• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1299-1311
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• 2017

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines using the sounds emitted by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We present here an automatic fault diagnosis system of hand drills using discrete wavelet transform (DWT) and pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The diagnosis system consists of three steps. Because of the presence of many noisy patterns in our signals, we first conduct a filtering analysis based on DWT. Second, the wavelet coefficients of the filtered signals are extracted as our features for the pattern recognition part. Third, PCA is performed over the wavelet coefficients in order to reduce the dimensionality of the feature vectors. Finally, the very first principal components are used as the inputs of an ANN based classifier to detect the wear on the drills. The results show that the proposed DWT-PCA-ANN method can be used for the sounds based automated diagnosis system.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.2
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• pp.235-241
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• 2019

The shoe manufacturing process is very low compared to other industries due to the labor-intensive process. As automation and smart factories are becoming more and more automated, changes in the shoe manufacturing process are also needed. In this paper, we want to standardize the fuse manufacturing process by modularizing it. First, we defined the terms of shoeupper and fuse process, the shoe upper fuse process by function and classified it as a modular process. The fuse process can be modularized with pattern supply module, pattern recognition module, pattern laminate module, pattern waiting module, adhesion module, heat pressing module, transmission module, etc.

• Proceedings of the Korean Society of Medical Physics Conference
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• 1999.11a
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• pp.425-426
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• 1999

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.525-527
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• 2003

Recently, the spatial resolution of earth observation satellites is significantly increased to a few meters. Such high spatial resolution images definitely will provide lots of information for detail-thirsty remote sensing users. However, it is more difficult to develop automated image algorithms for automated image feature extraction and pattern recognition. In this study, we propose a two-stage procedure to extract road information from high resolution satellite images. At first stage, a watershed segmentation technique is developed to classify the image into various regions. Then, a knowledge is built for road and used to extract the road regions. In this study, we use panchromatic and multi-spectral images of the IKONOS satellite as test dataset. The experiment result shows that the proposed technique can generate suitable and meaningful road objects from high spatial resolution satellite images. Apparently, misclassified regions such as parking lots are recognized as road needed further refinement in future research.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.198-206
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• 2000

This paper describes a research work of developing automated progressive process planning system for working electric products. An approach to the CAD system in based on the knowledge-based rules. Knowledge for the CAD system is formulated from plasticity theories experimental results and the empirical knowledge of field experts. The system has been written in AutoLISP on the AutoCAD with a personal computer and is composed of three main modules which are input and shape treatment flat pattern layout and strip layout module. Based on knowledge-based rules the system is design by considering several factors such as radius and angle of bend material and thickness of product complexities of blank geometry and punch profile bending sequence and availability of press. Strip layout drawing automatically generated by piercing with punch profiles divided into for external area is simulated in 3-D graphic forms including bending sequences for the product with piercing and bending. Results obtained using the modules enable the manufacture of electronic products to be more efficient in this field.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.54-61
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• 2000

A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• KSTLE International Journal
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• v.1 no.1
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• pp.34-42
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• 2000

A method and results of classification of four different metallic wear debris were presented by using their color features. The color image of wear debris was used far the initial data, and the color properties of the debris were specified by HSI color model. Particles were characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used fer the definition of a classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• International Journal of Contents
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• v.5 no.3
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• pp.50-56
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• 2009

Internet is being used for several activities by a great range of users. These activities include communication, e-commerce, education, and entertainment. Users are required to register regarding website in order to enroll web activities. However, registration can be done by automated hacking software. That software make false enrollments which occupy the resources of the website by reducing the performance and efficiency of servers, even stop the entire web service. It is crucial for the websites to have a system which has the capability of differing human users and computer programs in reading images of text. Completely Automated Public Turing Test to Tell Computers and Human Apart (CAPTCHA) is such a defense system against Optical Character Recognition (OCR) software. OCR can be defined as software which work for defeating CAPTCHA images and make countless number of registrations on the websites. This study proposes a new CAPTCHA context that is Korean CAPTCHA by means of the method which is splitting CAPTCHA images into several parts with random rotation values, and drawing random lines on a grid background by using Korean characters only. Lines are in the same color with the CAPTCHA text and they provide a distortion of image with grid background. Experimental results show that Korean CAPTCHA is a more secure and effective CAPTCHA type for Korean users rather than current CAPTCHA types due to the structure of Korean letters and the algorithm we are using: rotation and splitting. In this paper, the algorithm of our method is introduced in detail.

• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.1-7
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• 2003

This study is a research on character recognition based on image processing, using only color tape to mark guidelines instead of magnetic tape or electric wire used by existing AGV. AGV must follow given courses, and stop recognizing signs such as marks and numbers that indicate destinations. In this study. marks to stop AGV employed blue characters of the same font and size as those of number plates. Yellow driving lines and blue numeric characters were marked in corridors. AGV ran ing the characteristics of colors and detecting lines, and temporarily stopped recognizing numbers of 100％ through DP pattern matching. This study presented the image processing technique and the result of operating AGV.