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检索条件"任意字段=8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019"

共 336 条记录，以下是241-250 订阅

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Multi-Player Robust control of Stackelberg Games via Adaptive Dynamic Programming 11

Multi-Player Robust Control of Stackelberg Games via Adaptiv...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Zhang, Yongwei Zhang, Shunchao Zhao, Bo Liu, Derong Guangdong University of Technology School of Automation Guangzhou510006 China Beijing Normal University School of Systems Science Beijing100875 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, the hierarchical optimization problem of multi-player systems with matched uncertainties is investigated via adaptive dynamic programming. In the hierarchical optimization problem, there exist one leader and multiple followers, the leader chooses a policy in advance based on the actions of the followers, and the followers make optimal responses to the leader's policy. the optimal policies of the leader and the followers form the Stackelberg equilibrium. By designing appropriate value functions for the leader and the followers, the hierarchical optimization problem is formulated as a Stackelberg game and the robust stabilization problem is transformed into an optimal regulation problem. Moreover, the critic-only structure is established to obtain the approximate Stackelberg equilibrium. theoretical analysis shows that the developed ADP-based robust control guarantees the closed-loop system to be asymptotically stable. Finally, simulation example is adopted to verify the effectiveness of the present scheme. © 2022 ieee.

关键词： Robust control

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data-driven Modeling for PDF Shaping of Fiber Length Distribution in Refining Process 8

Data-Driven Modeling for PDF Shaping of Fiber Length Distrib...

引用

8th ieee Annual International conference on Cyber Technology in Automation, control, and Intelligent systems (ieee-CYBER)

作者： Li, Mingjie Zhou, Ping Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781538670576

A data-driven modeling approach is developed for the probability density function (PDF) shaping of the fiber length distribution (FLD) in the refining process. Firstly, a RBF neural network (NN) is used to approximate the output PDF of FLD, which consists of a set of weights and the corresponding RBF basis functions, the corresponding weight of the output PDF is estimated on the basis of the integration of the center and width of the RBF basis functions realized by an iterative learning law. Secondly, in order to improve the dynamic model precision between the input variables and the weights, considering that the random vector functional link networks (RVFLNs) has advantages of high accuracy and strung generalization ability, the dynamic relationship between the input variables and weights is obtained by using RVFLNs, thus the output FLD model of the refining process can be established. Ultimately, the experimental results show the effectiveness of the proposed approach.

关键词： data handling iterative methods learning (artificial intelligence) probability radial basis function networks Radial basis function networks data processing iterative methods Input variables Portable document format PDF gene basis functions Purification FIBER LENGth DISTRIBUTION Refining data driven

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A Novel Intelligent Online Fire Monitoring Approach for Underground Pipe Galleries 13

A Novel Intelligent Online Fire Monitoring Approach for Unde...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wang, Yongjing Wen, Chenying Zhu, Zheren Zhang, Xinming Song, Zhihuan Zhu, Zugang Fan, Xueer Zhejiang University The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Hangzhou Underground Pipeline Development Co. Ltd Hangzhou310064 China

ISBN: (纸本)9798350361674

Underground pipe galleries are constructed beneath urban areas, providing pathways for the installation of various municipal pipelines. these galleries help protect pipelines and efficiently utilize underground space, serving as a significant facilitator for the construction of smart cities. Due to the confined space inside the galleries and the simultaneous presence of multiple pipelines, there is a potential safety hazard of fire incidents. To ensure the safe operation of pipelines, online monitoring and timely warning of fire incidents are valuable tasks. Current traditional computer vision and machine learning approaches for object detection often experience slow response time. therefore the You Only Look Once (YOLO) series due to its fast response time, has become a popular method in real-time detection. Nonetheless, YOLOv8 still suffer from information fusion problems. In response to this issue, this paper proposed YOLOH algorithm, which improves YOLOv8 by employing the Gather-and-Distribute (GD) mechanism for intelligent online monitoring of fire incidents in underground pipe galleries. Finally, the effectiveness of YOLOH was validated using a combination of publicly available fire datasets and simulated fire datasets in underground pipe galleries. the YOLOH attains a 63.2 mAP, surpassing the YOLOv8-N baseline by +2.15 mAP. © 2024 ieee.

关键词： Fires

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Nuwa-RL: A Reinforcement learning based Receiver-side Congestion control Algorithm to Meet Applications Demands over Dynamic Wireless Networks 24

Nuwa-RL: A Reinforcement Learning based Receiver-side Conges...

引用

24th ieee International conference on High Performance Computing and Communications, 8th ieee International conference on data Science and systems, 20th ieee International conference on Smart City and 8th ieee International conference on Dependability in Sensor, Cloud and Big data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Gong, Guanghui Jiang, Xianliang Jin, Guang Xie, Yi Chen, Haiming Ningbo University Ningbo Zhejiang China

ISBN: (纸本)9798350319934

the advent of the wireless network has greatly increased the amount of data transmitted through the networks. Depending on the location of the user and the conditions of the network, the data transmitted over the network show the different transmission characteristics. data transmission over long periods of time is very sensitive to network vertical switching. It's highly susceptible to massive loss of throughput problems. therefore, it is essential to design a congestion algorithm for networks that switch frequently. However, traditional congestion control is a deployment based on a rule-based heuristics and tested on a predetermined set of benchmarks. As a result, these congestion control algorithms cannot solve the problem of access point switching under real networks. To address these issues, we propose Nuwa-RL, a receiver-side driven congestion control scheme. It combines the reinforcement learning algorithm PPOLSTM with the receiver-side congestion control algorithm for controlling the data transmission process. Nuwa-RL uses reinforcement learning to adjust the heuristic algorithm parameters in order to optimise the data transfer process. We verified that the algorithm is effective in reducing throughput degradation during network switching. Experience shows that Nuwa-RL can improve the data transmission ability of the congestion control algorithm in wireless networks. © 2022 ieee.

关键词： Reinforcement learning

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Covid-19 Epidemic Trend Prediction Based on CNN-StackBiLSTM 11

Covid-19 Epidemic Trend Prediction Based on CNN-StackBiLSTM

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Li, Zhenyu Wang, Yang Wang, Yanwei Zheng, Ying Su, Housheng School of Artificial Intelligence and Automation Huazhong University of Science and Technology China-Belt and Road Joint Laboratory On Measurement and Control Technology Wuhan430074 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Hubei Wuhan430073 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In late 2019, the novel coronavirus (COVID-19) became a major health hazard around the world. Recently, the COVID-19 has spread widely in most countries and regions and the number of infected people continues to grow rapidly. therefore, it is essential to research the development trend of the epidemic. the prediction of the number of infections and deaths is critical and helpful for developing health and epidemic prevention strategies and even curbing the epidemic. In this paper, a one-dimensional convolutional neural network combined with the stacked long-short-term-memory network model (CNN-StackBiLSTM) is proposed for the time-series prediction of cumulative cases and daily new cases. the local feature is extracted by CNN. the stacked BiLSTM captures the deeper characteristics of the time-series data. By combining the two networks, the proposed method simultaneously considers the information of temporal and spatial domains and can achieve accurate prediction results. Examples in Taiwan and Italy demonstrate the effectiveness of the proposed method. the proposed method is compared with LSTM, BiLSTM, and GRU. the mean absolute error, mean square error, R2 score, and root mean square error are calculated to quantificationally measure the different models. the results indicate the proposed method performs well in the prediction of both new daily confirmed cases and cumulative confirmed cases. © 2022 ieee.

关键词： COVID-19

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Puzzle Methods for data Science Applications 8

Puzzle Methods for Data Science Applications

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8th ieee International conference on Intelligent systems (IS)

作者： Jotsov, Vladimir Petrova, Pepa Iliev, Evtim ULSIT POB 161 Sofia 1113 Bulgaria ULSIT Sofia 1784 Bulgaria

ISBN: (纸本)9781509013531

Applications have been considered of Puzzle methods as a specific standard for improvement of different SAS Enterprise Miner tools. It is shown how the prognostic models have been improved using the considered original data-driven approaches or algorithms or by using different types of Binding, Pointing and classical constraints. As a result, new types of non-implicative causal relations are revealed. Different modifications of the proposed Puzzle methods have been researched aiming at better control of different types of constraints and elaboration of new, contemporary and more universal evolutionary applications of logical and statistical methods in one system.

关键词： data-driven Applications Puzzle Methods data Science Machine learning Agent Statistics

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A Multi-stage Optimized Fault Diagnosis Model for Imbalanced Fault data in Manufacturing Process 11

A Multi-stage Optimized Fault Diagnosis Model for Imbalanced...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Lai, Zhouhao Dong, Yan Ren, Hongru Lu, Renquan Guangdong University of Technology School of Computer Science and Technology and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Guangdong University of Technology School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Fault diagnosis of equipment in the manufacturing process is particularly important, and it is an indispensable link in intelligent manufacturing. the fault data generated by the equipment during the manufacturing process is often imbalanced. However, modeling and training on imbalanced dataset will result in a very high rate of misclassification. In order to solve this problem, and improve the accuracy of fault diagnosis in the manufacturing process. this paper proposes a multi-stage optimizatied fault diagnosis model based on bayesian optimization, synthetic minority oversampling technique(SMOTE) and stagewise additive modeling using a multi-class exponential loss function(SAMME), namely the BSS model. the multi-stage optimized fault diagnosis model proposed in this paper improves the diagnosis accuracy of imbalanced fault dataset from the aspects of dataset processing, model building and model training. Finally, the results of ultrasonic flowmeter failure diagnosis show that among the various evaluation indicators, the multi-stage optimized fault diagnosis model proposed in this paper is better than the traditional single machine learning model. © 2022 ieee.

关键词： Failure analysis

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Local-Global Dynamic Information Fusion Graph learning for Traffic Flow Prediction 13

Local-Global Dynamic Information Fusion Graph Learning for T...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Xu, Yuan Qin, Fan Ke, Wei He, Yan-Lin Zhang, Ming-Qing Zhu, Qun-Xiong Zhang, Yang College of Information Science & Technology Beijing University of Chemical Technology Beijing100029 China Engineering Research Center of Intelligent Pse Ministry of Education of China Beijing100029 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350361674

In the field of intelligent transportation systems, accurately predicting traffic flow is a challenging endeavor for enabling efficient services in urban road networks. While current research has made significant advancements in capturing nonlinear features and spatiotemporal dependencies at a fixed geographic node level, it often overlooks the dynamic interactions of traffic flow across different regions. In this work, we present a novel local-global dynamic information fusion graph learning model (LGDF-GL) for traffic flow prediction. Different from other graph learning model, we first design a dynamic adjacency matrix by region-adaptive parameter learning, and fuse it with a static adjacency matrix based on node geographic information by dynamic information fusion module, and the fused result serves as the diagonal elements of the local adjacency matrix. Subsequently, we input it into the graph convolutional network (GCN) and gated recurrent unit (GRU) aggregation model to extract local feature information containing dynamic spatiotemporal features from traffic flow data. the local feature information is pruned and concatenated to obtain global feature representations. then we achieve global feature representation prediction through multiple fully connected layers. Finally, we conducted experiments on two real-world traffic flow datasets to evaluate the performance of our proposed method. the results clearly demonstrate that the LGDF-GL method achieves superior prediction ability compared to several baseline models. © 2024 ieee.

关键词： Information fusion

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Rehabilitation Path Planning Based on Human Motion Output and Ergonomic Index Optimization 11

Rehabilitation Path Planning Based on Human Motion Output an...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： He, Tianyu Yan, Yangben Wang, Gang Liu, Yongbai Liu, Keping Sun, Zhongbo Changchun University of Technology Department of Control Engineering Changchun130012 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Rehabilitation robots help patients perform rehabilitation training according to the planned path, and reasonable planning of the rehabilitation path can enable patients to obtain better rehabilitation effects. In view of the problems of low degree of personification and poor comfort in the path planning of rehabilitation robots. In this paper, the normalized output function for human-like path planning is obtained, which is based on the inherent virtual constraints or output of the human lower limb, and the constraints are optimized through ergonomic index. First, the human body output data of the subject is collected through the optical motion capture system, after that the original normalized output function is obtained by Fourier fitting, and then the constraints are based on ergonomic indicators. Second, the constrained human body output data is calculated by the golden section algorithm, and finally the final human body standardized output function is obtained by Fourier fitting. Last, the standardized output function based on human output is applied in the path planning of rehabilitation robots, which can make the training of rehabilitation robots have a higher degree of human simulation and comfort, so as to achieve better rehabilitation effect. © 2022 ieee.

关键词： Motion planning

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Vehicle Tracking Method Based on Attention-YOLOv5 and Optimized DeepSort Models 11

Vehicle Tracking Method Based on Attention-YOLOv5 and Optimi...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Li, Zhuang Tian, Xincheng Liu, Yan Shi, Xiaorui Shandong University Center for Robotics School of Control Science and Engineering Jinan250061 China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China Sinotruk Jinan Power Co. Ltd. Sinotruk Industry Park Zhangqiu Jinan250220 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Vehicle tracking plays a vital role in traffic management and autonomous driving. In order to further improve the accuracy of vehicle tracking, reduce the number of ID-Switch and enhance the anti-interference ability to the outside world, we propose a vehicle detection and tracking model based on Attention-YOLOv5 and optimized DeepSort. Firstly, YOLOv5 with attention mechanism was used as the front-end target detector, that is, add CBAM and ECA attention mechanisms to the backbone network of the benchmark YOLOv5 separately to improve the feature extraction ability. then use the transfer learning idea to train it on the BDD dataset, it is concluded that ECA-YOLOv5 has the highest detection accuracy. Further we optimize the DeepSort algorithm, in order to overcome the disadvantages of the pre-training model not targeting traffic vehicles, we improve the re-recognition network structure and optimize the VeRi re-identification dataset, next perform re-identification pre-training on the Improved-VeRi dataset based on transfer learning. In order to make the vehicle features evenly distributed around the center of the class and improve feature resolution, a loss function L/CL combining the Softmax loss function and Center loss function is proposed. In the experimental part, collecting some highway traffic videos of different scenes. At first, conduct visual analysis, then perform ablation experiments, and evaluate by using MOTA, MOTP and ID-Switch. Compared with the benchmark algorithm, the tracking performance is significantly improved. © 2022 ieee.

关键词： Benchmarking

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