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共 716 条记录，以下是551-560 订阅

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Automatic Extraction Technology of Ionospheric Vertical data with Handwritten Font

Automatic Extraction Technology of Ionospheric Vertical Data...

引用

data driven control and learning systems (ddcls)

作者： Guichang Su Ruikun Zhang Xiangpeng Liu School of Mathematics and Physics Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

To solve the problems such as large size, dense text area and various handwriting styles, an automatic data extraction technology based on DB and CRNN algorithms is proposed, which mainly includes four modules: image preprocessing, form text detection, text recognition and layout processing. Firstly, contour extraction and tilt correction are used to preprocess the scanned images of handwriting vertical data. then, Hough transform is used for table detection and DB algorithm is used for text detection. the image segmentation function is added before DB detection algorithm to improve detection accuracy. Finally, CRNN algorithm is used for text recognition, and coordinate fusion algorithm is used to save the recognition results into Excel standardized format to realize automatic data extraction and saving. the experimental results show that the text detection recall rate of the proposed algorithm is 96.77%, and the F-value of the comprehensive evaluation index of text recognition is 95.2%. Compared with other algorithms, its effectiveness is verified. therefore, the automatic data extraction technology proposed in this paper has high practicability and can meet the actual needs of engineering applications.

关键词： Image segmentation Handwriting recognition Image recognition Text recognition Layout Text detection Transforms

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HFRRU-Net: Hybrid Features Ringed Residual U-Net for Image Splicing Forgery Detection

HFRRU-Net: Hybrid Features Ringed Residual U-Net for Image S...

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data driven control and learning systems (ddcls)

作者： Mengjing Sun Haijiang Zhu College of Information & Technology Beijing University of Chemical Technology Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the rapid development of computers and the internet, digital image forgery detection has become one of the important research hot topics in the field of computer vision. In this article, we propose a dual stream network to detect image tampering and locate forged areas. Our network includes the ringed residual U-Net, RGB stream and DCT stream, which is named as a hybrid feature ringed residual U-Net (HFRRU-Net) because of combining frequency domain features with RGB features. Compared to the existing works, HFRRU-Net is an end-to-end training approach without image preprocessing or post-processing, and this network may speed up the training and prediction process. In the experments, the HFRRU-Net is evaluated on five public image forgery datasets, CASIA v1.0, Columbia, In-the- Wild, Nist2016, and Realistic. the results indicate that the F1 scores of the HFRRU-Net outperforms other methods in four datasets. And visual comparisons further demonstrate the proposed method.

关键词： Training learning systems Visualization Digital images Splicing Streaming media Feature extraction

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Collaborative Optimization Strategy of Network-Load-Storage for Peak Load Shifting of Power System

Collaborative Optimization Strategy of Network-Load-Storage ...

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data driven control and learning systems (ddcls)

作者： Pu Yang Li Shuo Yanhong Luo Yixuan Lu Yanru Chen Northeastern University Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

As the integration of advanced power electronic technologies like microgrids, distributed generation, and energy storage systems gains momentum, the conventional passive and unidirectional mode of power supply encounters significant challenges. these advancements have brought about issues such as voltage instability due to the mismatch between energy supply and demand. In response, this research proposes a collaborative optimization strategy of network load and storage for peak load transfer. the objective is to harmonize the interaction between the distribution network, the load, and the energy storage segments, thereby leveraging various controllable assets to enhance the efficiency of energy resource utilization. the methodology begins by leveraging the predictive capabilities of the Long Short-Term Memory (LSTM) network for voltage analysis. then, informed by the projected voltage operation trends, the Network-Load-Storage (NLS) coordination strategy is employed. this strategy optimizes the energy storage's charge and discharge cycles to shave peak demand and bolster the load during periods of low demand. Comparative simulations demonstrate that the optimization approach presented in this study effectively enhances voltage stability, with the optimization scheduling yielding favorable outcomes.

关键词： Electricity Distribution networks Power system stability Market research data models Forecasting Optimization

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Entropy Weight Method-Based Multi-Attribute Decision-Making for Web Service Selection

Entropy Weight Method-Based Multi-Attribute Decision-Making ...

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data driven control and learning systems (ddcls)

作者： Xiyu Zhang Zhichun Jia Yiwen Wang Hongda Wang Bo Shao Xing Xing College of Information Science and Technology Bohai University

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Web services continue to proliferate and users are faced with more choices. learning to choose Web services wisely is essential in order to meet demand and receive the best service. this paper focuses on the problem of selecting the best Web service in a multi-attribute state. Our goal is to select quality service streams from a vast array of multi-attribute services to meet our users' needs. We consider the user's requirements as a workflow that includes a number of tasks and each task consisting of various services that are also part of the user's needs. It is our responsibility to pick the best services from each task and integrate them into a seamless service process. To solve this problem, in this work, we utilized a normalization algorithm for the attributes of the candidate services and then used entropy weighting method to calculate the weights and the optimal particle swarm algorithm for service flow selection. Although similar research methods exist in the existing literature, experimental results demonstrate that the time performance of this method in this paper can be significantly improved.

关键词： learning systems Web services Computational modeling Decision making control systems Entropy data models

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Detection of Melanoma Based on Improved YOLOv8

Detection of Melanoma Based on Improved YOLOv8

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data driven control and learning systems (ddcls)

作者： Honghai Ji Xuechen Zheng Shuangshuang Xiong Lingling Fan Li Wang School of Electronics & Control Engineering North China University of Technology Beijing China School of Automation Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the incidence of skin cancer is increasing globally, with melanoma being the most formidable form of skin cancer. Melanoma originates from melanocytes, which exhibit a high degree of visual similarity. Moreover, dermoscopy image data often suffer from hair occlusion and the lesion area appears relatively small in the images, making it challenging for traditional methods to achieve accurate recognition and detection results. In this regard, we propose a YOLOv8 framework based on multi-scale sequence fusion(MSSF) for melanoma detection. this approach enhances the Neck structure of YOLOv8 and improves the network's ability to extract multi-scale information by introducing the Multi-scale Sequence Fusion module. In addition, we implemented a morphological black hat based hair processing method to reduce hair occlusion. In the ISIC datasets, compared with the unimproved algorithm, the mean average precision(Map) at 50 IoU is increased by 5.7%, Map between 50 and 95 IoU is increased by 7.5%, and the number of parameters is decreased by 12.5%.

关键词： Hair YOLO learning systems Visualization Solid modeling Accuracy Image recognition

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Lateral Trajectory Tracking of Unmanned Vehicles Based on Active Disturbance Rejection Model Predictive control

Lateral Trajectory Tracking of Unmanned Vehicles Based on Ac...

引用

data driven control and learning systems (ddcls)

作者： Wei Ai Tong Zhang Yang Wang Xiangyang Li College of Automation Science and Technology South China University of Technology Guangzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In addition to internal state constraints such as lateral velocity and cross-swing angular velocity, external factors, such as a complex time-varying environment, directly impact the tracking performance. For a typical underdriven incomplete constraint system of a vehicle, it is possible to explicitly handle the physical constraints of the system while achieving high accuracy and stable trajectory tracking in the presence of parameter uncertainties and internal/external disturbances. this paper proposes a lateral trajectory tracking control algorithm based on active disturbance rejection model predictive control. By employing the concept of disturbance compensation, a multivariate extended state observer is designed to real time observe and estimate the system state and unknown disturbances, effectively compensating for them to enhance the system's robustness. Furthermore, adopting the principle of optimal control, the optimal control input is determined using a partially known adaptation model, considering the physical constraints of the system, thereby ensuring the stability of vehicle driving. Finally, comparative simulation experiments are conducted to validate the effectiveness of the proposed algorithm.

关键词： Adaptation models Uncertainty Trajectory tracking Vehicle driving Optimal control Observers Prediction algorithms

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An Internal Model control Method Based on Reversible Residual Neural Network Model Applied to Turntable Servo systems

An Internal Model Control Method Based on Reversible Residua...

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data driven control and learning systems (ddcls)

作者： Cheng Xie Ting Chen Yang Liu Songlin Chen Control and Simulation Center School of Astronautics Harbin Institute of Technology Harbin P. R. China Institute for Micro Nano Satellites Innovation Academy for Microsatellites of CAS Shanghai P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

To meet the special requirements of high dynamic and performance for turntable servo systems, an internal model control (IMC) method based on reversible residual neural network model is proposed in this paper. In order to improve the accuracy and modeling efficiency of the forward and inverse models in the internal model controller and ensure the correlation between these two models, a reversible residual neural network model based on the forward and inverse dynamic characteristics of turntable servo systems is proposed. through the special design of the network structure, the forward and inverse models can be obtained by one-way training. then, the forward and inverse models are applied to the traditional IMC algorithm framework, the IMC method based on the reversible network model is obtained, and the stability of the control system is proved. the simulation results show that the proposed IMC method has better nonlinear compensation ability, tracking effect, and dynamic performance than the traditional IMC method. In addition, the design process of the proposed internal model controller is simpler and more practical. the method described in this paper has important value and significance for the intelligent development of traditional servo control.

关键词： Training learning systems Simulation Process control control systems data models Stability analysis

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Iterative Fault-Tolerant control Strategy for Urban Traffic Signals Under Signal Light Failure

Iterative Fault-Tolerant Control Strategy for Urban Traffic ...

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data driven control and learning systems (ddcls)

作者： Shuo Liang Fei Yan College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China College of Automation and Information Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Most existing signal control methods in urban transportation areas have not taken into account the impact of signal light failures in actual road networks. By introducing a signal fault model on the basis of a macroscopic traffic flow model, a traffic signal control strategy based on iterative learning fault-tolerant control is proposed to address the overall signal control problem of urban road networks in the presence of traffic signal faults. When a signal light in the road network malfunctions and the fault is restored, by controlling the traffic signal, the queue length of vehicles in each lane at the intersection of the road network tends to be balanced, thereby improving road traffic efficiency. At the same time, state feedback is introduced into the control law to reduce the number of vehicles entering the intersection with faulty signal lights, reduce the impact on traffic flow when the signal light fails, and suppress the impact caused by periodic disturbances within the region. Finally, the convergence of the method was proved through mathematical derivation, and a linear matrix inequality was given to determine the controller gain. the simulation experiment results show that the proposed method can effectively reduce the impact of signal light failures on urban traffic flow.

关键词： Fault tolerance State feedback Roads Fault tolerant systems Transportation Real-time systems Mathematical models

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Affine Ultra-Local Based Model-Free Predictive control on PMSM Drives with $\varepsilon$-Approximation State Compensation

Affine Ultra-Local Based Model-Free Predictive Control on PM...

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data driven control and learning systems (ddcls)

作者： Yao Wei Weiyuan Ma Haotian Xie Dongliang Ke Fengxiang Wang Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Jinjiang China College of Mechanical and Electronic Engineering Fujian Agriculture and Forestry University Fuzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the accuracy of a data-driven model directly affects the control performance of model-free predictive control (MFPC). To take into account the affine motion of the plant, a model-free predictive current control (MF-PCC) strategy is proposed in this paper and applied to a permanent magnet synchronous motor (PMSM) driving system. To enhance model accuracy, we adopt an affine ultra-local model, which is designed to accurately represent the system. Online estimation of all affine operators is performed using the recursive least squares (RLS) algorithm to ensure a robust fit. To improve control performance, we integrate a state compensation technique with the model. the compensation gain is selected using the $\varepsilon$ -approximation approach to meet terminal constraints. the stability of the proposed method is theoretically analyzed, and its effectiveness is verified through experimental results. the proposed method offers improved model accuracy and current quality, along with enhanced robustness compared to conventional methods.

关键词： Adaptation models Accuracy Limiting System performance Predictive models Permanent magnet motors Stability analysis

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A Two-Level Active Equalization for Battery Packs Using Breadth First Search Algorithm

A Two-Level Active Equalization for Battery Packs Using Brea...

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data driven control and learning systems (ddcls)

作者： Jiaqiang Tian Tianhong Pan Qingping Zhang Guozhi Yang School of Eelectrical Engineering and Automation Anhui University Hefei P. R. China Power Research Institute of State Grid Ningxia Electric Power Co. Ltd Ningxia P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Equalization management is an effective approach for addressing energy inconsistencies in battery systems, and active equalization has emerged as the dominant direction for battery equalization management due to its low energy consumption and high efficiency. In this study, we propose a two-level active equalization scheme for battery packs, employing the Breadth First Search (BFS) Algorithm. Firstly, a flexible reconstruction topology is introduced to enable a two-layer balancing scheme. Specifically, a switched capacitor scheme is used to balance cells within modules, while the module balancing is achieved through flexible reconstruction. Subsequently, the equalization process is modeled using graph theory, and an equalization strategy based on the BFS algorithm is developed to determine the optimal energy path. Finally, the proposed balancing scheme is compared with traditional switched capacitor balancing schemes. Experimental results demonstrate that the proposed scheme exhibits superior balancing performance, with an optimal switching period of 2 seconds and a duty cycle of 55%.

关键词： learning systems Energy consumption Capacitors Switches control systems Graph theory Batteries

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