scholarly journals Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

2020 ◽  
Vol 10 (19) ◽  
pp. 6799
Author(s):  
Zhuoran Ma ◽  
Liang Gao ◽  
Yanglong Zhong ◽  
Shuai Ma ◽  
Bolun An
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Detection Method ◽  
Low Cost ◽  
Detection Methods ◽  
Data Conversion ◽  
Track Geometry ◽  
Slab Track ◽  
Detection Model ◽  
Characteristics Analysis

During the long-term service of slab track, various external factors (such as complicated temperature) can result in a series of slab damages. Among them, slab arching changes the structural mechanical properties, deteriorates the track geometry conditions, and even threatens the operation of trains. Therefore, it is necessary to detect slab arching accurately to achieve effective maintenance. However, the current damage detection methods cannot satisfy high accuracy and low cost simultaneously, making it difficult to achieve large-scale and efficient arching detection. To this end, this paper proposed a vision-based arching detection method using track geometry data. The main works include: (1) data nonlinear deviation correction and arching characteristics analysis; (2) data conversion and augmentation; (3) design and experiments of convolutional neural network- based detection model. The results show that the proposed method can detect arching damages effectively, and the F1-score reaches 98.4%. By balancing the sample size of each pattern, the performance can be further improved. Moreover, the method outperforms the plain deep learning network. In practice, the proposed method can be employed to detect slab arching and help to make maintenance plans. The method can also be applied to the data-based detection of other structural damages and has broad prospects.

scholarly journals A Fast Intrusion Detection Method for High-Speed Railway Clearance Based on Low-Cost Embedded GPUs

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7279
Author(s):  
Yao Wang ◽  
Peizhi Yu
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
High Speed ◽  
Large Scale ◽  
Feature Fusion ◽  
Low Cost ◽  
Obstacle Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Real Time Processing

The efficiency and the effectiveness of railway intrusion detection are crucial to the safety of railway transportation. Most current methods of railway intrusion detection or obstacle detection are inappropriate for large-scale applications due to their high cost or limited coverage. In this study, we present a fast and low-cost solution to intrusion detection of high-speed railways. As the solution to heavy computational burdens in the current convolutional-neural-network-based detection methods, the proposed method is mainly a novel neural network based on the SSD framework, which includes a feature extractor using an improved MobileNet and a lightweight and efficient feature fusion module. In addition, aiming to improve the detection accuracy of small objects, the feature map weights are introduced through convolution operation to fuse features at different scales. TensorRT is employed to optimize and deploy the proposed network in the low-cost embedded GPU platform, NVIDIA Jetson TX2, to enhance the efficiency. The experimental results show that the proposed methods achieved 89% mAP on the railway intrusion detection dataset, and the average processing time for a single frame was 38.6 ms on the Jetson TX2 module, which satisfies the need of real-time processing.

scholarly journals Identification of Temperature-Induced Deformation for HSR Slab Track Using Track Geometry Measurement Data

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5446 ◽  
Author(s):  
Zai-Wei Li ◽  
Xiao-Zhou Liu ◽  
Yue-Lei He
Keyword(s):  
High Speed ◽  
Detection Method ◽  
Measurement Data ◽  
Railway Track ◽  
Discrete Wavelet ◽  
High Speed Rail ◽  
Track Geometry ◽  
Slab Track ◽  
Geometry Measurement ◽  
Inspection Data

Slab track is widely used in many newly built high-speed rail (HSR) lines as it offers many advantages over ballasted tracks. However, in actual operation, slab tracks are subjected to operational and environmental factors, and structural damages are frequently reported. One of the most critical problems is temperature-induced slab-warping deformation (SWD) which can jeopardize the safety of train operation. This paper proposes an automatic slab deformation detection method in light of the track geometry measurement data, which are collected by high-speed track geometry car (HSTGC). The characteristic of track vertical irregularity is first analyzed in both time and frequency domain, and the feature of slab-warping phenomenon is observed. To quantify the severity of SWD, a slab-warping index (SWI) is established based on warping-sensitive feature extraction using discrete wavelet transform (DWT). The performance of the proposed algorithm is verified against visual inspection recorded on four sections of China HSR line, which are constructed with the China Railway Track System II (CRTSII) slab track. The results show that among the 24,806 slabs being assessed, over 94% of the slabs with warping deformation can be successfully identified by the proposed detection method. This study is expected to provide guidance for efficiently detecting and locating slab track defects, taking advantage of the massive track inspection data.

Research on a Novel Automation Software Reliability Detection Method

2014 ◽  
Vol 556-562 ◽  
pp. 2928-2931
Author(s):  
Wen Lai Liu
Keyword(s):  
Software Reliability ◽  
Large Scale ◽  
Design Principle ◽  
Detection Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Transition Diagram ◽  
Detection Model ◽  
Module Design ◽  
Improved Methods

The reliability detection method for the traditional large-scale automation software is based on the module design principle of the automation software which detects the reliability features one by one. It does not consider the concurrent reliable chain problems for the automation software which cause the low detection accuracy. The paper proposes a novel automation software system reliability detection method based on the path-based interfaces. The detection model integrates the features of the automation software. The established stochastic points process and state probability transition diagram overcome the shortcomings of the traditional large-scale automation software reliability detection methods. The experiment results illustrate the improved methods can increase the detection accuracy of the large-scale automation software which can be widely applied.

scholarly journals Multiview deep learning based on tensor decomposition and its application in fault detection of overhead contact systems

2021 ◽  
Author(s):  
Xuewu Zhang ◽  
Yansheng Gong ◽  
Chen Qiao ◽  
Wenfeng Jing
Keyword(s):  
High Speed ◽  
Tensor Decomposition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Training Time ◽  
Detection Model ◽  
Railway Line ◽  
Result Show ◽  
Deep Layers

AbstractThis article mainly focuses on the most common types of high-speed railways malfunctions in overhead contact systems, namely, unstressed droppers, foreign-body invasions, and pole number-plate malfunctions, to establish a deep-network detection model. By fusing the feature maps of the shallow and deep layers in the pretraining network, global and local features of the malfunction area are combined to enhance the network's ability of identifying small objects. Further, in order to share the fully connected layers of the pretraining network and reduce the complexity of the model, Tucker tensor decomposition is used to extract features from the fused-feature map. The operation greatly reduces training time. Through the detection of images collected on the Lanxin railway line, experiments result show that the proposed multiview Faster R-CNN based on tensor decomposition had lower miss probability and higher detection accuracy for the three types faults. Compared with object-detection methods YOLOv3, SSD, and the original Faster R-CNN, the average miss probability of the improved Faster R-CNN model in this paper is decreased by 37.83%, 51.27%, and 43.79%, respectively, and average detection accuracy is increased by 3.6%, 9.75%, and 5.9%, respectively.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

Fast and Efficient Mutation Detection Method Using Multiplex PCR and Cycle Sequencing

2000 ◽  
Vol 83 (02) ◽  
pp. 244-247 ◽  
Author(s):  
J. M. Costa ◽  
P. Ernault ◽  
D. Vidaud ◽  
M. Vidaud ◽  
D. Meyer ◽  
...  
Keyword(s):  
Multiplex Pcr ◽  
Mutation Detection ◽  
Detection Method ◽  
Deleterious Mutation ◽  
Low Cost ◽  
Detection Methods ◽  
Cycle Sequencing ◽  
Haemophilia B ◽  
Mild Haemophilia

SummaryA method using multiplex PCR followed by cycle-sequencing has been developed to detect mutations in the FIX gene. The procedure was evaluated in 45 severe or mild haemophilia B patients from 45 unrelated families. At least one deleterious mutation was identified in every haemophiliac demonstrating the efficiency of the method. Furthermore the described procedure offers many advantages compared to other screening detection methods: it is fast (less than 48 h), simple (partly automated) and of relatively low cost (it requires only one PCR).

scholarly journals The Assisted Positioning Technology for High Speed Train Based on Deep Learning

2020 ◽  
Vol 10 (23) ◽  
pp. 8625
Author(s):  
Yali Song ◽  
Yinghong Wen
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Detection Method ◽  
Generative Adversarial Networks ◽  
Single Shot ◽  
High Speed Train ◽  
Data Set ◽  
Detection Model ◽  
Model Based ◽  
Cumulative Error

In the positioning process of a high-speed train, cumulative error may result in a reduction in the positioning accuracy. The assisted positioning technology based on kilometer posts can be used as an effective method to correct the cumulative error. However, the traditional detection method of kilometer posts is time-consuming and complex, which greatly affects the correction efficiency. Therefore, in this paper, a kilometer post detection model based on deep learning is proposed. Firstly, the Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm is introduced to construct an effective kilometer post data set. This greatly reduces the cost of real data acquisition and provides a prerequisite for the construction of the detection model. Then, by using the existing optimization as a reference and further simplifying the design of the Single Shot multibox Detector (SSD) model according to the specific application scenario of this paper, the kilometer post detection model based on an improved SSD algorithm is established. Finally, from the analysis of the experimental results, we know that the detection model established in this paper ensures both detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. Thus, our model realizes the rapid and accurate detection of kilometer posts and improves the assisted positioning technology based on kilometer posts by optimizing the detection method.

scholarly journals Lead Selenide Polycrystalline Coatings Sensitized Using Diffusion and Ion Beam Methods for Uncooled Mid-Infrared Photodetection

Coatings ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 444 ◽  
Author(s):  
Hao Yang ◽  
Xiaojiang Li ◽  
Guodong Wang ◽  
Jianbang Zheng
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Low Cost ◽  
Ion Beam ◽  
Lead Selenide ◽  
Cmos Technology ◽  
Oxide Semiconductor ◽  
Recombination Suppression ◽  
Mid Infrared ◽  
Physical Effects

Polycrystalline lead selenide material that is processed after a sensitization technology offers the additional physical effects of carrier recombination suppression and carrier transport manipulation, making it sufficiently sensitive to mid-infrared radiation at room temperature. Low-cost and large-scale integration with existing electronic platforms such as complementary metal–oxide–semiconductor (CMOS) technology and multi-pixel readout electronics enable a photodetector based on polycrystalline lead selenide coating to work in high-speed, low-cost, and low-power consumption applications. It also shows huge potential to compound with other materials or structures, such as the metasurface for novel optoelectronic devices and more marvelous properties. Here, we provide an overview and evaluation of the preparations, physical effects, properties, and potential applications, as well as the optoelectronic enhancement mechanism, of lead selenide polycrystalline coatings.

Large-scale drop test on perlite–metal syntactic foam

2018 ◽  
Vol 53 (4) ◽  
pp. 515-520 ◽  
Author(s):  
T Fiedler ◽  
M Taherishargh
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Low Cost ◽  
Syntactic Foam ◽  
Drop Test ◽  
Passenger Vehicle ◽  
High Speed Imaging ◽  
Cellular Metal ◽  
Energy Absorbing ◽  
Impact Mass

Perlite–metal syntactic foam is a low-cost cellular metal intended for use in automotive impact protection. To test the viability of the material a 2.5 ton drop test was conducted. Impact mass and energy were selected to replicate the conditions of a frontal impact between a large passenger vehicle and a crash cushion. A hollow syntactic foam cylinder was manufactured to decelerate the drop weight in a controlled manner. Accelerometers and high-speed imaging were utilized to evaluate the performance of the energy absorbing element.

scholarly journals A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096133
Author(s):  
Jianhua Wang ◽  
Bang Ji ◽  
Feng Lin ◽  
Shilei Lu ◽  
Yubin Lan ◽  
...  
Keyword(s):  
Event Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Scheme ◽  
Event Stream ◽  
Detection Model ◽  
Efficient Detection ◽  
High Efficient ◽  
Complex Events

Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.

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