Song Wang^1,*, Fei Xie¹, Fengye Yang¹, Shengxuan Qiu¹, Chuang Liu², Tong Li³

Energy Engineering, Vol.120, No.10, pp. 2273-2285, 2023, DOI:10.32604/ee.2023.030107 - 28 September 2023

Abstract Winding is one of the most important components in power transformers. Ensuring the health state of the winding is of great importance to the stable operation of the power system. To efficiently and accurately diagnose the disc space variation (DSV) fault degree of transformer winding, this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor (KNN) algorithm and the frequency response analysis (FRA) method. First, a laboratory winding model is used, and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding. Then,… More > Graphic Abstract

G. Niranjani^1,*, K. Umamaheswari²

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 457-472, 2022, DOI:10.32604/iasc.2022.024264 - 05 January 2022

Abstract COVID-19 pandemic has imposed many threats. One among them is the accumulation of waste in hospitals. Waste should be disposed regularly and safely using sustainable methods. Sustainability is self development with preservation of society and its resources. The main objective of this research is to achieve sustainability in waste collection by minimizing the cost factor. Minimization of sustainable-cost involves minimization of three sub-components – total travel-cost representing economical component, total emission-cost representing environmental component and total driver-allowance-cost representing social component. Most papers under waste collection implement Tabu search algorithm and fail to consider the environmental More >

Juan Li

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 35-48, 2019, DOI:10.31209/2018.100000003

Abstract K-Nearest Neighbor algorithm (KNN) is a simple and mature classification method. However there are susceptible factors influencing the classification performance, such as k value determination, the overlarge search space, unbalanced and multi-class patterns, etc. To deal with the above problems, a new classification algorithm that absorbs tree structure, tree pruning and adaptive k value method was proposed. The proposed algorithm can overcome the shortcoming of KNN, improve the performance of multi-class and unbalanced classification, reduce the scale of dataset maintaining the comparable classification accuracy. The simulations are conducted and the proposed algorithm is compared with several existing More >