The stochastic optimization algorithm and the random signal nonlinear optimal filtering algorithm are typical representatives of intelligentoptimization algorithms in the rapidly developing scientific research and en...
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In the context of the rapid development of computer technology, people39;s lives have been inseparable from high-tech technology, which can bring convenience to people. In the field of computer vision, human activit...
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Robot localization is a critical challenge present in autonomous robots to estimate their position within the environment, ensuring accurate navigation and task execution. This research project compares the position e...
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Early diagnosis of such maladies can avoid crop diseases and increase agricultural yield. We attempt to propose deep learning-based approach for the classification of diseases in an apple crop using the New Plant illn...
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Research analyses the deployment of Convolutional-Neural-Networks (CNN) equipped with the CReLU (Concatenated Rectified Linear Units) activation function for identifying brain tumors MRI scan images. The CNN model is ...
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This paper presents a comprehensive review of Artificial Intelligence (AI), Machine learning (ML), and Deep learning (DL) technologies, focusing on their impact in driving AI automation across diverse sectors with a s...
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Real-time transmission line switching control is a cost-effective and efficient measure to alleviate line overload. However, due to the nonlinear characteristics of the transmission network structure optimization prob...
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ISBN:
(纸本)9798350375145;9798350375138
Real-time transmission line switching control is a cost-effective and efficient measure to alleviate line overload. However, due to the nonlinear characteristics of the transmission network structure optimization problem, conventional optimization methods often struggle to find solutions quickly. In recent years, data-driven methods have been widely applied in fields such as power system dispatch optimization. This paper proposes a method based on deep neural networks. By using historical and simulation data of the power system, the neural network is trained to perform exploratory learning and find optimal topology control strategies when line overload occurs. This method is trained and tested on the IEEE 14-bus open-source dataset, and the test results verify its effectiveness.
Aiming at the requirement of intelligent control of energy power system (EPS), this paper proposes an intelligent control algorithm combined with reinforcement learning (RL), and designs and implements it in the micro...
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With the continuous increase in vehicle numbers, traffic violations have become prevalent. Traditional detection approaches are often labor-intensive and constrained by geographical and temporal limitations. This pape...
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With changes in speed, cranes inevitably experience swinging, making precise lifting difficult. Taking a suspended crane as an example, this study analyzes the mathematical characteristics of the crane. Through MATLAB...
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ISBN:
(纸本)9798350366105;9798350366099
With changes in speed, cranes inevitably experience swinging, making precise lifting difficult. Taking a suspended crane as an example, this study analyzes the mathematical characteristics of the crane. Through MATLAB/SIMULINK simulation analysis, it focuses on examining the operational performance of the crane under different working conditions. Conventional PID control parameter optimization is applied to the crane's load swing angle. Comparative analysis is conducted on experiments with and without an anti-swing control system. The results indicate that the PID controller can rapidly and effectively reduce swinging angles. This research provides valuable references for the dynamic optimization, system parameter selection, and swing control of suspended cranes.
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