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RISS 인기검색어
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- 저자 : Simon Y. Foo (검색결과 3 건)
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Deng, Yuhang,Foo, Simon Y.,Li, Hui The Korean Institute of Power Electronics 2011 JOURNAL OF POWER ELECTRONICS Vol.11 No.4
The bidirectional dc-dc converter, being the interface between Energy Storage Element (ESE) and DC bus, is an essential component of the power management system for vehicle applications including electric vehicle (EV), hybrid electric vehicle (HEV), and fuel cell vehicle (FCV). In this paper, a novel multiphase bidirectional dc-dc converter interfacing with battery to supply and absorb the electric energy in the FCV system was studied with the help of real time digital simulator (RTDS). The mathematical models of fuel cell, battery and dc-dc converter were derived. A power management strategy was developed and first simulated in RTDS. A Power Hardware-In-the-Loop (PHIL) simulation using RTDS is then presented. The main challenge of this PHIL is the requirement for a highly dynamic bidirectional Simulation-Stimulation (Sim-Stim) interface. This paper describes three different interface algorithms. The closed-loop stability of the resulting PHIL system is analyzed in terms of time delay and sampling rate. A prototype bidirectional Sim-Stim interface is designed to implement the PHIL simulation.
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Yuhang Deng,Simon Y. Foo,Hui Li 전력전자학회 2011 JOURNAL OF POWER ELECTRONICS Vol.11 No.4
The bidirectional dc-dc converter, being the interface between Energy Storage Element (ESE) and DC bus, is an essential component of the power management system for vehicle applications including electric vehicle (EV), hybrid electric vehicle (HEV), and fuel cell vehicle (FCV). In this paper, a novel multiphase bidirectional dc-dc converter interfacing with battery to supply and absorb the electric energy in the FCV system was studied with the help of real time digital simulator (RTDS). The mathematical models of fuel cell, battery and dc-dc converter were derived. A power management strategy was developed and first simulated in RTDS. A Power Hardware-In-the-Loop (PHIL) simulation using RTDS is then presented. The main challenge of this PHIL is the requirement for a highly dynamic bidirectional Simulation-Stimulation (Sim-Stim) interface. This paper describes three different interface algorithms. The closed-loop stability of the resulting PHIL system is analyzed in terms of time delay and sampling rate. A prototype bidirectional Sim-Stim interface is designed to implement the PHIL simulation.
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A comparison of machine learning algorithms for diabetes prediction
Jobeda Jamal Khanam,Simon Y. Foo 한국통신학회 2021 ICT Express Vol.7 No.4
Diabetes is a disease that has no permanent cure; hence early detection is required. Data mining, machine learning (ML) algorithms, and Neural Network (NN) methods are used in diabetes prediction in our research. We used the Pima Indian Diabetes (PID) dataset for our research, collected from the UCI Machine Learning Repository. The dataset contains information about 768 patients and their corresponding nine unique attributes. We used seven ML algorithms on the dataset to predict diabetes. We found that the model with Logistic Regression (LR) and Support Vector Machine (SVM) works well on diabetes prediction. We built the NN model with a different hidden layer with various epochs and observed the NN with two hidden layers provided 88.6% accuracy.
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