Browse Items (9795 total)
Sort by:
-
Optimal Switching Operations of Soft Open Points in Active Distribution Network for Handling Variable Penetration of Photovoltaic and Electric Vehicles Using Artificial Rabbits Optimization
Global warming, rising fuel prices, and limited conventional fuel supplies are driving the use of renewable energy, battery energy storage, and electric vehicles, transforming traditional electrical distribution networks into active distribution networks. Stochastic technologies can present operational and control challenges, especially for radially configured active distribution networks. In this scenario, strengthening the existing active distribution networks is necessary. This study optimally integrates soft open points for dynamic network reconfiguration to handle uncertainty in active distribution networks. The location, size, and reconfiguration of the soft open points were obtained for the hourly load profile, which included electric vehicle fleet load penetration and PV distributed generation. The proposed multi-objective function uses active power loss, voltage profile, and reliability indices. The proposed multivariable optimization problem was solved using artificial rabbits optimization. The simulations were performed on a modified IEEE 33-bus radial distribution system. The computational efficiency of artificial rabbits optimization is competitive with other prominent algorithms. The proposed approach of optimal soft open points and dynamic network reconfiguration is utilized to cope with uncertainty and run the present active distribution networks with better technical and reliability characteristics. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Realization of Green 5G Cellular Network Role in Medical Applications: Use of ChatGPT-AI
Wireless communication in medical applications improves patient monitoring, care coordination, early disease detection, and patient empowerment. It improves healthcare and patient outcomes. The design and configuration of a solar-powered emergency battery backup system for 5G telecommunication base stations, including medical applications, may vary depending on local climate, power requirements, and resources. In this connection, uninterrupted power supply to the base stations become crucial. The author utilizes the ChatGPT-AI features and prepared this comprehensive letter for realizing the role of sustainable practices towards climatic changes. 2023, The Author(s) under exclusive licence to Biomedical Engineering Society. -
Optimal allocation of solar photovoltaic distributed generation in electrical distribution networks using Archimedes optimization algorithm
This paper proposes to resolve optimal solar photovoltaic (SPV) system locations and sizes in electrical distribution networks using a novel Archimedes optimization algorithm (AOA) inspired by physical principles in order to minimize network dependence and greenhouse gas (GHG) emissions to the greatest extent possible. Loss sensitivity factors are used to predefine the search space for sites, and AOA is used to identify the optimal locations and sizes of SPV systems for reducing grid dependence and GHG emissions from conventional power plants. Experiments with composite agriculture loads on a practical Indian 22-bus agricultural feeder, a 28-bus rural feeder and an IEEE 85-bus feeder demonstrated the critical nature of optimally distributed SPV systems for minimizing grid reliance and reducing GHG emissions from conventional energy sources. Additionally, the voltage profile of the network has been enhanced, resulting in significant reductions in distribution losses. The results of AOA were compared to those of several other nature-inspired heuristic algorithms previously published in the literature, and it was observed that AOA outperformed them in terms of convergence and redundancy when solving complex, non-linear and multivariable optimization problems. The Author(s) 2022. -
Coyote optimization algorithm for optimal allocation of interline Photovoltaic battery storage system in islanded electrical distribution network considering EV load penetration
In current times, there is a need to do power system planning to endure situations of any kind. An islanding operation is one such unavoidable situation that may be required in many cases for both technical and economic reasons. First and foremost, this paper focuses on the determination of the best allotment of Interline-Photovoltaic (I-PV) system as per Electric Vehicle (EV) load penetration in the network. With different operational constraints, a multi-objective optimization using real power loss and voltage deviation index is formulated and solved using the Coyote Optimization Algorithm (COA).The paper highlights the computational efficiency of COA with Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), in addition to various literary works, and the results suggest the superiority of COA by its global optima. The required battery energy storage system (BESS) capacity for supplying an islanded network's entire load demand for a day is determined in the second stage. The simulations were carried out on the IEEE 33-bus electrical distribution network (EDN) contemplating different levels of EV load penetration. The proposed methodology results have proved that the required energy is provided by optimal I-PV-BESS backup for a daylong islanding operation and its adaptability for practical situations. 2021 Elsevier Ltd -
Response of ChatGPT for Humanoid Robots Role in Improving Healthcare and Patient Outcomes
Humanoid robotics is characterized by constant developments, which are supported by several research facilities across the world. Humanoid robots are used in many different industries. In this setting, this letter, written by people, makes use of ChatGPT answers to examine how humanoid robots might be used in the medical industry, particularly in light of the COVID-19 pandemic and in future. Although humanoid robots can help with certain jobs, it is important to recognize the indispensable importance of human healthcare professionals who have knowledge, empathy, and the capacity for critical judgment. Although humanoid robots can complement healthcare initiatives, they shouldnt be viewed as a full-fledged replacement for human care. 2023, The Author(s) under exclusive licence to Biomedical Engineering Society. -
Digital education for a resilient new normal using artificial intelligenceapplications, challenges, and way forward
As society and technology advance to meet Industry 4.0 requirements, the educational system has also undergone many transformative changes in the past decade. Education is regarded as one of the most important tools for developing individuals, families, businesses, and the economy. New digital technologies are making a great revolution by transforming all aspects of education in teaching, learning, assessment, and feedback. The COVID-19 pandemic has led to the proliferation of digital education and its replacement of traditional education in the educational system. The developments in artificial intelligence (AI) are indispensable in all sectors, including education. AI-integrated learning helps management, teachers, students, parents, and other stakeholders gain insight into their performance to impact the process positively. This chapter aims to throw light on the emerging need and technologies used for digital education and to examine the role of AI in education with examples from the perspectives of teaching, learning, and assessment in the new normal. The application of AI in education and its effectiveness is explored through six publicly available datasets along with strengths, weaknesses, opportunities, challenges, and the future of digital education. This chapter discusses several examples and benefits of AI applications that enhance the educational experience and also emphasizes the need to align it with technology and curriculum to achieve the intended learning outcomes. 2023 Elsevier Ltd. All rights reserved. -
Effect of multiwalled carbon nanotube alignment on the tensile fatigue behavior of nanocomposites
The one-dimensional structure of carbon nanotubes makes them highly anisotropic, making them to possess unusual mechanical properties, and hence employed as promising nanofiller for the composite structures. However, various carbon nanotube properties are not completely utilized when they are used as reinforcement in composites due to inadequate and immature processing techniques. In the present work, an attempt has been made to utilize the strong anisotropic nature of multi-walled carbon nanotubes (MWCNTs) for improving the fatigue life of nanocomposites only by considering a very low weight percentage (<0.5 wt%). The anisotropy of MWCNTs was imparted into the nanocomposites by aligning them in the epoxy matrix with DC electric field during composite curing. Nanocomposites were made for three MWCNT loadings (0.1, 0.2, and 0.3 wt%). The tensile fatigue behavior was investigated under stress control by applying cyclic sinusoidal load with the frequency range of 13 Hz and stress ratio, R = 0.1. The specimens were tested for the fatigue load until the failure or 1E+05 cycles. The fractured surfaces were examined through scanning electron microscope to analyze the fatigue fracture behavior. A small weight percentage of MWCNT loading (0.2 wt%) into the polymer composite has enhanced on an average 13% to 15% fatigue life, which is encouraging to develop the low cost, improved fatigue life composite structures. Also, the energy dissipation mechanism in MWCNT dispersed nanocomposites has shown a reduced crack propagation rate. The Author(s) 2017. -
Nature's Lament: A Comparative Psychoanalytical Reading of Childhood Trauma in Select War Narratives
Sustainable Development has become an inevitable need of the hour. This paper problematizes the trauma of children as represented in the narratives, Beasts of No Nation by Uzodinma Iweala and A Long Way Gone by Ishmael Beah. The incomprehensibility of trauma, it's varied representation in fiction, dissociation of child psyche, and its detrimental effect on children is substantiated using psychoanalytic theory of trauma proposed by Cathy Caruth and contemporary trauma theorists. The paper argues the atrocities children are forced to be involved into, causes profound trauma in themselves leading to, encumbering of sustainable developmental goals. A comparative study of interpretive textual analysis is employed to study the havoc the society endears as a result of war, that wrecks the child, hindering the overall sustainable development. As it voices out the voiceless trauma of children the paper also aims in divulging the decisive influence of the select literary narratives in sensitizing the society in achieving societal as well as environmental sustainability. The Electrochemical Society -
Development of an efficient real-time H.264/AVC advanced video compression encryption scheme
Multimedia is the combination of media such as text, graphics, video clips, and audio files. In todays world, multimedia plays an important role in many applications that we use in our daily lives. It is used in educational software, animation, sound, and text, as well as multi-media software. H.264/AVC video compression is extremely efficient in terms of compression. Despite this, H.264/AVC requires a lot of processing and consumes a lot of power insdespite of the fact that its compression efficiency is lower than that of H.264/AVC. We examine the various methods of Video H.264 Advanced Video Compression Standard Encryption Schemes in this paper. The performance of all types of encryption techniques will be evaluated using parameters such as cost overhead, delay, and encryption quality. This will provide us with a detailed comparative analysis of video encryption schemes, allowing us to determine which one is far more efficient for H.264/AVC. 2021 Taru Publications. -
21st Century Teacher Educator
Golden Research Thoughts, Vol. 2, Issue 11, pp. 40-45, ISSN No. 2231-5063 -
Hybrid AI Talent Acquisition Model: An Opinion Mining and Topic based approach
Artificial Intelligence models have found their usage in the human resource domain. In this paper, job reviewers' opinions on online discussion boards have been captured. The relative importance of factors has been established through an extensive literature review. First, LDA Topic modelling by adopting PCA is performed on unstructured text data has been analyzed. Second, sentiment analysis using the Li-Hu method has been employed to understand job seekers' satisfaction with job portals. The proposed model, 'Hybrid AI Talent Acquisition Model,' follows a novel approach to streamlining the jobseeker opinion related to online outlets. 2022 IEEE. -
Strategic perspective of internal branding: A critical review /
European Journal of Business and Management, Vol.6, Issue 34, pp.98-105, ISSN No: 2222-1905 (Print), 2222-2839 (Online). -
Brand together: How co-creation generates innovation and re-energizes brands /
Vels Management Journal, Vol-1 (2), pp. 98-100. ISBN-978-0-7494-6325-0 -
Stacked LSTM a Deep Learning model to predict Stock market
The goal of Stock Market Prediction is to forecast the future value of a company's financial stocks. The use of machine learning and deep learning technologies in stock market prediction technologies is a recent trend. Machine learning makes predictions based on the values of current stock market indices by training on their previous values in sequential timely order using the artificial neural network, while deep learning makes predictions based on the values of current stock market indices by training on their previous values in sequential timely order using the artificial neural network. 2022 IEEE. -
Deep Convolution Neural Network for RBC Images
The suggested study's objectives are to develop an unique criterion-based method for classifying RBC pictures and to increase classification accuracy by utilizing Deep Convolutional Neural Networks instead of Conventional CNN Algorithm. Materials and Procedures A dataset-master image dataset of 790 pictures is used to apply Deep Convolutional Neural Network. Convolutional Neural Network and Deep Convolutional Neural Network comparison using deep learning has been suggested and developed to improve classification accuracy of RBC pictures. Using Gpower, the sample size was calculated to be 27 for each group. Results: When compared to Convolutional Neural Network, Deep Convolutional Neural Network had the highest accuracy in classifying blood cell pictures (95.2%) and the lowest mean error (85.8 percent). Between the classifiers, there is a statistically significant difference of p=0.005. The study demonstrates that Deep Convolutional Neural Networks perform more accurately than Conventional Neural Networks while classifying photos of blood cells[1]. 2022 IEEE. -
Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes
Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects: Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures. Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises. 2023 -
Assessing Individual Intention in Adoption of Green Loans for Solar Rooftop Projects
Amidst a global surge in environmental consciousness, this study investigates the adoption dynamics of green loans for solar rooftop projects, capitalizing on the increasing awareness of individuals seeking to embrace environmentally friendly technological innovations. Against the backdrop of escalating environmental concerns, this research explores the intricate relationships between financial considerations, heightened environmental awareness, regulatory support, and access to information in shaping individual intentions to adopt green loans for solar rooftop initiatives. Leveraging a Structural Equation Modeling (SEM) approach and a sample size of 225 respondents, this study provides nuanced insights into the factors influencing sustainable financial choices, contributing to the ongoing discourse on the intersection of green financing, renewable energy, and individual decision-making. The findings hold implications for policymakers, financial institutions, and individuals striving to align their financial practices with eco-conscious initiatives to pursue a sustainable future. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
The Employees Demographic Profile of Startups in India with Special Reference to Bangalore City: A Case Study
In the current scenario, startups play a pivotal role and exert a significant influence on the promotion of economic growth. Authorities perceive their substantial impact, considering factors such as job creation, economic development, and contributions to technological upgrading (Thornton and Assocham in Startups Indiaan overview, 2016; Jain in Int J Appl Res 2:152154, 2016; Sarangi in Why do most Indian startups fail? Computer Science and Engineering, Indian Institute of Technology, Delhi, 2015). There has been a noteworthy effort to promote entrepreneurship through the establishment and support of various incubation centers. Despite the positive reception and development initiatives, the alarming startup failure rates in India persist due to various reasons. Available data indicates that 12 states in India have 1000 recognized startups each, with Karnataka being particularly well-recognized for progress and brand creation, especially in the city of Bangalore. In light of this, the study aims to identify the demographic profile of employees in startups in India, with a special focus on Bangalore city. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
High-performance reconfigurable FET for a simple variable gain buffer amplifier design
Design and simulation of variable gain analog buffer amplifier using single gate reconfigurable field-effect transistor (SG-RFET) with strained silicon channel are proposed. The design simplicity makes SG-RFET device a potential candidate compared to the multi-gate RFET devices. The gain of the proposed configuration is varied by tuning the feedback voltage. The voltage gain of the proposed configuration can be tuned from 0.97V/V to 5V/V with an output load of 1 k?. The operational transconductance amplifier (OTA) using the SG-RFET device is used in the proposed buffer amplifier design. 2021 Informa UK Limited, trading as Taylor & Francis Group.