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Cross-modality person re-identification using hybrid mutual learning

IET COMPUTER VISION 2023年第1期17卷 1-12页

作者： Zhang, Zhong Dong, Qing Wang, Sen Liu, Shuang Xiao, Baihua Durrani, Tariq S. Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Strathclyde Elect & Elect Engn Glasgow Lanark Scotland

Cross-modality person re-identification (Re-ID) aims to retrieve a query identity from red, green, blue (RGB) images or infrared (IR) images. Many approaches have been proposed to reduce the distribution gap between RGB modality and IR modality. However, they ignore the valuable collaborative relationship between RGB modality and IR modality. Hybrid Mutual Learning (HML) for cross-modality person Re-ID is proposed, which builds the collaborative relationship by using mutual learning from the aspects of local features and triplet relation. Specifically, HML contains local-mean mutual learning and triplet mutual learning where they focus on transferring local representational knowledge and structural geometry knowledge so as to reduce the gap between RGB modality and IR modality. Furthermore, Hierarchical Attention Aggregation is proposed to fuse local feature maps and local feature vectors to enrich the information of the classifier input. Extensive experiments on two commonly used data sets, that is, SYSU-MM01 and RegDB verify the effectiveness of the proposed method.

关键词： feature extraction local representational knowledge local features Sensor fusion image classification image representation query identity learning (artificial intelligence) RGB modality Algebra Machine learning (artificial intelligence) classifier input cross-modality person re-identification hierarchical attention aggregation image fusion Computer vision and image processing techniques local feature vectors RegDB red green blue images infrared images Image recognition cross-modality person Re-ID distribution gap SYSU-MM01 hybrid mutual learning triplet relation local-mean mutual learning triplet mutual learning infrared imaging image colour analysis structural geometry knowledge local feature maps Image sensors IR modality vectors

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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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Artificial Immune system of Secure Face Recognition Against Adversarial Attacks

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2024年第12期132卷 5718-5740页

作者： Ren, Min Wang, Yunlong Zhu, Yuhao Huang, Yongzhen Sun, Zhenan Li, Qi Tan, Tieniu Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst MAIS Beijing Peoples R China China Acad Railway Sci Postgrad Dept Beijing Peoples R China

Deep learning-based face recognition models are vulnerable to adversarial attacks. In contrast to general noises, the presence of imperceptible adversarial noises can lead to catastrophic errors in deep face recognition models. The primary difference between adversarial noise and general noise lies in its specificity. Adversarial attack methods give rise to noises tailored to the characteristics of the individual image and recognition model at hand. Diverse samples and recognition models can engender specific adversarial noise patterns, which pose significant challenges for adversarial defense. Addressing this challenge in the realm of face recognition presents a more formidable endeavor due to the inherent nature of face recognition as an open set task. In order to tackle this challenge, it is imperative to employ customized processing for each individual input sample. Drawing inspiration from the biological immune system, which can identify and respond to various threats, this paper aims to create an artificial immune system to provide adversarial defense for face recognition. The proposed defense model incorporates the principles of antibody cloning, mutation, selection, and memory mechanisms to generate a distinct "antibody" for each input sample, wherein the term "antibody" refers to a specialized noise removal manner. Furthermore, we introduce a self-supervised adversarial training mechanism that serves as a simulated rehearsal of immune system invasions. Extensive experimental results demonstrate the efficacy of the proposed method, surpassing state-of-the-art adversarial defense methods. The source code is available here, or you can visit this website: https://***/RenMin1991/SIDE

关键词： Adversarial defense Face recognition Artificial immune system Self-supervised adversarial learning

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A Novel Framework to Generate Synthetic Video for Foreground Detection in Highway Surveillance Scenarios

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systemS 2023年第6期24卷 5958-5970页

作者： Li, Xuan Duan, Haibin Liu, Bingzi Wang, Xiao Wang, Fei-Yue Peng Cheng Lab Shenzhen 518000 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Key Lab Virtual Real Technol & Syst Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Foreground detection (FD) plays an important role in the domain of video surveillance for highway. The design of advanced FD algorithms requires large-scale and diverse video dataset. However, collecting and labeling real dataset is still time-consuming, labor-intensive, and highly subjective. To address this issue, we first use computer graphics (CG) to clone real highway scenarios (HS) and generate synthetic multi-challenge video datasets, called "Synthetic-HS (CG)", automatically labeled with accurate pixel-level ground truth. The Synthetic-HS (CG) dataset contains eight imaging condition sequences for computer vision research. Then, we design an image translation (IT) model that translates source domain (Synthetic-HS (CG)) to target domain (real). This model uses skip connections and attention module to generate realistic synthetic images "Synthetic-HS (IT)". We use publicly available Synthetic-HS in combination with the corresponding real video sequence to conduct experiments. The experiment results suggest that: 1) The Synthetic-HS (CG) dataset enables us to provide precise quantitative evaluation of the drawbacks of foreground detection methods 2) The realistic Synthetic-HS (IT) images can be used to promote the visual perception in highway video surveillance.

关键词： Computational modeling Surveillance Road transportation Generative adversarial networks Synthetic data Solid modeling Visualization Foreground detection highway surveillance parallel intelligent synthetic dataset scenarios engineering

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Analyzing the delivery determination problem of new retail stores considering crowdsourcing under the background of blockchain

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RESEARCH IN TRANSPORTATION BUSINESS AND management 2024年 52卷

作者： Li, Yue Zhao, Jianyou Chen, Zenghua Changan Univ Sch Automobile Xian 710061 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 310013 Peoples R China

The parcel delivery industry is experiencing rapid growth due to the rise of new retail stores. However, the traditional self-operated delivery model often leads to idle and wasteful distribution resources due to uneven temporal and spatial distribution of delivery orders. In this study, we propose a delivery system that combines crowdsourcing and self-operated delivery for new retail stores using blockchain technology to optimize the delivery algorithm. Our proposed system solves the problem of wasted resources by introducing crowdsourcing delivery and completes the distribution task allocation on a blockchain platform to protect customer privacy and quantify the performance of crowdsourcing couriers. We present a mixed-integer nonlinear programming formulation for the 2-Echelon Winning Determination Problem, in which the delivery decision is based on the lowest total cost. The cost of crowdsourcing delivery is a dynamic cost comprising of crowdsourcing courier service errors and bid prices recorded on the tamper-evident blockchain system. We also analyzed the impact of different crowdsourcing bidding prices on the choice of delivery method and total cost. Synthetic experiments show that our proposed hybrid delivery model ensures high-quality and efficient delivery of new retail stores while saving delivery costs.

关键词： Transportation and logistics Blockchain Crowdsourcing and self-operated delivery system 2-Echelon winning determination problem Vehicle routing problem Mixed-integer linear program

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Advances in Flexible Robotic Manipulator systems - Part II: Planning, control, Applications, and Perspectives

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第3期29卷 1680-1689页

作者： Li, Bai Li, Xinyuan Gao, Hejia Wang, Fei-Yue Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This work offers a review of techniques related to flexible robotic manipulator (FRM) systems. Our review is divided into two parts. The first part presents the background of FRMs, a survey of previous surveys, and FRM dynamic modeling methods. This article is the second part, which reviews the advances in FRM motion planners and controllers, along with the emerging applications and perspectives. Compared with previous FRM surveys, our work presents a more detailed categorization and extensive review of emerging research trends since 2020. The ongoing development of FRMs is marking advancements in the field of robotics.

关键词： Planning Vibrations Trajectory Uncertainty Neural networks End effectors Splines (mathematics) Artificial intelligence flexible material flexible robotic manipulator vibration suppression

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Coordinated Rescheduling of Train Timetable and Crew Scheme for Passenger-Freight Collinear Railway

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第5期11卷 5828-5838页

作者： Wang, Rui Zhou, Min Wang, Hongwei Yang, Bo Dong, Hairong Wang, Fei-Yue Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China China Railway Beijing Grp Co Ltd Dept Dispatching Beijing 100038 Peoples R China Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

On a passenger-freight collinear railway, the freight train operation level is comparatively low, frequently resulting in significant deviations from the original timetable and crew plan in the presence of various interferences. This article focuses on the problem of coordinated rescheduling of train timetable and crew scheme in the presence of disruptions on a double-track passenger-freight collinear railway. We develop a mixed-integer linear program (MILP) model considering the distinct priorities of passenger and freight trains, as well as crew operations, thereby surpassing the current practice of independently adjusting train timetable and crew plan to achieve a collaborative solution. The objective is to minimize delays for passenger trains and deviations in crew schedule, while maximizing the delivery rate of freight trains at railway Bureau boundary stations prior to the settlement time. Furthermore, for large-scale delays, we design a solution algorithm based on the rolling horizon approach to enhance computational efficiency. To validate the effectiveness of the proposed model, simulation experiments are conducted using actual running data from the Beijing-Shanghai railway. The experimental results illustrate that our coordinated model enhances the feasibility of adjustment outcomes during emergencies, in contrast to the model that neglects crew connections. Additionally, our proposed algorithm guarantees a solving error of under 5% and reduces solving time by over 60% compared with the results obtained by CPLEX. Moreover, three additional comparison experiments are conducted to further demonstrate the impact of crew activities on train operation adjustments, which also indicate that our approach can provide dispatchers with more feasible train operation adjustment schemes in terms of crew utilization.

关键词： Rail transportation Delays Mathematical models Schedules Interference Dispatching Optimization Crew operation interference passenger-freight collinear railway rolling horizon algorithm train operation adjustment

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A New Integral Critic Learning for Optimal Tracking control with Applications to Boiler-Turbine systems

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OPTIMAL control APPLICATIONS & METHODS 2023年第2期44卷 830-845页

作者： Wei, Qinglai Liu, Yujia Lu, Jingwei Ling, Jun Luan, Zhenhua Chen, Mingliang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China China Nucl Power Engn CO LTD State Key Lab Nucl Power Safety Monitoring Techno Shenzhen Guangdong Peoples R China

Optimal control theory and reinforcement learning are gradually being used in the field of industrial control. In this article, a new optimal tracking control scheme for 160 MW boiler-turbine systems is proposed based on an online policy iteration integral reinforcement learning (IRL) method. Firstly, a self-learning state tracking control with a cost function is developed to deal with the optimal tracking control problems for the boiler-turbine nonlinear system. Then with a modified cost function, a policy iteration-based IRL method is introduced to obtain the optimal control law. Finally, the monotonicity and the convergence of the cost function is analyzed and the detailed implementation of the policy iteration-based IRL method is provided via neural networks. The simulation results show that the control of the boiler-turbine system can converge in a short time by using this online iterative method. Through a theoretical simulation case, it can be concluded that the proposed method is more advanced compared with the MPC method.

关键词： adaptive dynamic programming boiler-turbine system integral reinforcement learning neural network policy iteration

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Decomposing Visual and Semantic Correlations for Both Fully Supervised and Few-Shot Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1658-1668页

作者： Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Most image classification methods are designed to either boost the classification accuracies with abundant supervision, or cope with the shortage of supervision information. This is often achieved by using the visual and semantic information of other sources. However, these methods use the visual and semantic information within the same class independently, leaving their intrinsic correlations unconsidered. Objects and disturbing components as well as noise exist on the same image. Besides, semantic representations also contain noisy information. To solve the problems mentioned above, we propose a novel method for both fully supervised image classification and few-shot image classification by decomposing visual and semantic correlations. We jointly explore the intrinsic correlations of visual and semantic information of images within the same class. For each class, we decompose its visual and semantic correlations using low-rank and sparse constraint respectively. The decomposed low-rank parts character the intrinsic correlations of images can be used in a linear transformation way. Using the decomposed parts of each class, Classification can be achieved by reconstruction error minimization. We conduct experiments on several datasets for both fully supervised image classification and few-shot image classification. Experimental results well show the effectiveness of the proposed method. © 2020 IEEE.

关键词： Semantics

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Parallel Vision for Intelligent Transportation systems in Metaverse: Challenges, Solutions, and Potential Applications

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2023年第6期53卷 3400-3413页

作者： Zhang, Hui Luo, Guiyang Li, Yidong Wang, Fei-Yue Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Metaverse and intelligent transportation system (ITS) are disruptive technologies that have the potential to transform the current transportation system by decreasing traffic accidents and improving driving safety. The integration of Metaverse and transportation technology, called metaverse transportation system (MTS), can greatly improve the intelligence of real transportation system. The digital models built in MTS help to simulate the full life cycle of physical entities, which equip the virtual space with controllability and flexibility. In this article, we concentrate on the field of environment perception, which is the basic function of intelligent vehicles in MTS. To overcome the poor scalability of traditional environment perception methods, we develop the framework of parallel vision for ITS in metaverse (PVITS), consisting of construction of virtual transportation space, model learning based on computational experiments, and feedback optimization based on parallel execution. This article highlights opportunities brought by PVITS in terms of model precision and generalization improvement. Then, the challenges of PVITS are discussed, i.e., distribution difference between virtual and real transportation space, structure design and theoretical interpretation of vision models, and data security and privacy in virtual transportation space. After that, we present several solutions to tackle the application challenges and fully exploit the superior characteristics of PVITS while attenuating their negative side effects. Some potential applications are also given to represent the effectiveness and reliability of PVITS.

关键词： Intelligent transportation system (ITS) metaverse parallel vision

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