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Nickel-cadmium alkaline batteries are the core energy source for auxiliary devices of electric multiple units. Hence, an accurate estimation of their state of charge (SOC) is significantly important for prolonging battery life and improving energy efficiency. Given the limitations of existing SOC estimation methods when dealing with small-sample battery cycling data, this paper proposes an attention mechanism integrated convolutional neural network-gated recurrent unit (CNN-GRU) model for battery SOC estimation, and experimental validation is conducted on the LPH140A model nickel-cadmium batteries used in electric multiple units. The model employs a convolutional neural network (CNN) to extract short-term feature dependencies from long sequences within the battery cycling data. Then, an attention mechanism-integrated gated recurrent unit (GRU) is adopted to capture long spatial distance dependencies of the extracted feature data, resulting in more precise battery SOC estimation. To precisely estimate the SOC of small-sample battery cycling data, this paper transforms the continuous regression model into a classification problem, discretizes the battery SOC ranges, and converts the final prediction result into discrete SOC values. The experimental results show that compared with the CNN-GRU algorithm, the proposed approach improves three key metrics — root mean square error, mean absolute error, and mean relative error by 18.90%, 17.92% and 19.78%, respectively, demonstrating impressive prediction accuracy and stability.

Mapping relationships of multidimensional architectures play an essential role in the autonomous transportation system (ATS), which can help understand the complex relationships between multidimensional architectures. The current mapping relationship discovery for multidimensional architectures in ATS requires significant manual involvement, leading to the underutilization of textual data and the intense subjectivity of results. In order to address the above issues, it is necessary to mine and further utilize the semantic information in the textual data. This study applies the text-matching model to the mapping relationship discovery of multi-dimensional architectures, which can calculate the semantic similarity between texts. On this basis, a method based on the Siamese-BERT-wwm-Bi-LSTM model is proposed, which incorporates Chinese BERT with whole word masking (BERT-wwm), bidirectional long-short term memory (Bi-LSTM), and the Siamese Network. A series of experiments are conducted with different text-matching models. The results show that the precision rate, recall rate, and F1-score exceed 80% for most applied methods, which verifies the feasibility of using the text-matching model for mapping relationship discovery. These results expect to provide a method with good performance that can automatically perform mapping relationship discovery., Author(s): You Liu [1]; Haonan Tuo [1]; Mingfan He [1]; Qiang Fu [1]; Tianjian Yu (corresponding author) [1]; Jinjun Tang (corresponding author) [1] 1. Introduction With the development of emerging [...]

To achieve precise prediction of the air pressure changes in the critical components of the air brake system, including brake pipes (BP), auxiliary reservoirs (AR), and brake cylinders (BC), this paper proposed a neural network-based air brake model developed from the brake application and release experimental data with different brake applications (50, 70, 100, and 150 kPa) conducted on a heavy haul train (HHT) test platform. The physical structures of the air brake system and the working mechanisms of a neural network are introduced, and the neural network-based air brake model scheme is presented by analysing the variation of air pressure within the air brake system under typical braking conditions (50 kPa). Besides, by comparing with other models, the superiority of the proposed model is verified, and the proposed model achieves higher computational efficiency, which increased by about 2900 times compared to the fluid dynamic model under the same conditions. Then, the model is applied to the calculation of the longitudinal dynamics of a 10,000-ton HHT, and the results demonstrate that the proposed model could serve as a promising tool for solving the braking excitation problem of longitudinal dynamics of trains and the study of intelligent braking control of trains. [ABSTRACT FROM AUTHOR]

Vibration contributes large increases in railway passenger discomfort during long-term sitting. Discomfort caused by vibration may differ in different operation conditions. This paper conducted field measurements to investigate the interrelationships between the three. Participants completed a 240-min train journey with their whole-body vibration, subjective comfort ratings and train operating parameters being recorded. A large correlation was observed between the estimated vibration dose value and subjective comfort. The relationship that vibration magnitude significantly increases with increasing the train speed and tunnel density was also found and quantified. A vibration exposure limit of 2.08 m/s1.75 corresponding to the boundary between subjective ratings of comfortable and discomfortable was obtained. Based on the exposure limit and the quantified relationship, a vibration comfort prediction method that can calculate the passenger's maximum tolerance time under a given operation condition was proposed and may help in determining the optimal operating speed and tunnels distribution to alleviate vibration discomfort. Practitioner summary: Similar to the guide to effect of vibration on health in current standard, a vibration exposure limit regarding comfort was provided for reference when assessing long-term vibration comfort. Meanwhile, a prediction method was proposed for determining the best train operating speed and tunnels distribution, thereby alleviating railway passengers' vibration discomfort. [ABSTRACT FROM AUTHOR]

Ultrasonic surface rolling processing (USRP) is a new method to improve the fatigue performance of titanium alloy, and repeated processing pass is an important factor that affects its strengthening effect. The effect of USRP passes on the surface microstructure, residual stress, fatigue performance and corrosion resistance of titanium alloy is researched via SEM, X-ray diffractometer, rotating–bending fatigue test and electrochemical impedance spectroscopy. The results show that Ti6Al4V alloy undergoes cumulative plastic deformation during USRP process, the surface grains are refined and a residual compressive stress field with a thickness of 500 μm is introduced, which together improve the fatigue performance of the Ti6Al4V alloy. Increasing the repeated processing passes will deepen the grain refinement layer and increase the surface hardening effect, but the fatigue life of the Ti6Al4V alloy does not increase with an increase in processing passes. A five-passes processing under a static force of 550 N can result in a greater gain for the fatigue resistance of the Ti6Al4V alloy; the fatigue life of a five-passes-processed sample under 600 MPa is 8 times higher than that of an untreated sample, and its fatigue crack source initiates at the subsurface away from the surface of 180 μm. Furthermore, Ti6Al4V alloys treated by USRP show a better corrosion resistance in both neutral and acidic solutions, especially for the five-passes-processed sample. [ABSTRACT FROM AUTHOR]

The accurate prediction of the state of charge (SOC) of Ni-Cd batteries is critical for developing battery management systems for high-speed trains. To address the challenges of the large floating charge voltage of Ni-Cd batteries and the vulnerability of a battery's SOC to environmental factors such as temperature, this paper proposes an adaptive adjustment mechanism-based particle swarm optimization (APSO) generalized regression neural network (GRNN) model. The proposed model introduces the concept of the particle aggregation degree to quantify the convergence of the particle swarm optimization (PSO) algorithm. Furthermore, the speed weight of the particle swarm is adaptively adjusted using a comprehensive loss function to optimize the parameters of the GRNN model. To validate the proposed method, simulation experiments are conducted under test conditions using Ni-Cd batteries, and the prediction accuracies of various algorithms are compared. The experimental results demonstrate that the APSO-GRNN model significantly reduces the model's prediction error. In addition, under the influence of different temperatures and noises, this method demonstrates strong robustness and high practical application value by accurately predicting the SOC, even with limited data samples. [ABSTRACT FROM AUTHOR]

A new, optimal thermal-based adaptive frequency control (OTC) for a bidirectional DC–DC converter with full-range zero-voltage switching (ZVS) is presented in this paper. The proposed OTC achieves ZVS for a bidirectional DC–DC converter without any zero-crossing detection (ZCD) circuit or current sensor. In order to ensure a ZVS over a wide voltage and load range, the optimal switching frequency was adjusted by detecting and tracking the lowest junction temperature of the semiconductor device. Because the proposed OTC does not need a ZCD circuit, the complexity of the circuit and the susceptibility of the sampling noise are reduced. Additionally, compared with the expensive current sensor, the proposed method of temperature detection by the NTC thermistor decreases the cost. In addition, the proposed OTC does not need accurate circuit parameters and voltage or current sampling results, so the dependence on the parameters is reduced. Moreover, the temperature of the switch is directly monitored in the proposed OTC, which can effectively protect the device. A prototype with 16–32 V input and 48 V output was built, and the experiment results prove the effectiveness and feasibility of it. [ABSTRACT FROM AUTHOR]

Trip destination prediction plays an important role in exploring urban travel patterns. Accurate prediction can improve the efficiency of traffic management and the quality of location‐based services. Here, a deep learning structure that contains three components: travel information extraction, classification learning mechanism, and output module is proposed. Three types of information (the partial trajectory of on‐going trips, historical trajectories, and related external information) are extracted in the first component. Then, the classification learning mechanism chooses different methods (i.e. Long Short‐Term Memory network and Embedding technology) according to the characteristic of variables. Finally, an output layer that integrates the prior information about destinations is constructed. Two open‐source trajectory datasets are used to validate the effectiveness of the proposed model. Results show that the proposed model outperforms benchmark models using only part of the information or using all of the information but ignore the classification learning mechanism. The performance of the proposed model under different call types and travel durations is further explored. The result of this study will help understand travel behaviour in urban cities. [ABSTRACT FROM AUTHOR]

The safety and reliability of inverters play a significant role in stable power electronic system operations. Considering a railway vehicle air-conditioning inverter as a case study, this paper presents a generic open-circuit fault diagnosis method that can be applied to various inverter and control schemes. First, a detailed model of three-phase output currents under both normal and fault status is derived, and then the concept of space trajectory is introduced based on the three-phase output currents. The open-circuit faults of the inverter lead to output phase current distortion. Therefore, different types of faults have different space trajectories, and the corresponding shape of the space trajectory is considered to be a fault feature. Then, the fault is located by the direction vector and center-of-mass coordinate. The effectiveness and reliability of the diagnosis method are verified and analyzed using a hardware-in-the-loop test platform. [ABSTRACT FROM AUTHOR]

The instability of the switching frequency in an inverter with hysteresis current results in a high failure rate of the power switching components of the inverter. To address this hurdle, a low-cost fault-tolerant control strategy is proposed in this paper to effectively correct the faults. [ABSTRACT FROM AUTHOR]

This paper is concerned with finite-time stabilization (FTS) analysis for a class of uncertain switched positive linear systems with time-varying delays. First, a new definition of finite-time boundedness (FTB) is introduced for switched positive system. This definition can simplify FTS analysis. Taking interval and polytopic uncertainties into account, a robust state feedback controller is built such that the switched positive linear system is finite-time bounded. Finally, an example is employed to illustrate the validities of obtained results. [ABSTRACT FROM AUTHOR]

Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, but rarely both. To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling relationships change across the distribution of crash rates considering the effects of spatial autocorrelation. Three types of explanatory variables, i.e., demographic, traffic networks and volumes, and land-use patterns, were considered. Using data collected in New York City from 2017 to 2019, the results show that: (1) the SARQ model outperforms the traditional quantile regression model in prediction and fitting performance; (2) the effects of variables vary with the quantiles, mainly classifying three types: increasing, unchanged, and U-shaped; (3) at the high tail of crash rate distribution, the effects commonly have sudden increases/decrease. The findings are expected to provide strategies for reducing the crash rate and improving road traffic safety. [ABSTRACT FROM AUTHOR]

AbstractIn this study, a novel modular multicell structure was proposed to achieve tunable energy absorption characteristics. Dovetail grooves and protrusions were designed on square thin-walled tubes as interfaces to realize the modular assembly. The quasi-static compression experimental results showed that single thin-walled tubes made of thermoplastic polyurethanes and short carbon-fiber-reinforced polyamide exhibited high elasticity and high compressive strength, respectively. Due to the different mechanical properties of the two materials, energy absorption performance of modular structures reached different levels with the variation of cell materials. The concept of modular multicell structure provided possibility to customize energy absorbers with desired properties. [ABSTRACT FROM AUTHOR]

Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, but rarely both. To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling relationships change across the distribution of crash rates considering the effects of spatial autocorrelation. Three types of explanatory variables, i.e., demographic, traffic networks and volumes, and land-use patterns, were considered. Using data collected in New York City from 2017 to 2019, the results show that: (1) the SARQ model outperforms the traditional quantile regression model in prediction and fitting performance; (2) the effects of variables vary with the quantiles, mainly classifying three types: increasing, unchanged, and U-shaped; (3) at the high tail of crash rate distribution, the effects commonly have sudden increases/decrease. The findings are expected to provide strategies for reducing the crash rate and improving road traffic safety.

Breast cancer has been shown to live in the tumor microenvironment, which consists of not only breast cancer cells themselves but also a significant amount of pathophysiologically altered surrounding stroma and cells. Diverse components of the breast cancer microenvironment, such as suppressive immune cells, re-programmed fibroblast cells, altered extracellular matrix (ECM) and certain soluble factors, synergistically impede an effective anti-tumor response and promote breast cancer progression and metastasis. Among these components, stromal cells in the breast cancer microenvironment are characterized by molecular alterations and aberrant signaling pathways, whereas the ECM features biochemical and biomechanical changes. However, triple-negative breast cancer (TNBC), the most aggressive subtype of this disease that lacks effective therapies available for other subtypes, is considered to feature a unique microenvironment distinct from that of other subtypes, especially compared to Luminal A subtype. Because these changes are now considered to significantly impact breast cancer development and progression, these unique alterations may serve as promising prognostic factors of clinical outcome or potential therapeutic targets for the treatment of TNBC. In this review, we focus on the composition of the TNBC microenvironment, concomitant distinct biological alteration, specific interplay between various cell types and TNBC cells, and the prognostic implications of these findings.

In this paper, the design of a low power heterogeneous wearable multi-sensor system, built with Zynq System-on-Chip (SoC), for human activity evaluation is presented. The powerful data processing capability and flexibility of this SoC represent significant improvements over our previous ARM based system designs. The new system captures and compresses multiple color images and sensor data simultaneously. Several strategies are adopted to minimize power consumption. Our wearable system provides a new tool for the evaluation of human activity, including diet, physical activity and lifestyle.