Call for Papers

Abstract: This paper presents a Long Short-Term Memory (LSTM) network-based method to identify faults in a 3-phase induction motor. Various external faults in a 3-phase induction motor are first described. A brief introduction to LSTM technique is then presented. The LSTM is trained to classify external faults using 3-phase voltages and currents collected from a 1/3 hp induction motor in real-time. MATLAB is used for training and testing the LSTM method. Results show that the proposed LSTM based method is effective in classifying different external faults in the 3-phase induction motor. The performance of the LSTM method is observed to be better than some of the previous methods in model formation and testing accuracy.

Keywords: Long Short-Term Memory, Induction Motor, Fault Identification, Machine Learning, Protective Relays.

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Abstract

The Effect of Chromium Additions on the Microstructure and Some Mechanical Properties of Delta Steel Company Nodular Cast Iron

S.O Jimoh, N. N.Ehigiamusoe

DOI: 10.17148/IJIREEICE.2020.8902

Abstract: The effects of chromium additions on the microstructure and some mechanical properties of nodular cast iron have been determined. The nodular cast iron samples containing from 0.6% to 2.6% chromium were produced at Delta steel company. The mechanical properties studied include: Tensile strength, hardness and percentage elongation. The results indicate that the tensile strength and hardness of the samples increased with percentage chromium additions from 449N/𝑚𝑚2 at 0% chromium to 544N/𝑚𝑚2 at 2.6% chromium and from 195BHN at 0% chromium to 323 BHN at 2.6% chromium. However, the percentage elongation of the nodular cast iron sample decreased with increasing percentage chromium additions from 9% at 0% 𝑐ℎ𝑟omium to 2%chromium additions. The microstructure revealed an increase in degree of pearlite phase together with some precipitates of Chromium carbide phase as the chromium level increased.

Keywords: Chromium, Nodular Cast Iron, Ferrous Metals.

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Abstract

Economic Benefit of Melting Low Manganese Pig Iron

Jimoh S. O, Lozovich, S and Nwuguru A. A

DOI: 10.17148/IJIREEICE.2020.8903

Abstract: The study about economic effectiveness of melting low Manganese pig iron is presented. In this study, mathematical models were developed to evaluate of the economic effectiveness of the exclusion of the use of Manganese ore in agglomeration and blast furnace process with its transfer to converter process. It was found that by obviating the use of manganese ore in blast furnace, there is economy of resources and reduction in the cost of liquid pig iron. The processing of low manganese Pig-iron in a converter with the use of manganese ore also created positive economic effect, and it can get up to 6.4USD/ton of steel.

Keywords: Economic Benefit; Mathematical models; blast furnace process; melting low manganese pig-iron and steel.

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Abstract

Satellite Image Processing to Detect Buildings using Deep Learning

Claudia P Dsouza, Dr. K. P. Shashikala

DOI: 10.17148/IJIREEICE.2020.8904

Abstract: Detecting Buildings from high resolution satellite images is incredibly helpful in map making, land use analysis, urban planning etc. If a human expert wants to manually extract this valuable data, it is difficult. So there is one potential solution to extract this data by using automatic techniques. In this project we are going to use deep learning approach to detect buildings from the satellite images. We have downloaded a large amount of dataset because deep learning model requires a huge dataset for training the model. Then pre-processing of these image like cropping, resizing and removal of noise by using Gaussian filter is being done. Split the dataset into training and validation set. After gathering all the dataset, we should train the model. In training process, the dataset undergoes through convolutional neural networks for different layers. Once training is completed the model has been built and we test the model by applying different input images and finally we get only the detected buildings as output. In this project we have also done the greenery detection by using HSV (Hue, Saturation, Value) colour format. We are using python for coding purpose.

Keywords: Satellite Images, Building Detection, Convolutional Neural Networks and Deep learning.

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Abstract

Machine Learning Based News Validation

Reetodeep Hazra, Megha Banerjee, Judhajit Sanyal

DOI: 10.17148/IJIREEICE.2020.8905

Abstract: The use of machine learning has become widespread in recent years, especially due to the impact of media content on the general population. In particular, the validation of truth in the context of news has become a critical necessity, due to its ready availability from verified and unverified sources, and its ability to influence people majorly. The present work outlines a LSTM (long short-term memory) based approach to news validation. The results obtained by the model are presented in terms of the training data set and contrasted with the results obtained from the test data set. Appreciable accuracy is achieved through the model, seen through the corresponding loss curves and confusion matrix.

Keywords: Machine Learning, LSTM, News Validation, Loss Curves, Confusion Matrix.

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Abstract

ECG Processor with Optimised Hybrid Classifier for Cardiovascular Arrhythmia

Nikhyda.N, Dr.B.Paulchamy, Dr.S.Kavitha

DOI: 10.17148/IJIREEICE.2020.8906

Abstract: This paper presents the design of an energy-efficient Electrocardiogram (ECG) processor for arrhythmia detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiac conditions. It provides better time and frequency resolution of the Electrocardiogram (ECG) signal, which helps in decoding important information of an ECG, which improves the arrhythmia classification. The decisions can be achieved by determining different intervals such as PR Interval, RR Interval, Heart Rate etc. and those intervals will be compared with the ideal intervals. During the whole process Modelsim was used & ECG signals were taken from PhysioBank ATM. The proposed QRS detection architecture deals with almost all the ECG signal artifacts, such as low-frequency noise, baseline drift, and high-frequency interference with minimum hardware resources. Few metrics needed to be concerned during the implementation of ECG processor are Accuracy (low detection error-rate), Area (hardware resource utilization), Power efficiency (low power consumption), Speed (delay), Sensitivity (real time monitoring) And Predictivity. Thereby, in this proposed project we are going to simulate the results and compare few performance metrics with the existing methodology.

Keywords: Electrocardiogram, QRS complex, hybrid classifier, multi-scaled product, soft-threshold algorithm, wavelet ECG detector.

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Abstract

Standalone Hybrid Wind and Solar Photovoltaic Cell Power Generation

Arvind Kumar Pal, Dr. Dolly Thankachan

DOI: 10.17148/IJIREEICE.2020.8907

Abstract: This paper presents a combination of voltage source converter and capacitor banks which is called as Static Compensator (STATCOM), Voltage Source Converter (VSC) based diversion connected Flexible AC Transmission System (FACTS) devices which improves the dynamic and static voltage control in transmission in addition with distribution system. It is a reactive power return device which can produce and absorb the reactive power. The power quality has ongoing to play an important role in electronic and electrical industries. A system concerning the power quality measurement is voltage flicker and harmonics. Normally DSTATCOM is connected in distribution system. Optimizing the parameters of a bio-inspired algorithm’s naturally the main path to improve its performance. The distribution used in the displacement and creation of new solutions is also a factor to consider when enhancing its capacity. In this work, the efficiency of Gravitational Search Algorithm (GSA) and Cuckoo Search Algorithms (CSA) is investigated through MATLAB Simulink model. InThe IGBT is used as a power switch which has low switching loss and the turn off is very simple. In this paper THD is evaluated, disturbances obtained in the current waveforms are reduced by means of DSTATCOM. The distribution system with DSTATCOM and the control scheme used for power quality enhancement is simulated and validated.

Keywords: Static Compensator (STATCOM), Voltage Source Converter (VSC), Cuckoo Search Algorithms (CSA), Gravitational Search Algorithm (GSA).

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Abstract

Gender Differences in Neuroticism, Psychoticism and Extraversion Between Under Graduate Students

Dr. Pramod.M.Katkar

DOI: 10.17148/IJIREEICE.2020.8908

Abstract: In the last few years the wind power generation has been increased significantly. Due to development in renewable energy technologies and continued rise in prices of petroleum products hybrid renewable energy systems are gaining more importance for supplying the power to meet the today’s increasing energy demands either as a stand-alone system or as a grid connected system. In this work a model of stand-alone connected hybrid system consists of wind and solar photovoltaic cell system is studied and implemented in MATLAB/SIMULINK. The proposed system consists of a wind turbine, induction generator, a PV solar cell array, boost converter, an inverter and a battery storage system. A simple control technique has been proposed to track the operating point at which maximum power can be coerced from the solar/wind system under continuously changing environmental conditions. A relative study of hybrid model PV/wind system has been made.

Keywords: Photovoltaic Cell, Wind System, Hybrid System, MATLAB / SIMULINK.

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Abstract

Fraudulent Credit Card Transactions Prediction Using 2D-Convolutional Neural Network

DOI: 10.17148/IJIREEICE.2020.8910

Abstract: Fraudulent credit card is one of the most alarming concerns in this modern age. With few misuse of credit card, thousands of dollars can be mishandled. This paper focused on detecting fraud credit card transaction using 2D- Convolutional neural network. The paper reports the categorization of two designated categories- Genuine and fraud transactions. In the pre-processing stage we applied under-sampling technique to handle imbalance dataset. For the improvement of the accuracy, we have decreased the number of convolutional layers with a sigmoid layer at the top. The proposed technique achieved an accuracy of 94%

Keywords: Credit card fraud detection, convolutional neural network, under-sampling, imbalanced dataset.

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Abstract

Personality Characteristics Between Table-Tennis and Lawn-Tennis Players

Ajay Arora

DOI: 10.17148/IJIREEICE.2020.8911

Abstract: The objective of the study is to determine the differences of personality characteristics of Table-tennis and Lawn Tennis Players. The data was collected through respondents from 150 Lawn-tennis and 150 Table –tennis players. Eysenck Personality Questionnaire - Revised (EPQ-R) was used to measure four personality characteristics of Table- tennis and Lawn Tennis Players. The result of the shows the there were significant difference between neuroticism, psychoticism and extraversion between Table tennis and lawn tennis players. However no significant difference was found in lie scale between these two groups players. Table-tennis player’s having more, neurotic psychotic tendency as compared to Lawn-tennis players

Keywords: Personality, Table Tennis, Lawn Tennis.

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Abstract

Implementation of Basic Reversible Logic Gates using Quantum Dot Cellular Automata

Vineeta Gupta, Aniket Saha, Abhimanyu Roy

DOI: 10.17148/IJIREEICE.2020.8912

Abstract: Quantum-dot Cellular Automata (QCA) is attaining worldwide recognition of researchers for its outstanding efficiency in terms of size, energy consumption, and latency as compared to similar nanotechnologies like Complementary Metal-Oxide-Semiconductor (CMOS), a Single Electron Technology (SET), nanowire transistor, Carbon Nanotube Field-Effect Transistor (CNTFET) [1]. The productive implementation of reversible logic is very crucial to carry out quantum computing. Reversible logic reduces the number of gates, quantum cost, and garbage output as information lost during the computation is zero. Reversible logic is an invertible process which means that for each final state of the system, a unique initial state can be determined. There are various implementations of Reversible Logic Gates and Quantum Computing in today’s world, viz., DNA computing, low power CMOS, nanotechnology, etc. This paper provides efficient and modified designs of seven reversible gates using Quantum-dot Cellular Automata.

Keywords: Reversible Logic Gate, Quantum-dot Cellular Automata, Quantum Computing, VLSI.

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