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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501-510 订阅

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Event Triggered control of Ship Automatic Berthing Based on Network Attack

Event Triggered Control of Ship Automatic Berthing Based on ...

引用

data driven control and learning systems (ddcls)

作者： Xin Liu Qiang Zhang QiWen Wang GuoQing Xue Bo Xu School of Navigation of Shipping Shandong Jiaotong University Wei Hai China Shandong Port Group Co. Ltd Qing Dao China Qingdao Pilot Station China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this article proposes an adaptive neural network event triggered control scheme based on virtual parameter learning for ship automatic berthing control under false data injection (FDI) attack environment, which is simultaneously affected by external interference, uncertain dynamics, and input saturation. In the controller design process, neural network technology and adaptive virtual parameter learning methods are combined to perform online reconstruction compensation for actuator signal mutations, uncertain dynamics, and external disturbances caused by FDI attacks. In addition, event triggering mechanism is adopted to reduce communication load and channel occupancy rate. Finally, the stability of the control law was analyzed using Lyapunov theory, and the effectiveness of the scheme was verified through comparative simulation experiments. the simulation results showed that the control algorithm proposed in this paper has superior control performance and steady-state performance in FDI attack environments. In practical engineering applications, this scheme can provide theoretical support for the automatic berthing of intelligent ships.

关键词： learning systems Actuators Adaptive systems Additives Simulation Neural networks Interference

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Detection of Anatomical Landmarks During Laparoscopic Cholecystectomy Surgery Based on Improved Yolov7 Algorithm

Detection of Anatomical Landmarks During Laparoscopic Cholec...

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data driven control and learning systems (ddcls)

作者： Zongjin Yang Guang Yang Xingyu Chen Zhenhao Chen Na Qin Deqing Huang Institute of Systems Science and Technology Southwest Jiaotong University Chengdu P.R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Many adverse events during laparoscopic cholecystectomy surgery occur due to errors in visual perception and judgment by surgeons, leading to misunderstandings of anatomical structures, particularly in surgeries performed by young and inexperienced doctors. this paper proposes a YOLOv7_CA model for detecting anatomical landmarks during laparoscopic cholecystectomy surgery, based on an attention mechanism. the network efficiently detects the gallbladder, Calot's triangle, and common bile duct, which are prone to intraoperative mis-manipulations. We introduce data augmentation and transfer learning methods to address the issue of small datasets. the mAP of YOLOv7_CA is 69.34%, with improvements of 3.22%, 5.59%, and 6.10% in mAP, Precision, and Recall, respectively, compared to YOLOv7. Experimental results demonstrate the network's excellent performance in detecting critical intraoperative anatomical landmarks, thereby achieving real-time assistance for surgeons. It also provides new ideas for the application of deep learning in the surgical field of vision.

关键词： Laparoscopes Deep learning Attention mechanisms Transfer learning Surgery Medical services Object detection

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Fault Detection Method for Closed-Loop control Loop of Water Jet Propulsion Device Based on Convolutional Autoencoder and Support Vector data Description

Fault Detection Method for Closed-Loop Control Loop of Water...

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data driven control and learning systems (ddcls)

作者： Zhaoyang Li Cheng Yang Gangqiang Li Meixi Wu China Institute of Marine Technology & Economy Beijing China Science and Technology on Water Jet Propulsion Laboratory Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the waterjet propulsion device, as an efficient power propulsion method, has been widely used in various types of ships. However, due to its complex structure, harsh working environment, and frequent failures caused by long-term high-load operation, it seriously affects the ship's mission performance. this paper proposes a data-driven fault detection method that introduces convolutional autoencoder (CAE) networks to implement adaptive feature extraction on the raw signal samples of closed-loop control systems, through convolutional encoding and decoding operations, while reducing the data dimension, the nonlinear inherent information in the raw signal samples is obtained, reducing the interference of redundant information. Using Particle swarm optimization and support vector data description (PSO-SVDD) and the Kernel Density Estimation (KDE) to obtain Statistics and control Limits for Feature Space and Residual Space. then Using composite statistics combining feature statistics and residual statistics to characterize health status of closed-loop control systems. the effectiveness of the proposed method is verified by physical experiments based on NI Compact RIO. the experimental results indicate that the proposed method has strong engineering value and can improve the reliability of waterjet propulsion devices.

关键词： Performance evaluation Training Machine learning algorithms Convolution Fault detection Propulsion Aerospace electronics

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Discretization Strategies for Improved Health State Labeling in Multivariable Predictive Maintenance systems 13

Discretization Strategies for Improved Health State Labeling...

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13th International conference on data Science, Technology and Applications, data 2024

作者： Autran, Jean-Victor Kuhn, Véronique Diguet, Jean-Philippe Dubois, Matthias Buche, Cédric ArianeGroup Issac France IRL CROSSING CNRS Adelaide Australia ENIB Brest France

ISBN: (纸本)9789897587078

In machine learning, effective data preprocessing, particularly in the context of predictive maintenance, is a key to achieving accurate predictions. Predictive maintenance datasets commonly exhibit binary health states, offering limited insights into transitional phases between optimal and failure states. this work introduces an approach to label data derived from intricate electronic systems based on unsupervised discretization techniques. the proposed method uses data distribution patterns and predefined failure thresholds to discern the overall health of a system. By adopting this approach, the model achieves a nuanced classification that not only distinguishes between healthy and failure states but also incorporates multiple transitional states. these states act as intermediary phases in the system’s progression toward potential failure, enhancing the granularity of predictive maintenance assessments. the primary objective of this methodology is to increase anomaly detection capabilities within electronic systems. through the utilization of unsupervised discretization, the model ensures a data-driven approach to system monitoring and health evaluation. the inclusion of multiple transitional states in the labeling process facilitates a more precise predictive maintenance framework, enabling informed decision-making in maintenance strategies. this article contributes to advancing the effectiveness of predictive maintenance applications by addressing the limitations associated with binary labeling, ultimately encouraging a more nuanced and accurate understanding of system health. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Predictive maintenance

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Model Analysis and Application of Quantum Computers in Credit Scoring Card Combination Optimization

Model Analysis and Application of Quantum Computers in Credi...

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data driven control and learning systems (ddcls)

作者： Xiaohan Zhou Ruikun Zhang School of Mathematics and Physics Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the context of business such as bank credit cards or related loans, banks score customers through credit scoring cards. Loan interest income and bad debt losses determined by the combination of thresholds of different credit scoring cards are key factors that determine the bank's final income. this paper mainly studies the combination optimization of credit scoring cards by establishing an optimization model and transforming it into a Quadratic Unconstrained Binary Optimization (QUBO) problem. First, the optimization problem of a single credit scoring card is studied, the data is preprocessed and zero-one integer programming model is established, then convert the objective function into a Hamiltonian to obtain an optimization model in the form of QUBO, and obtain the maximum bank income and the threshold of a single credit scoring card through the Simulated Annealing. then, the credit scoring card combination optimization problem is studied. On the basis of the above research, variable substitution is used to reduce function degree, and a penalty function is introduced to obtain an unconstrained QUBO model for the combination problem. the D-Wave quantum annealing algorithm is used to obtain the optimal combination solution.

关键词： learning systems Analytical models Annealing Optimization models Computational modeling Process control Simulated annealing

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An Improved VAE-Based Quality Prediction Method with Application to a Hot Strip Rolling Mill Process

An Improved VAE-Based Quality Prediction Method with Applica...

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data driven control and learning systems (ddcls)

作者： Yundan Liu Kai Zhang Xiaowen Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing PR China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing PR China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In a hot strip rolling mill (HSRM) process, the control of the crown quality of a strip steel is of paramount importance. In this paper, a distributed crown prediction model based on an improved Variational Autoencoder (VAE) framework is proposed. the model integrates a multi -layer VAE architecture to better accommodate the complex distribution of actual production data. Utilizing real production data from Ansteel's HSRM process, the model was subjected to extensive training and validation to optimize its predictive accuracy. then in comparison with centralized VAE and VAE models with specialized prediction layers, the distributed model demonstrated exceptional predictive performance, with evaluation metrics including mean absolute error, root mean squared error and mean absolute percentage error. It can be seen from the application result that: the proposed method shows a better prediction result compared with the traditional methods.

关键词： Training Strips Technological innovation Accuracy Process control Production Predictive models

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Dense-Descriptor Transporter Networks for Multidimensional Deformable Object Manipulation

Dense-Descriptor Transporter Networks for Multidimensional D...

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data driven control and learning systems (ddcls)

作者： Yang Cao Daoxiong Gong Jianjun Yu Faculty of Information Technology Beijing University of Technology Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the continuous development of robotics, there are emerging new trends in robotic manipulation of deformable objects, calling for improved adaptability and flexibility to better cope with complex environments and task requirements. Due to the high-dimensional state space and complex dynamics of deformable objects during deformation, it is challenging for neural networks to directly extract object features such as shape, texture and color from images. In this paper, we propose an imitation learning approach that utilizes dense descriptor modules to improve its perception of different deformable objects and deformations. the learned descriptors can extract the features of deformable objects from RGB-D images and map them to the descriptor space, making the representation of objects more compact and robust. Furthermore, our proposed approach demonstrates better competitiveness in simulation environments featuring fabrics, ropes, or bags when compared to the baseline methods.

关键词： Visualization Deformation Shape Image color analysis Imitation learning Neural networks Feature extraction

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Mobile Robot Navigation with Incremental Self-Organizing Developmental Network Learned Based on Modified RRT

Mobile Robot Navigation with Incremental Self-Organizing Dev...

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data driven control and learning systems (ddcls)

作者： Mengchao Shi Dongshu Wang School of Electrical & Information Engineering Zhengzhou University Zhengzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

When a mobile robot performs tasks, it may encounter changeable unknown environments. When navigating in the changing unknown environment, mobile robot should be able to learn incrementally to gradually improve their navigation capabilities. Considering that the fixed network structure will limit the continuous learning ability of the robot, this paper proposes an incremental self-organized developmental network, which does not need to define the number of neurons in advance, and its memory capacity adaptively increases with the learned knowledge. the network is employed to learn continuously in multiple complex environments without forgetting previously learned knowledge and without neuronal redundancy or insufficiency. In addition, this paper integrates the Rapidly Exploring Random Tree (RRT) algorithm into autonomous continuous learning process of incremental self-organizing developmental network, using the modified RRT algorithm to perform a global search of the environment to obtain a feasible path that the network can learn. this method can target the information in the specific environment, reduce the sample data for training the developmental network, improve the learning efficiency, and the network can show better generalization ability after continuous learning. the experimental results validate the effectiveness and potential of the proposed network as well as its learning method.

关键词： learning systems Knowledge engineering Training Navigation Neurons Termination of employment control systems

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Aerodynamic Parameter Identification of Aircraft Based on GA-BP Algorithm

Aerodynamic Parameter Identification of Aircraft Based on GA...

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data driven control and learning systems (ddcls)

作者： Yu Zhao Shihui Wang You Sun Si Chen Tianpeng Lan Yingbin Zhang Beijing Aerospace Automatic Control Institute China Academy of Launch Vehicle Technology Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Aiming at the problem of aerodynamic parameter identification of a certain type aircraft, the GA-BP algorithm is used to identify the coefficients of aerodynamic force. Firstly, the coefficients are solved by using flight trajectory data obtained from wind tunnel simulation experiments. then, the relationships between aerodynamic force coefficients and Mach, angle of attack, rudder pendulum angle, etc. are fitted by BP neural network. the thresholds and weights between neurons are optimized by genetic algorithm during the training of neural network, so that the optimal structure of neural network can be obtained through training of multiple sets of flight trajectory data under different flight conditions. Simulation results show that the aerodynamic force coefficients obtained by the GA-BP algorithm have higher accuracy compared with traditionally used engineering methods.

关键词： Training Parameter estimation Accuracy Wind tunnels Force Aerodynamics Trajectory

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Class-Imbalance and Client-Imbalance Federated learning for Fault Diagnosis

Class-Imbalance and Client-Imbalance Federated Learning for ...

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data driven control and learning systems (ddcls)

作者： Shu Sun Jinchuan Qian Xinmin Zhang Zhihuan Song State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the development of big data, data-driven fault diagnosis models have experienced rapid growth. However, as the complexity of models increases, there is a corresponding rise in the demand for data. To obtain a substantial amount of data, traditional approaches involve centralizing datasets from multiple factories for training. However, due to heightened emphasis on data privacy from enterprises and regulatory bodies, this method becomes more challenging to implement and poses risks of data leakage. In response to this challenge, this paper designs a novel federated learning-based fault diagnosis modeling approach. Each local device trains its model on its local dataset and subsequently uploads updated model parameters to a central server for global model aggregation and updating. However, it is worth noting that in industrial processes, there may be concurrent class imbalance and data client imbalance, which brings challenges to fault diagnosis modeling based on federated learning. To address this challenge, we further propose a dynamic client aggregation fault diagnosis method based on category loss in federated learning. Experimental results indicate that the proposed approach not only protects data privacy in imbalanced class and client scenarios but also achieves outstanding fault recognition accuracy and robustness.

关键词： Fault diagnosis Training data privacy Accuracy Federated learning Heuristic algorithms data models

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