Modeling and Simulation of Solar Photovoltaic Cell for the Generation of Electricity in UAE

This paper proposes the implementation of a circuit based simulation for a Solar Photovoltaic (PV) cell in order to get the maximum power output. The model is established based on the mathematical model of the PV module. As the PV cell is used to determine the physical and electrical behavior of the cell corresponding to environmental factors such as temperature and solar irradiance, this paper evaluates thirty years solar irradiation data in United Arab Emirates (UAE), also analyzes the performance parameters of PV cell for several locations. Based on the Shockley diode equation, a solar PV module is presented. However, to analyze the performance parameters, Solarex MSX 120, a typical 120W module is selected. The mathematical model for the chosen module is executed in Matlab. The consequence of this paper reflects the effects of variation of solar irradiation on PV cell within UAE. Conclusively, this paper determines the convenient places for implementing the large scale solar PV modules within UAE.Comment: To be published in 5th International Conference on Advances in Electrical Engineering (ICAEE-2019

Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers

The central goal of this paper is to establish two commonly available dimensionality reduction (DR) methods i.e. t-distributed Stochastic Neighbor Embedding (t-SNE) and Multidimensional Scaling (MDS) in Matlab and to observe their application in several datasets. These DR techniques are applied to nine different datasets namely CNAE9, Segmentation, Seeds, Pima Indians diabetes, Parkinsons, Movement Libras, Mammographic Masses, Knowledge, and Ionosphere acquired from UCI machine learning repository. By applying t-SNE and MDS algorithms, each dataset is transformed to the half of its original dimension by eliminating unnecessary features from the datasets. Subsequently, these datasets with reduced dimensions are fed into three supervised classification algorithms for classification. These classification algorithms are K Nearest Neighbors (KNN), Extended Nearest Neighbors (ENN), and Support Vector Machine (SVM). Again, all these algorithms are implemented in Matlab. The training and test data ratios are maintained as ninety percent: ten percent for each dataset. Upon accuracy observation, the efficiency for every dimensionality technique with availed classification algorithms is analyzed and the performance of each classifier is evaluated.Comment: 2020 IEEE Region 10 Symposium (TENSYMP), 5-7 June 2020, Dhaka, Banglades

Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers

The central aim of this paper is to implement Deep Autoencoder and Neighborhood Components Analysis (NCA) dimensionality reduction methods in Matlab and to observe the application of these algorithms on nine unlike datasets from UCI machine learning repository. These datasets are CNAE9, Movement Libras, Pima Indians diabetes, Parkinsons, Knowledge, Segmentation, Seeds, Mammographic Masses, and Ionosphere. First of all, the dimension of these datasets has been reduced to fifty percent of their original dimension by selecting and extracting the most relevant and appropriate features or attributes using Deep Autoencoder and NCA dimensionality reduction techniques. Afterward, each dataset is classified applying K-Nearest Neighbors (KNN), Extended Nearest Neighbors (ENN) and Support Vector Machine (SVM) classification algorithms. All classification algorithms are developed in the Matlab environment. In each classification, the training test data ratio is always set to ninety percent: ten percent. Upon classification, variation between accuracies is observed and analyzed to find the degree of compatibility of each dimensionality reduction technique with each classifier and to evaluate each classifier performance on each dataset.Comment: 2nd International Conference on Innovation in Engineering and Technology (ICIET

Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced performances for clusters with different densities. Therefore, in this paper, an adaptive DBSCAN is proposed which can work significantly well for identifying clusters with varying densities.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Auxetic Yarn: Fundamentals, Influencing Parameters, Application Areas and Challenges

The mechanical behaviour of auxetic materials and structures is the most distinctive characteristic, which differs from that of conventional engineering materials due to the negative Poisson’s ratio. Auxetic materials have the fascinating feature of widening when stretched and contracting when compressed. In recent times, the research of auxetic materials based on textile structures has received a lot of interest. Auxetic effect development at the yarn phase is a new and exciting field of study. Many researchers already developed different types of auxetic yarns, such as the helical auxetic yarn, the plied auxetic yarn, the semi-auxetic yarn etc. The helical auxetic yarn (HAY) is the most commonly mentioned auxetic yarn. It is made up of a rigid wrap and an elastic core yarn. However, it is interesting that auxetic yarns can be produced from conventional non-auxetic fibres through the conventional spinning system as well. The helical auxetic yarn is a new type of yarn with a wide variety of possible applications. Moreover, pore-opening characteristics of auxetic yarns make it a potential candidate in the fields of technical textiles, such as medical textiles, filter application, protective textiles etc. Fabrication of auxetic textiles by utilizing auxetic yarns through simple weaving and knitting technology opens the door to new applications. The aim of this paper is to address the fundamentals of auxetic yarns, such as structure, shortcomings, production techniques, as well as the influencing process parameters. From various research works, it is evident that the wrap helical angle, the core/wrap diameter ratio, and the initial moduli of wrap component are the most vital processing parameters during the production of auxetic yarns. Finally, some potential application areas and challenges of auxetic yarns are also addressed briefly in this paper