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检索条件"任意字段=3rd International Conference on Robotics and Control Engineering, RobCE 2023"

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Torque Analysis of Spherical Permanent Magnetic Motor with Magnetic Equivalent Circuit and Maxwell Stress Tensor

Torque Analysis of Spherical Permanent Magnetic Motor with M...

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2011 3rd international Asia conference on Informatics in control, Automation and robotics(CAR 2011)

作者： Bin Li Chao Liu Hongfeng Li Guidan Li School of Electrical Engineering and Automation Tianjin University

The paper discusses the torque characteristics of spherical permanent magnetic(PM)motor adopting three-dimensional(3D)magnetic equivalent circuit(MEC).Taking into account the unsaturation of the magnetic field,the rotor and the stator are meshed in spherical coordinates and cylindrical coordinates respectively,and then the expressions of the reluctances and magnetomotive force(MMF)have been *** to the relative position between the coordinate systems,the magnetic field in the air gap has been obtained by transforming the coordinate *** on this,the Maxwell stress tensor(MST)method is applied to analyze the electromagnetic force and torque,the corresponding torque matrix is given,and the relationship between the motor torque and winding location is *** with the finiteelement method(FEM),this method is proved to have a high accuracy and helpful to the optimization design of the PM spherical motor.

关键词： PM spherical motor,Electromagnetic torque,Maxwell Stress Tensor,3D Magnetic Equivalent Circuit

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Pavement Crack Segmentation Network Based on Local Diagonal Gray Scale Transformation Algorithm

Pavement Crack Segmentation Network Based on Local Diagonal ...

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Computer Science, Electronic Information engineering and Intelligent control Technology (CEI), IEEE international conference on

作者： Tian Xie Song Wang Tao Li Chen Chang School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Pavement crack segmentation aims to identify and localize crack objects, in order to solve the problems such as the influence of complex texture noise in the crack background on segmentation and the misidentification of fine cracks, a crack segmentation network based on local diagonal grayscale transformation algorithm (LDGT-IterNet) is proposed. Firstly, the Local Diagonal Grayscale Transformation (LDGT) algorithm is used to preprocess the crack image, which converts the grayscale image into a 4-channel image with constant contrast according to the grayscale order between each pixel and its diagonal neighboring pixels along the four directions, preserving the structural intrinsic features of the crack curves and at the same time making the contrast change robust; Then, Convolutional Block Attention Module (CBAM) is introduced between the basic and refined modules of IterNet segmentation network to improve the performance of the segmentation network by mining the feature map detail information. In order to verify the robustness of the proposed method, experimental comparison and validation is carried out on Crack500, CFD and Crack200 datasets. The F 1 -score of the method is 85.47%, 87.95% and 86.82% on the three datasets, respectively. It shows that the method can effectively segment pavement cracks and provide more accurate information for road maintenance.

关键词： Image segmentation Roads Noise Process control Gray-scale Predictive models Robustness

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Research on Battery Energy control Strategy Based on Neural Network

Research on Battery Energy Control Strategy Based on Neural ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Xianghao Liu Jinsong Li Yi Wang Hainan University Haikou Hainan China China Automotive Engineering Research Institute Chongqing China

With the development of science and technology, battery energy plays an increasingly important role in modern society. The role of battery energy includes but is not limited to providing power for mobile devices and powering cars. It is also the main energy source for new energy vehicles. Energy storage systems provide energy storage. It provides power for aerospace and can also be used as a backup power supply for households and industries to deal with sudden power outages and other situations. Neural networks are also widely used in battery energy. For example, neural networks can be used to predict the remaining service life of lithium-ion batteries. This paper takes the commonly used 18650 power lithium battery as the main research object, and proposes a neural network battery state of charge (SOC) estimation method based on the LEVY flight step-based group search optimization algorithm (LEVY-GSO-BP). The group search optimization algorithm after Levy flight step optimization is used to optimize the design of the established SOC neural network model to improve the accuracy of the neural network's estimation of SOC related parameters. BP neural network is a powerful optimization algorithm that can solve unimodal function and multimodal function optimization problems of high-dimensional data. It has the advantages of simplicity, efficiency and accuracy, so it is widely used in various problems. However, the calculation of BP neural network is more difficult, takes a long time, and the convergence speed drops significantly at the end of the calculation, and may even stagnate. In order to solve these problems, we propose an algorithm improvement method based on Levy flight step size. Levy flight is an optimization algorithm with excellent global search capabilities that can find the optimal solution in a short time. It uses a combination of short-distance frequent search and occasional jumping long-distance search to improve local search capabilities and global sear

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Design and Realization of Unmanned Vehicle Autonomous Navigation System Based on Binocular Camera

Design and Realization of Unmanned Vehicle Autonomous Naviga...

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Computer Science, Electronic Information engineering and Intelligent control Technology (CEI), IEEE international conference on

作者： Tianxing Guo Lianlei Lin Huanyu Liu Junbao Li Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Aiming at the navigation problem of unmanned vehicles in extreme environments such as communication interference and limited GPS signals, this study proposes an autonomous navigation method based on binocular cameras. The method enables the unmanned vehicle to complete the task of localization and map building using only binocular images, without the need for other sensors or signal sources. Meanwhile, this study also proposes a local path planning obstacle avoidance method based on depth map, which, when combined with global localization information, can take into account both local obstacle avoidance and global path navigation, and can cope with random environmental changes. The system uses a single sensor for autonomous obstacle avoidance and navigation, which reduces the computational requirements and ensures the low cost of the navigation system. In order to verify the reliability and effectiveness of the system under signal-constrained conditions, the system is evaluated in a simulation environment and a real field scenario, respectively. The experimental results show that the system is able to achieve reliable localization and path planning under signal-constrained conditions.

关键词： Location awareness Navigation Reliability theory Path planning Sensor systems Sensors Collision avoidance

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Research on Temperature Adaptive control Method of Auxiliary Dialysis Equipment Based on RBF Neural Network

Research on Temperature Adaptive Control Method of Auxiliary...

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Computer Science, Electronic Information engineering and Intelligent control Technology (CEI), IEEE international conference on

作者： Zhaoming Liang Yong Li Shuo Yan Yuzhe Hao Jiangsu Automation Research Institute Jiangsu JARI Medical Technology Co. Ltd Lianyungang China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Aiming at the difficulties of slow heating rate, slow temperature response, easy to be affected by ambient temperature and low precision in the heating process of manual peritoneal dialysis auxiliary equipment, this paper deeply analyzes the heating control process of the equipment, designs a temperature adaptive control method of auxiliary dialysis equipment based on RBF neural network, and explores the efficient heating control system of auxiliary dialysis equipment. On the basis of collecting a large number of actual data in the heating process, the intelligent data processing algorithm is introduced. The RBF neural network algorithm is used to adaptively compensate the dialysate temperature, and the temperature detection value without compensation is compared. The temperature adaptive control method of auxiliary dialysis equipment based on RBF neural network can adjust the control parameters in real time and accurately, solve the problems of slow heating speed and low temperature accuracy caused by easy environmental impact and uneven heating in the heating process, and realize real-time accurate monitoring of dialysate temperature. The treatment accuracy reaches $\pm \mathbfcontrol\ {{}^{\circ}\mathbf{C}}$ , with high stability and heating efficiency. The temperature control effect is significantly improved, ensuring the safety and comfort of the product. It has significant application value for clinical treatment of peritoneal dialysis.

关键词： Heating systems Temperature measurement Heat treatment Neural networks Process control Manuals control systems

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Skin Cancer Detection and control Techniques Using Hybrid Deep Learning Techniques

Skin Cancer Detection and Control Techniques Using Hybrid De...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Nagaraj P Suresh Kumar S V. Saijagadeeshkumar G. Pavan Kumar K. Yerriswamyreddy K. Jaya Krishna Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India

More than 70% of skin cancer fatalities are caused by the deadliest type of the disease, melanoma. Malignant melanocytes give rise to melanomas. Melanoma would have a better ranking based on years lost to cancer because relatively young people are affected by this cancer. The average age at which melanoma is discovered in patients is relatively young, and the overall number of melanoma sufferers is rising. As a result, it is hypothesized that Caucasian populations are experiencing an increase in the overall burden of melanoma. There is a large potential benefit for the prevention of melanoma because the overall burden of the disease is rising, prognosis strongly depends on the time of diagnosis, as well as the majority crucially, effective treatments for advanced stages are missing. However, the majority of the known risk factors for melanoma, including pale skin tone, freckles, light eyes, advanced age, history of sunburns, clinical atypical nevi, past melanoma, and family history of the disease, are not treatable. The only possible outcomes are sun exposure and sunburns. Melanoma preventive strategies include sun protection techniques. Comprehensive sun protection programs have been in place for more than ten years in several high-risk nations, and public awareness of the benefits of sun protection is strong. The general public's sun exposure habits have not changed even though these public health efforts have raised awareness of skin cancer and the harmful effects of excessive sun exposure. Several researchers have expressed an interest in melanoma early detection, and a tremendous amount of work has been done on melanoma diagnosis. Several approaches to early detection were explored in this research work, along with the benefits and drawbacks of each approach.

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A network reconfiguration method of distribution networks considering dynamic characteristics of the distributed renewable energy sources

A network reconfiguration method of distribution networks co...

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12th international conference on Renewable Power Generation (RPG 2023)

作者： J. Li X. Yu B. Yang X. Li W. Xu Z. Xuan State Grid Beijing Electric Power Research Institute 34 South 3rd Ring Road Middle Road Fengtai District Beijing People's Republic of China State Key Laboratory of Reliability and Intelligence of Electrical Equipment (Hebei University of Technology) Department of Electrical Engineering 5340 Xiping Road Beichen District Tianjin People's Republic of China

The existing research on distribution network restoration control lacks the consideration of the dynamic recovery characteristics of distributed renewable energy sources (dRESs). By taking the output of dRESs as invariant, the voltage of network could be excessive low, which leads to the failure of system restoration. Given this, a restoration control method of distribution network considering the output fluctuation of dRESs is proposed in this paper. Firstly, the dynamic restoration control model of distribution network with dRESs integration is constructed. Then, the Gaussian mutation, which can effectively improve the global search ability and computing speed, are introduced into the traditional Binary Particle Swarm Optimization (BPSO) algorithm. The examples validate that the proposed method can calculate the optimal reconstruction scheme of the dRESs-penetrated distribution network within dozens of seconds. The off-normal voltage and failure reconnection of dRESs can be avoided, boosting the restoration of distribution system.

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engineering Social Learning Mechanisms for Minimalistic Multi-agent Robots

Engineering Social Learning Mechanisms for Minimalistic Mult...

引用

international conference on control and Robots (ICCR)

作者： Owais Hamid Kerstin Dautenhahn Chrystopher L. Nehaniv Systems Design Engineering University of Waterloo Waterloo ON Canada

ISBN: (数字)9781728175621

ISBN: (纸本)9781728175638

Social learning, which can include complex or simple social mechanisms, allow us to understand cooperation and communication in animals, giving them better chances to survive for longer and thrive as a society. In order to translate this understanding into socially rich behavior among multi-agent robots, this study utilizes social learning mechanisms that are simple yet quite effective. Small and simple swarm robots are utilized to understand how such social mechanisms might play a role in establishing rules for emergent group behavior and how social rules might be engineered to gain useful effects in a group of robots. The study investigates exploratory behavior without interaction (asocial) and with interaction (social) among a group of robots. The results from this exploratory study suggest that deterministic asocial exploration is best performed by Spiral exploration mechanisms. However, these asocial exploration strategies are eclipsed by certain types of social reward sharing strategies as long as sharing occurs for at least half the lifetime of the robots. Sharing locations of reward caches for all time is of course the most optimal, but comes at the cost of communicating longer and hence using more energy both on the sender and receiver's end. An analysis of a compromise strategy between completely asocial exploration and social reward location sharing is performed using strategies termed critical and conditional learning. It is found that the number of reward caches located through critical and conditional learning are intermediary to the two extremes, namely completely asocial and completely social foraging.

关键词： Learning systems Spirals Animals Switching systems Swarm robotics Switches Robots

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A Sensitivity-based Adaptive Sliding Mode control for Perturbed Autonomous Underwater Vehicles

A Sensitivity-based Adaptive Sliding Mode Control for Pertur...

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RSI/ISM international conference on robotics and Mechatronics

作者： Mahnaz Abolvafaie Hamid Reza Koofigar Maryam Malekzadeh University of Isfahan Department of Electrical Engineering Isfahan Iran University of Isfahan Department of Mechanical Engineering Isfahan Iran

ISBN: (纸本)9781467372350

Sensitivity analyses in the turning circle and zigzag maneuvers, as two main standard ones, are proposed here as a preliminary step to motion control design for an Autonomous Underwater Vehicle (AUV). The most effective parameters of the AUV are then adopted to be estimated by some adaptation mechanisms. Adaptive sliding mode control is developed to ensure the robust stability and performance, without a priori knowledge about the bound of perturbations. A simulation study is also presented to demonstrate the effectiveness of the method.

关键词： Hydrodynamic coefficients Sensitivity analysis Adaptive control Autonomous underwater vehicle (AUV)

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Research on Bad Data Identification Method for Power Information Communication Process

Research on Bad Data Identification Method for Power Informa...

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Computer Science, Electronic Information engineering and Intelligent control Technology (CEI), IEEE international conference on

作者： Fuxiang Zhang Zheng Li Meng Li Chong Li Haibo Sun State Grid Liaoning Information and Communication Company Shenyang China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

In traditional electrical power communication processes, the denoising ability of the existing methods for identifying faulty data is inadequate, which results in a diminished identification efficiency. To address this, the present study devises a novel method for identifying faulty data in electrical power information communication processes, grounded in wavelet analysis. Leveraging the singularity characteristic of the wavelet transformation, this method conducts localized singularity detection on the power information communication process. Following the detection, it discriminates between normal and faulty data utilizing neural network algorithms. Subsequently, to eliminate noise-containing signals within the faulty data, both soft and hard threshold denoising techniques are employed. After calculating the noise intensity, the wavelet transformation coefficients within a certain scale are arranged in ascending order based on their squares, and the likelihood estimation vector is computed. The method then proceeds to remove the noise segments within the signal, guided by the minimum and maximum values in the vector. Empirical results illustrate that this approach significantly enhances the denoising capability and bolsters identification efficiency, showcasing its potential as a robust solution in improving the quality and reliability of electrical power communication processes.

关键词： Wavelet transforms Fault diagnosis Noise reduction Noise Neural networks Process control Estimation

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