Bio: Zhang Yan is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Histogram & Fuzzy logic. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
TL;DR: The simulation results show that the proposed image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.
01 Oct 2022
TL;DR: In this paper , a faulty feeder detection method based on the characteristics of transient zero-mode current (TZMC) in multi-frequency bands is proposed, which is tested through simulations on the PSCAD/EMTDC platform.
Abstract: In a small-current grounding system, the pole-to-ground fault may cause the voltage drop in the fault pole and the voltage rise in the other poles. In flexible DC distribution systems, Severe voltage variation may shorten the insulation lifetime of the equipment, which leads to great concerns on the safety issue. In addition, the existence of high transition resistance degrades the accuracy of fault detection methods, thus further affecting the reliability of the system. Therefore, it is essential to explore an advanced technology for faulty feeder detection. This study proposes a faulty feeder detection method based on the characteristics of transient zero-mode current (TZMC) in multi-frequency bands. The change rate of zero-mode voltage is applied as the protection activation criterion. Then, the characteristic matrix is constructed via computing the fuzzy entropy of TZMC in each frequency band. Finally, the faulty feeder can be identified by conducting fuzzy C-means on the characteristic matrix. This proposed method is tested through simulations on the PSCAD/EMTDC platform, which successfully demonstrates its outstanding adaptability, reliability, and accuracy.
TL;DR: Experimental results show that the fuzzy degree of the image is reduced by the improved algorithm, and the clarity of the adaptive smoothness constraint image is improved effectively.
Abstract: For the problems of poor enhancement effect and long time consuming of the traditional algorithm, an adaptive smoothness constraint image multilevel fuzzy enhancement algorithm based on secondary color-to-grayscale conversion is proposed. By using fuzzy set theory and generalized fuzzy set theory, a new linear generalized fuzzy operator transformation is carried out to obtain a new linear generalized fuzzy operator. By using linear generalized membership transformation and inverse transformation, secondary color-to-grayscale conversion of adaptive smoothness constraint image is performed. Combined with generalized fuzzy operator, the region contrast fuzzy enhancement of adaptive smoothness constraint image is realized, and image multilevel fuzzy enhancement is realized. Experimental results show that the fuzzy degree of the image is reduced by the improved algorithm, and the clarity of the adaptive smoothness constraint image is improved effectively. The time consuming is short, and it has some advantages.