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Enhancing Short Circuit Fault Detection In Three-Phase Power Systems: A Wavelet-Based Approach
Get PDFThis work aimed to improve fault detection accuracy in three-phase power systems, addressing a critical need for reliable and uninterrupted electrical power supply. Existing approaches often fluctuate in accuracy. The study explored the gaps in current literature, emphasizing the need for precise fault detection strategies. The technique to enhance fault detection is an innovative methodology combining MATLAB simulation with Wavelet analysis. This study explored the application of Wavelet transforms, including Daubechies 4, Haar, Symlet 5, and Discrete Approximation Meyer, to the current signals in a three-phase power system. By extracting and comparing the current signal’s detailed coefficients against predefined threshold values for fault detection and identifying optimal Wavelets using Wavelet coefficients\u27 energy analysis, this study provided a comprehensive solution to the complex problem of fault detection in power systems.
The study utilized both qualitative and quantitative methodologies to gather and analyze data. By combining expert insights with numerical data, the study aimed to gain a comprehensive understanding of the fault detection process. The integration of qualitative and quantitative approaches allowed for a more holistic exploration of the subject, providing valuable insights into the complexity of fault detection.
The findings highlight the significance of adaptability in choosing the most suitable Wavelet for specific fault scenarios. The study revealed no universal optimal Wavelet for all fault types, emphasizing the need for tailored approaches. Applying Wavelet analysis combined with a threshold approach and energy-based analysis enhances the reliability and stability of power systems. This study addressed existing gaps and introduced innovative methodologies, thereby contributing to the advancement of power systems fault detection.
In summary, the study presents significant insights into the realm of electrical engineering and power systems fault detection. With the potential to enhance power system reliability, the findings contribute valuable knowledge to the field. Moreover, the study aims to deepen the overall understanding of fault detection processes, offering further advancements in this crucial aspect of electrical engineering.
Index terms—Detailed coefficients, fault detection, fault types, short circuit fault, Wavelet transforms
Identity-Centered User-Generated Content to Create Occupational Identity with Visual Computing
No full textThis talk reports on an approach to computer science education for pre- and early-adolescents in which the goal is the formation of occupational identity with visual computing developed collaboratively between a large game studio, a research-intensive university, and a historically black college/university (HBCU). This ongoing project takes place in a rural public-school setting in the United States. Our project is structured around the idea that identity-centered user-content creation projects can positively influence student self-professed performance and interest in science, technology, engineering, art, and math (STEAM) related subjects and interest in STEAM careers. The projects in our curriculum engage students in real-time 3DCG coding and asset creation activities commonly associated with game development. We describe the process of working with school administrators and teachers to create a technology-infused environment in which remote external partners play a collaborative role in curriculum development and delivery. This work has significance for efforts to remotely engage with rural students at an age when occupational identity development is forming and thus develop a potential to expand the pathway for underrepresented minorities
Simulating soil-carbon-water interactions in two profiles to select precision cover for soil-health and drought-resilience
No full textBackground: The disseminated information to the stakeholders is mostly regional as the urge to meet global production goals and adoption of intensive agricultural practice have been amplified. Regional management strategies (MS) are mainly based on a broad-spectrum of ecoregions and climate scenarios and thus, are not always successful to prescribe a cover without failure. This produces a dilemma which restrains the farmers from adapting soil-covers as a climate-smart conservation practice. Simulating agroecosystems for better-understanding of soil-plant-water relationships within a soil profile would improve the societal acceptance of using cover crops. We intended to bring light into the uncertain conditions of production during drought and inconsistencies in soil organic carbon (SOC) sequestration through analyzing the impacts of soil cover, residue decomposition and inherent soil characteristics on soil moisture variability. Method: In-situ effects of cover, residues and manure application on specific soil-layers were considered to simulate above and belowground biomass contents and impacts of applied residues, C, nitrogen and moisture content of soils at different crop-growth phases. Soil-environmental factors of seven MS were modeled to reveal the subtle conditions within the profiles of two similar soils of closely-located sites. Decision support systems with Strategic Analysis along with the simulated-results were used for long-term impact-analysis and select a precision cover based on soil and site. Moisture movement through whole soil profiles during drought was analyzed to understand the scope of improving the resiliency of the forage systems. We also compared the impacts of fallow, manure application along with the residue incorporation from rye, clover and winter wheat on two loamy fine-sand soils with similar reaction but contrasting permeability and clay +silt contents. Results: Irrespective of soil types, impacts of management strategies on SOC were found statistically significant for both 0–20 cm and 20–40 cm soil layers. Slight differences in clay+silt fractionation resulted in different SOC in soil layers and ultimately the moisture availability status. Due to complex interaction effects of management-strategies and soil properties, N leaching was different within two soil profiles. N-uptake was found definable by SOC up to 96% in residue-only treatments and 83% in manure-based MS. Conclusions: A cover of winter-wheat with 50–50 manure +fertilizer and residue application could prove as the best management strategy to improve soil-health and long-term productivity. A precision-cover of clover which results in maximum residue, surface residual-C and SOC addition, and minimum N-leaching could be recommended for resource-use efficiency on low-fertile acid loamy sand soils during drought
Machine learning soil-environmental impacts on agroecosystems for relating microbial biomass to soil carbon sequestration
No full textBackground and Aims: Storing carbon (C) within soils is significant for maintaining soil-health and reinforces the feedback loop of C loss from soils as CO2 to the atmosphere. Seasonal variation with increased temperatures and inconsistent precipitation as climate change consequences also affect the soil C-sequestration process globally. Soil-health management practices (SHMPs) such as cover crops, crop residues and manures increase organic components as well as soil-organic C (SOC) pool in an agroecosystem. While, soil microbial-biomass (SMB) which is considered as a soil-health metric to understand microbial community response, is still not modelled to relate with SOC and seasonal impacts to identify suitable SHMPs. Methods: Cover crops followed by 100% residue addition and combinations of manure +organic-fertilizer were the SHMPs for a winter-wheat system in our field study. Seasonal data regarding SOC, nitrogen, SMB-C and labile-C content of soils were used to machine-learn the system and understand the influence of different drivers on SMB-C. Results: The test models based on ‘Multivariate Linear Regression’ could explain 70% of the variability and predicted seasonal-variation as a dominant variant followed by SHMPs and soil-moisture. AdaBoost and Random Forest Models performed better than others if ‘Ensemble Learning’ was used. ‘Feature Importance’ predicted labile-C and aboveground-biomass as the two most important drivers impacting SMB-C. Conclusions: Ensemble Learning’ method of Machine-Learning could be successfully implied to understand the SMB-C in an agroecosystem and set benchmark-strategies for soil-health improvement. 50% manure+ 50% fertilizer with crop-residue could be recommended for maximum labile-C and SOC in surface soil-layers
Algorithmic And Computational Approaches For Improving The Efficiency Of Mobile Genomic Element Discovery, A Bioinformatics Framework
Get PDFThrough this research, we are showcasing the application of computational approaches to the discoveries in the life sciences spectrum. Our current research not only focused on mobile genetic elements but also developed the computational methods that enabled these findings. We combined the biology sciences and computer science in our research, which is essentially multidisciplinary. To that end, this research intricately probed the role and implications of mobile genetic elements, emphasizing transposable elements. These dynamic components wielded substantial influence over genomic architecture\u27s structure, function, and evolutionary adaptations. An integral component of our study is the innovative computational tool, Target/IGE Retriever (TIGER), employed to detect and map these mobile genetic elements. Given the pronounced impact of these elements on gene regulation and their involvement in various genetic diseases, their precise detection and mapping within a genome were crucial for understanding intricate genetic dynamics and disease etiology.
Addressing computational challenges, the study introduces three new algorithms to enhance TIGER\u27s performance, tested using E. coli genomes. This testing aimed to determine the impact of database size reduction on result accuracy and performance. Findings indicate that while prophage yields are less affected by database size, non-phage islands show sensitivity, suggesting performance improvements with smaller databases. Furthermore, the research conducts a comparative analysis of TIGER and BLAST outputs, focusing on validating transposons identified in E. coli genomes. This involves cross-referencing with established databases and employing statistical methods for match categorization, enhancing the authenticity of transposon location identification.. Within the purview of this rigorous analytical process, particular attention is accorded to evaluating sequence alignment results and the quality of BLAST hits, focusing specifically on identifying direct repeats within insertion sequences. The study underscores TIGER\u27s efficacy in transposon discovery and yields critical insights into its performance relative to BLAST. This research illuminates potential avenues for enhancing computational tools in bioinformatics, all within the larger framework of contributing significantly to genomics and bioinformatics research\u27s ongoing advancements. Our work deepens our understanding of the role and influence of mobile genetic elements on genomic architecture.
Index Term: Computational biology, bioinformatics, mobile genetic elements, transposon, validation, database
(R2027) A New Class of Pareto Distribution: Estimation and its Applications
Get PDFThe classical Pareto distribution is a positively skewed and right heavy-tailed lifetime distribution having a lot many applications in various fields of science and social science. In this work, via logarithmic trans-formed method, a new three parameter lifetime distribution, an extension of classical Pareto distribution is generated. The different structural properties of the new distribution are studied. The model parameters are estimated by the method of maximum likelihood and Bayesian procedure. When all the three parameters of the distribution are unknown, the Bayes estimators cannot be obtained in a closed form and hence, the Lindley’s approximation under squared error loss function is used to compute the Bayes estimators. A Monte Carlo simulation study is also conducted to compare the performance of these estimators using mean square error. The application of the new distribution for modelling earthquake insurance and reliability data are illustrated using two real data sets
(R2054) Convergence of Lagrange-Hermite Interpolation using Non-uniform Nodes on the Unit Circle
Get PDFIn this research article, we brought into consideration the set of non-uniformly distributed nodes on the unit circle to investigate a Lagrange-Hermite interpolation problem. These nodes are obtained by projecting vertically the zeros of Jacobi polynomial onto the unit circle along with the boundary points of the unit circle on the real line. Explicitly representing the interpolatory polynomial as well as establishment of convergence theorem are the key highlights of this manuscript. The result proved are of interest to approximation theory