Train Model Research Articles

Smart Face Attendance System using Facial Recognition

The SmartFace Attendance system using facial recognition represents a modern solution to automate attendance tracking processes in educational institutions and organizations. Leveraging advanced facial recognition technology, it offers accurate and efficient attendance management while addressing privacy concerns and ensuring user acceptance. The system utilizes state-of-the-art algorithms such as Convolutional Neural Networks (CNN), Histogram of Oriented Gradients (HOG), and Support Vector Machines (SVM) to detect and recognize faces in real-time captured images. By providing touchless operation and seamless integration with existing infrastructure, SmartFace Attendance system offers convenience and scalability for users across diverse environments. With robust security measures, adherence to ethical considerations, and compliance with legal regulations, it is poised to revolutionize attendance tracking practices, enhancing operational efficiency and improving user experiences in educational and organizational settings. Keywords—Online Attendance System, Energy Efficiency, Facial Recognition, Support Vector Machine(SVM), Decision Tree, Histogram of Oriented Gradients(HOG), Convolutional Neural Network(CNN), Database Management, Model Train- ing.

Dynamic Response Analysis of Moving Trains Passing Through the Stationary Thunderstorm Downburst Wind

The safety of the high-speed train traveling through the stationary thunderstorm downburst wind was studied. First, a thunderstorm wind test device was used to simulate the stationary thunderstorm downburst wind. Based on the rigid model pressure measurement tests, the aerodynamic forces of the train traveling along different paths through the stationary thunderstorm downburst wind were measured. The influence of the radial distance of the crossing path on the aerodynamic force coefficients of the train was investigated. On this basis, an unsteady aerodynamic model of the high-speed train crossing through the stationary thunderstorm downburst wind was established, and dynamic response analysis was carried out using the SIMPACK multibody dynamics simulation software to explore further the safety of the high-speed train crossing through the stationary thunderstorm downburst wind. The research showed that the stationary thunderstorm downburst wind field has significant spatial variation characteristics compared with the atmospheric boundary layer wind field. When the train passes through the thunderstorm downburst wind, the radial wind speed and wind yaw angle experienced by the train constantly change, and the change curve shows a symmetrical distribution. The aerodynamic force of the train will undergo sudden loading and unloading processes, and the lateral force coefficient of the train on different paths shows a “pulse-type” variation. Moreover, the lateral force coefficient increases with the increase of wind yaw angle. Under the influence of the thunderstorm downburst wind, the variation trend of the aerodynamic force coefficients of the train is consistent with that under crosswind. However, there are significant differences in the numerical values. Therefore, it is impossible to simply use the formula for calculating the aerodynamic force coefficients of the train under crosswinds to predict the aerodynamic force coefficients of the train under the thunderstorm downburst wind. While passing through the thunderstorm downburst wind, the overturning coefficient index plays a decisive role in the safety of train operations. Train rollover is the main form of train safety accidents, while derailment accidents are not easy. The numerical results obtained in this study are significant for evaluating the operational safety while moving trains traversing the stationary thunderstorm downburst wind.

The Scenario-Situational Modeling of Professional Training of Future Lawyers in College: Organization Features and Results of Experimental Work

The Scenario-Situational Modeling of Professional Training of Future Lawyers in College: Organization Features and Results of Experimental Work

Flow and sound fields of scaled high-speed trains with different coach numbers running in long tunnel

Segregated incompressible large eddy simulation and acoustic perturbation equations were used to obtain the flow field and sound field of 1:25 scale trains with three, six and eight coaches in a long tunnel, and the aerodynamic results were verified by wind tunnel test with the same scale two-coach train model. Time-averaged drag coefficients of the head coach of three trains are similar, but at the tail coach of the multi-group trains it is much larger than that of the three-coach train. The eight-coach train presents the largest increment from the head coach to the tail coach in the standard deviation (STD) of aerodynamic force coefficients: 0.0110 for drag coefficient (Cd), 0.0198 for lift coefficient (Cl) and 0.0371 for side coefficient (Cs). Total sound pressure level at the bottom of multi-group trains presents a significant streamwise increase, which is different from the three-coach train. Tunnel walls affect the acoustic distribution at the bottom, only after the coach number reaches a certain value, and the streamwise increase in the sound pressure fluctuation of multi-group trains is strengthened by coach number. Fourier transform of the turbulent and sound pressures presents that coach number has little influence on the peak frequencies, but increases the sound pressure level values at the tail bogie cavities. Furthermore, different from the turbulent pressure, the first two sound pressure proper orthogonal decomposition (POD) modes in the bogie cavities contain 90% of the total energy, and the spatial distributions indicate that the acoustic distributions in the head and tail bogies are not related to coach number.

Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Abstract: This paper pivots on (DR) is a leading cause of blindness worldwide. Early detection and effective management are crucial in preventing damage to the retina. In recent years, (DL) techniques have shown promise in aiding the DR. This paper aims to investigate the diagnosis of using (CNNs) for automated DR screening. The study utilized a available patient dataset of retinal images to train and testing CNN model for DR detection and classification. Results showed that given (DL) techniques achieved with accurately in both tasks, outperforming existing state-of-the-art methods. The findings suggest the (DL) algorithms can aid in the early detection and management of DR, potentially reducing the burden of the condition on healthcare systems and improving patient outcomes

Palmprint spoofing detection by using deep learning technique on Multispectral database

The protection of Biometric systems against attacks is crucial as biometric devices proliferate in the field of personal authentication. The presentation assault is the most prevalent type of attack on biometric systems; it entails presenting a fake copy (artefact) of the true biometric to the sensor in order to gain unauthorised access. The vulnerability in palmprint-based biometric systems has not received much attention despite the substantial threat posed by these assaults. In this research, we show how to detect a spoof palmprint image. Spoofing attacks involving faked images pose a significant threat to biometric systems. For the suggested method, we use the CASIA palmprint database, from which we constructed our own spoof database using printed photos. After that, we did some pre-processing to obtain the ROI image and a noise-free image for feature extraction using the SIFT approach. We use the convolution neural network for classification and the SVM for comparison. We obtained a result of 96.2% for our proposed palmprint system identification and 89% for SVM. But our main goal is to train the model for spoof detection, so we take some normal images and some spoof images for our train model and use the confusion matrix to calculate the accuracy of our model. We obtain an overall accuracy of 86% for our spoof detection by computing the confusion matrix.

On the implementation of hydraulic-interconnected-suspensions at the primary suspension stage of high-speed rail vehicles

In recent years, huge investments have been made to improve the dynamic performance of high-speed trains. Research into innovative suspension components has been part of the development of this transport system for decades. Innovative devices can allow rail vehicles to deal with the constantly increasing speed required by the global market. Among the most innovative suspension layouts proposed in railway dynamics in past years, limited attention has been given to Hydraulic Interconnected Suspensions (HIS). This layout is composed of two hydraulic cylinders with external hydraulic connections. Hydraulic Interconnected Suspensions allow promising tuning capabilities due to their ability to offer different responses based on the specific inputs given to the cylinders. This layout is rarely considered for rail vehicles, and the few previous works related to this topic considered the HIS layout to be applied at the secondary suspension stage. In this context, this paper proposes applying an HIS layout to the primary suspension stage of rail vehicles, in order to overcome the trade-offs between ride comfort, running safety and maximum car body displacement that need to be considered by bogie manufacturers when designing and optimising these mechanical systems. A nonlinear physical model of the HIS is proposed for co-simulation with a Multi-body (MB) model of a high-speed train. The improvement provided implementing an HIS at the primary suspension stage is then compared to similar enhancements that could be made when tuning and varying the standard suspension components of a bogie.

Comparative study on lateral vibration characteristics of the head and tail cars of high-speed trains under unsteady aerodynamic loadings

With the increasing speed of high-speed trains, abnormal vibrations in the tail car pose a threat to its overall stability and running safety. In this study, the aerodynamic and multibody dynamics models for 3-car and 8-car trains are established. The flow field characteristics and dynamic performance around the head and tail cars under both steady and unsteady aerodynamic loadings at a speed of 400 km/h are compared. The results reveal that the surrounding flow field exhibits highly pronounced unsteady characteristics, directly leading to a significant impact of unsteady aerodynamic loadings on the train's dynamic response. Under unsteady loadings, the lateral stability indexes, displacement, velocity, and acceleration of the tail car are all worser compared to those of the head car. Additionally, the larger oscillation amplitudes and frequencies closer to the suspension modal frequencies under unsteady aerodynamic loadings contribute to more pronounced lateral displacements and accelerations, constituting a crucial factor in the observed ‘tail swing' phenomenon for the high-speed train.

Development of New Passenger and Freight Train Load Models for Dynamic Assessment of Railway Bridges

The dynamic response of a bridge to a train crossing needs to be assessed when planning new railway bridges on tracks with a line speed above or equal 120 km/h and when recalculating existing bridges for new train configurations planned to put into operation or when increasing the locally permissible line speed. In the assessment, the model trains HSLM-A and HSLM-B need to be applied as a sequence of moving loads on two- or three-dimensional mechanical models of the bridge structure as defined in Eurocode EN 1991-2. However, these load models were developed in the 1990s while in the last 25 years many new trains have been developed and put into operation with different configurations compared to the normative model trains in terms of axle distances and axle loads. As a result, the dynamic bridge response for actually operating trains can significantly exceed the permissible values, in particular the vertical bridge acceleration. The normatively regulated model trains HSLM-A and HSLM-B are therefore no longer adequate for the dynamic assessment of railway bridges, and there is an urgent need to develop new load models for passenger and freight trains, to cover the trains currently operating on the European railway network. For the reasons mentioned above, an international consortium consisting of TU Darmstadt, KU Leuven, AIT and REVOTEC was commissioned by the German Centre for Rail Transport Research (DZSF) in 2019 to develop new dynamic passenger and freight train load models for the dynamic calculation of railway bridges. This article presents the development steps carried out, the optimization methods used herein, as well as the validation of the newly developed load models on a large set of existing railway bridges.

Numerical study of aerodynamics drag and flow characteristics of the high-speed train head design

A native design of the Indonesian high-speed train has been developed as an improvement of the previous medium-speed train model. As the design speed is proposed to be increased from 160 km/h to 250 km/h, the nose shape and nose length of the train head were modified into a sharper and longer nose. In this study, numerical simulation of computational fluid dynamics was used to investigate the aerodynamic phenomena and flow behavior around the modified train head and carbodies. The CFD simulation was used as the verification of the shape design and a tool for rapid design changes and optimization. The model configuration is three cars on a real scale by ignoring other parts like the train window and door, pantograph, and train connecting. A k-ε turbulence model was used in the simulation. From this investigation, it is found that the coefficient of drag on the model is calculated to be 0.34. Meanwhile, an experimental wind tunnel test validates the result with a coefficient drag is 0.38 or 10% divergence from the numerical method.

Study on the adaptive relationship between train nose lengths and variable-section tunnel parameters: Aiming at mitigating micro-pressure waves

Environmental problems caused by micro-pressure waves (MPWs) are increasingly prominent with increase in train speed. Considering different models of trains, which may have differences in nose lengths (Ln), and certain extreme terrains where it is unsuitable to construct the buffer hood outside the tunnel, this study numerically investigated the adaptive relationship between Ln and variable-section tunnel parameters [i.e., the length (L1) and area (S1) of the entrance expansion section] for minimizing the MPW. The results show that, as Ln rises from 5 to 13 m, the formation of the MPW peak is continuously delayed, and the peak value of the MPW decreases; while the optimal L1 remains at 20 m for different Ln. Additionally, the optimal S1 scarcely changes with the increase in Ln as well, between 151 and 156 m2, and its ratio to the section area of the main tunnel (70 m2) is approximately 2.2. The optimal parameters of the entrance expansion section play a universal role for trains with different Ln, which can alleviate the MPW amplitude with a value of 58% or higher. The findings can provide significant support for designing tunnels and tunnel hoods, focusing on alleviating acoustic noise.

A seven-year analysis of the tourism sector of India with a prediction model—A case study of smart tourism

<p>The tourism sector requires an in-depth analysis and forecasting to bring a clear picture of various factors that affect the visit of foreign tourists to certain countries. In this context, the work carried out in this paper provides an in-depth analysis of the number of tourists to India and the revenue generated from them from the year 2014 to 2020. Furthermore, the analysis of different states of India to which the tourists visited the most and quarterly analysis of the tourists to India are also presented. The impact of corona pandemic on the tourism sector of India is also shown by comparing the number of tourists in 2019 and 2020. Support vector regression (SVR) is trained with the historical data of the number of tourists from 2001 to 2016 and validated for 2017 to 2019. This trained model is used to forecast the number of tourists from 2020 to 2023 to study the impact of corona pandemic on the number of foreign tourists to India. Similarly, the historical data of foreign exchange fees from 2001 to 2016 is used to train the model while it is validated with data from 2017 to 2019. This train model is used to predict the Foreign Exchange Earning (FEE) for the years 2020 to 2022. The actual FEE is compared with the predicted FEE to show the impact of the coronavirus pandemic on the revenue generated from tourism by India.<strong></strong></p>

A Model of Accident Avoiding System for Trains

Abstract: Safety must be the top most priority in train running and it plays an important role in safe running of trains in our country. Already many systems are existed to avoid accidents, even though an attempt is made in this project work to study the subject of train accidents avoiding systems to enhance the technology further. The Train Collision Avoidance System (TCAS) is the major subject to detect unsafe situations arising due to over speed and train collisions in station area. The advanced accident prevention methods must be incorporated those measures under which the trains will be in constant communication with the protection systems through electronic communication systems. Although, Indian Railways have safe running devices, we have made an effort to cover few more important features like, 1 – to detect the track break in running rail track, 2 – object detection in between the tracks with auto control, and 3 – auto stop when train detects Red signal present over the track side signal post. These are the three important parameters incorporated in our project work and for live demo, a mini model train will be constructed over which all required sensors and their control circuits will be installed to prove the theme practically.

Nonlinear impedance characteristics of a gas hydraulic energy absorber and performance prediction for railway transportation of valuable goods

It is meaningful to investigate the nonlinear impedance characteristics of gas hydraulic energy absorbers (GHEAs) and their potential application in modern railway transportation of valuable goods. Fluid-mechanics-based full parametric modelling of a GHEA used in a high-speed train is performed, both simulations with the developed mathematic model and physical experiments are carried out on the static and dynamic characteristics of the GHEA. The agreement between the results of the simulation and the experiment with tolerable deviations validates the nonlinear mathematic model. Longitudinal train dynamics (LTD) modelling and simulation of a Chinese rapid freight train with the validated GHEA model or the conventional MT-2 friction-type energy absorber model is finally conducted. The results show that when the train uses GHEAs instead of MT-2 energy absorbers, the coupler forces and vehicle accelerations of the train are significantly reduced, and the vehicles operate more steadily in situations such as train startup, emergency braking and shunting operations; thus, gas hydraulic energy absorber is protective and would be alternative for equipping rapid freight trains for the transportation of valuable goods. The obtained nonlinear mathematic model in terms of the structural, oil and gas property parameters of the GHEA is useful and meaningful in further parameter design optimization of GHEAs and optimal specification of GHEAs for rapid freight trains.

Optimal allocation method of electric/air braking force of high-speed train considering axle load transfer

Reasonable distribution of braking force is a factor for a smooth, safe, and comfortable braking of trains. A dynamic optimal allocation strategy of electric-air braking force is proposed in this paper to solve the problem of the lack of consideration of adhesion difference of train wheelsets in the existing high-speed train electric-air braking force optimal allocation strategies. In this method, the braking strategy gives priority to the use of electric braking force. The force model of a single train in the braking process is analyzed to calculate the change of adhesion between the wheel and rail of each wheelset after axle load transfer, and then the adhesion of the train is estimated in real time. Next, with the goal of maximizing the total adhesion utilization ratio of trailer/motor vehicles, a linear programming distribution function is constructed. The proportional coefficient of adhesion utilization ratio of each train and the application upper limit of braking force in the function is updated according to the change time point of wheelset adhesion. Finally, the braking force is dynamically allocated. The simulation results of Matlab/Simulink show that the proposed algorithm not only uses the different adhesion limits of each trailer to reduce the total amount of braking force undertaken by the motor vehicle, but also considers the adhesion difference of each wheelset. The strategy can effectively reduce the risk and time of motor vehicles during the braking process and improve the stability of the train braking.

Learning Domain Specific Sub-layer Latent Variable for Multi-Domain Adaptation Neural Machine Translation

Domain adaptation proves to be an effective solution for addressing inadequate translation performance within specific domains. However, the straightforward approach of mixing data from multiple domains to obtain the multi-domain neural machine translation (NMT) model can give rise to the parameter interference between domains problem, resulting in a degradation of overall performance. To address this, we introduce a multi-domain adaptive NMT method aimed at learning domain specific sub-layer latent variable and employ the Gumbel-Softmax reparameterization technique to concurrently train both model parameters and domain specific sub-layer latent variable. This approach facilitates the learning of private domain-specific knowledge while sharing common domain-invariant knowledge, effectively mitigating the parameter interference problem. The experimental results show that our proposed method significantly improved by up to 7.68 and 3.71 BLEU compared with the baseline model in English-German and Chinese-English public multi-domain datasets, respectively.

Multi-objective optimisation of wheel-rail adhesion for high-speed trains with aerodynamic lift wings

A high-speed train with aerodynamic lift wings (aero-wings) is designed to improve train speed and mitigate energy consumption. Nevertheless, the impact of aero-wings on wheel-rail adhesion and safety remains uncertain. In this examination, a multi-body dynamics (MBD) model of a high-speed train integrated with aero-wings was first created. The influences of train speed, wheel-rail contact conditions, and aerodynamic lift on the wheel-rail adhesion, wheel wear, and dynamics performance were briefly investigated, indicating that aerodynamic lift degrades wheel-rail adhesion and dynamics performance but reduced wheel wear. Then, the multi-parameters matching optimisation design for sufficient wheel-rail adhesion and minimal wheel wear was conducted. The results indicate that, under dry contact and traction conditions, the minimum wear and optimal wheel-rail adhesion can be achieved when the train speed is about 440.62 km/h, the aerodynamic lift is about 24.48% of the vehicle weight, and the wheel wear optimisation rate is 25.38%. While the train speed of 549.54 km/h and an aerodynamic lift of 12.39% corresponds to the optimal results with wear reduction of 11.62%, under wet and braking conditions. This study provides valuable insights for determining aerodynamic load limits and formulating traction/braking control strategies for high-speed trains with aero-wings.

Floor vibration predictions based on train-track-building coupling model

With the objective of rational urban space utilization, the development of elevated metro depots and over-track buildings is progressing rapidly, with a particular focus on addressing the vibrations caused by trains. Unlike traditional metro depots, trains in elevated depots run on concrete floors, which may lead to excessive floor vibrations. Therefore, it is essential to evaluate the impact of these vibrations during the design phase. In this paper, a vertical vibration prediction model for trains running on building floors is proposed by integrating the train, track structure, and building structure into a unified system. The train model is conceptualized as a multi-rigid body connected by springs and dampers. The track structure comprises rails, fasteners, and track slabs. The building floor is simplified as a rectangular plate composed of multiple orthogonal structural girders and columns in vertical contact with the floor. To verify the proposed method, field measurements were conducted in an elevated metro depot. The proposed model provided estimates of the vibration transmission with good agreement from track to building floor by comparing with the measured vibrations. The model presented in this paper can be utilized to predict the base vibrations of buildings and further predict the floor vibrations.

Dynamic model of high-speed maglev train-guideway bridge system with a nonlinear suspension controller

To improve the anti-interference ability of maglev trains, a dynamic model of the high-speed maglev train with a nonlinear suspension controller for the guideway system is proposed in this paper. Based on the nonlinear characteristics of the magnetic suspension system, a nonlinear decoupling controller is designed using the feedback linearization theory. Then, a high-speed maglev train model is refined with a guideway coupling system, consisting of a maglev train simulated as a multi-body dynamics model with 537 degrees of freedom and a spatial finite element model of the guideway. Taking the Shanghai high-speed maglev train as an example, the correctness of the computational model is verified by comparing the modeling results with field measurement data, and the control effectiveness of the nonlinear controllers and the traditional PD controllers is compared considering different train speeds and disturbance forces. The results show that the suspension gap under the decoupling control is smaller than that under the PD control during the train operations. Under the same disturbance force, the decoupling control exhibits better control performance than the PD control. The variation amplitudes of the magnetic pole gaps are generally linearly related to the disturbance force.

МЕТОДИКА ОЦЕНКИ КОМФОРТНОСТИ ПЕРЕВОЗКИ ПАССАЖИРОВ ЖЕЛЕЗНОДОРОЖНЫМ ТРАНСПОРТОМ ДЛЯ МИНИМИЗАЦИИ РИСКОВ СВЯЗАННЫХ С ЧЕЛОВЕЧЕСКИМ ФАКТОРОМ В ПРОЦЕССЕ ВЕДЕНИЯ ПОЕЗДА

The paper solves the problems of assessing the smooth running of a passenger train, the quality and comfort of passenger transportation by rail, as well as determining the level of professional competencies of locomotive crew members. A comprehensive computer model of a passenger train is developed, which makes it possible to study all the processes of controlling traction equipment and train brakes that affect energy efficiency, safety and dynamics of rolling stock. Using fuzzy logic tools, a technique is developed for rating employees of locomotive crews according to the criterion of ensuring smooth running of the train. The results of experimental trips by passenger trains on sections of the North Caucasus Railway are presented. The dependences of the locomotive traction force, the speed and the longitudinal jerk of the passenger train cars are obtained.