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This work presents a new method for sleeper crack identification based on cascade convolutional neural network (CNN) to address the problem of low efficiency and poor accuracy in the traditional detection method of sleeper crack identification. The proposed algorithm mainly includes improved You Only Look Once version 3 (YOLOv3) and the crack recognition network, where the crack recognition network includes two modules, the crack encoder-decoder network (CEDNet) and the crack residual refinement network (CRRNet). The improved YOLOv3 network is used to identify and locate cracks on sleepers and segment them after the sleeper on the ballast bed is extracted by using the gray projection method. The sleeper is inputted into CEDNet for crack feature extraction to predict the coarse crack saliency map. The prediction graph is inputted into CRRNet to improve its edge information and local region to achieve optimization. The accuracy of the crack identification model is improved by using a mixed loss function of binary cross-entropy (BCE), structural similarity index measure (SSIM), and intersection over union (IOU). Results show that this method can accurately detect the sleeper crack image. During object detection, the proposed method is compared with YOLOv3 in terms of directly locating sleeper cracks. It has an accuracy of 96.3%, a recall rate of 91.2%, a mean average precision (mAP) of 91.5%, and frames per second (FPS) of 76.6/s. In the crack extraction part, the F-weighted is 0.831, mean absolute error (MAE) is 0.0157, and area under the curve (AUC) is 0.9453. The proposed method has better recognition, higher efficiency, and robustness compared with the other network models. [ABSTRACT FROM AUTHOR]

The track line suffers from track irregularity, subgrade settlement, rail surface defects, and gauge distortion. Measuring the track geometry quality plays an important role in rail administrations to safe operation and track maintenance. In order to meet the requirements of track daily inspection, this article describes a comprehensive detection system for the track geometry, which is designed by four cameras, an inclinometer, and an inertial measurement unit. Moreover, the detection system has the characteristics of simple structure, automatic detection, and multiple measurement parameters. First, to measure track alignment and irregularity, a multisensing calibration method is established to fuse visual sensors and inertial sensors. An accelerometer and a Kalman filter are used to eliminate accumulated unrealistic drifts of accelerometer and gyroscope. Second, to measure the rail surface defects and gauge, the model of laser and machine vision is deduced to obtain the coordinate values in a unified coordinate system, and an improved iterative closest point method is developed to improve the matching accuracy. Finally, a series of experiments is carried out to validate the measuring performance. The results indicate that the proposed method can accurately measure the track geometry, with the maximum deviations of track alignment, track irregularity, rail defects, and gauge that are less than 0.5, 0.4, 0.3, and 0.1 mm, respectively. The detection system is not only simple in structure but also has multiple detection parameters, which is especially suitable for track daily maintenance. [ABSTRACT FROM AUTHOR]

This paper aims to create a prediction model for car body vibration acceleration that is reliable, effective, and close to real-world conditions. Therefore, a huge amount of data on railway parameters were collected by multiple sensors, and different correlation coefficients were selected to screen out the parameters closely correlated to car body vibration acceleration. Taking the selected parameters and previous car body vibration acceleration as the inputs, a prediction model for car body vibration acceleration was established based on several training algorithms and neural network structures. Then, the model was successfully applied to predict the car body vibration acceleration of test datasets on different segments of the same railway. The results show that the proposed method overcomes the complexity and uncertainty of the multiparameter coupling analysis in traditional theoretical models. The research findings boast a great potential for application. [ABSTRACT FROM AUTHOR]

Electricity load forecasting is becoming one of the key issues to solve energy crisis problem, and time-series Bayesian Neural Network is one popular method used in load forecast models. However, it has long running time and relatively strong dependence on time and weather factors at a residential level. To solve these problems, this article presents an improved Bayesian Neural Networks (IBNN) forecast model by augmenting historical load data as inputs based on simple feedforward structure. From the load time delays correlations and impact factors analysis, containing different inputs, number of hidden neurons, historic period of data, forecasting time range, and range requirement of sample data, some advices are given on how to better choose these factors. To validate the performance of improved Bayesian Neural Networks model, several residential sample datasets of one whole year from Ausgrid have been selected to build the improved Bayesian Neural Networks model. The results compared with the time-series load forecast model show that the improved Bayesian Neural Networks model can significantly reduce calculating time by more than 30 times and even when the time or meteorological factors are missing, it can still predict the load with a high accuracy. Compared with other widely used prediction methods, the IBNN also performs a better accuracy and relatively shorter computing time. This improved Bayesian Neural Networks forecasting method can be applied in residential energy management. [ABSTRACT FROM AUTHOR]

In this paper, a novel tangent error maximum power point tracking algorithm based on perturb and observe method is presented to achieve the high efficiency for photovoltaic system under fast multi-changing solar irradiances. In order to get a proper initial value, the mathematical model of optimal voltage is established. By using tangent error method, a dynamic perturbation step is calculated. Meanwhile, the characteristics for tracking direction is analyzed in detail to distinguish oscillating power from changes in irradiances and perturbation step. Moreover, the tracking performance of the proposed algorithm is investigated by modeling, simulation and experiment of PV system. Furthermore, errors and statistical analyses are carried out to illustrate the accuracy of the tangent error algorithm. Compared with previous methods in other works, the results demonstrate that the proposed method not only has the fast tracking speed but also the high efficiency. In addition, it is found that the overall energy conversion efficiency of the photovoltaic module has increased by approximately 3% during the whole time. Besides, due to its ease implementation, high efficiency and reliability under fast varying irradiances, the proposed method can be applied to provide energy for moving carriers, such as UAVs, cars and trains. [ABSTRACT FROM AUTHOR]

• The proposed method can accurately predict the vibration status of the train body. • Without installing any new sensors and other monitoring equipment on the train. • The novel method can reduce maintenance costs and prevent potential safety risks. • The daily measurement data from GJ-5 rail inspection vehicle is used as the datasets. • A multiple correlation coefficients method is used for the data pre-processing. • A prediction of train body vibrations base on the bagged regression tree is established. • The proposed method has the best performance, and the accuracy is up to 98%. • The number of valuable training data sets is reduced by 78.3%. Prediction of the train body vibrations induced by the train running is desirable and useful to ensure comfortable service, reliable, safe, and secure operation of railway systems. By using daily measurement data from GJ-5 rail detection vehicle, this paper presents a novel prediction algorithm, which is based on bagged tree ensemble regression with multiple correlation coefficients. To obtain the valuable data sets from a large amount of inspection data, an approach of multiple correlation coefficients is used for the data pre-processing. Then the prediction model of train body vibrations is established by combining regression tree algorithm and bagged ensemble algorithm. By training the valuable data sets, the prediction results are calculated by the bagged tree ensemble regression method. Finally, the proposed method is evaluated with experimental data and the traditional method. The experimental results show that the proposed method not only has higher accuracy but also can effectively reduce the number of the data sets, the accuracy is up to 98% and the number of valuable training data sets is reduced by 78.3%. The new method proposed in the paper can accurately predict the vibration status of the train body without installing any new sensors and other monitoring equipment on the train, which can reduce maintenance costs and prevent potential safety risks. [ABSTRACT FROM AUTHOR]

As a result of long-term pressure from train operations and direct exposure to the natural environment, rails, fasteners, and other components of railway track lines inevitably produce defects, which have a direct impact on the safety of train operations. In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R-CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang-Taiyuan high-speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop an improved adhesion model to better reproduce the low adhesion condition of the anti-skid control for rail vehicles under braking condition. Design/methodology/approach: In view of the low adhesion characteristics for rail vehicles under braking conditions, the Polach adhesion model was improved based on the sliding power and sliding energy. The wheel–rail low adhesion model suitable for braking condition was given. The analysis of braking anti-skid control under emergency braking condition was carried out through the co-simulation, and compared with the test data; the effectiveness and practicability of the improved low adhesion model were verified. Findings: The results showed that the improved adhesion model is simple and efficient and the parameters involved are less, and it can be directly applied to the real-time simulation of anti-skid control in the process of train braking. Originality/value: This paper can provide a theoretical reference for the reasons of change and improvement of adhesion between wheel and rail caused by the adjustment of braking force under anti-skid control, which can fulfill a need to the study of sliding energy on the contact surface, the removal effect of pollutants on the wheel–rail surface and the improvement and recovery of adhesion caused. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2020-0244/ [ABSTRACT FROM AUTHOR]

Rail fastener is a crucial component equipment to ensure the safe operation of the train, and it is very paramount to detect the loose state of the fastener. In this paper, the vertical vibration acceleration signal of wheelset is taken as the research object, and the loose state of fastener is identified by separating and calculating the key IMF energy entropy. Firstly, based on the finite element theory and the principle of multibody dynamics, the rigid-flexible coupling simulation model of vehicle track is established. Then, the vertical vibration acceleration signals of the wheelset under the speed of 200 km/h are obtained by setting the different loosening degrees of the fastener. Finally, we use optimized HHT to process signals, and the orthogonal empirical mode decomposition method (OEMD) is proposed to optimize the orthogonality of the intrinsic mode function, to eliminate the IMF component having poor correlation with the original signal; the Hilbert time spectrum and information entropy theory are combined to calculate the energy entropy of the key IMF, and the HHT energy entropy evaluation algorithm of the vertical acceleration response signal of the train wheelset is proposed. The simulation results show that the HHT energy entropy of 100% fastener looseness is less than 25%, 50%, and 75%, decreasing trend. The algorithm can recognize the looseness of track fastener through the experiment under different working conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper presents a new dynamic 2D fusion model and analysis method for analyzing the dynamic characteristics of floating photovoltaic modules under continuous changes in motion, irradiance, and temperature caused by actual wind and waves conditions. • The coupling relationship between the motion state and the environmental factors is established to achieve accurate measurement of floating photovoltaic modules. • The complementary filter is used to eliminate the low-frequency interference signals of the gyroscope and the high-frequency interference signals of the accelerometer. • A new dynamic 2D fusion model of floating photovoltaic modules is set up to obtain the dynamic output characteristics, which integrates mechanical motion state information with the electrical parameters. • The experimental results show that proposed model and analysis method have high performance, and power generation efficiency can be increased by 2.5%. As a new application form, floating photovoltaic power generation is a green and sustainable energy source with immense development potential. However, built on marine or riverine surface, the floating photovoltaic system not only faces challenges from environment factors that land-based photovoltaic system meets, but also faces challenges from the motion of the floating bodies which are caused by the continuous external forces of wind and waves. Modelling and analyzing the output characteristics of floating photovoltaic modules is desirable and useful to improve power generation efficiency. This paper presents a new dynamic 2D fusion model and analysis method for analyzing the dynamic characteristics of floating photovoltaic modules under continuous changes in motion, irradiance, and temperature caused by actual wind and waves conditions. To achieve accurate measurement of floating photovoltaic modules, the coupling relationship between the motion state and the environmental factors is established. Then the motion state modeling of floating photovoltaic modules based on multi-sensors information fusion is established to acquire the absolute attitude angle with the continuous superposition of high and low frequency motion in the geographical coordinate system. On this basis, a new dynamic 2D fusion model of floating photovoltaic modules is set up to obtain the dynamic output characteristics, which integrates mechanical motion state information with the electrical parameters. Finally, the proposed modelling and analyzing method is evaluated with experimental data. The experimental and analytical results show that the accuracy of the 2D fusion model is higher than 98.7%, the output efficiency of floating photovoltaic modules is only 94.2% compared to the static state, and power generation efficiency can be increased by 2.5% by using the proposed method. The new modelling method proposed in the paper can accurately obtain the output characteristics of the floating photovoltaic system under actual operating conditions, which can effectively improve the efficiency of power generation. [ABSTRACT FROM AUTHOR]

Commonly used fastener positioning methods include pixel statistics (PS) method and template matching (TM) method. For the PS method, it is difficult to judge the image segmentation threshold due to the complex background of the track. For the TM method, the search in both directions of the global is easily affected by complex background, as a result, the locating accuracy of fasteners is low. To solve the above problems, this paper combines the PS method with the TM method and proposes a new fastener positioning method called local unidirectional template matching (LUTM). First, the rail positioning is achieved by the PS method based on the gray-scale vertical projection. Then, based on the prior knowledge, the image of the rail and the surrounding area of the rail is obtained which is referred to as the 1-shaped rail image; then, the 1-shaped rail image and the produced offline symmetrical fastener template is pre-processed. Finally, the symmetrical fastener template image is searched from top to bottom along the rail and the correlation is calculated to realize the fastener positioning. Experiments have proved that the method in this paper can effectively realize the accurate locating of the fastener for ballastless track and ballasted track at the same time. [ABSTRACT FROM AUTHOR]

This article proposes an innovative RGBD saliency model, that is, attention-guided feature integration network, which can extract and fuse features and perform saliency inference. Specifically, the model first extracts multimodal and level deep features. Then, a series of attention modules are deployed to the multilevel RGB and depth features, yielding enhanced deep features. Next, the enhanced multimodal deep features are hierarchically fused. Lastly, the RGB and depth boundary features, that is, low-level spatial details, are added to the integrated feature to perform saliency inference. The key points of the AFI-Net are the attention-guided feature enhancement and the boundary-aware saliency inference, where the attention module indicates salient objects coarsely, and the boundary information is used to equip the deep feature with more spatial details. Therefore, salient objects are well characterized, that is, well highlighted. The comprehensive experiments on five challenging public RGBD datasets clearly exhibit the superiority and effectiveness of the proposed AFI-Net. [ABSTRACT FROM AUTHOR]

Rail fastener status recognition and detection are key steps in the inspection of the rail area status and function of real engineering projects. With the development of and widespread interest in image processing techniques and deep learning theory, detection methods that combine the two have yielded promising results in practical detection applications. In this paper, a semantic-segmentation-based algorithm for the state recognition of rail fasteners is proposed. On the one hand, we propose a functional area location and annotation method based on a salient detection model and construct a novel slab-fastclip-type rail fastener dataset. On the other hand, we propose a semantic-segmentation-framework-based model for rail fastener detection, where we detect and classify rail fastener states by combining the pyramid scene analysis network (PSPNet) and vector geometry measurements. Experimental results prove the validity and superiority of the proposed method, which can be introduced into practical engineering projects. [ABSTRACT FROM AUTHOR]

This paper proposes an effective method to elevate the performance of saliency detection via iterative bootstrap learning, which consists of two tasks including saliency optimization and saliency integration. Specifically, first, multiscale segmentation and feature extraction are performed on the input image successively. Second, prior saliency maps are generated using existing saliency models, which are used to generate the initial saliency map. Third, prior maps are fed into the saliency regressor together, where training samples are collected from the prior maps at multiple scales and the random forest regressor is learned from such training data. An integration of the initial saliency map and the output of saliency regressor is deployed to generate the coarse saliency map. Finally, in order to improve the quality of saliency map further, both initial and coarse saliency maps are fed into the saliency regressor together, and then the output of the saliency regressor, the initial saliency map as well as the coarse saliency map are integrated into the final saliency map. Experimental results on three public data sets demonstrate that the proposed method consistently achieves the best performance and significant improvement can be obtained when applying our method to existing saliency models. [ABSTRACT FROM AUTHOR]

Abstract: Based on a brief retrospect of the method in establishing T g of the bulk metallic glasses (BMGs), some perplexities concerning this are pointed out. With the experimental results of Zr–Al–Ni–Cu–X (Nb,Ti) BMGs, a electrical resistivity method is proposed to determine the glass transition temperature of BMGs. With the method, we define two kinds of characteristic temperature related to the glass transition, T g-dep and T g-int, respectively. By comparing T g-dep and T g-int with T g determined by the DSC method, we have found that, for the same alloy at the same heating rate, T g-dep is very close to T g-onset while T g-int is approximate to T g-mid. As a method to determine the glass transition temperature, the electrical resistivity method has proved to be more convenient and practical in comparison with the DSC method, especially when the DSC curve cannot show the glass transition character of BMGs. In addition, we would emphasize that when we refer to T g, it is necessary to expatiate on the way of denoting the glass transition temperature, such as T g-dep or T g-int (T g-onset or T g-mid), and on the heating rate, in order to avoid ambiguity. [Copyright &y& Elsevier]