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FIFAWC:a dataset with detailed annotation and rich semantics for group activity recognition

Frontiers of Computer Science 2024年第6期18卷 271-272页

作者： Duoxuan PEI Di HUANG Yunhong WANG State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Intelligent Recognition and Image Processing Lab. School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c... 详细信息

关键词： has collective gained

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A Distributed Algorithm for Large-Scale Multi-Agent MINLPs 63

A Distributed Algorithm for Large-Scale Multi-Agent MINLPs

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Dong, Sheng Xia, Weiguo Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering China

ISBN: (纸本)9798350316339

In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collab.ratively optimize the sum of local objective functions through their own continuous and/or discrete decision variables, subject to global coupling constraints and local constraints. The resulting Mixed-Integer Nonlinear Programmings (MINLPs) are NP-hard, non-convex, and large-scale. Therefore, this paper aims to design distributed algorithms to find feasible suboptimal solutions with a guaranteed bound. To this end, considering dual decomposition as an effective method to decompose large-scale constraint-coupled optimization problems, we first show, based on the convexification effects of large-scale MINLPs, that the primal solutions from the dual are near-optimal under certain conditions. This expands recent results in Mixed-Integer Linear Programmings (MILPs) to the nonlinear case but requires additional efforts on the proof. Utilizing this result to tighten the coupling constraints, we develop a fully distributed algorithm for the tightened problem, based on dual decomposition and consensus protocols. The algorithm is guaranteed to provide feasible solutions for the original MINLP. Moreover, asymptotic suboptimality bounds are established for the obtained solution. Finally, the efficacy of the method is verified through numerical simulations. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalab.lity. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Preclinical Assessment of Upper Limb Tremor in Parkinson's Disease with Deep Learning and Wearable Technology

Preclinical Assessment of Upper Limb Tremor in Parkinson's D...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lin, Fang Wang, Zhelong Qiu, Sen Zhao, Hongyu School of Control Science and Engineering Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Liaoning Dalian China

ISBN: (纸本)9798350337020

Tremors are typically experienced by patients at the beginning of Parkinson's disease (PD). Clinicians evaluate clinical symptoms based on scale and experience, but mild tremors do not have significant characteristics and are difficult to observe with the naked eye. Implementing an intelligent and objective method to identify PD patients with early tremor symptoms and healthy controls (HC) is necessary. This study used wearable sensors to collect 9-axis inertial signals and 2-channel sEMG signals at the wrists of 13 HC and 24 PD patients from Dalian Municipal Central Hospital. Based on a Long Short-Term Memory Network (LSTM), an attention mechanism, and a Fully Convolutional Network (FCN), we develop a model to classify and recognize data from PD patients. Compared the proposed method with several classification methods, the results showed that the proposed method achieved higher classification accuracy (91.78%), precision (100%), recall (87.50%), and F1-score (93.33%) of PD class than Support Vector Machine, FCN, and LSTM. The computing time of the proposed method is approximately 1 second. The proposed method identifies early PD patients by pre-clinical assessment of mild upper limb tremors, which is valuable for early treatment and rehabilitation. © 2023 IEEE.

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Mechanistic data-driven combined modelling and predictive control method for coal-fired power unit 5

Mechanistic data-driven combined modelling and predictive co...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Hu, Jingyu Han, Zhongyang Zhao, Jun Wang, Wei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Liaoning 116024 China

ISBN: (纸本)9798350325294

With the increase of the proportion of new energy sources connected to the grid, challenges are also emerged for modeling and control of the conventional coal-fired power units to increase their operational flexibility of deep peak-regulation. Traditional data-driven modelling (DDM) requires a large amount of training data containing a variety of operating conditions, which are hard to be fully satisfied in engineering practice. This paper investigates the mechanics and dynamic characteristics of boiler-turbine systems in the coal-fired power unit, and proposes a mechanistic data-driven combined modelling (M-DDM) method. It not only requires less amount of data comparing with the DDM based method, but also covers a wide range of operating conditions by considering the dynamic characteristics of the coal-fired power unit. A model predictive control (MPC) based method was subsequently designed as the control vehicle. In order to improve the computing efficiency and reduce the probability of model mismatch considering the involved ordinary differential equations (ODEs) and nonlinearities. reinforcement learning is embedded into the MPC. Finally, simulations are conducted for demonstrating the superior performance of the proposed methods comparing with other commonly deployed modeling and control methods. © 2023 IEEE.

关键词： Model predictive control

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A Novel Point Cloud Deep Learning Method for Predicting Variable Airfoil Flow Fields 43

A Novel Point Cloud Deep Learning Method for Predicting Vari...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Xinyao Cao, Junzhe Dalian University of Technology Control Science and Engineering Dalian116024 China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Airfoil flow field is of great significance to the design optimization of aircraft shape. Convolutional Neural Networks (CNNs) have been applied in flow field prediction studies, which greatly reduce the cost of computation time in traditional Computational Fluid Dynamics (CFD) methods. However, such CNN-based approaches normally bring extra errors due to the procedure of interpolating irregular grid data into Cartesian grids, which may lead to underperforming prediction accuracy. In this paper, an improved PointNet++ point cloud deep learning method was proposed for the airfoil flow field prediction. The unstructured grid vertices in the CFD domain were treated as point cloud as well as the input of neural network, and only the original information of the data was adopted to avoid interpolation. Neighborhood relationship features were defined and spliced with neighborhood point features, and the features were integrated by simultaneously solving for the maximum, minimum, sum, and average values to extract the local or global features of the system to enhance the performance of deep network for extracting the information. Finally, the proposed network was trained and tested by using 549 sets of flow field data formed by 61 airfoils under 9 angle-of-attack conditions, and further predicted for an additional 32 datasets with different airfoils and conditions. The simulation results show that the method proposed in this study gets lower prediction errors in terms of pressure, Mach number, x-direction velocity, and y-direction velocity than the two other current point cloud deep networks, PointNet and PointNet++. At the same time, the computation speed of this method is about 14 times faster than the traditional CFD method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational fluid dynamics

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Scheduling Optimization of Coal Washing Process Based on Two-Stage Fuzzy Heuristic Algorithm 13

Scheduling Optimization of Coal Washing Process Based on Two...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Zhou, Hui Wang, Zhiyuan Han, Zhongyang Wang, Linqing Zhao, Jun Wang, Wei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350361674

Coal washing and sorting plays a vital role for coal production. Reasonable scheduling of washing and sorting equipment, crushers and various coal silos in the coal washing and sorting process can effectively increase the total value of product coal and reduce the total energy consumption of equipment. In this paper, a multi-constrained nonlinear mixed integer programming model considering the start and stop of each equipment in the washing process is established to realize the specific regulation of the washing process. In order to overcome the problem such as local convergence when solving high-dimensional space problems, a two-stage fuzzy heuristic algorithm is proposed in this paper. In the first stage, a variety of population genetic algorithms based on fuzzy adaptive strategy control are designed for global search to establish an elite population bank to preserve high-quality individuals produced by various groups. In the second stage, an improved simulated annealing algorithm is used to perform a local search with a backtracking process on specific neighbors of individuals in the elite population bank, which can improve the stability of the algorithm and reduce the situation of falling into local convergence. Then the real data of a Chinese coal mining company was used for experiments, where the proposed algorithm is compared with other commonly used methods. The experimental results demonstrate that the proposed algorithm can increase the total coal value of the washed product by at least 1.7% compared to other algorithms, and the average total number of starts and stops of the equipment is reduced by about 7.3%, which are effectively beneficial for the related enterprise. © 2024 IEEE.

关键词： Coal mines

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Efficient Mobile Deployment of Lightweight CNN-Transformer Model for Crop Disease Diagnosis in Smart Agriculture 4

Efficient Mobile Deployment of Lightweight CNN-Transformer M...

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4th Asia Conference on Information engineering, ACIE 2024

作者： Lv, Zirong China University of Geosciences Hubei Key Lab. of Adv. Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan China

ISBN: (纸本)9798350371925

Crop disease diagnosis is of great significance for crop yield and agricultural production. Deep learning methods have become a major research direction for solving crop disease diagnosis. However, recent studies have shown that deploying complex neural network models on mobile devices poses a significant challenge to mobile hardware performance. To address this issue, this paper proposes a lightweight network model based on the Transformer and deploys it on mobile devices. Compared to other lightweight network models, our approach significantly reduces computational complexity using Transformer modules and exhibits a particular advantage in recognition accuracy. It achieves a recognition rate of 97.76% for ten categories of tomato diseases, while the model size is only 3.86 MB. Furthermore, we deploy the model on embedded devices and apply it to tomato crop recognition, enabling farmers to monitor crop conditions in real-time and realizing smart agriculture. © 2024 IEEE.

关键词： Deep learning

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Fault identification of rotating machinery based on multiscale convolution and deep residual networks 9

Fault identification of rotating machinery based on multisca...

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9th International Symposium on Sensors, Mechatronics, and Automation System, ISSMAS 2023

作者： Wang, Huihui Fan, Tianqi Qiu, Sen Liu, Long School of Intelligence and Electronic Engineering Dalian Neusoft University of Information Dalian116023 China Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9781510672802

Rotating machinery and equipment have a wide range of industrial applications, and it is important to perform intelligent fault detection during their operation. In order to fully capture the multi-scale features of mechanical fault data while solving the degradation problem of deep networks, we propose an intelligent fault detection model combining multi-scale convolution and deep residual networks. We validate it on four publicly availab.e datasets, and the results demonstrate the excellent performance of the proposed method. © 2024 SPIE.

关键词： Convolution Feature extraction Deep learning Machine learning Vibration Data modeling Lithium Convolutional neural networks Data centers Signal processing

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