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Fatigue driving is a serious threat to road safety, which is why accurately identifying fatigue driving behavior and warning drivers in time are of great significance in improving traffic safety. However, accurately recognizing fatigue driving is still challenging due to large intra-class variations in facial expression, continuity of behaviors, and illumination conditions. A fatigue driving recognition method based on feature parameter images and a residual Swin Transformer is proposed in this paper. First, the face region is detected through spatial pyramid pooling and a multi-scale feature output module. Then, a multi-scale facial landmark detector is used to locate 23 key points on the face. The aspect ratios of the eyes and mouth are calculated based on the coordinates of these key points, and a feature parameter matrix for fatigue driving recognition is obtained. Finally, the feature parameter matrix is converted into an image, and the residual Swin Transformer network is presented to recognize fatigue driving. Experimental results on the HNUFD dataset show that the proposed method achieves an accuracy of 96.512%, thus outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the rapid growth of the world's population and urbanization, people are increasingly seeking higher-quality medical services to improve their lives. The classification method based on deep convolutional neural networks (CNNs) is widely used in smart healthcare systems along with advancements in communication and hardware technology. Unfortunately, for conventional deep CNN algorithms, most of the regions do not participate in the convolution operation, resulting in the loss of feature information and the correlation of information between the features. To address this issue, this paper proposes a new strategy of aggregation decentralized down-sampling to prevent the loss of feature information. The regions that are not involved in the convolution operation are re-convoluted and stacked onto depth information in the forward propagation layer and the short-circuit layer, ensuring gradual convergence of the feature map and avoiding the loss of feature information. The accuracy of the proposed residual network (ResNet) system for classification tasks showed an average improvement of 2.57% compared with the conventional ResNet strategies. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) methods for energy saving and greening have recently appeared in the field of autonomous driving. In inter-vehicle communication (IVC), a feasible and increasingly popular research direction of RL is to obtain the optimal action decision of agents in a special environment. This paper presents the application of reinforcement learning in the vehicle communication simulation framework (Veins). In this research, we explore the application of reinforcement learning algorithms in a green cooperative adaptive cruise control (CACC) platoon. Our aim is to train member vehicles to react appropriately in the event of a severe collision involving the leading vehicle. We seek to reduce collision damage and optimize energy consumption by encouraging behavior that conforms to the platoon's environmentally friendly aim. Our study provides insight into the potential benefits of using reinforcement learning algorithms to improve the safety and efficiency of CACC platoons while promoting sustainable transportation. The policy gradient algorithm used in this paper has good convergence in the calculation of the minimum energy consumption problem and the optimal solution of vehicle behavior. In terms of energy consumption metrics, the policy gradient algorithm is used first in the IVC field for training the proposed platoon problem. It is a feasible training decision-planning algorithm for solving the minimization of energy consumption caused by decision making in platoon avoidance behavior. [ABSTRACT FROM AUTHOR]

Fatigue driving behavior recognition in all-weather real driving environments is a challenging task. Accurate recognition of fatigue driving behavior is helpful to improve traffic safety. The facial landmark detector is crucial to fatigue driving recognition. However, existing facial landmark detectors are mainly aimed at stable front face color images instead of side face gray images, which is difficult to adapt to the fatigue driving behavior recognition in real dynamic scenes. To maximize the driver's facial feature information and temporal characteristics, a fatigue driving behavior recognition method based on a multi-scale facial landmark detector (MSFLD) is proposed. First, a spatial pyramid pooling and multi-scale feature output (SPP-MSFO) detection model is built to obtain a face region image. The MSFLD is a lightweight facial landmark detector, which is composed of convolution layers, inverted bottleneck blocks, and multi-scale full connection layers to achieve accurate detection of 23 key points on the face. Second, the aspect ratios of the left eye, right eye and mouth are calculated in accordance with the coordinates of the key points to form a fatigue parameter matrix. Finally, the combination of adaptive threshold and statistical threshold is used to avoid misjudgment of fatigue driving recognition. The adaptive threshold is dynamic, which solves the problem of the difference in the aspect ratio of the eyes and mouths of different drivers. The statistical threshold is a supplement to solve the problem of driver's low eye threshold and high mouth threshold. The proposed methods are evaluated on the Hunan University Fatigue Detection (HNUFDD) dataset. The proposed MSFLD achieves a normalized mean error value of 5.4518%, and the accuracy of the fatigue driving recognition method based on MSFLD achieves 99.1329%, which outperforms that of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Cl-initiated oxidation of methacrolein (MACR, C4H6O) under NOx-free conditions has been investigated in a fast flow tube by using a home-made vacuum ultraviolet (VUV) photoionization mass spectrometer complemented by high-level theoretical calculations. The key species such as intermediates and radicals together with products involved in the oxidation are observed online and confirmed in photoionization mass spectra. The reaction potential energy surfaces of the transient C4H5O and C4H6OCl radicals, formed from the hydrogen-abstraction reaction and the addition reaction of MACR with Cl atoms, with oxygen have been theoretically calculated to illuminate the formation of the peroxy radicals of C4H5OO2 and C4H6OClO2. The photoionization processes of these peroxy radicals, whose cations are not stable, and their individual self-reactions as well as bimolecular reactions with HO2 radical are studied and discussed. In addition, kinetic experiments are also performed to get the time evolution of specific products and compared with theoretical models, providing a detailed insight into the reaction mechanism of the Cl-initiated oxidation of MACR. [ABSTRACT FROM AUTHOR]

This article aims to improve the performance of deep learning-based defective fastener inspection method. Due to the defective fasteners are insufficient and far less than the defect-free ones in real railway, it is difficult to train a robust fastener inspection model on such imbalanced dataset. In view of this problem, a novel image generation method called four-discriminator cycle-consistent adversarial network (FD-Cycle-GAN) is proposed to generate the defect fastener images using a large number of defect-free ones. Extensive experiments are conducted on the real fastener images and generated images. Experimental results demonstrate that the defect fastener images generated by our proposed method have better quality and richer diversity than those generated by other state-of-the-art methods. In addition, compared with the CNN-only baseline, the performance of the fastener inspection model trained on the expanded dataset containing the defect fastener images generated by FD-Cycle-GAN is improved significantly. The detection accuracy and relative IMP reach 93.25% and 21.59% respectively. [ABSTRACT FROM AUTHOR]

Visual inspection techniques for rail surface defects have become prevalent approaches to obtain information on rail surface damage. However, uneven illumination leads to illegibility of local information, and the change of the wheel-rail area results in the changeful background of the rail surface, both of which pose challenges to the visual inspection. This paper proposes a novel algorithm that detects rail surface defects via partitioned edge features (PEF). PEF eliminates the effect of uneven illumination by effectively extracting edge features and building homogeneous background on the rail surface. In the process of edge feature extraction, the thresholding based on adaptive partition of rail surface (APRS) plays an indispensable role. In APRS, the rail surface is adaptively partitioned into three types of regions according to the wheel-rail contact degree. After that, the dynamic threshold is set adaptively for each region type on the basis of the prior information of defect proportion. Subsequently, based on neighborhood information and fuzzy decision, the spatial information of adjacent pixels and the direction information of fracture edges are utilized to realize the effective recovery of incomplete defect contours. In addition, defect contours are precisely filled via a flexible combination of morphological hole filling operation and defect region extraction based on improved background difference. The accuracy of this PEF algorithm was confirmed by experiments and comparisons with related algorithms. The experiment results show that PEF detects defects with 92.03% recall and 88.49% precision, which achieves higher accuracy than the established detection algorithms for rail surface defects. [ABSTRACT FROM AUTHOR]

Rail surface defect inspection based on machine vision faces challenges against the complex background with interference and severe data imbalance. To meet these challenges, in this article, we regard defect detection as a key-point estimation problem and present the proposed attention neural network for rail surface defect detection via consistency of Intersection-over-Union(IoU)-guided center-point estimation (CCEANN). The CCEANN contains two crucial components. The two components are the stacked attention Hourglass backbone via cross-stage fusion of multiscale features (CSFA-Hourglass) and the CASIoU-guided center-point estimation head module (CASIoU-CEHM). Furthermore, the CASIoU-guided center-point estimation head module integrating the delicate coordinate compensation mechanism regresses detection boxes flexibly to adapt to defects’ large-scale variation, in which the proposed CASIoU loss, a loss regressing the consistency of intersection-over-union (IoU), central-point distance, area ratio, and scale ratio between the targeted defect and the predicted defect, achieves higher regression accuracy than state-of-the-art IoU-based losses. The experiments demonstrate that the CCEANN outperforms competitive deep learning-based methods in four surface defect datasets. [ABSTRACT FROM AUTHOR]

Rail corrugation (RC) is one of the most important indicator to evaluate the quality of rail, which is used to describe the irregularity of rail surface, also known as rail’s vertical flatness. However, the definition of RC is still an empirical description for low-speed measurement devices. In this paper, the process of RC measurement is divided into two steps, sampling and extraction, which helps the users to understand more clearly. Then a new mathematic model is proposed to make the process of RC’s extraction be an executable operation for machine calculation. The model adopts a new concept of flexible virtual ruler to perform sliding filtering on an overall rail and extract the instantaneous RC with a successive approximation algorithm according to user’s requirements and national standards. The proposed FVR model can not only be fully compatible with the traditional extraction method of RC, but also provide a new idea for evaluating RC’s quality. Comparing with the current popular methods, the proposed model gives a meaningful strategy for rail maintenance with a complete mathematic description having more degrees of freedom. Experiment results demonstrate its validity and reliability for both indoor simulation and actual outdoor experiments. [ABSTRACT FROM AUTHOR]

Rail corrugation is the geometric structure of the rail top surface, and it directly related to the safety and comfort of rail transportation. The monitoring of rail corrugation is the key work for rail maintenance department. Currently, the 2D laser displacement sensor is being used to measure the rail corrugation. However, the 2D sensor is easily affected by pitching vibration under complex measurement environment, which leads to large errors in the final measurement results. For solving the problem of pitching vibration in dynamic measuring rail corrugation, a data splicing method based on deviation recognition and calibration is proposed in this study. Firstly, the blind recognition of sensor deviation is applied to identify the measurement state of sensor in each data sampling. Then, a mapping method is proposed to position the actual repeated measurement interval according to geometric relationship between the deviation angle and the offset in the longitudinal direction. Finally, the modified DTW model is proposed for data registration and the data splicing can be achieved according to the registration results. Extensive experiments are carried on both indoor experimental platform and outdoor platform. The experimental results indicate that our proposed method can accurately recognize the deviation of sensor and can calibrate the deviated data set, and its performance on the rail corrugation measurement under the pitching vibration case outperforms previous data splicing work. Thus, it is with the potential engineering application. [ABSTRACT FROM AUTHOR]

For rail wear measurement, the process of profile registration is critical. Nowadays, the widely used method is the rail waist double circle segment method (DCS). However, this method may be invalid in actual applications because of the influence of the following two factors: one is the outliers mixed into profiles and the other is the profile diversity. The former induces the misalignment between the measured profile and the standard one, and the latter may destroy the integrity of rail waist, which makes that the measured profile cannot be matched with the standard one through DCS. To solve the two problems, a hybrid profile registration method is proposed in this paper. First, by locating new matching primitives to realize coarse registration, we check the profile integrity fast. Then, for complete profile, we rematch its original measured profile with the standard one finely based on DCS to detect and remove the outliers correctly. The efficiency and superiority of the proposed method were verified by numerous experiments. The results show that the average score of F1-Measure for outlier detection reaches 0.95, which outperforms some classical models obviously. Meanwhile, the result of the profile integrity check is also basically coincided with the real face of test zones. Furthermore, the system can run at a speed of 21.95 km/h under our experimental setup, which is far higher than that of the rail maintenance train (up to 5 km/h). [ABSTRACT FROM AUTHOR]

Fasteners are an important part of railway security. The application of computer vision and image processing on fastener detection has become a research hot spot. However, most of the researches have focused on the missing and damage detection of fasteners up to now. In this paper, a method based on image processing is proposed to locate the center of the hexagon nut in the railway fastener. The precise position of the hexagon nut can be used for the automatic replacement of the nut, which is of great significance for the automatic railway construction and maintenance. The locating method proposed in this paper can be divided into two steps. First, the improved Canny detector which combines surrounding suppression and iterative excitation is used to extract the contours of the fastener. Then, a multifeature hierarchical locating (MHL) algorithm is proposed, which integrates the circle contour of the gasket and the hexagon features of the nut. The experimental results show that the improved Canny detector suppresses noises effectively and highlights the contours of fastener. The MHL algorithm in the next step improves the locating accuracy remarkably while guaranteeing the robustness meantime. [ABSTRACT FROM AUTHOR]

Owing to the inherent sparse channel feature in multi-carrier modulation systems, compressed sensing (CS)-based techniques have been used for channel estimation with less pilot subcarriers for orthogonal frequency division multiplexing (OFDM) systems. Because of a sparse multipath channel’s characteristic of fractional delay, the channel measurement matrix that is generated by conventional time-domain sampling approaches cannot perfectly recover the channel impulse response (CIR), especially in the case of two adjacent paths that both have with non-negligible power. In this study, a fractional delay multipath channel model is used to simulate the wireless multipath channel. In addition, a time-domain oversampling based doubly reduced matching pursuit (DRMP) algorithm at the receiver is proposed to improve the estimation accuracy while reducing the computational complexity. Simulation results show that the proposed method can improve both the bit error rate (BER) performance and estimation time compared to conventional methods in an environment with fractional delay. [ABSTRACT FROM AUTHOR]

Rail corrugation dynamic measurement techniques are critical to guarantee transport security and guide rail maintenance. The chord-based method is one of the most common methods nowadays. However, for corrugation broadband measurement varied from 30 mm to 60 m, this method is faced with two severe challenges in actual applications: one is that the effective measuring range of traditional single chord models cannot cover the required broadband, and the other is that the common frequency sampling method (FSM) is unavailable to design the FIR inverse filters for asymmetrical chord and multipoints chord models. To solve the two problems, a practical combination-chord model (CCM) is proposed in this paper. For the first problem, the model is constructed on the existing three-point asymmetrical chord platform. Through the flexible combination of three sensors, CCM integrates the advantages of long-wave measurement from the two-point chord model and shortwave measurement from the three-point asymmetrical chord model, and realizes the corrugation broadband measurement in one platform successfully. For the second problem, the least square (LS) is employed to design the inverse filter. Compared with FSM, the design process is more unlimited and easier for the user. More important, even with the same output filter order, the filter performance designed by LS is better than that by FSM. The efficiency of CCM and the superiority of inverse filter designed by LS were verified by vast experiments. The results could achieve the design target. [ABSTRACT FROM PUBLISHER]

Dynamically measuring rail profile using the structured-light vision suffers from random vibrations on the line laser projector, which would cause distorted rail profiles. This paper presents a simple and effective distortion rectifying method to address this issue. The distorted rail profile is rectified by easily projecting it onto an auxiliary plane which is parallel to the cross section of rail. In order to establish the auxiliary plane, three profiles formed by radiating multiline structured light on rail are used to fit the rail longitudinal axis. More importantly, only one of the light planes is required to be calibrated beforehand. The others are calibrated online with the proposed self-calibration method, which is based on collinearity and parallelity constraints on the scene points of different rail profiles and requires only one image of the scene. Apart from evaluating the implementation with comprehensive experiments, we compare our method against other published works. The results exhibit its effectiveness and superiority in terms of the dynamic measurement of the rail profile. [ABSTRACT FROM AUTHOR]

Measuring rail profile in the presence of multiple degrees of freedom vibration is a very challenging task. This paper presents a novel method based on the local affine invariant feature descriptor to calibrate distorted profiles, which are obtained by traditional rail measurement system. It has three major modules: local affine invariant (LAI) feature descriptor, affine transformation estimation and parameters refinement. LAI feature descriptor is based on the affine geometry invariant and generated by calculating the proportions of different areas. Using the proposed LAI descriptor, we implement a three-stage profile calibration including matching, estimation, and refinement based on grouping and fast iterative closest point (FICP) algorithm. The performance of proposed LAI descriptor and calibrating method is tested by performing extensive experiments. The experimental results show that our LAI descriptor is highly descriptive and robust with respect to varying resolution and noise, and the LAI descriptor based calibration is effective and repeatable. [ABSTRACT FROM AUTHOR]

Rail section profile accurate measurement techniques are critical to guarantee the transport security and reduce the maintenance cost. However, the profile may make an affine transformation under vehicle vibration. It is a challenge to identify and calibrate the affine distorted profiles in the 2-D laser displacement system (LDS). By profound observation and discussion on triple primitives including rail jaw, railhead inner line, and rail waist curve, we found the difference between normal profiles and distorted ones. To measure the difference quantitatively, we proposed a novel curve similarity measurement method, i.e., point set mapping (PSM), in this paper. By constructing PSM from the standard profile to the measured one on the triple matching primitives, the fast recognition and accurate calibration of rail profile under affine distortion were implemented. The accuracy and efficiency of the system were verified by experiments and comparisons with related methods. The results show that the PSM is faster and more robust than the universal Hausdorff distance on the similarity measure, and the performance of the modified particle swarm optimization is superior for profile distortion calibration. Furthermore, the LDS can run at a speed of 12.86 km/h under our experimental setup, which is far higher than that of the rail maintenance train (up to 5 km/h). [ABSTRACT FROM PUBLISHER]

Compressive sensing based channel estimation has shown its advantage of accurate reconstruction for sparse signal with less pilots for OFDM systems. However, high computational cost requirement of CS method, due to linear programming, significantly restricts its implementation in practical applications. In this paper, we propose a reduced complexity channel estimation scheme of modified orthogonal matching pursuit with sliding windows for ISDB-T (Integrated Services Digital Broadcasting for Terrestrial) system. The proposed scheme can reduce the computational cost by limiting the searching region as well as making effective use of the last estimation result. In addition, adaptive tracking strategy with sliding sampling window can improve the robustness of CS based methods to guarantee its accuracy of channel matrix reconstruction, even for fast time-variant channels. The computer simulation demonstrates its impact on improving bit error rate and computational complexity for ISDB-T system. [ABSTRACT FROM AUTHOR]

The high-speed dynamics of nodes and rapid change of network topology in vehicular ad hoc networks (VANETs) pose significant challenges for the design of routing protocols. Because of the unpredictability of VANETs, selecting the appropriate next-hop relay node, which is related to the performance of the routing protocol, is a difficult task. As an effective solution for VANETs, geographic routing has received extensive attention in recent years. The Greedy Perimeter Coordinator Routing (GPCR) protocol is a widely adopted position-based routing protocol. In this paper, to improve the performance in sparse networks, the local optimum, and the routing loop in the GPCR protocol, the Weighted-GPCR (W-GPCR) protocol is proposed. Firstly, the relationship between vehicle node routing and other parameters, such as the Euclidean distance between node pairs, driving direction, and density, is analyzed. Secondly, the composite parameter weighted model is established and the calculation method is designed for the existing routing problems; the weighted parameter ratio is selected adaptively in different scenarios, so as to obtain the optimal next-hop relay node. In order to verify the performance of the W-GPCR method, the proposed method is compared with existing methods, such as the traditional Geographic Perimeter Stateless Routing (GPSR) protocol and GPCR. Results show that this method is superior in terms of the package delivery ratio, end-to-end delay, and average hop count. [ABSTRACT FROM AUTHOR]

The Cl-initiated oxidation of methacrolein (MACR, C 4 H 6 O) under NO x -free conditions has been investigated in a fast flow tube by using a home-made vacuum ultraviolet (VUV) photoionization mass spectrometer complemented by high-level theoretical calculations. The key species such as intermediates and radicals together with products involved in the oxidation are observed online and confirmed in photoionization mass spectra. The reaction potential energy surfaces of the transient C 4 H 5 O and C 4 H 6 OCl radicals, formed from the hydrogen-abstraction reaction and the addition reaction of MACR with Cl atoms, with oxygen have been theoretically calculated to illuminate the formation of the peroxy radicals of C 4 H 5 OO 2 and C 4 H 6 OClO 2 . The photoionization processes of these peroxy radicals, whose cations are not stable, and their individual self-reactions as well as bimolecular reactions with HO 2 radical are studied and discussed. In addition, kinetic experiments are also performed to get the time evolution of specific products and compared with theoretical models, providing a detailed insight into the reaction mechanism of the Cl-initiated oxidation of MACR.