Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempt...Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data.The existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation.When comparing to a single model,this technique offers for improved prediction.Ensemble based feature selections parallel multiple expert’s judgments on a single topic.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.Further,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work.KNN(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning approach.The results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.展开更多
This paper presents design feasibility study and development of a new hybrid excitation flux switching motor (HEFSM) as a contender for traction drives in hybrid electric vehicles (HEVs). Initially, the motor general ...This paper presents design feasibility study and development of a new hybrid excitation flux switching motor (HEFSM) as a contender for traction drives in hybrid electric vehicles (HEVs). Initially, the motor general construction, the basic working principle and the design concept of the proposed HEFSM are outlined. Then, the initial drive performances of the proposed HEFSM are evaluated based on 2D-FEA, in which the design restrictions, specifications and target performances are similar with conventional interior permanent magnet synchronous motor (IPMSM) used in HEV. Since the initial results fail to achieve the target performances, deterministic design optimization approach is used to treat several design parameters. After several cycles of optimization, the proposed motor makes it possible to obtain the target torque and power of 333 Nm and 123 kW, respectively. In addition, due to definite advantage of robust rotor structure of HEFSM, rotor mechanical stress prediction at maximum speed of 12,400 r/min is much lower than the mechanical stress in conventional IPMSM. Finally, the maximum torque and power density of the final design HEFSM are approximately 11.41 Nm/kg and 5.55 kW/kg, respectively, which is 19.98% and 58.12% more than the torque and power density in existing IPMSM for Lexus RX400h.展开更多
文摘Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data.The existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation.When comparing to a single model,this technique offers for improved prediction.Ensemble based feature selections parallel multiple expert’s judgments on a single topic.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.Further,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work.KNN(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning approach.The results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.
文摘This paper presents design feasibility study and development of a new hybrid excitation flux switching motor (HEFSM) as a contender for traction drives in hybrid electric vehicles (HEVs). Initially, the motor general construction, the basic working principle and the design concept of the proposed HEFSM are outlined. Then, the initial drive performances of the proposed HEFSM are evaluated based on 2D-FEA, in which the design restrictions, specifications and target performances are similar with conventional interior permanent magnet synchronous motor (IPMSM) used in HEV. Since the initial results fail to achieve the target performances, deterministic design optimization approach is used to treat several design parameters. After several cycles of optimization, the proposed motor makes it possible to obtain the target torque and power of 333 Nm and 123 kW, respectively. In addition, due to definite advantage of robust rotor structure of HEFSM, rotor mechanical stress prediction at maximum speed of 12,400 r/min is much lower than the mechanical stress in conventional IPMSM. Finally, the maximum torque and power density of the final design HEFSM are approximately 11.41 Nm/kg and 5.55 kW/kg, respectively, which is 19.98% and 58.12% more than the torque and power density in existing IPMSM for Lexus RX400h.
