Dr Mehran Taghipour-Gorjikolaie

Current is no longer sinusoidal in modern electric networks because of widespread use of power electronic-based equipments and nonlinear loads. Usually AC loss is calculated for pure sinusoidal current, while it is no longer accurate when current is nonsinusoidal. On the other hand, efficiency of cooling system in large scale power devices is dependent on accurate estimation and prediction of the heat load caused by AC loss in design stage. Therefore, estimation of nonsinusoidal AC loss of high temperature superconducting (HTS) material would be of great interest for designers of large-scale superconducting devices. In this paper, at first nonsinusoidal AC loss of a typical HTS tape was calculated under distorted currents using H-formulation finite element method. Then, a range of artificial intelligence (AI) models were implemented to predict AC loss of a typical HTS tape. In order to find the best and more adaptive AI model for nonsinusoidal AC loss prediction, different regression models are evaluated using Support Vector Machine regression model, Generalized Linear regression model, Decision Tree regression model, Feed Forward Neural Network based model, Adaptive Neuro Fuzzy Inference System based model, and Radial Basis Function Neural Network (RBFNN) based model. In order to evaluate robustness of developed models cross-validation technique is implemented on experimental data. To compare the performance of different AI models, four prediction measures were used: Theil's U coefficients (U_Accuracy and U_Quality), Root Mean Square Error (RMSE) and Regression value (R-value). Obtained results show that best performance belongs to RBFNN based model and then ANFIS based model. U coefficients and RMSE values are obtained less than 0.005 and R-Value is become close to one by using RBFNN based model for testing data, which indicates high accuracy prediction performance.

Features play an important role in the performance of machine learning and classification applications. Usually, separability of classes by using raw or original features are so low, and it is necessary to use complex classifiers with high computational costs or use enrichment modules to increase distinctiveness of features. In this paper, a deep feature enrichment method is proposed to increase the distinguishing power of features using an adaptive neural network-based structure. Proposed method adaptively uses linear/non-linear activation functions for coding, and the dimension of the coding space adaptively adjusted to be lower, the same, or higher than the original feature space. Then the best neural network structure (number of layers and neurons per layers) and the optimum weights for the proposed neural structure are optimized using an evolutionary optimization algorithm. Optimized modules can map/code raw input features into an enriched feature space that can increase the separability of the data points among classes. In fact, our obtained enriched features can adapt themselves to the nature of the training data and they can improve the generalization power also the performance of conventional classifiers. Experimental results on popular UCI datasets such as Glass, Liver, Iris, Wine, Breast cancer and seeds show increase of significant correct recognition rates (11.63% for Glass, 4.35% for Liver, 13.34% for Iris, 27.78% for Wine, 0.72% for Breast cancer and 11.9% for seeds) and also improvement of more than 1.5% of verification rate and 2% of Identification rate for the Yale face database. (C) 2020 Elsevier Ltd. All rights reserved.

•Converting Raw MRI images to deep image features by using CAE.•Using Ensemble of CAEs for extracting more knowledge from raw image and merging them into one deep feature image.•Using CNN for extracting deep feature to classify Alzheimer’s disease in different conditions.•Presenting better accuracy and reliability in comparison with other methods in literatures. Alzheimer’s disease is one of the famous causes of death among elderly. Diagnosis of this disease in the early stage is so difficult by conventional methods. Machine learning methods are one of the best choice for improving the accuracy and performance of diagnosis procedure. The heterogeneous dimensions and structure among the data of this disease have complicated the diagnosis process. Therefore proper features are needed to solve this complexity. In this research, proposed method is introduced in two main steps. In the first step, ensemble of pre-trained auto encoder based feature extraction modules are used to generate image feature from 3D input image and in the second step convolutional neural network is used to diagnosis Alzheimer’s disease. Three different classification cases, namely; Alzheimer’s Disease (AD) versus Normal Condition (NC), AD versus Mild Cognitive Impairment (MCI) and MCI versus NC are studied. Obtained results show that accuracy rate for AD/NC, AD/MCI and MCI/NC are 95%, 90% and 92.5%, respectively. Also, for all cases sensitivity and specially sensitivity rates for proposed method confirm that it could be reliable for diagnosis AD in early stage and has less error to detect normal condition.