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1,794 results on '"Kumar, Pavan"'

10. Linear codes with few weights over $\mathbb{F}_{p}+u\mathbb{F}_{p}$

Author

Kumar, Pavan and Khan, Noor Mohammad

Subjects
Computer Science - Information Theory, 94B05, 11T71
Abstract

For any positive integer $m$ and an odd prime $p$; let $\mathbb{F}_{q}+u\mathbb{F}_{q}$, where $q=p^{m}$, be a ring extension of the ring $\mathbb{F}_{p}+u\mathbb{F}_{p}.$ In this paper, we construct linear codes over $\mathbb{F}_{p}+u\mathbb{F}_{p}$ by using trace function defined on $\mathbb{F}_{q}+u\mathbb{F}_{q}$ and determine their Hamming weight distributions by employing symplectic-weight distributions of their Gray images.

Published
2024

11. Five-Lee-weight linear codes over $\mathbb{F}_{q}+u\mathbb{F}_{q}$

Author

Kumar, Pavan and Khan, Noor Mohammad

Subjects
Computer Science - Information Theory, 94B05, 11T71
Abstract

In this study, linear codes having their Lee-weight distributions over the semi-local ring $\mathbb{F}_{q}+u\mathbb{F}_{q}$ with $u^{2}=1$ are constructed using the defining set and Gauss sums for an odd prime $q $. Moreover, we derive complete Hamming-weight enumerators for the images of the constructed linear codes under the Gray map. We finally show an application to secret sharing schemes.

Published
2024

31. Chapter Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University

Author

Chen, Chien-Wen, Kumar, Pavan, Hsieh, Shang-Hsien, Pal, Aritra, Chang, Yun-Tsui, and Wu, Chen-Hung

Subjects
Sustainable campus, renewable energy, point cloud segmentation, deep learning, thema EDItEUR::U Computing and Information Technology
Abstract

As climate change intensifies, we must embrace renewable solutions like solar energy to combat greenhouse gas emissions. Harnessing the sun's power, solar energy provides a limitless and eco-friendly source of electricity, reducing our reliance on fossil fuels. Rooftops offer prime real estate for solar panel installation, optimizing sun exposure, and maximizing clean energy generation at the point of use. For installing solar panels, inspecting the suitability of building rooftops is essential because faulty roof structures or obstructions can cause a significant reduction in power generation. Computer vision-based methods proved helpful in such inspections in large urban areas. However, previous studies mainly focused on image-based checking, which limits their usability in 3D applications such as roof slope inspection and building height determination required for proper solar panel installation. This study proposes a GIS-integrated urban point cloud segmentation method to overcome these challenges. Specifically, given a point cloud of a metropolitan area, first, it is localized in the GIS map. Then a deep-learning-based point cloud classification model is trained to detect buildings and rooftops. Finally, a rule-based checking determines the building height, roof slopes, and their appropriateness for solar panel installation. While testing at the National Taiwan University campus, the proposed method demonstrates its efficacy in assessing urban rooftops for solar panel installation

Published
2023
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32. Chapter FCM-Enabled Approach for Investigating Interdependencies of BIM Performance Factors in the Sustainable Built Environment

Author

Kumar, Pavan, Hsieh, Shang-Hsien, Pal, Aritra, and Chang, Yun-Tsui

Subjects
BIM performance, Fuzzy Cognitive Map (FCM), Built environment, thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization
Abstract

In pursuit of a sustainable built environment, BIM plays a crucial role in the project's performance and has egressed as a powerful technology in the construction industry, impacting the outcome and the project delivery workflows. Numerous dynamic and interdependent factors influence BIM performance. However, Existing literature prominently focuses on exploring the influencing factors for BIM performance, ignoring the impact and strength of the interplay of these factors on one another, therefore offering an inadequate picture of optimizing BIM performance. The evolving nature and degree of complexity of construction projects necessitate the identification and comprehensive understanding of the interdependencies between factors contributing to BIM performance in the sustainable built environment. A Fuzzy Cognitive Map (FCM) is a modeling method that represents and analyses the interplay between the factors in a complex system. So, this study proposes an FCM-enabled approach to investigate the interdependencies of factors contributing to BIM performance and conduct what-if scenarios, including predictive analysis. The developed FCM model can help reveal the hidden cause-effect relationships among a complex system of BIM performance factors, enabling stakeholders to develop more informed strategies and proactively plan to optimize BIM Performance

Published
2023
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33. Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model

Author

Singh, Ram Kumar, Rani, Meenu, Bhagavathula, Akshaya Srikanth, Sah, Ranjit, Rodriguez-Morales, Alfonso J, Kalita, Himangshu, Nanda, Chintan, Sharma, Shashi, Sharma, Yagya Datt, Rabaan, Ali A, Rahmani, Jamal, and Kumar, Pavan

Subjects
Public aspects of medicine, RA1-1270
Abstract

BackgroundThe coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. ObjectiveThe aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. MethodsThe comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. ResultsThe top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. ConclusionsThe observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

Published
2020
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