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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]

• 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]

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]

[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]