Training Speed Research Articles

Effect of Vehicle Vibration on Interior Noise of Railway Vehicles Passing Through Rail Corrugation Sections

This study involved experimental research to analyze the effect of rail corrugation on interior noise levels inside railway vehicles. Measurements taken on the Incheon Line 2 light rail indicated that the vehicle’s age and maintenance condition have minimal effects on interior noise. Although wheel wear slightly reduces interior noise, it is insufficient to address the issue of abnormal noise. The analysis of the relationship between vehicle vibrations and interior noise revealed that at 80 km/h and with a 24 mm rail corrugation wavelength, vibrations at 920 Hz in the axle box and car body increase, coinciding with the dominant interior noise frequency of 926.6 Hz. Furthermore, an analysis using a car body sweep confirmed a relative increase in noise in the 920 Hz range. Therefore, abnormal noise in rail corrugation sections is caused by vibrations at 920 Hz due to the corrugation wavelength and train speed, which align with the car body resonance frequency, leading to increased car body vibrations and interior noise.

Research and Simulation Application of Automatic Generation of Judging Curves for IoT Mobile Devices in Train Driving Operations Incorporating Hybrid Genetic Algorithms

A control system for automatically generating a train operation evaluation curve based on a hybrid genetic algorithm has been proposed in order to improve the safety of automatic train operation. The system is based on Internet of Things (IoT) mobile devices and utilizes various sensors such as accelerometers, gyroscopes, barometers, and GPS to collect real-time data on the driver’s acceleration, angular velocity, air pressure, and position, among other parameters. 5G wireless communication technology is used to achieve high-speed data transmission and real-time communication with the cloud. Based on cloud data, a spatial grid area planning model is constructed to automatically generate the train operation evaluation curve. Using a spatial dynamic programming method, the entire network of Electric Multiple Unit (EMU) trains is treated as a whole, and a dynamic model of the EMU train is constructed. The spatial area parameters of the EMU train’s automatic driving operation evaluation curve are combined with the dynamic model analysis method. By identifying and analyzing environmental parameters such as train speed and distance, the EMU train’s automatic driving operation evaluation curve is optimized. A hybrid genetic evolution learning optimization algorithm is used to fit the motion spatial parameters of the EMU train’s automatically generated driving operation evaluation curve, and a spatial behavior analysis simulation is created for the EMU train’s automatically generated control driving operation curve. Through the use of hybrid genetic evolution learning optimization technology, adaptive control and automatic driving operation curve simulation planning for the automatically generated driving operation evaluation curve of the maglev train are achieved, as well as simulation and algorithm optimization design for the automatically generated driving operation evaluation curve of the maglev train. The simulation results show that the method has good adaptability and strong automatic control capability. The experimental results demonstrate the control effect of the proposed method on energy consumption and train stopping error, indicating that the proposed method can effectively improve the parameter adjustment and offset correction ability of the evaluation curve generated by the high-speed train driving operation.

Rail Flaw B-Scan Image Analysis Using a Hierarchical Classification Model

AbstractAs railway traffic volumes and train speeds increase, rail maintenance is becoming more crucial to prevent catastrophic failures. This study aimed to develop an artificial intelligence (AI)-based solution for automatic rail flaw detection using ultrasound sensors to overcome the limitations of traditional inspection methods. Ultrasound sensors are well-suited for identifying structural abnormalities in rails. However, conventional inspection techniques like rail-walking are time-consuming and rely on human expertise, risking detection errors. To address this, a hierarchical classification model was proposed integrating ultrasound B-scan images and machine learning. It involved a two-stage approach—model A for fuzzy classification followed by Model EfficientNet-B7 was identified as the most effective architecture for both models through network comparisons. Experimental results demonstrated the model's ability to accurately detect rail flaws, achieving 88.56% accuracy. It could analyze a single ultrasound image sheet within 0.45 s. An AI-based solution using ultrasound sensors and hierarchical classification shows promise for automated, rapid, and reliable rail flaw detection to support safer railway infrastructure inspection and maintenance activities.

A multi modal fusion coal gangue recognition method based on IBWO-CNN-LSTM

Accurate identification of coal and gangue is a crucial guarantee for efficient and safe mining of top coal caving face. This article proposes a coal-gangue recognition method based on an improved beluga whale optimization algorithm (IBWO), convolutional neural network, and long short-term memory network (CNN-LSTM) multi-modal fusion model. First, the mutation and memory library mechanisms are introduced into the beluga whale optimization to explore the solution space fully, prevent falling into local optimum, and accelerate the convergence process. Subsequently, the image mapping of the audio signal and vibration signal is performed to extract Mel-Frequency Cepstral Coefficients (MFCC) features, generating rich sample data for CNN-LSTM. Then the multi-head attention mechanism is introduced into CNN-LSTM to speed up the training speed and improve the classification accuracy. Finally, the IBWO-CNN-LSTM coal-gangue recognition model is constructed by the optimal hyperparameter combination obtained by IBWO to realize the automatic recognition of coal-gangue. The benchmark function proves that IBWO is superior to other optimization algorithms. By building an experimental platform for the impact of coal and gangue falling on the tail beam of hydraulic support, multiple experimental data collection is carried out. The experimental results show that the proposed coal-gangue recognition model has better performance than other recognition models, and the accuracy rate reaches 95.238%. The multi-modal fusion strategy helps to improve the accuracy and robustness of coal-gangue recognition.

Research on the dynamic response of semi-enclosed sound barrier on viaduct under train pulsating wind and wheel-rail excitation

This study develops a coupled train-viaduct dynamic analysis model to investigate the dynamic response of semi-enclosed sound barriers on viaducts in light rail transit. During the iterative process of this model, fluctuating wind produced by the train is introduced and combined with wheel-rail loads to analyze the dynamic response of sound barriers. Based on the above model, in the time domain, there are significant differences in the vibration displacement and acceleration under combined excitation when the train head enters and the tail exits the sound barrier region. In the frequency domain, the vibration acceleration level under combined excitation increases significantly within the 0–5 Hz frequency. The parameter analysis results indicate that the total vibration acceleration level is higher at the measuring points close to the direction of travel. As the train speed increases, the effect of fluctuating wind becomes more significant in the low-frequency range (1–3.15 Hz). The smaller number of train formations also improved the vibration acceleration level difference between the combined and wheel-rail excitation.

Flow characteristics induced by a multiform windbreak in complex terrains with and without a train: A simplified method for calculating aerodynamic loads

This study aims to investigate common multiform windbreaks, aligned parallel to railway tracks and perpendicular to incoming wind, in complex terrains. Using unsteady simulations, the study analyzes airflow downstream of these windbreaks and the aerodynamic characteristics during train passage. It evaluates the wind-protection performance of various windbreak types and transitions and identifies factors that influence performance. Results indicate that the vertical surface walls offer stronger wind protection compared to slope walls or viaduct barriers. Flow patterns near transitions reveal that upstream airflow shifts longitudinally from high-performance windbreaks to lower-performance ones, reentering the railway line space from the latter. This suggests a design approach in which neighboring windbreaks exhibit similar performance to optimize protection. On aerodynamic characteristics of the train, the maximum side force on the leading vehicle is found proportional to wind speed and train speed to the powers of 1.6 and 0.5, respectively; train speed affects the pressure on the streamlined head and the vortices around the leeward side. A simplified calculation for aerodynamic loads on a vehicle is proposed and explored with a consideration of wind speed above the railway line. An error margin of the maximum side force by this simplified method is 8.4%, and the saving is at least 88.2% of the computational resources when assessing the crosswind stability of a vehicle. The proposed design for the multiform windbreak, along with the simplified calculation method, can improve the performance of a multiform windbreak and increase the efficiency of assessing crosswind safety for railway operations downstream of the windbreak.

Study on Soil Vibration Propagation Characteristics Caused by Subway near Foundation Pit

Abstract Based on the Shanghai Metro Line 12, a three-dimensional finite element calculation model is established to study the influence of adjacent foundation pits on subway train vibration propagation. A comparative analysis is conducted on the vibration response and propagation characteristics of the subway during single and bidirectional metro operation under free field and adjacent foundation pit conditions. The effects of subway train speed and tunnel depth on vibration propagation are discussed in detail. It is indicated that as the train speed increases, the ground vibration increases, and the increasing trend of vertical acceleration gradually slows down. The deeper the tunnel is buried, the smaller the ground vibration response. The variation of tunnel burial depth has no effect on the attenuation law of vertical vibration.

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models.

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that Adan finds an ϵ-approximate first-order stationary point within O(ϵ-3.5) stochastic gradient complexity on the non-convex stochastic problems (e.g., deep learning problems), matching the best-known lower bound. Extensive experimental results show that Adan consistently surpasses the corresponding SoTA optimizers on vision, language, and RL tasks and sets new SoTAs for many popular networks and frameworks, e.g., ResNet, ConvNext, ViT, Swin, MAE, DETR, GPT-2, Transformer-XL, and BERT. More surprisingly, Adan can use half of the training cost (epochs) of SoTA optimizers to achieve higher or comparable performance on ViT, GPT-2, MAE, etc, and also shows great tolerance to a large range of minibatch size, e.g., from 1 k to 32 k.

Dynamics Performance Research and Calculation of Speed Threshold Curve for High-Speed Trains Under Unsteady Wind Loads

Affected by strong wind environments, the vibration of trains will significantly intensify, which will severely impact the running quality of trains. To address such challenges, an improved wind load model is proposed in this paper to simulate the shock of strong wind on trains. The proposed model employs the integral approach to calculate the equivalent wind load on trains and applies it to the body of trains during the dynamics simulation process. Eventually, the two-level running quality threshold curve for passenger and freight trains is acquired through the conditional probability density function and the regularized regression model. This achievement covers train speed restrictions for wind speeds ranging from 0~25 m/s, providing a scientific basis for railway departments to adjust train speeds based on real-time wind speeds. It is of utmost importance for ensuring the safe and efficient operation of trains under strong wind conditions.

Environmental Health and Transportation: A Landscape Design of a Vital Healthy Area in An Ecological Town

Traveling long distances for specialized treatment causes physical and finanacial coasts. In return, convenient and rapid train transportation along scenic routes provides a suitable atmosphere for recovery in therapeutic health environments. Just a traveling by train saves time andcontributes to economicdevelopment and facilitaiting trade exchanges, aswell as stimulating the economic mobility of regions. Designing a train trajectory thatis not considered from an ecological perspective leads to damage to users and sometimes harms to population Through this research , the ecological space of high speed train station and a parking area on Motorway designed using biomimetics in a healthy area of OuzeraTown in order to avoid the damage resulting from carbon dioxide emissions which provides an appropriate therapeutic environment that provides rapid transportation and specialized healthcare sevices in natural landscape and optimal therapeutic atmosphere in One Health environment.

Improving facial expression recognition for autism with IDenseNet-RCAformer under occlusions.

The term 'autism spectrum disorder' describes a neurodevelopmental illness typified by verbal and nonverbal interaction impairments, repetitive behaviour patterns and poor social interaction. Understanding mental states from FEs is crucial for interpersonal interaction and social interaction. But when there are occlusions like glasses, facial hair or self-occlusion, it becomes harder to identify facial expressions accurately. This research tackles the issue of identifying facial expressions when parts of the face are occluded and suggests an innovative technique to tackle this difficulty. Creating a strong framework for facial expression recognition (FER) that better handles occlusions and increases recognition accuracy is the goal of this research. Therefore, we propose novel Improved DenseNet-based Residual Cross-Attention Transformer (IDenseNet-RCAformer) system to tackle the partial occlusion FER problem in autism patients. The recognition framework's efficacy is assessed using four datasets of facial expressions, and some preprocessing procedures are conducted to increase the expression recognition efficiency. After that, when recognizing expressions, a simple argmax function is applied to get a forecasted landmark position from a heatmap. Then feature extraction phase, local and global representation are captured from preprocessed images by adopting Inception-ResNet-V2 approach, Cross-Attention Transformer, respectively. Moreover, both features are fused by employing the FusionNet method, thereby enhancing system's training speed and precision. After the features are extracted, an improved DenseNet mechanism is applied to efficiently recognize some variety of facial expressions in partially occluded autism patients. A number of performance metrics are determined and analysed to demonstrate the proposed approach's effectiveness, where the IDenseNet-RCAformer performs best with an accuracy of 98.95%. According to the experimental findings, the proposed framework significantly outperforms the prior recognition frameworks in terms of recognition outcomes.

Utilizing pretrained convolutional neural networks for crack detection and geometric feature recognition in concrete surface images

Accurate detection of concrete cracks and identification of crack geometric characteristics are essential for ensuring structural safety. To address this, a concrete surface crack detection method was developed using pretrained convolutional neural networks (CNNs). Additionally, a comprehensive framework was proposed that integrates the pretrained CNN as a feature extractor with various regression algorithms to recognize concrete crack features, including the crack area, maximum width, and average width. Results demonstrate that the detection accuracy and training speed of modified CNN models based on pretrained networks, such as VGG16 and MobileNet, outperform those of CNN models trained from scratch. Moreover, the established CNNs achieve high accuracy in handling diverse images affected by environmental disturbances and noise. The developed comprehensive framework successfully recognized crack geometric features in concrete surface images. Evaluation of four regression algorithms revealed that support vector regression (SVR) achieved R2 values of 0.863 for predicting crack area and 0.764 for predicting the crack average width, while the XGBoost regression algorithm yielded an R2 of 0.782 for predicting the crack maximum width.

Abrupt changing aerodynamic loads resulting in diminished ride comfort when two high-speed trains intersect in a tunnel

The lateral force exerted when two trains pass each other can adversely affect train safety, and this adverse effect becomes more pronounced as the train speed increases. When trains cross paths in tunnels, the aerodynamics differ from those in open lines due to the restrictive nature of tunnel walls. Utilizing the Renormalization Group (RNG) k-ε turbulence model and the “Mosaic” grid method, this research examines changes in aerodynamic load of the train and ride comfort during a intersection at 400 km/h in a tunnel and contrasts this with conditions at 350 km/h. The results indicate that the change in aerodynamic load on each carriage is more pronounced when the head train of the oncoming train passes than when its tail train passes, with the largest variation observed during the passing of both the head and tail trains. This alteration in aerodynamic load is primarily attributed to the air being pushed in the locomotive area and the negative pressure from the vortex structure between trains. When the speed is increased from 350 km/h to 400 km/h, the aerodynamic load on the train increases by approximately 20 % to 40 %, and the acceleration of the head train grows by 20 % to 50 %. The most noticeable decrease in ride comfort is observed in the head train, with the highest increase in the head train’s Overall Vibration Total Value (OVTV), which rises by 30.1 %.

Performance prediction of sintered NdFeB magnet using multi-head attention regression models

The preparation of sintered NdFeB magnets is complex, time-consuming, and costly. Data-driven machine learning methods can enhance the efficiency of material synthesis and performance optimization. Traditional machine learning models based on mathematical and statistical principles are effective for structured data and offer high interpretability. However, as the scale and dimensionality of the data increase, the computational complexity of models rises dramatically, making hyperparameter tuning more challenging. By contrast, neural network models possess strong nonlinear modeling capabilities for handling large-scale data, but their decision-making and inferential processes remain opaque. To enhance interpretability of neural network, we collected 1,200 high-quality experimental data points and developed a multi-head attention regression model by integrating an attention mechanism into the neural network. The model enables parallel data processing, accelerates both training and inference speed, and reduces reliance on feature engineering and hyperparameter tuning. The coefficients of determination for remanence and coercivity are 0.97 and 0.84, respectively. This study offers new insights into machine learning-based modeling of structure-property relationships in materials and has potential to advance the research of multimodal NdFeB magnet models.

Exploration on the application of dynamics principles in police physical education curriculum teaching

The police physical education curriculum focuses on the integration of physical training and police skills training, aiming to enhance students’ physical fitness and police skill levels. By reasonably applying the principles of dynamics, teachers can develop more scientific and reasonable training plans, optimizing aspects such as strength training, speed training, endurance training, and skill instruction. The application of dynamics principles not only helps improve students’ physical fitness but also assists teachers in better understanding the mechanical principles involved when students perform movements, thereby enabling more effective skill instruction. Therefore, in the teaching of police physical education, emphasis should be placed on the integration and practice of dynamics principles to improve teaching quality and effectiveness, providing strong support for cultivating high-quality police talents.

A novel multidisciplinary hierarchical collaborative optimization of the high-speed pantograph considering the uncertainty of joint clearances

The pantograph–catenary system is a critical subsystem of the train power supply system. However, the increasing train speed brings many challenges. In particular, the interaction impacts of different disciplines are intensified. To solve the optimization problem of the high-speed pantograph, a multidisciplinary hierarchical collaborative optimization (MHCO) method is proposed in this article. MHCO divides the subsystem-level optimization process of design variables into various layers based on the shared design variables between disciplines. At the overall system level, the consistency constraint condition is constructed to obtain optimal values according to the optimized subsystem-level results. Four optimization indicators [translational motion of the pantograph head, pantograph lightweighting, pantograph–catenary contact force (PCCF) and active control] are established to conduct he design optimization of a high-speed pantograph. Compared with the initial value, the four indicators have improved by 78.7076%, 16.6516%, 38.1037% and 15.5542%. MHCO provides a new method for designing complex mechanical systems.

V^3: Viewing Volumetric Videos on Mobiles via Streamable 2D Dynamic Gaussians

Experiencing high-fidelity volumetric video as seamlessly as 2D videos is a long-held dream. However, current dynamic 3DGS methods, despite their high rendering quality, face challenges in streaming on mobile devices due to computational and bandwidth constraints. In this paper, we introduce V 3 (Viewing Volumetric Videos), a novel approach that enables high-quality mobile rendering through the streaming of dynamic Gaussians. Our key innovation is to view dynamic 3DGS as 2D videos, facilitating the use of hardware video codecs. Additionally, we propose a two-stage training strategy to reduce storage requirements with rapid training speed. The first stage employs hash encoding and shallow MLP to learn motion, then reduces the number of Gaussians through pruning to meet the streaming requirements, while the second stage fine tunes other Gaussian attributes using residual entropy loss and temporal loss to improve temporal continuity. This strategy, which disentangles motion and appearance, maintains high rendering quality with compact storage requirements. Meanwhile, we designed a multi-platform player to decode and render 2D Gaussian videos. Extensive experiments demonstrate the effectiveness of V 3 , outperforming other methods by enabling high-quality rendering and streaming on common devices, which is unseen before. As the first to stream dynamic Gaussians on mobile devices, our companion player offers users an unprecedented volumetric video experience, including smooth scrolling and instant sharing. Our project page with source code is available at https://authoritywang.github.io/v3/.

DLT-GAN: Dual-Layer Transfer Generative Adversarial Network-Based Time Series Data Augmentation Method

In actual production processes, analysis and prediction tasks commonly rely on large amounts of time-series data. However, real-world scenarios often face issues such as insufficient or imbalanced data, severely impacting the accuracy of analysis and predictions. To address this challenge, this paper proposes a dual-layer transfer model based on Generative Adversarial Networks (GANs) aiming to enhance the training speed and generation quality of time-series data augmentation under small-sample conditions while reducing the reliance on large training datasets. This method introduces a module transfer strategy based on the traditional GAN framework which balances the training between the discriminator and the generator, thereby improving the model’s performance and convergence speed. By employing a dual-layer network structure to transfer the features of time-series signals, the model effectively reduces the generation of noise and other irrelevant features, improving the similarity of the generated signals’ characteristics. This paper uses speech signals as a case study, addressing scenarios where speech data are difficult to collect and the limited number of speech samples available for effective feature extraction and analysis. Simulated speech timbre generation is conducted, and the experimental results on the CMU-ARCTIC database show that, compared to traditional methods, this approach achieves significant improvements in enhancing the consistency of generated signal features and the model’s convergence speed.

Dynamic response analysis of concrete filled steel tube tied arch bridge on a slab foundation under moving train load

The analysis of bridge vibration response under train loads is crucial for the operational safety of railway bridge structures. In this study, a three-dimensional coupled dynamic model of train-track-truss arch bridge is established. Based on the numerical simulation results, the effects of different train axle loads and speeds on the vibration response of the truss arch bridge are analyzed, and the time-history changes of the displacement and stress at critical sections of the bridge are revealed. The results show that: during the train operation, the maximum vertical dynamic stress and maximum vertical displacement are linearly related to the train axle load and speed. The greater the train axle load and speed, the larger the maximum vertical dynamic stress and maximum vertical displacement. The maximum vertical acceleration generated during train operation increases linearly with train speed and exponentially with train axle load. The most unfavorable section occurs at the mid-span of the bridge, where the maximum vertical displacement, maximum vertical dynamic stress, and maximum vertical acceleration are all at their peak. This research has significant implications for engineering safety and operation.

Novel Method for Evaluating Wetland Ecological Environment Quality Based on Coupled Remote Sensing Ecological Index and Landscape Pattern Indices: Case Study of Dianchi Lake Wetlands, China

Wetlands serve as crucial ecological buffers, significantly influencing temperature reduction, carbon storage, regional climate regulation, and urban wastewater treatment. To elucidate the relationship between wetland landscape patterns and ecological environment, and to accurately assess lake ecosystems, this study proposes a semi-supervised classification method based on RSEI and K-Means. By integrating landscape pattern indices, the Remote Sensing Ecological Index (RSEI), and disturbance proximity, a comprehensive evaluation of the ecological quality of the Dianchi wetlands was conducted. The results indicate that the RSEI-K-Means method, with K set to 50, achieved overall accuracies (OAs) and Kappa values of 0.91 and 0.88, surpassing the SVM’s 0.85 and 0.80. This method effectively combines ecological and landscape indices without relying on extensive training samples, enhancing accuracy and speed in wetland information extraction and addressing the challenges of spatial heterogeneity. This study reveals that from 2007 to 2009, and 2013 to 2015, landscape patterns were significantly influenced by the rapid expansion of Kunming city, exacerbating wetland fragmentation. Notably, significant ecological quality changes were observed in 2009 and 2013, with gradual recovery post-2013 due to strengthened environmental protection policies. The RSEI disturbance proximity analysis indicated that the affected areas were primarily concentrated in regions of high human activity, confirming the method’s high sensitivity and effectiveness. This study can help in wetland ecosystem research and management.