Establishment and Optimization of Status Assessment Variables for Heavy Haul Railway Line Service Performance

In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status evaluation variable set construction and reduction optimization method is proposed. Firstly, the status of heavy haul railway line is analyzed, and an initial set of evaluation variables affecting the line status is constructed. Then, based on the association rule and the principal component analysis method, key variables are extracted from the initial variable set to establish the evaluation system. Finally, this method is verified with actual data of a line. The results show that the service performance of heavy haul railway line can still be evaluated accurately when the evaluation variables are reduced by 60% in the proposed method.

Numerical Simulations of Snow Accumulation in the Bogie Region of a Train Considering Snow Particle Rotation

To investigate the influence of snow particle rotational motion on the accumulation of snow in the bogie region of high-speed trains,an Euler‒Lagrange numerical approach is adopted.The study examines the effects of snow particle diameter and train speed on the ensuing dynamics.It is shown that considering snow particle rotational motion causes significant deviation in the particle trajectories with respect to non-rotating particles.Such a deviation increases with larger snow particle diameters and higher train speeds.The snow accumulation on the overall surface of the bogie increases,and the amount of snow on the vibration reduction device varies greatly.In certain conditions,the amount of accumulated snow can increase by several orders of magnitudes.

Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder

In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.

Robust Parameter Identification Method of Adhesion Model for Heavy Haul Trains

A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.

Real-Time Mosaic Method of Aerial Video Based on Two-Stage Key Frame Selection Method

A two-stage automatic key frame selection method is proposed to enhance stitching speed and quality for UAV aerial videos. In the first stage, to reduce redundancy, the overlapping rate of the UAV aerial video sequence within the sampling period is calculated. Lagrange interpolation is used to fit the overlapping rate curve of the sequence. An empirical threshold for the overlapping rate is then applied to filter candidate key frames from the sequence. In the second stage, the principle of minimizing remapping spots is used to dynamically adjust and determine the final key frame close to the candidate key frames. Comparative experiments show that the proposed method significantly improves stitching speed and accuracy by more than 40%.

Enhanced and tunable Imbert–Fedorov shift based on epsilon-near-zero response of Weyl semimetal

We theoretically investigate the reflected spatial Imbert–Fedorov(IF)shift of transverse-electric(TE)-polarized beam illuminating on a bulk Weyl semimetal(WSM).The spatial IF shift is enhanced significantly at two different frequencies close to the epsilon-near-zero(ENZ)frequency,where large values of reflection coefficients|r_(pp)|/|r_(ss)|are obtained due to the ENZ response induced different rapid increasing trends of|r_(pp)|and|r_(ss)|.Particularly,the tunable ENZ effect with tilt degree of Weyl cones and Fermi energy enables the enhanced spatial IF shift at different frequencies.The enhanced spatial IF shift also shows the adjustability of WSM thickness,incident angle and Weyl node separation.Our findings provide easy and available methods to enlarge and adjust the reflected IF shift of TE-polarized light with a WSM.

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

Image Stitching Method of Non-Ferrous Smelting Scene Based on SIFT Algorithm and Color Constancy

In non-ferrous smelting scenes, complex environments, uneven lighting conditions, and inaccurate automatic exposure parameters result in the panoramic image having obvious seams and significant brightness differences in different regions. To address these issues, a novel image stitching method based on Scale-Invariant Feature Transform (SIFT) and color constancy theory was proposed. Initially, the input image was preprocessed followed by feature extraction and matching. Subsequently, color constancy processing based on matching points was performed to acquire an image with consistent brightness to be spliced. This process was complemented by the integration of multi-band fusion to enhance the original fusion procedure. Eventually, a spliced image with seamless blending and even luminosity was generated. The method proposed in this paper can obtain panoramic images that were more suitable for human eyes to observe, and greatly improve the subjective and objective performance of panoramic images. In the non-ferrous smelting scene, the PSNR and SSIM scores were improved by 3.555 dB and 0.16 respectively.

Train wheel-rail force collaborative calibration based on GNN-LSTM

Accurate wheel-rail force data serves as the cornerstone for analyzing the wheel-rail relationship.However,achieving continuous and precise measurement of this force remains a significant challenge in the field.This article introduces a calibration algorithm for the wheel-rail force that leverages graph neural networks and long short-term memory networks.Initially,a comprehensive wheel-rail force detection system for trains was constructed,encompassing two key components:an instrumented wheelset and a ground wheel-rail force measuring system.Subsequently,utilizing this system,two distinct datasets were acquired from the track inspection vehicle:instrumented wheelset data and ground wheel-rail force data,a feedforward neural network was employed to calibrate the instrumented wheelset data,referencing the ground wheel-rail force data.Furthermore,ground wheel-rail force data for the locomotive was obtained for the corresponding road section.This data was then integrated with the calibrated instrumented wheelset data from the track inspection vehicle.Leveraging the GNN-LSTM network,the article establishes a mapping relationship model between the wheel-rail force of the track inspection vehicle and the locomotive wheel-rail force.This model facilitates continuous measurement of locomotive wheel-rail forces across three typical scenarios:straight sections,long and steep downhill sections,and small curve radius sections.

Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network

Multisensor data fusionmethod can improve the accuracy of bearing fault diagnosis,in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis,a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network(MCMI-GCFN)is proposed in this paper.Firstly,a Convolutional Autoencoder(CAE)and Squeeze-and-Excitation Block(SE block)are used to extract features of raw current and vibration signals.Secondly,the model introduces source domain classifiers and domain discriminators to capture modal invariance between different modal data based on domain adversarial training,making use of the redundancy and complementarity between multimodal data.Then,the spatial aggregation property of Graph Convolutional Neural Networks(GCN)is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic information.Finally,the validation is conducted on the public bearing damage current and vibration dataset from Paderborn University.The experimental results showed that the delivered fusion method achieved a bearing fault diagnosis accuracy of 99.6%,which was about 9%–11.4%better than that with nonfusion methods.

Research on Track Fastener Service Status Detection Based on Improved Yolov4 Model

As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.

Online Capacitor Voltage Transformer Measurement Error State Evaluation Method Based on In-Phase Relationship and Abnormal Point Detection

The assessment of the measurement error status of online Capacitor Voltage Transformers (CVT) within the power grid is of profound significance to the equitable trade of electric energy and the secure operation of the power grid. This paper advances an online CVT error state evaluation method, anchored in the in-phase relationship and outlier detection. Initially, this method leverages the in-phase relationship to obviate the influence of primary side fluctuations in the grid on assessment accuracy. Subsequently, Principal Component Analysis (PCA) is employed to meticulously disentangle the error change information inherent in the CVT from the measured values and to compute statistics that delineate the error state. Finally, the Local Outlier Factor (LOF) is deployed to discern outliers in the statistics, with thresholds serving to appraise the CVT error state. Experimental results incontrovertibly demonstrate the efficacy of this method, showcasing its prowess in effecting online tracking of CVT error changes and conducting error state assessments. The discernible enhancements in reliability, accuracy, and sensitivity are manifest, with the assessment accuracy reaching an exemplary 0.01%.

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.

YOLOv8 for Fire and Smoke Recognition Algorithm Integrated with the Convolutional Block Attention Module

The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods.

WiFi Indoor Positioning and Tracking Algorithm Based on Compressive Sensing and Sage-Husa Adaptive Kalman Filter

Aiming at the problem that the positioning accuracy of WiFi indoor positioning technology based on location fingerprint has not reached the requirements of practical application, a WiFi indoor positioning and tracking algorithm combining adaptive affine propagation (AAPC), compressed sensing (CS) and Kalman filter is proposed. In the off-line phase, AAPC algorithm is used to generate clustering fingerprints with optimal clustering effect performance;In the online phase, CS and nearest neighbor algorithm are used for position estimation;Finally, the Kalman filter and physical constraints are combined to perform positioning and tracking. By collecting a large number of real experimental data, it is proved that the developed algorithm has higher positioning accuracy and more accurate trajectory tracking effect.

Image Processing for Denoising Using Composite Adaptive Filtering Methods Based on RMSE

As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images contaminated by unknown noise has gradually become one of the research focuses. In order to achieve blind denoising and separation to restore images, this paper proposes a method for image processing based on Root Mean Square Error (RMSE) by integrating multiple filtering methods for denoising. This method includes Wavelet Filtering, Gaussian Filtering, Median Filtering, Mean Filtering, Bilateral Filtering, Adaptive Bandpass Filtering, Non-local Means Filtering and Regularization Denoising suitable for different types of noise. We can apply this method to denoise images contaminated by blind noise sources and evaluate the denoising effects using RMSE. The smaller the RMSE, the better the denoising effect. The optimal denoising result is selected through comprehensively comparing the RMSE values of all methods. Experimental results demonstrate that the proposed method effectively denoises and restores images contaminated by blind noise sources.

Multi-Antenna Jammer Assisted Covert Communications in Data Collected IoT with NOMA

In this paper,we investigate covert communications in data collected IoT with NOMA,where the paired sensor nodes S_(m) and S_(n) transmit covert messages to a legitimate receiver(Bob)in the presence of a Warden(Willie).To confuse the detection at Willie,an extra multi-antenna friendly jammer(Jammer)has been employed to transmit artificial noise(AN)with random power.Based on the CSI of Willie is available or not at Jammer,three AN transmission schemes,including null-space artificial noise(NAN),transmit antenna selection(TAS),and zeroforcing beamforming(ZFB),are proposed.Furthermore,the closed-form expressions of expected minimum detection error probability(EMDEP)and joint connection outage probability(JCOP)are derived to measure covertness and reliability,respectively.Finally,the maximum effective covert rate(ECR)is obtained with a given covertness constraint.The numerical results show that ZFB scheme has the best maximum ECR in the case of the number of antennas satisfies N>2,and the same maximum ECR can be achieved in ZFB and NAN schemes with N=2.Moreover,TAS scheme also can improve the maximum ECR compared with the benchmark scheme(i.e.,signal-antenna jammer).In addition,a proper NOMA node pairing can further improve the maximum ECR.

Model-Free Sliding Mode Control for PMSM Drive System Based on Ultra-Local Model

This paper presents a novel model-free sliding mode control(MFSMC)method to improve the speed response of permanent magnet synchronous machine(PMSM)drive system.The ultra-local model(ULM)is first derived based on the input and the output of the PMSM.Then,the novel MFSMC method is presented,and the controller is designed based on ULM and MFSMC.A sliding mode observer(SMO)is constructed to estimate the unknown part of the ULM.The estimated unknown part is feedbacked to MFSMC controller to performcompensation for parameter perturbations and external disturbances.Compared with the sliding mode control(SMC)method,the results of simulation and experiment demonstrate that the presented MFSMC method improves the dynamic response and robustness of the PMSM drive system.

Enhanced potassium storage of carbon nanofibers as binderfree anodes enabled by coupling ultra-small amorphous Sb_(2)O_(3),graphene modification and sulfur doping

Considering the intrinsic advantages of natural copiousness and cost-effectiveness of potassium resource,potassium-ion batteries(KIBs) are booming as prospective alternatives to lithium-ion batteries(LIBs) in large-scale energy storage scenarios. Nevertheless, lacking desirable electrodes for reversibly hosting the bulky K+hinders the widespread application of KIBs, and it needs to be urgently solved. Hereon, the porous S-doped Sb_(2)O_(3)-graphene-carbon(SAGC) nanofibers are manufactured through an adjustable and facile approach, which involves electrospinning, in situ etching and sulfuration. The synthesized SAGC is featured by the ultra-small amorphous Sb_(2)O_(3) homogeneously wrapped inside the carbon matrix, as well as the co-incorporation of graphene and sulfur. Tentatively,the SAGC nanofiber sheets are applied as binder-free anodes for KIBs, exhibiting a prominent cycling life(256.72 m Ah·g^(-1) over 150 cycles at 100 m A·g^(-1)) and rate·g^(-1) over 100 cycles at 1 A·g^(-1)). The positive synergy among all the active components accounts for the distinguished performances of the SAGC. By reinforcing the tolerability to the swelling stress, producing the valid electrochemical active sites, and promoting the charge transferring for reversible K+uptake, the SAGC finally renders the excellent cyclability, capacity, and rate capability. Moreover, the extrinsic electrochemical pseudocapacitance characteristics induced by the porous carbon substrate elevate the K-storage capacity of the SAGC as well. It is hoped that the conclusions drawn may offer new insights into a direction for the high-performance binderfree KIB anodes.

A bearing fault diagnosis method based on a convolutional spiking neural network with spa tial-tempor al fea ture-extr action capability

Convolutional neur al netw orks(CNNs)ar e widel y used in the field of fault diagnosis due to their strong feature-extraction capability.How ever,in eac h timeste p,CNNs onl y consider the curr ent input and ignor e any cyclicity in time,ther efor e pr oducing difficulties in mining temporal features from the data.In this w ork,the third-gener ation neur al netw ork-the spiking neur al netw ork(SNN)-is utilized in bearing fault diagnosis.SNNs incorpor ate tempor al concepts and utilize discrete spike sequences in communication,making them more biolo gically e xplanatory.Inspired by the classic CNN LeNet-5 fr amew ork,a bearing fault diagnosis method based on a convolutional SNN is proposed.In this method,the spiking convolutional network and the spiking classifier network are constructed by using the inte gr ate-and-fire(IF)and leaky-inte gr ate-and-fire(LIF)model,respectively,and end-to-end training is conducted on the overall model using a surrogate gradient method.The signals are adaptively encoded into spikes in the spiking neuron layer.In addition,the network utilizes max-pooling,which is consistent with the spatial-temporal characteristics of SNNs.Combined with the spiking con volutional la y ers,the netw ork fully extracts the spatial-temporal featur es fr om the bearing vibration signals.Experimental validations and comparisons are conducted on bearings.The results show that the proposed method achieves high accuracy and takes fewer time steps.