Early forest fire image recognition plays an important role in timely fire fighting. This paper proposes an early forest fire recognition method based on C-GhostNet network. The C-GhostNet network is an improved versi...
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This paper presents a new ESD clamp circuit which is co-triggered by RC and diode. It is immune to the false triggering caused by fast power-up events due to the diode detection mechanism. With the addition of a commo...
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Today, the world economy is in the stage of rapid development, followed by the rising quantity, the decreasing moisture content, and the increasing heat value of Municipal Solid Waste (MSW). Since waste incineration h...
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Fault diagnosis and isolation is important for industrial system. In this paper, a kernel canonical variate analysis(KCVA) is proposed for fault isolation. KCVA is originally used as a data dimension reduction techniq...
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Fault diagnosis and isolation is important for industrial system. In this paper, a kernel canonical variate analysis(KCVA) is proposed for fault isolation. KCVA is originally used as a data dimension reduction technique which can account for nonlinearity and correlations in the industrial dynamical process data. But there are some difficulties using KCVA in the construction of the contribution for the fault isolation. On the one hand, it is difficult to compute the contributions of individual variables because it is scarcely possible to find an inverse mapping from the feature space to the original space. On the other hand, a smearing effect is hardly avoided. To solve the problem, a KCVA-based contributions is proposed using the state subspace and the residual subspace which can isolate the faulty variables effectively. Simulations are conducted on the Tennessee Eastman process to verify the performance of the proposed method.
This paper focuses on the vision-based autonomous landing mission of a quadrotor unmanned aerial vehicle (UAV). A double-layered nested Aruco landing marker is designed which can adapt to the situation that the field ...
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It is necessary to regularly detect faults to maintain the safety and stability of power lines. Insulators are one of the important electrical components in high-voltage transmission lines. It is extremely necessary t...
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ISBN:
(数字)9798350357882
ISBN:
(纸本)9798350357899
It is necessary to regularly detect faults to maintain the safety and stability of power lines. Insulators are one of the important electrical components in high-voltage transmission lines. It is extremely necessary to check the working status of insulators regularly. Traditional manual inspection is inefficient because it requires a significant amount of labor costs. In this paper, a method for detecting insulators' missing defect based on aerial images is proposed to address the issue by unmanned aerial vehicle (UAV). Firstly, the improved Faster R-CNN (region-based convolutional neural network) is used to identify and locate insulators in aerial images. Secondly, the U-Net image segmentation network segments insulators from the images. The adaptive threshold segmentation method completely separates the insulator from the background. Then the binary image of the insulator is obtained. Finally, the binary image is converted into a fault curve which is used for determining the missing insulators based on the distribution of the fault curve. By using collected insulator datasets on a 330kV overhead transmission line using a DJI M300 UAV platform and an onboard H20T camera/sensor, the detection accuracy of glass insulators is as high as 0.98 with the proposed algorithm. The positioning accuracy of the proposed algorithm is also higher than other algorithms. This method has high detection accuracy for missing defects in insulators. The experimental results show that compared with similar algorithms, this method has higher accuracy and efficiency.
In the actual production of slot die coating,the minimum coating thickness and the maximum substrate moving speed could only be judged by production experience,and there was no accurate prediction model due to the non...
In the actual production of slot die coating,the minimum coating thickness and the maximum substrate moving speed could only be judged by production experience,and there was no accurate prediction model due to the nonlinear characteristics of fluid ***,building a reasonable and efficient prediction model for slot die coating is now an urgent and challenging *** this paper,an optimized extreme learning machine(ELM) based on improved beetle antennae search(IBAS) algorithm is proposed for slot die coating *** optimized ELM model can well learn the nonlinear characteristics of the system and make accurate predictions,thus solving the traditional inaccurate empirical *** the prediction accuracy of ELM depends on the selection of weights and biases,the IBAS optimization algorithm is used to quickly search for the optimal value of weights and biases in the ELM *** algorithm improves the generation mechanism of antennae on the basis of the original algorithm,so that the algorithm can converge *** the same time,the search strategy of the algorithm is improved to avoid falling into the local optimal *** predicting the production data of slit coating,the feasibility and effectiveness of IBAS-ELM model are proved.
This paper discusses the H∞ consensus problem of leader-follower multi-agent systems. The controller for each agent is crafted to utilize comprehensive information from all connected agents, while an innovative event...
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The recognition of early forest fires can reduce the resource loss caused by fire combustion. A real-time forest fire image recognition method based on r-shufflenetv2 network is proposed. R-shufflenetv2 is mainly comp...
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In this paper, an insulator missing defect detection method is proposed based on unmanned aerial vehicles to solve the problem of glass insulator burst fault detection in high-voltage transmission lines. Firstly, the ...
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ISBN:
(数字)9798350354409
ISBN:
(纸本)9798350354416
In this paper, an insulator missing defect detection method is proposed based on unmanned aerial vehicles to solve the problem of glass insulator burst fault detection in high-voltage transmission lines. Firstly, the proposed method utilizes the improved Mask R-CNN (region-based convolutional neural network) algorithm to segment insulator strings in aerial images. Then, the constructed encoder-decoder network is used to extract and reconstruct features of the insulators, resulting in residual images. Finally, the residual images preserve the location information of the fault and obtains the result of missing insulators. The experiment shows that the proposed algorithm has high segmentation accuracy for insulators and high recognition accuracy for insulator missing faults.
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