Huizhi Gou^1,2,*, Yuncai Ning¹

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 803-822, 2021, DOI:10.32604/cmes.2021.015922

Abstract Accurate photovoltaic (PV) power prediction can effectively help the power sector to make rational energy planning and dispatching decisions, promote PV consumption, make full use of renewable energy and alleviate energy problems. To address this research objective, this paper proposes a prediction model based on kernel principal component analysis (KPCA), modified cuckoo search algorithm (MCS) and deep convolutional neural networks (DCNN). Firstly, KPCA is utilized to reduce the dimension of the feature, which aims to reduce the redundant input vectors. Then using MCS to optimize the parameters of DCNN. Finally, the photovoltaic power forecasting method of KPCA-MCS-DCNN is established. In… More >

Memoona Malik^*, Iftikhar Azim, Amir Hanif Dar, Sohail Asghar

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 165-182, 2021, DOI:10.32604/iasc.2021.017437

Abstract Despeckling of SAR imagery is a crucial step prior to their automated interpretation as information extraction from noisy images is a challenging task. Though a huge despeckling literature exists in this regard, there is still a room for improvement in existing techniques. The contemporary despeckling techniques adversely affect image edges during the noise reduction process and are thus responsible for losing the significant image features. Therefore, to preserve important features during the speckle reduction process, a two phase hybrid despeckling filter is proposed in this study. The first phase of the hybrid filter focuses on edge preservation by employing a… More >

Hongshuo Zhang¹, Bo Zhu^1,*, Kaimin Pang¹, Chunmei Chen¹, Yuwei Wan²

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 797-810, 2021, DOI:10.32604/iasc.2021.017232

Abstract Using machine learning method to recognize abnormal patterns covers the shortage of traditional control charts for autocorrelation processes, which violate the applicable conditions of the control chart, i.e., the independent identically distributed (IID) assumption. In this study, we propose a recognition model based on support vector machine (SVM) for the AR (1) type of autocorrelation process. For achieving a higher recognition performance, the cuckoo search algorithm (CS) is used to optimize the two hyper-parameters of SVM, namely the penalty parameter c and the radial basis kernel parameter g. By using Monte Carlo simulation methods, the data sets containing samples of… More >

Haolong Chen^1,2, Bo Yu¹, Huanlin Zhou^1*, Zeng Meng¹

CMC-Computers, Materials & Continua, Vol.53, No.4, pp. 271-290, 2017, DOI:10.3970/cmc.2017.053.271

Abstract The cuckoo search algorithm (CS) is improved by using the conjugate gradient method(CGM), and the CS-CGM is proposed. The unknown inner boundary shapes are generated randomly and evolved by Lévy flights and elimination mechanism in the CS and CS-CGM. The CS, CGM and CS-CGM are examined for the prediction of a pipe’s inner surface. The direct problem is two-dimensional transient heat conduction in functionally graded materials (FGMs). Firstly, the radial integration boundary element method (RIBEM) is applied to solve the direct problem. Then the three methods are compared to identify the pipe’s inner surfacewith the information of measured temperatures. Finally,… More >