system, miners provide computing power to confirm transactions in pursuit of transaction fees, while users compete by bidding transaction fees for faster confirmation. This process is in essence analogous to online ad auctions, where advertisers bid for more prominent ad slots. Therefore, inspired by ad auction research, we propose to apply the Generalized Second Price (GSP) auction mechanism in the dynamic confirmation game on Bitcoin transactions. Our model is targeted to deal with the problems caused by instability and low efficiency in the currently-adopted Generalized First Price (GFP) auction model in Bitcoin confirmation games. Besides, we use the "rank-by-cost" rule to replace the "rank-by-fee" rule, where each transaction's cost is calculated by the user-submitted fee and the waiting time. Aiming to probe users' equilibrium strategy, we first discuss the GSP game with complete information under synchronous submissions, and show that it has the Locally Envy-Free equilibrium. Then, we study the GSP game with incomplete information under asynchronous submissions, and define two types of strategies, i.e., the Farsighted Balanced (FB) strategy and the Instant Balanced (IB) strategy. The FB strategy is in line with users' practical needs of determining fees so as to maximize the long-term payoffs;however it cannot generate a stable equilibrium. Alternatively, the IB strategy focuses on the instant payoff maximization, and if all users follow the IB strategy, their equilibrium fees can finally converge to a stable profile. Finally, we design computational experiments to validate our theoretical models and analysis. Our research findings indicate that this novel GSP mechanism is superior to the currently adopted GFP mechanism. Besides, the convergence of the GSP game under the IB strategy has also been illustrated by the computational experiments.

关键词： Bitcoin Games Pipelines Computational modeling Nickel Analytical models Blockchain bitcoin transaction confirmation game generalized second price mechanism transaction fee

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A Vectorized Representation Model for Trajectory Prediction of Intelligent Vehicles in Challenging Scenarios

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第10期8卷 4301-4306页

作者： Guo, Lulu Shan, Ce Shi, Tengfei Li, Xuan Wang, Fei-Yue Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Trajectory prediction for challenging scenarios has always been a significant problem in the field due to the complexity of dynamic scenarios and interactions. Furthermore, there is often a dynamic gap between evaluating and validating methods on fixed datasets and real driving scenarios. This letter forms part of a series of reports emanating from the IEEE Transactions on Intelligent Vehicles's Decentralized and Hybrid Workshops (DHW) dedicated to the field of Scenarios Engineering. Our research proposes a scenario engineering-based calibration and validation framework for trajectory prediction of autonomous vehicles to more effectively validate the performance of the method in challenging scenarios. First, Scenarios Engineering (SE) uses OpenSCENARIO and real dataset to generate high-definition maps for challenging driving scenarios. Then, the vectorization approach is employed to extract contextual details from the scene and agent trajectory information from the HD map, and the graph neural network is used to model the high-order interaction to realize the interactive trajectory prediction. Compared with the traditional method, the trajectory prediction can be calibrated through SE so that the prediction process can use more traffic information and attribute characteristics, and improve the evaluation index of prediction. The DHW discusses a practical case to verify the potential of the trajectory prediction framework based on scenarios generation in improving the authenticity and accuracy of trajectory prediction.

关键词： Index Terms-Autonomous vehicles graph neural network HD maps OpenSCENARIO scenarios engineering trajectory prediction

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A Vagueness Adaptive Efficiency Evaluation Method of Science and Technology Resources Opening and Sharing Platforms Based on FAHP and BP Neural Network

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IEEE TRANSACTIONS ON ENGINEERING management 2024年 71卷 2049-2062页

作者： Zhang, Hui Feng, Xin Wei, Yigang Li, Zonggan Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm Beijing 100190 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Beihang Univ Lab Low Carbon Intelligent Governance Beijing 100191 Peoples R China Beihang Univ Beijing Key Lab Emergency Support Simulat Technol Beijing 100191 Peoples R China

With the vigorous development of big data and information technology, promoting science and technology resources opening and sharing (STROS) has become a promising strategy to develop national innovation capacity. China has established various STROS platforms (STROSP) at the national and regional levels to encourage resources and knowledge sharing. However, STROSP efficiency evaluation is challenged in a vague environment, in which subjective and imprecise information in acquiring evaluation preferences of decision makers is a key obstacle. To solve these problems, an innovative model for STROSP efficiency evaluation is developed. An indicator assessment system explicitly considers the key evaluation aspects, namely, service quantity, service quality, and service effect. This article combines fuzzy analytic hierarchy process (FAHP) and backpropagation (BP) neural network algorithm into an integrated model to quantify the efficiency of STROS. Prior knowledge of experts was fully utilized by FAHP. The neural network algorithm enabled the intelligent extraction and rapid inference of sample data features. The proposed model maximizes fuzzy mathematics in solving fuzzy and nonquantifiable problems and utilizes the advantages of the BP neural network on nonlinear mapping. The accuracy and reliability of the model are validated by a case study of six national STROSP in China. Estimation results demonstrate that the model is a powerful method for the real-time evaluation of STROS efficiency evaluation.

关键词： Backpropagation (BP) neural network China efficiency evaluation fuzzy analytic hierarchy process (FAHP) science and technology resources opening and sharing vague environment

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