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Optimization design of a new type of Halbach array motor with magnetic axis offset

Optimization design of a new type of Halbach array motor wit...

引用

IEEE Conference on Cybernetics and Intelligent Systems

作者： Shaoru Zhang Muyan Jiang Yihan Liu Zhanping Huang Xiuju Du Jielu Zhang Fanglin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

This paper proposes a novel Halbach array structure based on the traditional radial and tangential magnetized Halbach array permanent magnet motor. This structure enables the magnetic field lines to be concentrated, thereby enhancing the motor torque. Concurrently, the magnetic axis offset is designed on this basis by combining ferrite with low magnetic conductivity and NdFeB with high magnetic conductivity. This design effectively reduces the gap between the electromagnetic torque and the peak current angle of the reluctance torque, and fully utilizes both the electromagnetic and reluctance torques to further improve the total output torque of the motor. The newly proposed motor, known as the Magnetic Axis Offset Halbach Motor (MAOHM), is a result of this design. Furthermore, this paper also employs a finite element method to study and compare the proposed motor with the traditional motor, serving to verify the rationality and effectiveness of the design.

关键词： Torque Shape Rotors Random access memory Conductivity Motors Permanent magnets

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Crop-picking Robot Grasping System Based on Deep Learning

Crop-picking Robot Grasping System Based on Deep Learning

引用

IEEE Conference on Cybernetics and Intelligent Systems

作者： Qike Wu Yihan Liu Rui Ma Shaoru Zhang Sai Gao Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

The crop-picking system plays a key role in automatic picking. In this study, a set of crop-picking robot grasping system based on convolutional neural network (CNN) was designed by using deep learning technology. Firstly, a variety of preprocessing methods are used to expand and process the image data to ensure the diversity and integrity of the sample data. Then, CNN and gradient descent (GD) algorithm were combined to optimize the training model to identify the types of crops accurately. Then, the lidar used ToF (Time of Flight) technology and AGglomerative NESting(AGNES) algorithm to obtain the key positioning points. After excluding invalid cluster centers, the identification results were accurately located. The experimental results show that compared with the 3D pose estimation method of the manipulator based on multi-view, the calculation error of the 3D coordinates of crops is smaller, and the reasoning speed for a single image is only 0.000316 seconds. Finally, the comprehensive accuracy of the system reaches 97.58%, which provides practical technical support for automatic crop picking.

关键词： Training Three-dimensional displays Accuracy Laser radar Robot kinematics Pose estimation Crops

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Deep Learning-based Visual Recognition System for Fruit-Picking Robots

Deep Learning-based Visual Recognition System for Fruit-Pick...

引用

IEEE Conference on Cybernetics and Intelligent Systems

作者： Rui Ma Yihan Liu Qike Wu Shaoru Zhang Nan Wang Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Fruit recognition plays an important role in automated picking, in order to improve the accuracy and real-time picking, this study uses deep learning methods to design a fruit-picking robot visual recognition system. First, multiple preprocessing methods are used to expand the sample data, and the images are proportionally cropped and scaled to make the image dataset more complete; subsequently, to improve the accuracy of image recognition, a multilayer Convolutional Neural networks (CNN) is established; and the Adam algorithm is utilized to train the model parameters several times to determine the optimal hyperparameters, thus overcoming the defects of the multilayer neural network that has a local optimal search. The experimental results show that compared with the random forest method, this system has the characteristics of high-speed recognition and high accuracy in fruit picking, and can quickly and accurately recognize fruit images, with the recognition speed of a single image taking only 0.081 seconds, and the recognition accuracy reaching more than 97.35%. The method has important theoretical and application value and provides an effective means for automatic fruit recognition.

关键词： Visualization Image recognition Accuracy Random access memory Nonhomogeneous media Real-time systems Character recognition

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The short-term photovoltaic power generation forecasting based on Fruit Fly Optimization Algorithm optimized BP neural network

The short-term photovoltaic power generation forecasting bas...

引用

IEEE Conference on Cybernetics and Intelligent Systems

作者： Shaoru Zhang Haoxuan Zhu Likun Yang Zhanping Huang Xiuju Du Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Accurately predicting photovoltaic power generation is crucial for ensuring the safe operation of power grids and advancing solar energy development and utilization. For the issue of large errors in current prediction methods, this paper introduces a forecasting method for photovoltaic power generation. The method utilizes Fruit Fly Optimization Algorithm (FOA) to optimize Back Propagation (BP) neural network. This involves analyzing the correlations among factors impacting photovoltaic power generation and selecting relevant meteorological data via the Pearson correlation coefficient method. Fruit Fly Algorithm demonstrates rapid convergence, a minimal parameter set and ease of adjustment, rendering it applicable across various domains. Employing Fruit Fly Algorithm to optimize weights and thresholds within BP neural network leads to the final prediction outcomes. Simulation results confirm the superior prediction accuracy of the FOA-BP model for photovoltaic power generation, particularly during spring, autumn and winter, showcasing its practical utility.

关键词： Photovoltaic systems Correlation coefficient Atmospheric modeling Electricity Neural networks Predictive models Prediction algorithms

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