Journal of Science and Engineering

Technical Analysis of Grid Connected 1 kWP Rooftop Solar PV for Residential Consumer at Urban Areas of Lalitpur, Nepal

Rupesh Gautam, Hari Bahadur Darlami — Fri, 31 Dec 2021 00:00:00 +0000

Integration of rooftop solar PV by a residential consumer to the grid is beneficial to both consumer and grid if integrated optimally. The minimization of power loss by improving the voltage profile on the grid side and reducing the electricity bill by improving energy deficiency faced by the residential consumer is the benefit of the integrated PV system. The preparation of this paper is based on the study of grid impact analysis and performance analysis of solar among the urban areas of Lalitpur. First, the grid impact and optimum size calculation are performed in IEEE-33 and IEEE-15 bus test system, and Pulchowk feeder is taken for the study of real scenario. Improvement of voltage profile, loss reduction, and the number of houses feasible for injecting PV generation has been demonstrated graphically. The power loss is found to be reduced by 62.5% after injecting solar PV into the grid. PVsyst has been used for the performance analysis of the PV array and the performance ratios are within limits with a capacity factor of 0.177 and performance ratio of 0.74 and system yield factor of 4.24 kWh/kWp/day. From all the results and performance analysis, it can be concluded that integration of 1 kWp rooftop solar is technically feasible if the optimum number of houses are allowed to inject PV array power at the optimum location.

A Few Methods of Solving Optimal Control Problem in Hamilton-Jacobi-Bellman Equation Form

Bhimsen Khadka, Durga Jang K.C. — Fri, 31 Dec 2021 00:00:00 +0000

Non-linear optimal control problem arises in many different areas, for example, engineering, medical sciences, economics, industries, etc. The solution of Hamilton-Jacobi-Bellman equation is connected with the non -linear optimal control problem. In this paper, we formulate the Hamilton-Jacobi-Bellman equation using nonlinear optimal control problem. We also discuss its solutions using Adomian decomposition method, Laplace transform-Homotopy perturbation method and variational iteration method.

A Review on the Lebesgue Spaces

Santosh Ghimire, Bimala Mishra — Fri, 31 Dec 2021 00:00:00 +0000

In this article, we begin with classical Lebesgue spaces Lp with p being constant and review the various properties such as completeness and duality of the space. To this end, we also discuss the boundedness of Hardy-Littlewood maximal function and interpolation on such spaces. Finally, we focus our attention on variable exponent Lebesgue spaces and review various results on it. Moreover, we also see the differences in between these Lebesgue spaces.

Privacy and Security Issues in Social Networking Sites (SNS)

Naresh Khatri, Sunil Paudel — Fri, 31 Dec 2021 00:00:00 +0000

Social Networking Sites (SNS) have become very fashionable during the past decade, as they permit users to both express their personal feelings and meet friends, relatives, and people with similar interests. There are many potential threats to data privacy and security like fraud, identity theft, and disclosure of sensitive information. Many users are still not conscious of these threats. Moreover, the privacy settings provided by SNSs aren't flexible and reliable enough to protect user data. Users don't have any control over what others reveal about them. We conducted a preliminary study among internet users of Nepal which examines the privacy and security issues in SNSs because the users share their information and put varieties of data on SNSs. This study intends to protect the user information from any misuse, to make sure of data security, and to permit users to choose what information to share and with whom. Another objective of this study is to measure the level of steps taken by the SNS operators. We identified privacy and security issues in SNS and propose a Privacy and Security Framework as a foundation to deal with these problems.

Detection and Classification of MRI-Based Brain Tumor via Jaya Algorithm and Twin Support Vector Machine

Dinesh Ghemosu, Shashidhar Ram Joshi — Fri, 31 Dec 2021 00:00:00 +0000

Brain tumor detection and classification is one of the challenging tasks in the medical image application. Early detection of a brain tumor can help diagnosis and treatment of the patients. Magnetic Resonance Imaging (MRI) is widely used for the detection of brain tumor. Manual analysis of brain MRI, and classification of brain tumor is a tedious and time-consuming job. This paper introduces a novel approach to brain tumor segmentation and classification using BRATS 2015 datasets. Our system exploits the benefits of Jaya Algorithm (JA) as an optimization technique for finding multi-level thresholds to segment the tumor part from the MRI. Feature extraction is implemented by Gray Level Co-occurrence Matrix (GLCM), followed by Principal Component Analysis (PCA) for feature reduction. Due to its inherent distinct features and advantages, a machine-learning approach, Twin Support Vector Machine (TSVM) is used as a classifier. The prediction accuracy of the proposed system yielded up to 97.89 % with sensitivity 96.48%, 98.97 precision, 97.91% F1 Score, and 0.0798 MSE. The accuracy, sensitivity, F1 Score, and MSE are found comparable to the other state-of-arts machine learning methods.

Risk Management in Construction Firms in Nepal

Umesh Sukamani — Fri, 31 Dec 2021 00:00:00 +0000

The main purpose of this study is to identify and focus on major factors that affect risk management in construction sites in developing countries like Nepal. Relative Importance Index (RII) analysis is carried out to rank major factors that affect risk management. Besides, ANOVA analysis is carried out to test the hypothesis. Findings show that there is no difference between small and large-scale project groups in their perception of the significance level of factors affecting Project Risk Management. Moreover, “a) payment delay b) project funding problem, and c) defective design” were top three major factors that affect risk management in developing countries like Nepal. The managerial level of construction firms mainly in developing countries, should focus on the top ten critical factors for better improvement of risk management no matter its size- be it is small- or large scale construction firms.

Nepali Handwritten Letter Generation using GAN

Basant Babu Bhandari, Aakash Raj Dhakal, Laxman Maharjan, Asmin Karki — Fri, 31 Dec 2021 00:00:00 +0000

The generative adversarial networks seem to work very effectively for training generative deep neural networks. The aim is to generate Nepali Handwritten letters using adversarial training in raster image format. Deep Convolutional generative network is used to generate Nepali handwritten letters. Proposed generative adversarial model that works on Devanagari 36 classes, each having 10,000 images, generates the Nepali Handwritten Letters that are similar to the real-life data-set of total size 360,000 images. The generated letters are obtained by simultaneously training the generator and discriminator of the network. Constructed discriminator networks and generator networks both have five convolution layers and the activation function is chosen such that generator networks generate the image and discriminator networks check if the generated image is similar to a real-life image dataset. To measure the quantitative performance, Frechet Inception Distance (FID) methodology is used. The FID value of 18 random samples, generated by networks constructed, is 38413677.145. For a qualitative measure of the model let the reader judge the quality of the image generated by the generator trained model. The Nepali letters were generated by the adversarial network as required. The evaluation helps the generative model to be better and further enables a better generation that humans have not thought of.