Research on Experiment and Technique of Material Identification for Debris of Engineering Equipment Based on Computer Vision

2012 ◽  
Vol 479-481 ◽  
pp. 1115-1118
Author(s):  
Qun Zhang Tu ◽  
Ming Pan ◽  
Jian Xun Zhao ◽  
Da Zhen Su ◽  
Guo Tao Wang
Keyword(s):  
Computer Vision ◽  
Fault Diagnosis ◽  
Identification Accuracy ◽  
New Method ◽  
Characteristic Parameters ◽  
Material Identification ◽  
Engineering Equipment

Based on micro-image, the material identification of iron and copper debris in working oil of engineering equipment is researched. By selecting four characteristic parameters of debris image, a debris recognition classifier is designed based on multi-SVM. The materials and types of debris can be fast identified after the model is trained and the identification accuracy is high, thus a new method for fault diagnosis of engineering equipment is provided.

scholarly journals Improving skeleton algorithm for helping Caenorhabditis elegans trackers

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Pablo E. Layana Castro ◽  
Joan Carles Puchalt ◽  
Antonio-José Sánchez-Salmerón
Keyword(s):  
Computer Vision ◽  
Caenorhabditis Elegans ◽  
Transformation Function ◽  
Simple Solution ◽  
New Method ◽  
Vision Systems ◽  
Distance Transformation ◽  
Automatic Computer ◽  
C Elegans ◽  
Computer Vision Systems

AbstractOne of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.

A New Method for Railway Fastener Detection Using the Symmetrical Image and Its EA-HOG Feature

2019 ◽  
Vol 34 (02) ◽  
pp. 2055006
Author(s):  
Jiajia Liu ◽  
Jianying Yuan ◽  
Yongfang Jia
Keyword(s):  
Computer Vision ◽  
Sparse Representation ◽  
Efficient Method ◽  
Important Task ◽  
New Method ◽  
Automatic Inspection ◽  
Test Image ◽  
Recognition Result ◽  
The Status ◽  
Railway Operation

Railway fastener recognition and detection is an important task for railway operation safety. However, the current automatic inspection methods based on computer vision can effectively detect the intact or completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In our method, we exploit the EA-HOG feature fastener image, generate two symmetrical images of original test image and turn the detection of the original test image into the detection of two symmetrical images, then integrate the two recognition results of symmetrical image to reach exact recognition of original test image. The potential advantages of the proposed method are as follows: First, we propose a simple yet efficient method to extract the fastener edge, as well as the EA-HOG feature of the fastener image. Second, the symmetry images indeed reflect some possible appearance of the fastener image which are not shown in the original images, these changes are helpful for us to judge the status of the symmetry samples based on the improved sparse representation algorithm and then obtain an exact judgment of the original test image by combining the two corresponding judgments of its symmetry images. The experiment results show that the proposed approach achieves a rather high recognition result and meets the demand of railway fastener detection.

Study on the Structural Model of Human Pose

2013 ◽  
Vol 464 ◽  
pp. 387-390
Author(s):  
Wei Hua Wang
Keyword(s):  
Computer Vision ◽  
Human Behavior ◽  
Structural Model ◽  
New Method ◽  
Domain Modeling ◽  
Key Technology ◽  
Broad Application ◽  
Structural Relation ◽  
Human Pose ◽  
Human Image

The analysis and understand of human behavior is broad application in the computer vision domain, modeling the human pose is one of the key technology. In order to simplify the model of the human pose and expediently describe the human pose, a lot of condition was appended to confine the process of human pose modeling or the application environments in the current research. In this paper, a new method for modeling the human pose was proposed. The human pose was modeled by the structural relation according to the physiological structural, the advantages of the model are the independent of move, the independent of scale of the human image and the dependent of view angle, it can be used to modeling the human behavior in video.

A New Method for Power Converter Fault Diagnosis in SRM Drives

2012 ◽  
Vol 48 (2) ◽  
pp. 653-662 ◽  
Author(s):  
Natália S. Gameiro ◽  
Antonio J. Marques Cardoso
Keyword(s):  
Fault Diagnosis ◽  
Power Converter ◽  
New Method

A New Design Method for the Fault Diagnosis on the Rotating Rectifier of Aeronautic Synchronous Generator

2012 ◽  
Vol 548 ◽  
pp. 544-547
Author(s):  
Yong Zhi Liu ◽  
Cong Liu
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Design Method ◽  
Synchronous Generator ◽  
New Method ◽  
Test Analysis ◽  
Exciting Current ◽  
On Line

A new method of fault diagnosis on the rotating rectifier of aeronautic synchronous is raised in the work. Firstly, the condition, truth and approach of EMD are introduced, and the method and steps of building up the feature vector are also included, Secondly the theories of LS-SVM and the arithmetic in the classification are also included. Finally taking the faults of one and two diodes turning off for example, after extracting the feature vector of exciting current based on EMD and establishing the classifying method based on Gauss RBF LS-SVM, the test, analysis and comparison can be on between LS-SVM and NN the conclusion can be got that the classified method referred in the work owns higher exactness, takes less time and has more application on the on-line fault diagnosis NN.

scholarly journals Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 36
Author(s):  
Xiaoan Yan ◽  
Yadong Xu ◽  
Daoming She ◽  
Wan Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Identification Accuracy ◽  
Learning Ability ◽  
Rolling Bearings ◽  
Softmax Classifier ◽  
Vibration Data ◽  
Fault Recognition ◽  
Fault Features ◽  
Latent Features

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.

scholarly journals New Edge Detection Method for Indonesian Batik

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Thomas Adi Purnomo Shidi ◽  
Suyoto Suyoto
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Detection Method ◽  
New Method ◽  
Analysis Method ◽  
Unique Pattern ◽  
Detection Analysis ◽  
Edge Detection Method

Abstrak. Metode Baru Deteksi Tepi untuk Batik Indonesia. Didalam paper ini, diusulkan sebuah metode pendeteksi baru untuk motif batik. Deteksi tepi sudah sangat sering digunakan didalam pemrosesan gambar. Batik motif adalah salah satu contoh gambar yang memiliki bentuk yang unik dan menarik untuk dianalisis. Metode yang digunakan pada paper ini adalam metode canny dan prewit dan akan menghasilkan metode baru yaitu metode Thomas. Perbedaan antara metode dan hasil akan dilihat dari sisi ketepatan, qualitas hasil dan kejelasan. Contoh batik yang akan digunakan adalah motif parang, motife lereng dan udan liris. Ketiga batik tersebut memiliki pola  yang unik. Kata kunci : Canny, Prewitt, Thomas, Batik, Parang, Lereng, Udan liris. Abstract. New Edge Detection Method for Indonesian Batik. In this paper, we propose a new edge detection analysis method on batiks motif. Edge detection has been oftenly  used in computer vision and image processing. Indonesian  Batiks motif are some example of graphic picture that has unique pattern that interesting to analyse. The method that used for example on this paper are canny and prewit and produce a new method, thomas method. the different  amongs the method, the result of comparison appears on quality, accuracy and clarity. The example that we use are parang batiks motive, lereng batiks motive, and udan liris batiks motive. Three of batiks motive above are have unique pattern. Keywords: Canny, Prewitt, Thomas, Batik, Parang, Lereng, Udan liris.

Sign in / Sign up

Export Citation Format

Share Document