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In India, Rail mode of transport serves as frequently preferrable transit systems operating with the optimal cost. Typically, the Indian Railways transport thousands of people on a day-to-day basis in addition to transporting large consignment of goods. Therefore, it is important for the trains to ensure that they run on quality tracks. At times, these tracks are challenged by the friction generated by continuous passage of trains in addition to over corrosions that occur due to their environmental imbalance. Preventive Track Maintenances (PTMs) have been recently introduced by railways for enhancing the quality of railway tracks, but on the contrary, it was failed to focus on the actual needs or emergencies of railway tracks. Moreover, none of the existing methods have been tested with real time datasets. Specifically, holding only two class labels are being considered resulting in the reduction of classification performances. But the major challenging task is that the real-time datasets fall under the category of multi-variant data. Hence, this study aims to provide a Decision Support System (DSS) that predicts the Railway Track Quality (RTQ) from the real time datasets available on the track inspection data of the Indian metro rail system. The proposed research uses clustering and classification processes for achieving Predictive Track maintenance (PTM). Furthermore, the proposed method of RPTMs includes five steps namely data collection, data transformations, clustering of data, preventive maintenances, and evaluations. The undertaken datasets are transformed into numeric formats for the creation of clusters using Kernel Mean Weight Fuzzy Local Information C Means (KMWFLICMs). The resultant clusters from the data have five major types of clusters such as Normal, Low risks, Medium, High, and Emergency Risks based on the parameters of gauge, cross level attributes, turnouts and versine of mainline. From the inferred cluster results, the dataset was further classified to choose maintenance status from four major classes namely No Actions, Fixed Maintenances, Investigate Maintenances, and Emergency Maintenance pertinent to the outcomes of FWCNNs (Fuzzy Weight Convolution Neural Networks). The proposed system was experimented on MATLAB and evaluated against various machine learning approaches. Therefore, the obtained statistical results confirmed that the proposed FWCNN model had afforded higher accuracy in predicting the maintenance interventions based on relevant risk category. [ABSTRACT FROM AUTHOR]

The paper focuses on a method to synthesize the track vertical irregularities, with the possibility of application in the simulation of the dynamics in the railway vehicles. The basic elements of the method are the power spectral density of the track irregularities and the quantities describing the track geometry quality, as they have been adopted in compliance with the ORE B 176 report and UIC 518 leaflet. A major contribution of this method is the extension of the interval specific to the wavelength of the track vertical irregularities, so that this interval be representative for the frequency range of the vertical vibrations in the railway vehicle. The results of the numerical simulations of the dynamic behavior in a vehicle while running on a tangent track with vertical irregularities being synthesized as above demonstrate its efficiency. [ABSTRACT FROM AUTHOR]

This work describes an analytical approach to determine what degree of accuracy is required in the definition of the rail vehicle models used for dynamic simulations. This way it would be possible to know in advance how the results of simulations may be altered due to the existence of errors in the creation of rolling stock models, whilst also identifying their critical parameters. This would make it possible to maximise the time available to enhance dynamic analysis and focus efforts on factors that are strictly necessary. In particular, the parameters related both to the track quality and to the rolling contact were considered in this study. With this aim, a sensitivity analysis was performed to assess their influence on the vehicle dynamic behaviour. To do this, 72 dynamic simulations were performed modifying, one at a time, the track quality, the wheel–rail friction coefficient and the equivalent conicity of both new and worn wheels. Three values were assigned to each parameter, and two wear states were considered for each type of wheel, one for new wheels and another one for reprofiled wheels. After processing the results of these simulations, it was concluded that all the parameters considered show very high influence, though the friction coefficient shows the highest influence. Therefore, it is recommended to undertake any future simulation job with measured track geometry and track irregularities, measured wheel profiles and normative values of the wheel–rail friction coefficient. [ABSTRACT FROM AUTHOR]

We propose an adaptive framework to estimate the quality of video tracking algorithms without ground-truth data. The framework is divided into two main stages, namely, the estimation of the tracker condition to identify temporal segments during which a target is lost and the measurement of the quality of the estimated track when the tracker is successful. A key novelty of the proposed framework is the capability of evaluating video trackers with multiple failures and recoveries over long sequences. Successful tracking is identified by analyzing the uncertainty of the tracker, whereas track recovery from errors is determined based on the time-reversibility constraint. The proposed approach is demonstrated on a particle filter tracker over a heterogeneous data set. Experimental results show the effectiveness and robustness of the proposed framework that improves state-of-the-art approaches in the presence of tracking challenges such as occlusions, illumination changes, and clutter and on sequences containing multiple tracking errors and recoveries. [ABSTRACT FROM PUBLISHER]

As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF. [ABSTRACT FROM AUTHOR]