Features in educational data are ambiguous which leads to noisy features and curse of dimensionality problems.These problems are solved via feature selection.There are existing models for features selection.These mode...Features in educational data are ambiguous which leads to noisy features and curse of dimensionality problems.These problems are solved via feature selection.There are existing models for features selection.These models were created using either a single-level embedded,wrapper-based or filter-based methods.However single-level filter-based methods ignore feature dependencies and ignore the interaction with the classifier.The embedded and wrapper based feature selection methods interact with the classifier,but they can only select the optimal subset for a particular classifier.So their selected features may be worse for other classifiers.Hence this research proposes a robust Cascade Bi-Level(CBL)feature selection technique for student performance prediction that will minimize the limitations of using a single-level technique.The proposed CBL feature selection technique consists of the Relief technique at first-level and the Particle Swarm Optimization(PSO)at the second-level.The proposed technique was evaluated using the UCI student performance dataset.In comparison with the performance of the single-level feature selection technique the proposed technique achieved an accuracy of 94.94%which was better than the values achieved by the single-level PSO with an accuracy of 93.67%for the binary classification task.These results show that CBL can effectively predict student performance.展开更多
Medium voltage DC(MVDC)system is considered as a promising technology to improve the efficiency and power density of electric aircraft propulsion(EAP)drives.To adapt to the MVDC voltage level and achieve high drive pe...Medium voltage DC(MVDC)system is considered as a promising technology to improve the efficiency and power density of electric aircraft propulsion(EAP)drives.To adapt to the MVDC voltage level and achieve high drive performance,a five-level active neutral point clamped(5L-ANPC)inverter consisting of three-level ANPC and flying capacitor circuits is investigated,which possesses higher voltage capability,lower output harmonics,as well as mitigated dv/dt and common-mode voltage.To fulfill the requirements of high-speed operation and pursue further enhanced efficiency and power density of the inverter for the next-generation EAP drives,Silicon Carbide(SiC)semiconductor devices are considered for implementing the 5L-ANPC inverter.However,the large commutation loops associated with certain switching states of the inverter lessen the benefits of configuring all the switches as SiC devices.As a result,a hybrid Si/SiC 5L-ANPC inverter is developed with a synchronous optimal pulse(SOP)width modulation strategy for controlling the switches in cell 2 and finite-control-set model predictive controller(FCS-MPC)for those in cell 3 of the inverter.Consequently,in the proposed topology,the SiC devices are merely used for the high-frequency switches in cell 3 and the rest of the low-frequency switches are configured with Si IGBTs.This Si/SiC hybrid ANPC inverter concurrently provides high efficiency and low implementation cost at high-speed operation mode.Simulation and experimental results are provided to verify the effectiveness of the proposed hybrid inverter.展开更多
文摘Features in educational data are ambiguous which leads to noisy features and curse of dimensionality problems.These problems are solved via feature selection.There are existing models for features selection.These models were created using either a single-level embedded,wrapper-based or filter-based methods.However single-level filter-based methods ignore feature dependencies and ignore the interaction with the classifier.The embedded and wrapper based feature selection methods interact with the classifier,but they can only select the optimal subset for a particular classifier.So their selected features may be worse for other classifiers.Hence this research proposes a robust Cascade Bi-Level(CBL)feature selection technique for student performance prediction that will minimize the limitations of using a single-level technique.The proposed CBL feature selection technique consists of the Relief technique at first-level and the Particle Swarm Optimization(PSO)at the second-level.The proposed technique was evaluated using the UCI student performance dataset.In comparison with the performance of the single-level feature selection technique the proposed technique achieved an accuracy of 94.94%which was better than the values achieved by the single-level PSO with an accuracy of 93.67%for the binary classification task.These results show that CBL can effectively predict student performance.
文摘Medium voltage DC(MVDC)system is considered as a promising technology to improve the efficiency and power density of electric aircraft propulsion(EAP)drives.To adapt to the MVDC voltage level and achieve high drive performance,a five-level active neutral point clamped(5L-ANPC)inverter consisting of three-level ANPC and flying capacitor circuits is investigated,which possesses higher voltage capability,lower output harmonics,as well as mitigated dv/dt and common-mode voltage.To fulfill the requirements of high-speed operation and pursue further enhanced efficiency and power density of the inverter for the next-generation EAP drives,Silicon Carbide(SiC)semiconductor devices are considered for implementing the 5L-ANPC inverter.However,the large commutation loops associated with certain switching states of the inverter lessen the benefits of configuring all the switches as SiC devices.As a result,a hybrid Si/SiC 5L-ANPC inverter is developed with a synchronous optimal pulse(SOP)width modulation strategy for controlling the switches in cell 2 and finite-control-set model predictive controller(FCS-MPC)for those in cell 3 of the inverter.Consequently,in the proposed topology,the SiC devices are merely used for the high-frequency switches in cell 3 and the rest of the low-frequency switches are configured with Si IGBTs.This Si/SiC hybrid ANPC inverter concurrently provides high efficiency and low implementation cost at high-speed operation mode.Simulation and experimental results are provided to verify the effectiveness of the proposed hybrid inverter.
