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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是261-270 订阅

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Intelligent Drug Recognition System Based on Improved YOLOv7 and PP-OCRv3 13

Intelligent Drug Recognition System Based on Improved YOLOv7...

引用

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

作者： Chen, Zhenhao Chen, Yilin Yang, Zongjin Qin, Na Huang, Deqing Institute of Systems Science and Technology Southwest Jiaotong University Chengdu610031 China

ISBN: (纸本)9798350361674

the manual dispensing of drugs by pharmacists is time-consuming, labor-intensive, and has a low accuracy rate. In order to address this issue, this paper proposes an automated drug dispensing method based on improved object detection and text recognition. this method first embeds a Simple Attention Module (SimAM) into the network structure of YOLOv7, named YOLOv7_Sim, to achieve the localization and recognition of medicine bottles. Additionally, a lightweight PP-OCRv3 is employed for text recognition on liquid bags, and the similarity between the recognition results and the names of drugs on the prescription is used to determine a match. the novelty of this method lies in the combination of YOLOv7 and SimAM, which improves the accuracy of medicine bottle recognition and enhances the algorithm's robustness. Experimental results demonstrate that compared to YOLOv7, YOLOv7_Sim shows significant performance improvements, with a 3.12% increase in mAP, 0.69% increase in precision, and 2.83% increase in recall for the same type of medicine bottle. the proportion of fully recognized text on liquid bags is 90.6%. the drug recognition system, which combines YOLOv7_Sim and lightweight PP-OCRv3, has practical engineering significance for automating the drug dispensing process. © 2024 ieee.

关键词： Bottles

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Adaptive Super-Twisting Sliding Mode control for Friction Compensation of Servo System Via AESO and Parameters Estimation 13

Adaptive Super-Twisting Sliding Mode Control for Friction Co...

引用

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

作者： Wang, Shun Tao, Liang Deng, Xiongfeng Xu, Binzi Shi, Linlin College of Electrical Engineering Anhui Polytechnic University Wuhu241000 China College of Engineering South China Agricultural University Guangzhou510642 China

ISBN: (纸本)9798350361674

this paper presents a super-twisting sliding mode control strategy utilizing an adaptive extended state observer (AESO) for servo systems with nonlinear friction compensation to tackle the challenges of precise tracking. Firstly, an advanced online parameter identification mechanism is adopted to identify the unknown parameters of the continuous friction model within the system. this mechanism dynamically updates parameters in real-time, effectively converging to the true values of the parameters in response to the dynamic changes of the system. Subsequently, the AESO is employed to accurately estimate and compensate for the lumped disturbances, encompassing external disturbances and parameter estimation errors. then, an adaptive super-twisting sliding mode controller is proposed. the controller is meticulously engineered to ensure the system's state accurately adheres to a predefined reference trajectory, effectively reducing errors stemming from model uncertainties and the influence of external disturbances. To empirically substantiate the efficacy of the proposed methods, a comprehensive series of simulation experiments are conducted, showcasing the superiority of the proposed method in terms of tracking accuracy and system stability. © 2024 ieee.

关键词： Sliding mode control

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data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings 13

Data Augmentation Integrated with Feature-Enhanced Convoluti...

引用

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

作者： Wang, Li Qu, Qilin Wang, Yanwei Wong, David Shan-Hill Zheng, Ying 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 693 Chuxiong Road Wuhan430073 China National Tsing-Hua University Department of Chemical Engineering Hsinchu30071 Taiwan

ISBN: (纸本)9798350361674

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. this lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. the network's ability on feature extraction is improved by focusing on high quality features. thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task. © 2024 ieee.

关键词： Convolutional neural networks

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End-to-End Autonomous Driving Algorithm Based on PPO and Its Implementation 13

End-to-End Autonomous Driving Algorithm Based on PPO and Its...

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

作者： Zhao, Jianfeng Zhao, Yang Li, Weixia Zeng, Chaoqun School of Automative and Transportation Engineering Shenzhen Politechnic University Shenzhen518000 China

ISBN: (纸本)9798350361674

With the commercialization of vehicles equipped with partial autonomous driving capabilities, achieving fully autonomous driving remains a hot topic among researchers. In contrast to modular autonomous vehicle control, end-to-end autonomous driving aims to replicate human driving by directly translating sensor inputs into vehicle control outputs, making it the ultimate goal in autonomous driving. In this study, we propose an end-to-end autonomous driving algorithm based on the Proximal Policy Optimization (PPO) reinforcement learning algorithm and verify its feasibility. Firstly, we construct an autonomous driving algorithm based on the PPO algorithm, where pre-encoded images and vehicle state information are input to a policy based on deep neural networks, directly generating speed and steering controls for the vehicle, thus achieving true end-to-end autonomous driving. Furthermore, using the CARLA simulation environment for autonomous vehicles, we define a reward mechanism for driving behavior and establish a reinforcement learning environment to train and validate the proposed algorithm. Finally, we analyze the algorithm's feasibility based on rewards, cumulative average rewards, average episode length, and average driving distance. While the algorithm still has a long way to go before practical application, it lays the foundation for further implementation of end-to-end autonomous driving algorithms. © 2024 ieee.

关键词： Deep reinforcement learning

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Two-Dimensional Statistical Pattern Analysis for Effective Modeling and Monitoring of Limited Batch Process data in Food Industry 13

Two-Dimensional Statistical Pattern Analysis for Effective M...

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

作者： Zhu, Jinlin Zhang, Zheng Jiangnan University State Key Laboratory of Food Science and Resources Wuxi214122 China School of Food Science and Technology Jiangnan University Wuxi214122 China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering 999077 Hong Kong

ISBN: (纸本)9798350361674

this study presents a novel two-dimensional statistical pattern analysis (2D-SPA) method, specially designed to address the challenges inherent in monitoring and modeling processes within low-volume, high-quality food manufacturing settings. By ingeniously integrating robust statistical techniques within a two-dimensional sliding window framework, this approach significantly expands the analytical dataset by operating across both batch and time dimensions, effectively overcoming the constraints posed by limited batch sizes. this innovative strategy not only augments the detection and diagnosis capabilities for subtle process deviations and emerging trends but also capitalizes on the dynamic statistical variances observed across batches and over time, thus offering a comprehensive view of the process variability. the application of 2D-SPA is meticulously validated through an industrial case study focused on lactic acid bacteria fermentation, where it demonstrates marked improvements in accuracy and sensitivity for process monitoring. the results underscore the method's potential to significantly enhance quality assurance measures in food batch production. © 2024 ieee.

关键词： Food industry

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An Online Evaluation Based Extended-State Kalman Filter Approach to Navigation Via Signal of Opportunity 13

An Online Evaluation Based Extended-State Kalman Filter Appr...

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

作者： Liu, Junyuan Chen, Xuanwen Chen, Sen Bai, Wenyan Wang, Yinghui Zhao, Zhiliang Airborne Avionics Flight Test Institute Chinese Flight Test Establishment Xi'an710089 China Beijing Aerospace Automatic Control Institute Beijing100854 China School of Automation and Electrical Engineering University of Science and Technology Beijing100083 China School of Mathematics and Statistics Shaanxi Normal University Xi'an710119 China School of Electrical and Control Engineering North Central University Taiyuan030051 China

ISBN: (纸本)9798350361674

this paper studies the problem of multi-source opportunistic signal navigation of aircraft. Firstly, the paper provides the mathematical models for five types of opportunistic signals. Furthermore, the online evaluation algorithm based on T-test is proposed for seeking the biased opportunistic signals. By disposing of the opportunistic signals causing intolerant location error, an estimated location position is obtained by trusty opportunistic signals. Finally, to handle the unknown part of flight acceleration caused by external disturbances, the extended-state Kalman filter is constructed to promote the locating accuracy. the simulation results show the effectiveness of the proposed method. © 2024 ieee.

关键词： Extended Kalman filters

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A New Method of the Shortest Path Planning for Unmanned Aerial Vehicles 6

A New Method of the Shortest Path Planning for Unmanned Aeri...

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2017 ieee 6th data driven control and learning systems conference (ddcls’17)

作者： Darong Huang Dong Zhao Ling Zhao School of Information Science and Engineering Chongqing Jiaotong University Department of real estate equipment engineering Chongqing real estate college

ISBN: (纸本)9781509054626

In this paper, the optimal route and deployment scheme are designed to ensure the shortest retention time for unmanned aerial vehicles(UAV) in risk area. Firstly, according to the known data and radar scanning range, the regional distribution map of target grope and base are obtained, respectively. Secondly, based on the different scanning bandwidth of loads, target points are classified by using clustering analysis. this makes the target points fall on the scanning bandwidth of UAV as far as possible, accordingly reducing the UAV's scanning times. this problem can be regarded as a travelling salesman problem in radar scanning range. Finally, the deployment result and locally optimal route are obtained by 0-1 programming in LINGO. Furthermore, particle swarm optimization is used to improve the local optimal path and the global optimal route can then be generated.

关键词： UAV GA PSO the Shortest Path

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Image Recognition of Chinese Herbal Medicine Using Adaptive Gamma Correction Based on Convolutional Neural Network 13

Image Recognition of Chinese Herbal Medicine Using Adaptive ...

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

作者： Ji, Honghai Liu, Xinjing Wang, Li Fan, Lingling Liu, Shida School of Electronics & Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100096 China

ISBN: (纸本)9798350361674

Chinese herbal medicine (CHM) has gained significant attention due to its potential in treating various diseases. CHM image classification is crucial in fields such as smart medical care and medical teaching. However, accurately classifying CHM images has been a challenging task due to their fine-grained variability. To achieve automatic identification and classification of Chinese herbal medicines, we designed a method that combines image processing and deep learning. We established a dataset of CHM images, which includes 10 kinds of common Chinese herbal medicines, with 15,719 images in total. the dataset covers medicinal materials in various natural backgrounds and provides ample samples for training deep learning models. Regarding image processing, we proposed a method based on adaptive gamma correction(AGC) and compared it with CLAHE(Contrast Limited Adaptive Histogram Equalization)and traditional gamma correction(TGC) methods. Subsequently, we employed three pre-training models of convolutional neural networks based on transfer learning, namely GoogLeNet, ResNet-18, and MobileNetV2, to identify CHM images. the results of the verification indicate that the AGC-CNN model outperforms the CLAHE-CNN and TGC-CNN models in terms of classification accuracy and image quality improvement. © 2024 ieee.

关键词： Contrastive learning

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Observer-Based Fault Tolerant Formation control of Multi-Agent systems via Adaptive Dynamic Programming 13

Observer-Based Fault Tolerant Formation Control of Multi-Age...

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

作者： Zhang, Yongwei Zhao, Bo Liu, Derong College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Systems Science Beijing Normal University Beijing100875 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798350361674

this paper develops a fault tolerant formation control scheme for multi-agent systems (MASs) by using adaptive dynamic programming. In MASs, there exist one leader and multiple followers, where the leader is a generated ideal trajectory and the followers catch up with the leader using appropriate designed control inputs based on their own and neighbors' information. To obtain accurate fault information, a fault observer is established for each follower. Subsequently, a discounted cost function, which includes the estimated fault, the formation error, and the control laws, is developed for each following agent. In order to solve the coupled Hamilton-Jacobi-Bellman equation of each follower, a critic-only structure is built to obtain the approximate fault tolerant formation control law. Furthermore, it is proved that the uniform ultimate boundedness of the formation error of each following agent is guaranteed under the developed observer-based fault tolerant formation control law. It means that all following agents are able to maintain a specified formation and catch up with the leader. Finally, a simulation example is employed to validate the effectiveness of the present control approach. © 2024 ieee.

关键词： Cost functions

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On-Orbit Identification of Unstable Micro-Propulsion System Parameters Using Drag-Free control 13

On-Orbit Identification of Unstable Micro-Propulsion System ...

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

作者： Xu, Nuo Xia, Xiwang Li, Zhaoxiong He, Xiongfeng Guo, Ning Wang, Pengcheng Zhang, Yonghe Chinese Academy of Sciences Innovation Academy for Microsatellites Shanghai201203 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory for Satellite Digitization Technology Chinese Academy of Sciences Shanghai201203 China Lanzhou Institute of Physics Science and Technology on Vacuum Technology and Physics Lab Lanzhou730000 China

ISBN: (纸本)9798350361674

Space gravitational wave detection spacecraft is equipped with a drag-free control and micro-propulsion system, and adopts thrust closed-loop feedback control structure. An on-orbit high accuracy estimation algorithm of micro-thruster parameters based on drag-free control model is proposed in this paper in order to deal with the problem of unsteady nominal thrust caused by thruster failure or aging and and extend thrusters lifespan. In this method, the recursive gain and covariance are designed to estimate the parameter matrix of system. Adaptive forgetting factors are added to the covariance matrix for designing a fading memory window to determine the influence of prior data on the results. At the same time, the thruster state is deconstructed into three parts: thrust gain, thrust direction and lever arm to facilitate the redistribution of control force and torque. Simulations show that the on-orbit thrust identification errors of the proposed method in different micro-propulsion states are all at sub-micro-newton level, and the noise is less than 0.1μ N/Hz which has capability to meet the requirements corresponding to drag-free control system. © 2024 ieee.

关键词： Covariance matrix

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