Measurement accuracy is an important performance indicator for high-voltage direct current (HVDC) voltage dividers. The temperature rise effect for a HVDC voltage divider’s internal resistance has an adverse effect on measurement accuracy. In this paper, by building a solid model of a DC voltage divider, the internal temperature rise characteristic and error caused by the temperature rise in a resistance voltage divider were theoretically simulated. We found that with the increase in height and working time, the internal temperature of the voltage divider increased. The results also showed that the lowest temperature was near the lower flange and the highest temperature was near the upper flange in the middle of the voltage divider. The error caused by the temperature rise increased first and then decreased gradually with divider height, increasing with its working time. The measurement error caused by the internal temperature difference in steady state reached a maximum of 158.4 ppm. This study provides a theoretical basis to determine the structure and accuracy improvement for a resistive voltage divider, which is helpful for the selection of components and the optimization of the heat dissipation structure.
The current super-resolution methods cannot fully exploit the global and local information of the original low-resolution image, resulting in loss of some information. In order to solve the problem, we propose a multiscale residual dense network (MRDN) for image super-resolution. This network is constructed based on the residual dense network. It can integrate the multiscale information of the image and avoid losing too much information in the deep level of the network, while extracting more information under different receptive fields. In addition, in order to reduce the redundancy of the network parameters of MRDN, we further develop a lightweight parameter method and deploy it at different scales. This method can not only reduce the redundancy of network parameters but also enhance the nonlinear mapping ability of the network at different scales. Thus, it can better learn and fit the feature information of the original image and recover the satisfactory super-resolution image. Extensive experiments are conducted, which demonstrate the effectiveness of the proposed method.
A new view about underwater imaging was proposed in this paper that water self still is an imaging object, and a new synthetic model about underwater image was also presented, that is, the resulting image is a synthetic image made from the images of underwater object and water self. Seeing that the present situation which the visual quality of the resulting image made by current restoration methods about underwater image is not satisfied, a new restoration method about underwater image, that is a systematic method called removing water-compensating attenuation-optimization, was proposed,. The results demonstrated that here proposed method is better than some of other methods.
In recent years, small objects detection has received extensive attention from scholars for its important value in application. Some effective methods for small objects detection have been proposed. However, the data collected in real scenes are often foggy images, so the models trained with these methods are difficult to extract discriminative object features from such images. In addition, the existing small objects detection algorithms ignore the texture information and high-level semantic information of tiny objects, which limits the improvement of detection performance. Aiming at the above problems, this paper proposes a texture and semantic integrated small objects detection in foggy scenes. The algorithm focuses on extracting discriminative features unaffected by the environment, and obtaining texture information and high-level semantic information of small objects. Specifically, considering the adverse impact of foggy images on recognition performance, a knowledge guidance module is designed, and the discriminative features extracted from clear images by the model are used to guide the network to learn foggy images. Second, the features of high-resolution images and low-resolution images are extracted, and the adversarial learning method is adopted to train the model to give the network the ability to obtain the texture information of tiny objects from low-resolution images. Finally, an attention mechanism is constructed between feature maps of the same scale and different scales to further enrich the high-level semantic information of small objects. A large number of experiments have been conducted on data sets such as “Cityscape to Foggy” and “CoCo”. The mean prediction accuracy (mAP) has reached 46.2% on “Cityscape to Fogg”, and 33.3% on “CoCo”, which fully proves the effectiveness and superiority of the proposed method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.