scholarly journals Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 864
Author(s):  
Qian Li ◽  
Zhe Zhang ◽  
Fei Peng
Keyword(s):  
Integer Programming ◽  
Complex Network ◽  
Model Development ◽  
Accident Prevention ◽  
Hazard Identification ◽  
Identification Methods ◽  
Identification Method ◽  
Comparison Of Results ◽  
Accident Causation ◽  
Identification Model

This study investigates a critical hazard identification method for railway accident prevention. A new accident causation network is proposed to model the interaction between hazards and accidents. To realize consistency between the most likely and shortest causation paths in terms of hazards to accidents, a method for measuring the length between adjacent nodes is proposed, and the most-likely causation path problem is first transformed to the shortest causation path problem. To identify critical hazard factors that should be alleviated for accident prevention, a novel critical hazard identification model is proposed based on a controllability analysis of hazards. Five critical hazard identification methods are proposed to select critical hazard nodes in an accident causality network. A comparison of results shows that the combination of an integer programming-based critical hazard identification method and the proposed weighted direction accident causality network considering length has the best performance in terms of accident prevention.

Analysis of Radar Technology Identification Model for Potential Geologic Hazard based on Convolutional Neural Network and Big Data

2021 ◽  
Author(s):  
Feng He ◽  
Hongjiang Liu ◽  
Chunxue Liu ◽  
Guangjing Bao
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Convolutional Neural Network ◽  
New Technologies ◽  
Hazard Identification ◽  
Identification Accuracy ◽  
Radar Image ◽  
Geologic Hazard ◽  
Continuous Increase ◽  
Identification Model

Abstract To ensure the proper adoption of new technologies in identifying the potential geologic hazard on tourist routes, convolutional neural network (CNN) technology is applied in the radar image geologic hazard information extraction. A scientific and practical geologic hazard radar identification model is built, which is based on CNN’s image identification and big data algorithm calculation, and it can effectively improve the geologic hazard identification accuracy. By designing experiments, the geologic hazard radar image data are verified, and the practicality of radar image intelligent Identification under CNN and big data technology is also verified. The results show that the images of different resolution sizes all play a significant role in identification of geologic hazard performed by CNN. However, there are differences in the performance of different CNN models. With the continuous increase of training samples, the identification accuracy of various network models is also improved. By means of radar image test, the identification capability of CNN model is the best, the highest precision is 93.61%, and the geologic hazard recall rate is 98.27%. Apriori algorithm is introduced into data processing, and the running speed and efficiency of identification models are improved, with favorable identification effect in variable data sets. This research can provide theoretical ideas and practical value for the development of potential geologic hazard identification on tourist routes.

Analysis of Identification Methods of Key Nodes in Transportation Network

2022 ◽  
Author(s):  
Qiang Lai ◽  
Hong-hao Zhang
Keyword(s):  
Transportation Networks ◽  
Transportation Network ◽  
Practical Significance ◽  
Node Degree ◽  
Connected Subgraph ◽  
Network Efficiency ◽  
Identification Methods ◽  
Identification Method ◽  
Bus Networks ◽  
Key Nodes

Abstract The identification of key nodes plays an important role in improving the robustness of the transportation network. For different types of transportation networks, the effect of the same identification method may be different. It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks. Based on the knowledge of complex networks, the metro networks and the bus networks are selected as the objects, and the key nodes are identified by the node degree identification method, the neighbor node degree identification method, the weighted k-shell degree neighborhood identification method (KSD), the degree k-shell identification method (DKS), and the degree k-shell neighborhood identification method (DKSN). Take the network efficiency and the largest connected subgraph as the effective indicators. The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.

Face Recognition

2011 ◽  
pp. 163-254
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Recognition ◽  
Access Control ◽  
Individual Identification ◽  
Modern Life ◽  
Identification Methods ◽  
Personal Security ◽  
Identification Method ◽  
The Individual ◽  
Biometric Features

In the modern life, the need for personal security and access control is becoming an important issue. Biometrics is the technology which is expected to replace traditional authentication methods that are easily stolen, forgotten and duplicated. Fingerprints, face, iris, and voiceprints are commonly used biometric features. Among these features, face provides a more direct, friendly and convenient identification method and is more acceptable compared with the individual identification methods of other biometrics features. Thus, face recognition is one of the most important parts in biometrics.

scholarly journals A Parameter Identification Method for Stewart Manipulator Based on Wavelet Transform

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 257
Author(s):  
Chenyang Zhang
Keyword(s):  
Wavelet Transform ◽  
Parameter Identification ◽  
Coulomb Friction ◽  
Velocity Analysis ◽  
Dynamical Parameter ◽  
New Approach ◽  
Identification Methods ◽  
Identification Method ◽  
True Value ◽  
Joint Velocity

Aiming at inertial and viscous parameter identification for the Stewart manipulator regardless of the influence of Coulomb friction, a simple and effective dynamical parameter identification method based on wavelet transform and joint velocity analysis is proposed in this paper. Compared with previously known identification methods, the advantages of the new approach are that (1) the excitation trajectory is easy to design, and (2) it can not only identify the inertial matrix, but also the viscous matrix accurately regardless of the influence of Coulomb friction. Comparison is made among the identification method proposed in this paper, another identification method proposed previously, and the true value calculated with a formula. The errors from results of different identification methods demonstrate that the method proposed in this paper shows great adaptability and accuracy.

A Person Identification Method Using a Top-View Head Image from an Overhead Camera

2012 ◽  
Vol 16 (6) ◽  
pp. 696-703 ◽  
Author(s):  
Ryota Nakatani ◽  
◽  
Daichi Kouno ◽  
Kazutaka Shimada ◽  
Tsutomu Endo
Keyword(s):  
Body Size ◽  
Background Subtraction ◽  
Experimental Results ◽  
Hair Color ◽  
Identification Task ◽  
Person Identification ◽  
Identification Methods ◽  
Adaboost Algorithm ◽  
Identification Method ◽  
Top View

In this paper, we describe a novel image-based person identification task. Conventional face-based person identification methods have a low tolerance for occluded situations, such as overlapping of people in an image. We focus on an image from an overhead camera. Using the overhead camera reduces restrictions on the installation location of the camera and solves the problem of occluded images. First, our method identifies a person area in a captured image by using background subtraction. Then, it extracts four features from the area: (1) body size, (2) hair color, (3) hairstyle, and (4) hair whorl. We apply the four features to the AdaBoost algorithm. Experimental results show the effectiveness of our method.

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