The paper presents a mathematical model of special construction induction traction motor. On the base of predictive filtering, analytical studies, fuzzy logic control, relying on the virtual data generated by FEM （Fi...The paper presents a mathematical model of special construction induction traction motor. On the base of predictive filtering, analytical studies, fuzzy logic control, relying on the virtual data generated by FEM （Finite Element Method） and BEM （Boundary Element Method） is detected faults of induction motor. Digital predictive filter is used to separate a fundamental harmonic from spectrum current and voltage harmonics. Fuzzy logic control is used to identify a motor state. Magnetic fields distribution in the traction motor, of the wheel vehicle is presented in the paper. Modem diagnostics method has been used for faulty motor simulation and shows results of motor fault effects. Some computer programs were applied in calculation of magnetic fields distribution. On the base of magnetic field distributions were analyzed different failures situations. Some laboratory experiments realized for induction traction motor were verified by results of computer calculations.展开更多
Research on coke price forecasting is of theoretical and practical signiifcance. Here, the Kalman ifltering algorithm was used to analyze the price of coke. As the only state variable, the historical coke price is sor...Research on coke price forecasting is of theoretical and practical signiifcance. Here, the Kalman ifltering algorithm was used to analyze the price of coke. As the only state variable, the historical coke price is sorted out to build the state space model. The algorithm makes use of innovation composed of the difference between observed and predicted values, and alows us to obtain the optimal estimated value of the coke price via continuous updating and iteration of innovation. Our results show that this algorithm is effective in the ifeld of coke price tracking and forecasting.展开更多
The hourly values of the ionospheric F2 layer critical frequency, foF2, recorded at Wakkanai ionosonde station （45.4°N, 141.7°E） have been collected to construct a middle-latitude single-station model for ...The hourly values of the ionospheric F2 layer critical frequency, foF2, recorded at Wakkanai ionosonde station （45.4°N, 141.7°E） have been collected to construct a middle-latitude single-station model for forecasting foF2 under geomagnetic quiet and disturbed conditions. The module for the geomagnetic quiet conditions incorporates local time, seasonal, and solar vari- ability of climatological foF2 and its upper and lower quartiles. It is the first attempt to predict the upper and lower quartiles of foF2 to account for the notable day-to-day variability in ionospheric foF2. The validation statistically verifies that the model captures the climatological variations of foF2 with higher accuracy than IRI does. The storm-time module is built to capture the geomagnetic storm induced relative deviations of foF2 from the quiet time references. In the geomagnetically disturbed module, the storm-induced deviations are described by diumal and semidiumal waves, which are modulated by a modified magnetic activity index, the Kf index, reflecting the delayed responses of foF2 to geomagnetic activity forcing. The coeffi- cients of the model in each month are determined by fitting the model formula to the observation in a least-squares way. We provide two options for the geomagnetic disturbed module, including or not including Kalman filter algorithm. The Kalman filter algorithm is introduced to optimize these coefficients in real time. Our results demonstrate that the introduction of the Kalman filter algorithm in the storm time module is promising for improving the accuracy of predication. In addition, comparisons indicate that the IRI model prediction of the F2 layer can be improved to provide better performances over this region.展开更多
Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical importance.This study aims at forec...Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical importance.This study aims at forecasting the coffee price in Ethiopia. We used daily closed price data of Ethiopian coffee recorded in the period 25 June 2008 to 5 January 2017 obtained from Ethiopia commodity exchange（ECX） market to analyse coffee prices fluctuation. Here, the nature of coffee price is non-stationary and we apply the Kalman filtering algorithm on a single linear state space model to estimate and forecast an optimal value of coffee price. The performance of the algorithm for estimating and forecasting the coffee price is evaluated by using root mean square error（RMSE）. Based on the linear state space model and the Kalman filtering algorithm, the root mean square error（RMSE） is 0.000016375, which is small enough, and it indicates that the algorithm performs well.展开更多
文摘The paper presents a mathematical model of special construction induction traction motor. On the base of predictive filtering, analytical studies, fuzzy logic control, relying on the virtual data generated by FEM （Finite Element Method） and BEM （Boundary Element Method） is detected faults of induction motor. Digital predictive filter is used to separate a fundamental harmonic from spectrum current and voltage harmonics. Fuzzy logic control is used to identify a motor state. Magnetic fields distribution in the traction motor, of the wheel vehicle is presented in the paper. Modem diagnostics method has been used for faulty motor simulation and shows results of motor fault effects. Some computer programs were applied in calculation of magnetic fields distribution. On the base of magnetic field distributions were analyzed different failures situations. Some laboratory experiments realized for induction traction motor were verified by results of computer calculations.
基金National Natural Science Foundation in China(No.71173141),National Natural Science Foundation in China(No.71373170)development projects in Higher Education Institution of Shanxi Province of China(No.20111312)+1 种基金special funds projects in Higher Education Institution of Shanxi Province of China(No.201246)soft science research project in Shanxi Province of China(No.2013041015-04)
文摘Research on coke price forecasting is of theoretical and practical signiifcance. Here, the Kalman ifltering algorithm was used to analyze the price of coke. As the only state variable, the historical coke price is sorted out to build the state space model. The algorithm makes use of innovation composed of the difference between observed and predicted values, and alows us to obtain the optimal estimated value of the coke price via continuous updating and iteration of innovation. Our results show that this algorithm is effective in the ifeld of coke price tracking and forecasting.
基金supported by the CMA (Grant No. GYHY201106011)the National Basic Research Program of China ("973" Project) (Grant No. 2012CB- 825604)+1 种基金the National Natural Science Foundation of China (Grant Nos. 41074112, 41174137, 41174138)the Specialized Research Fund for State Key Laboratories
文摘The hourly values of the ionospheric F2 layer critical frequency, foF2, recorded at Wakkanai ionosonde station （45.4°N, 141.7°E） have been collected to construct a middle-latitude single-station model for forecasting foF2 under geomagnetic quiet and disturbed conditions. The module for the geomagnetic quiet conditions incorporates local time, seasonal, and solar vari- ability of climatological foF2 and its upper and lower quartiles. It is the first attempt to predict the upper and lower quartiles of foF2 to account for the notable day-to-day variability in ionospheric foF2. The validation statistically verifies that the model captures the climatological variations of foF2 with higher accuracy than IRI does. The storm-time module is built to capture the geomagnetic storm induced relative deviations of foF2 from the quiet time references. In the geomagnetically disturbed module, the storm-induced deviations are described by diumal and semidiumal waves, which are modulated by a modified magnetic activity index, the Kf index, reflecting the delayed responses of foF2 to geomagnetic activity forcing. The coeffi- cients of the model in each month are determined by fitting the model formula to the observation in a least-squares way. We provide two options for the geomagnetic disturbed module, including or not including Kalman filter algorithm. The Kalman filter algorithm is introduced to optimize these coefficients in real time. Our results demonstrate that the introduction of the Kalman filter algorithm in the storm time module is promising for improving the accuracy of predication. In addition, comparisons indicate that the IRI model prediction of the F2 layer can be improved to provide better performances over this region.
文摘Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical importance.This study aims at forecasting the coffee price in Ethiopia. We used daily closed price data of Ethiopian coffee recorded in the period 25 June 2008 to 5 January 2017 obtained from Ethiopia commodity exchange（ECX） market to analyse coffee prices fluctuation. Here, the nature of coffee price is non-stationary and we apply the Kalman filtering algorithm on a single linear state space model to estimate and forecast an optimal value of coffee price. The performance of the algorithm for estimating and forecasting the coffee price is evaluated by using root mean square error（RMSE）. Based on the linear state space model and the Kalman filtering algorithm, the root mean square error（RMSE） is 0.000016375, which is small enough, and it indicates that the algorithm performs well.
