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Institution

National University of Malaysia

EducationKuala Lumpur, Malaysia
About: National University of Malaysia is a education organization based out in Kuala Lumpur, Malaysia. It is known for research contribution in the topics: Population & Heat transfer. The organization has 26593 authors who have published 41270 publications receiving 552683 citations. The organization is also known as: NUM & Universiti Kebangsaan Malaysia.


Papers
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Journal ArticleDOI
TL;DR: Total lipids extracted from 20 species of freshwater fish in Malaysia were analyzed for their total fat and fatty acids composition and showed that total monounsaturated fatty acids were the highest, followed by saturated and polyuns saturated fatty acids.

196 citations

Journal ArticleDOI
TL;DR: In this paper, the authors presented the outcome of a new system architecture and control algorithm that can use both battery storage and manage the temperature of thermal appliances, which is an important part of the smart grid that enables residential customers to execute demand response programs autonomously.

195 citations

Journal ArticleDOI
TL;DR: In this article, the performance analysis of solar drying system for red chili was concerned with performance analysis, energy and exergy analyses of the solar drying process were performed, and the results showed that the efficiency was 28, 13, 45, and 57% at an average solar radiation of 420 W/m2 and a mass flow rate of 0.07

195 citations

Journal ArticleDOI
TL;DR: The authors examines the extent to which the religious factor has bearing on policy and development strategy affecting tourism in Muslim countries and suggests that, although the doctrine of Islam encourages travel and hospitable behavior, it has little influence on the mode of tourism development.

194 citations

Journal ArticleDOI
TL;DR: The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.
Abstract: The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.

194 citations


Authors

Showing all 26827 results

NameH-indexPapersCitations
Jonathan E. Shaw114629108114
Sabu Thomas102155451366
Biswajeet Pradhan9873532900
Haji Hassan Masjuki9750229653
Mika Sillanpää96101944260
Choon Nam Ong8644425157
Keith R. Abrams8635530980
Kamaruzzaman Sopian8498925293
Benedikt M. Kessler8238524243
Michel Marre8244439052
Peter Willett7647929037
Peter F. M. Choong7253218185
Nidal Hilal7239521524
Margareta Nordin7226719578
Teuku Meurah Indra Mahlia7033917444
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202382
2022363
20213,169
20202,808
20192,888
20183,299