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Institution

P A College of Engineering

About: P A College of Engineering is a based out in . It is known for research contribution in the topics: Dihedral angle & Ring (chemistry). The organization has 298 authors who have published 594 publications receiving 4888 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the effect of using diesel plastic oil blends in a CRDI diesel engine was evaluated and the experimental results inferring that, thermal efficiency of all blends gradually decreases compare to diesel at all loading condition.
Abstract: This research work is carried out to evaluate the effect of using diesel plastic oil blends in a CRDI diesel engine. This paper shows the potential of utilizing waste plastic oil extracted by pyrolysis of waste plastic as an alternative fuel for diesel engine. The plastic is not an ingredient component in environment due to the adhesive bond between their elements. The oil that can be removed from waste plastic contaminant is like inexhaustible energy resource. The plastic oil facilitate to reduce the obligation of fossil fuel as well as it can be used as an alternative fuel in engine without changing description of the engine as it can also be used in pure form or blended form. In this study the diesel engine is fuelled with plastic oil, diesel blends. The performance and emission characteristics is evaluated. The blends PO10,PO20 and PO30 are prepared on mass basis. The experimental results inferring that, thermal efficiency of all blends gradually decreases compare to diesel at all loading condition. CO emission is decreasing considerably with increasing the percentage of plastic oil in blends, NOX emission and CO2 emission is increasing slightly with increase in blends percentage and loads.

12 citations

Proceedings ArticleDOI
06 Mar 2020
TL;DR: A design for segregation and monitoring of waste using Message Queuing Telemetry Transport (MQTT) in order to manage the waste collection using embedded IoT system that will monitor the amount of waste deposited.
Abstract: In this paper, we propose a design for segregation and monitoring of waste using Message Queuing Telemetry Transport (MQTT) in order to manage the waste collection. Smart waste management is essential for maintaining clean environment. Because, now-a-days unorganized and non-systematic waste collection is the major issue in the society. Therefore, in need of solution to collect waste in effective manner. The solution for this problem is given by embedded IoT system that will monitor the amount of waste deposited, for which an integrated platform where segregation and monitoring of waste have been presented. In segregation part, wastes are separated as dry waste and wet waste and in monitoring part, the bins with dry and wet wastes are embedded with sensors and the level of bin is transmitted via IoT. IoT is the system of interrelated computing devices which transfer data without requiring human to human interaction, it also introduce economical solution for massive data collection. The proprietary communication protocol that is used for transmission is MQTT. This protocol is the very light weight protocol used for messaging, designed with embedded systems, sensors and mobile applications in mind.

12 citations

Journal ArticleDOI
TL;DR: The result shows the performance of the proposed KSVM obtained high performance compared with SVM and Hidden Markov Model (HMM), and indicates KSVM is more advantageous in accuracy which is used to monitor the object.
Abstract: Millions of security cameras were placed in public spaces, generating large quantities of video data. There is a need to develop smart techniques to identify and classify objects tracking instantly. Most of them concentrate on spatial information, resulting in exposure to noise and background movement. In addition, monitoring individuals in overcrowded scenes is a difficult task, due to the variation of movement and appearance created by the large amount of people in the scene. In this paper, initially, utilizing threshold value, the video is split into frames. Then segmentation of moving objects using Extended Kalman Filters (EKF) to improve the accuracy of the classification. Instead, to distinguish between the foreground object and the background object, the texture features is removed. The artifacts are then labelled using improved Learning Vector Quantization (LVQ) for efficient identification of anomalies. Also an effective classification of Kernel Support Vector Machine (KSVM) predicated on anomaly detection has been suggested utilizing spatio-temporal movement pattern models in overcrowded scenes to solve these problems. Hence, KSVM is more advantageous in accuracy which is used to monitor the object. The result shows the performance of the proposed KSVM obtained high performance compared with SVM and Hidden Markov Model (HMM).

12 citations

Journal ArticleDOI
TL;DR: In this paper, an indigenous FVM code is developed for numerical analysis of conjugate heat transfer and fluid flow, considering different problems, and the code is found to be around 90% of total execution time in solving the pressure (P) correction equation.
Abstract: Conjugate heat transfer and fluid flow is a common phenomenon occurring in parallel plate channels. Finite volume method (FVM) formulation-based semi-implicit pressure linked equations algorithm is a common technique to solve the Navier–Stokes equation for fluid flow simulation in such phenomena, which is computationally expensive. In this article, an indigenous FVM code is developed for numerical analysis of conjugate heat transfer and fluid flow, considering different problems. The computational time spent by the code is found to be around 90% of total execution time in solving the pressure (P) correction equation. The remaining time is spent on U, V velocity, and temperature (T) functions, which use tri-diagonal matrix algorithm. To carry out the numerical analysis faster, the developed FVM code is parallelized using OpenMP paradigm. All the functions of the code (U, V, T, and P) are parallelized using OpenMP, and the parallel performance is analyzed for different fluid flow, grid size, and boundary conditions. Using nested and without nested OpenMP parallelization, analysis is done on different computing machines having different configurations. From the complete analysis, it is observed that flow Reynolds number (Re) has a significant impact on the sequential execution time of the FVM code but has a negligible role in effecting speedup and parallel efficiency. OpenMP parallelization of the FVM code provides a maximum speedup of up to 1.5 for considered conditions.

12 citations

Journal ArticleDOI
01 Jan 2020
TL;DR: A statistical analysis and visualized reported cases of coronavirus disease 2019 (COVID-19) based on the open data collection provided by Johns Hopkins University is done to provide researchers, public health officials and the general public with exposure to the epidemic.
Abstract: A local outbreak of initially unknown cause pneumonia was detected in Wuhan (Hubei, China) in December 2019 and a novel coronavirus, the severe acute respiratory syndrome coronavirus 2, was quickly found to be causing it. Since then, the epidemic has spread to all of China's mainland provinces as well as 58 other countries and territories, with more than 87,137 confirmed cases around the globe, including 79,968 from China, 7169 from other countries as of 1 March 2020, as stated by the World Health Organization in the COVID-19 situation report-41. In response to this current public health emergency, this study done a statistical analysis and visualized reported cases of coronavirus disease 2019 (COVID-19) based on the open data collection provided by Johns Hopkins University. Where the location and number of confirmed infected cases have been shown, there have also been deaths, recovered cases and comparisons of the growth rates between the Globe countries. This was intended to provide researchers, public health officials and the general public with exposure to the epidemic.

12 citations


Authors

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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20223
2021120
202054
201935
201823
201723