Bug-fix time prediction models

Author(s):  
Pamela Bhattacharya ◽  
Iulian Neamtiu
2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cong Bai ◽  
Zhong-Ren Peng ◽  
Qing-Chang Lu ◽  
Jian Sun

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.


Author(s):  
Dariusz Góral ◽  
◽  
Franciszek Kluza ◽  
Walter E.L. Spiess ◽  
Katarzyna Kozłowicz ◽  
...  

Author(s):  
Hadi Malek ◽  
Sara Dadras ◽  
YangQuan Chen

Being one of the most used passive components in power electronics, electrolytic capacitors have the shortest life span due to their wear-out failure which is mainly caused by vaporization and deterioration of capacitor electrolyte. Knowing these two phenomena increase Equivalent Series Resistance (ESR) of the capacitor, tracking ESR value over the system operating time can be a good indicator for state of health of an electrolytic capacitor. In order to set the maintenance schedule, various ESR monitoring algorithms computing remaining time before failure have been investigated in literature. These real-time algorithms use classical models for ESR and life-time estimation which are not precise enough and leads the maintenance program to be either risky if the prediction is more than the actual life-time or more expensive if it is much less than the actual life span. This paper presents a generalized equivalent model using fractional order element for electrolytic capacitor to estimate the ESR and impedance of faultless running capacitor. Unlike other existing fractional order models, proposed model considers a fractional order dynamic only in the dielectric losses and the terminal capacitor remains integer order as observed in actual capacitor’s behavior. Furthermore, a novel failure predictive model using Mittage-Leffler function is proposed to track the ESR increment due to aging of the capacitor and estimate the failure time based on the information which are provided through ESR monitoring system. Using this model increase the life-time prediction accuracy. Hence the predictive maintenance of the system with capacitors nearing their failure time can be set more precisely. These two fractional order models are compared against classical ESR and life-time prediction models to illustrate the enhanced performances of the proposed models.


2015 ◽  
Vol 13 (9) ◽  
pp. 3088-3095
Author(s):  
David A. Monge ◽  
Matej Holec ◽  
Filip Zelezny ◽  
Carlos Garcia Garino

2012 ◽  
Vol 12 (2) ◽  
pp. 181-192 ◽  
Author(s):  
Mohammad Reza Ghaffariyan ◽  
Ramin Naghdi ◽  
Ismael Ghajar ◽  
Mehrdad Nikooy

Author(s):  
Chandra Mouly Kuchipudi ◽  
Steven I. J. Chien

Travel-time prediction has been an interesting research subject for decades, and various prediction models have been developed. A prediction model was derived by integrating path-based and link-based prediction models. Prediction results generated by the hybrid model and their accuracy are compared with those generated by the path-based and link-based models individually. The models were developed with real-time and historic data collected from the New York State Thruway by the Transportation Operations Coordinating Committee. In these models, the Kalman filtering algorithm is applied for travel-time prediction because of its significance in continuously updating the state variables as new observations. The experimental results reveal that the travel times predicted with the path-based model are better than those predicted with the link-based model during peak periods, and vice versa. The hybrid model derives results from the best model at a given time, thus optimizing the performance. A prototype prediction system was developed on the World Wide Web.


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