No abstract

No abstract

No abstract

No abstract

This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT), feature extraction, genetic algorithm (GA), and neural network (ANN) techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

No abstract

No abstract

Background/AimsMany prognostic models have been developed to help physicians make medical decisions on treating patients with pulmonary embolism. Among these models, the Pulmonary Embolism Severity Index (PESI) has been shown to be a successful risk stratification tool for patients with acute pulmonary embolism. The PESI, however, had not been applied to patients with pulmonary embolism in Korea.MethodsThe patients included in this study were diagnosed by computed tomography at Inje University's Ilsan Paik Hospital between December 1999 and March 2007. Risk stratification for the patients was performed using the PESI. The mortality rate was calculated according to each PESI risk class.ResultsOf the 90 patients enrolled in this study, ten were assigned to PESI class I, 29 to PESI class II, 22 to PESI class III, eight to PESI class IV, and ten to PESI class V. The mortality rate after 30 days in each class was 0, 10.3, 9.1, 0, and 50% (p=0.0016), respectively, whereas the respective hospital mortality rate was 4.8, 13.8, 13.6, 12.5, and 50% (p=0.0065). The overall mortality was 9.5, 27.6, 31.8, 50.0, and 60%, respectively (p=0.0019). The mortality rate was significantly associated with the PESI class.ConclusionsThe PESI class was found to be significantly correlated with the 30-day mortality rate, hospital mortality, and overall mortality. Our data indicate that the PESI can be used to predict the prognosis of patients with pulmonary embolism and in making medical decisions regarding the treatment of patients with pulmonary embolism.