Bus mass is an important factor that affects fuel consumption and one of the key input parameters associated with automatic shift and hybrid electric vehicle （HEV） energy management strategy. A city bus mass estimat...Bus mass is an important factor that affects fuel consumption and one of the key input parameters associated with automatic shift and hybrid electric vehicle （HEV） energy management strategy. A city bus mass estimation method based on kinetic energy theorem was proposed in this paper. The real-time data including vehicle speed and engine torque were collected by a remote data acquisition system. The samples in the process of being accelerated were selected to conduct vehicle mass estimation at the same bus stop with the same gear. The average estimation error is 2. 92% after the verification by actual data. Compared with the method based on recursive least squares, the algorithm based on kinetic energy theorem requires less sample length and the estimation error is smaller. Therefore, the method is more suitable for the bus mass estimation. The influences of gear, rolling resistance coefficient, wind resistance coefficient and road slope on mass estimation accuracy were analyzed.展开更多
Accurate blood pressure （BP） measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related ...Accurate blood pressure （BP） measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis （PCA） is fused with forward stepwise regression （SWR）, artificial neural network （ANN）, adaptive neuro-fuzzy inference system （ANFIS）, and the least squares support vector machine （LS-SVM） model for the prediction of BP reactivity to an unsupported back in norrnotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components, termed ＇principal components＇ （PCs）, from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM （PCA-LS-SVM） model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.展开更多
基金National International Cooperation in Science and Technology Special Project(No.2013DFG62890)
文摘Bus mass is an important factor that affects fuel consumption and one of the key input parameters associated with automatic shift and hybrid electric vehicle （HEV） energy management strategy. A city bus mass estimation method based on kinetic energy theorem was proposed in this paper. The real-time data including vehicle speed and engine torque were collected by a remote data acquisition system. The samples in the process of being accelerated were selected to conduct vehicle mass estimation at the same bus stop with the same gear. The average estimation error is 2. 92% after the verification by actual data. Compared with the method based on recursive least squares, the algorithm based on kinetic energy theorem requires less sample length and the estimation error is smaller. Therefore, the method is more suitable for the bus mass estimation. The influences of gear, rolling resistance coefficient, wind resistance coefficient and road slope on mass estimation accuracy were analyzed.
文摘Accurate blood pressure （BP） measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis （PCA） is fused with forward stepwise regression （SWR）, artificial neural network （ANN）, adaptive neuro-fuzzy inference system （ANFIS）, and the least squares support vector machine （LS-SVM） model for the prediction of BP reactivity to an unsupported back in norrnotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components, termed ＇principal components＇ （PCs）, from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM （PCA-LS-SVM） model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.
