In this paper, an approach to straight and circle track reconstruction is presented, which is suitable for particle trajectories in an homogenous magnetic field （or 0 T） or Cherenkov rings. The method is based on fl...In this paper, an approach to straight and circle track reconstruction is presented, which is suitable for particle trajectories in an homogenous magnetic field （or 0 T） or Cherenkov rings. The method is based on fllzzy c-regression models, where the number of the models stands for the track number. The approximate number of tracks and a rough evaluation of the track parameters given by Hough transform are used to initiate the fuzzy c-regression models. The technique effectively represents a merger between track candidates finding and parameters fitting. The performance of this approach is tested by some simulated data under various scenarios. Results show that this technique is robust and could provide very accurate results efficiently.展开更多
This paper proposes a proper methodology in data modification by using AHP （analytical hierarchy process） technique and FCM （fuzzy c-mean） model in the ICU （intensive care unit）. The binary data were created fro...This paper proposes a proper methodology in data modification by using AHP （analytical hierarchy process） technique and FCM （fuzzy c-mean） model in the ICU （intensive care unit）. The binary data were created from continuous data using FCM model, while the continuous data were constructed from binary data using AHP technique. The models used in this study are FCRM （fuzzy c-regression model）. A case study in scale of health at the ICU ward using the AI-IP, FCM model and FCRM models was conducted. There are six independent variables in this study. There are four cases which are considered as the result of using AHP technique and FCM model against independent data. After comparing the four cases, it was found that case 4 appeared to be the best model, because it has the lowest MSE （mean square error） value. The original data have the MSE value of 97.33, while the data in case 4 have the MSE value of 82.75. This means that the use of AHP technique can reduce the MSE value, while the use of FCM model can not reduce the MSE value. In other words, it can be proved that the AHP technique can increase the accuracy of prediction in modeling scale of health which is associated with the mortality rate in the ICU.展开更多
基金Supported by National Natural Science Foundation of China(11275109)
文摘In this paper, an approach to straight and circle track reconstruction is presented, which is suitable for particle trajectories in an homogenous magnetic field （or 0 T） or Cherenkov rings. The method is based on fllzzy c-regression models, where the number of the models stands for the track number. The approximate number of tracks and a rough evaluation of the track parameters given by Hough transform are used to initiate the fuzzy c-regression models. The technique effectively represents a merger between track candidates finding and parameters fitting. The performance of this approach is tested by some simulated data under various scenarios. Results show that this technique is robust and could provide very accurate results efficiently.
文摘This paper proposes a proper methodology in data modification by using AHP （analytical hierarchy process） technique and FCM （fuzzy c-mean） model in the ICU （intensive care unit）. The binary data were created from continuous data using FCM model, while the continuous data were constructed from binary data using AHP technique. The models used in this study are FCRM （fuzzy c-regression model）. A case study in scale of health at the ICU ward using the AI-IP, FCM model and FCRM models was conducted. There are six independent variables in this study. There are four cases which are considered as the result of using AHP technique and FCM model against independent data. After comparing the four cases, it was found that case 4 appeared to be the best model, because it has the lowest MSE （mean square error） value. The original data have the MSE value of 97.33, while the data in case 4 have the MSE value of 82.75. This means that the use of AHP technique can reduce the MSE value, while the use of FCM model can not reduce the MSE value. In other words, it can be proved that the AHP technique can increase the accuracy of prediction in modeling scale of health which is associated with the mortality rate in the ICU.
