Comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / Mohammad Yazdani-Asrami, Mehran Taghipour Gorjikolaie and S. Asghar Gholamian

In this paper, three versions of Particle Swarm Optimization (PSO) are proposed to estimate the equivalent circuit parameters of squirrel cage induction motor. It is believed that how inertia weight changes during iterations can impact on final results. Constricted coefficients, linear model and exponential version are used as inertia weight, each of them presents different variations for inertia weight and consequently for particle movements and speed of such movements. In the linear version, particles start searching process with high speed and their speed will decrease by constant ramp, this kind of variation let to search all solution space in a short time and local search at the final iterations with low speed, also exponential version presents same treatment as linear version with non-linear variations in inertia weight and speed of movement. But, mathematical analysis shows that they trap into local minima and scientists presents constricted version to solve this problem. In order to evaluate proposed versions additional to make changing in PSO’s version, sensitivity of proposed methods is analyzed using three sets of data. Results confirm the ability of proposed method which can estimate parameters with a possible least error

Artificial intelligence for superconducting transformers

Artificial intelligence (AI) techniques are currently widely used in different parts of the electrical engineering sector due to their privileges for being used in smarter manufacturing and accurate and efficient operating of electric devices. Power transformers are a vital and expensive asset in the power network, where their consistent and fault-free operation greatly impacts the reliability of the whole system. The superconducting transformer has the potential to fully modernize the power network in the near future with its invincible advantages, including much lighter weight, more compact size, much lower loss, and higher efficiency compared with conventional oil-immersed counterparts. In this article, we have looked into the perspective of using AI for revolutionizing superconducting transformer technology in many aspects related to their design, operation, condition monitoring, maintenance, and asset management. We believe that this article offers a roadmap for what could be and needs to be done in the current decade 2020-2030 to integrate AI into superconducting transformer technology

Joint feature fusion and optimization via deep discriminative model for mobile palmprint verification

With recent advances in pattern recognition and computer vision, mobile palmprint authentication has become an emerging field to provide better facilities and ubiquitous computing for scientific and commercial communities. To effectively streamline this issue, researchers focus on improving authentication performance by designing deep convolutional neural networks. Despite the high potential of the state-of-the-art methods, the challenges of preprocessing computation cost, lack of training samples for big data application, and discriminative feature optimization remain to be carefully addressed. A deep mobile palmprint verification framework focusing on discriminative feature representation is proposed. To this end, an automatic feature mapping is learned from two well-known deep architectures via an effective weighted loss function. Thereafter, a convolution-based feature fusion block is followed by a surrogate model in the feature-matching phase for palmprint verification. From a practical point of view, our framework is cost-effective and can represent discriminative features with high performance. We demonstrate the effectiveness of our framework and mobile database for palmprint verification task beating the state-of-the-art on standard benchmarks. Moreover, experimental results show that our model outperforms previous ones, especially for the few-shot learning application, achieving equal error rates of 0.0281% and 0.0197% for IIT Delhi Touchless Palmprint Database and Hong Kong PolyU Palmprint databases, respectively. It is notable that all codes are open-source and may be accessed online

Prediction of nonsinusoidal AC loss of superconducting tapes using Artificial Intelligence-based models

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 (UAccuracy and UQuality), 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

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online

Artificial intelligence for superconducting transformers

Artificial intelligence (AI) techniques are currently widely used in different parts of the electrical engineering sector due to their privileges for being used in smarter manufacturing and accurate and efficient operating of electric devices. Power transformers are a vital and expensive asset in the power network, where their consistent and fault-free operation greatly impacts the reliability of the whole system. The superconducting transformer has the potential to fully modernize the power network in the near future with its invincible advantages, including much lighter weight, more compact size, much lower loss, and higher efficiency compared with conventional oil-immersed counterparts. In this article, we have looked into the perspective of using AI for revolutionizing superconducting transformer technology in many aspects related to their design, operation, condition monitoring, maintenance, and asset management. We believe that this article offers a roadmap for what could be and needs to be done in the current decade 2020-2030 to integrate AI into superconducting transformer technology

Artificial intelligence for superconducting transformers

Artificial intelligence (AI) techniques are currently widely used in different parts of the electrical engineering sector due to their privileges for being used in smarter manufacturing and accurate and efficient operating of electric devices. Power transformers are a vital and expensive asset in the power network, where their consistent and fault-free operation greatly impacts the reliability of the whole system. The superconducting transformer has the potential to fully modernize the power network in the near future with its invincible advantages, including much lighter weight, more compact size, much lower loss, and higher efficiency compared with conventional oil-immersed counterparts. In this article, we have looked into the perspective of using AI for revolutionizing superconducting transformer technology in many aspects related to their design, operation, condition monitoring, maintenance, and asset management. We believe that this article offers a roadmap for what could be and needs to be done in the current decade 2020-2030 to integrate AI into superconducting transformer technology