• Smart Structures and Systems
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• v.17 no.2
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• pp.167-194
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• 2016

This paper investigates the Synergetics based Damage Detection Method (SDDM) for frame structures by using surface-bonded PZT (Lead Zirconate Titanate) patches. After analyzing the mechanism of pattern recognition from Synergetics, the operating framework with cooperation-competition-update process of SDDM was proposed. First, the dynamic identification equation of structural conditions was established and the adjoint vector (AV) set of original vector (OV) set was obtained by Generalized Inverse Matrix (GIM).Then, the order parameter equation and its evolution process were deduced through the strict mathematics ratiocination. Moreover, in order to complete online structural condition update feature, the iterative update algorithm was presented. Subsequently, the pathway in which SDDM was realized through the modified Synergetic Neural Network (SNN) was introduced and its assessment indices were confirmed. Finally, the experimental platform with a two-story frame structure was set up. The performances of the proposed methodology were tested for damage identifications by loosening various screw nuts group scenarios. The experiments were conducted in different damage degrees, the disturbance environment and the noisy environment, respectively. The results show the feasibility of SDDM using piezoceramic sensors and actuators, and demonstrate a strong ability of anti-disturbance and anti-noise in frame structure applications. This proposed approach can be extended to the similar structures for damage identification.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Progress in Medical Physics
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• v.26 no.1
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• pp.52-58
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• 2015

The automated dental cavity detection program for a new concept intra-oral dental x-ray imaging device, an auxiliary diagnosis system, which is able to assist a dentist to identify dental caries in an early stage and to make an accurate diagnosis, was to be developed. The primary theory of the automatic dental cavity detection program is divided into two algorithms; one is an image segmentation skill to discriminate between a dental cavity and a normal tooth and the other is a computational method to analyze feature of an tooth image and take an advantage of it for detection of dental cavities. In the present study, it is, first, evaluated how accurately the DRLSE (Direct Regularized Level Set Evolution) method extracts demarcation surrounding the dental cavity. In order to evaluate the ability of the developed algorithm to automatically detect dental cavities, 7 tooth phantoms from incisor to molar were fabricated which contained a various form of cavities. Then, dental cavities in the tooth phantom images were analyzed with the developed algorithm. Except for two cavities whose contours were identified partially, the contours of 12 cavities were correctly discriminated by the automated dental caries detection program, which, consequently, proved the practical feasibility of the automatic dental lesion detection algorithm. However, an efficient and enhanced algorithm is required for its application to the actual dental diagnosis since shapes or conditions of the dental caries are different between individuals and complicated. In the future, the automatic dental cavity detection system will be improved adding pattern recognition or machine learning based algorithm which can deal with information of tooth status.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.919-927
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• 2019

In this paper, we propose an accurate camera calibration method for acquiring multiview stereoscopic images. Generally, camera calibration is performed by using checkerboard structured patterns. The checkerboard pattern simplifies feature point extraction process and utilizes previously recognized lattice structure, which results in the accurate estimation of relations between the point on 2-dimensional image and the point on 3-dimensional space. Since estimation accuracy of camera parameters is dependent on feature matching, accurate detection of checkerboard corner is crucial. Therefore, in this paper, we propose the method that performs accurate camera calibration method through accurate detection of checkerboard corners. Proposed method detects checkerboard corner candidates by utilizing 1-dimensional gaussian filters with succeeding corner refinement process to remove outliers from corner candidates and accurately detect checkerboard corners in sub-pixel unit. In order to verify the proposed method, we check reprojection errors and camera location estimation results to confirm camera intrinsic parameters and extrinsic parameters estimation accuracy.

• Sleep Medicine and Psychophysiology
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• v.3 no.2
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• pp.65-76
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• 1996

Natural sleep pattern and its physiology in childhood are much different from those in adulthood. Several aspects of clinical evaluation for sleepiness in childhood are more difficult than in adulthood. These difficulties are due to several factors. First, excessive sleepiness in childhood do not always develop functional impairments. Second, objective test such as MSLT may not be reliable since it is hard to be certain that the child understand instructions. Third, sleepiness in children is often obscured by irritability. paradoxical hyperactivity, or behavioral disturbances. Anseguently, careful clinical evaluation is needed for the sleepy children. Usual causes of sleepiness in children are the disorders that induce insufficient sleep such as sleep apnea syndrome, schedule disorder, underlying medical and psychiatric disorder, and so forth. After excluding such factors, we can diagnose the hypersomnic disorders such as narcolepsy, Kleine-Levin syndrome, and idiopathic central nervous system hypersomnia. Among the variety of those causes of sleepiness, I reviewed the clinical difference of narcolepsy and obstructive sleep apnea syndrome in childhood compared with in adulthood. Recognition of the childhood narcolepsy is difficult because even severely sleepy children often do not develop pathognomic cataplexy and associated REM phenomena until much later. Since childhood narcolepsy give srise to many psychological, academical problem. Practicers should be concerned about these aspects. Childhood obstructive sleep apnea syndrome is different from adult obstructive sleep apnea syndrome too. Several aspects such as pathophysiology. clinical feature, diagnostic criteria, complication, management, and prognosis differ from those in the adult syndrome. An important feature of childhood obstructive sleep apnea syndrome is the variety of severe complications such as behavioral disorders, cognitive impairment, cardiovascular symptoms, developmental delay, and ever death. Fortunately, surgical interventions like adenotosillectomy or UPPP are more effective for Childhood OSA than adult form. CPAP is a "safe, effective, and well-tolerated" treatment modality too. So if early detection and proper management of childhood OSA were done, the severe complication would be prevented or ever cured.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.190-200
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• 2014

The video-based system of the broadcasters and the video-related institutions have shifted from analogical to digital in worldwide. This migration process can generate a defect, digital dropout, in the quality of the contents. Moreover, there are limited researches focused on these kind of defects and those related have limitations. For that reason, we are proposing a new method for feature extraction emphasizing in the peculiar block pattern of digital dropout based on discrete cosine transform (DCT). For classification of error block, we utilize support vector machine (SVM) which can manage feature vectors efficiently. Further, the proposed method overcome the limitation of the previous one using continuity of frame by frame. It is using only the information of a single frame and works better even in the presence of fast moving objects, without the necessity of specific model or parameter estimation. Therefore, this approach is capable of detecting digital dropout only with minimal complexity.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1087-1097
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• 2017

As the penetration rate of smartphones increases, the number of malicious codes targeting smartphones is increasing. I 360 Security 's smartphone malware statistics show that malicious code increased 437 percent in the first quarter of 2016 compared to the fourth quarter of 2015. In particular, malicious applications, which are the main means of distributing malicious code on smartphones, are aimed at leakage of user information, data destruction, and money withdrawal. Often, it is operated by an API, which is an interface that allows you to control the functions provided by the operating system or programming language. In this paper, we propose a mechanism to detect malicious application based on the similarity of API pattern in normal application and malicious application by learning pattern of API in application derived from static analysis. In addition, we show a technique for improving the detection rate and detection rate for each label derived by using the corresponding mechanism for the sample data. In particular, in the case of the proposed mechanism, it is possible to detect when the API pattern of the new malicious application is similar to the previously learned patterns at a certain level. Future researches of various features of the application and applying them to this mechanism are expected to be able to detect new malicious applications of anti-malware system.

• Fashion & Textile Research Journal
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• v.18 no.4
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• pp.468-476
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• 2016

Recent advancements in optics technology enable us to realize fast scans of hands using two-dimensional (2D) image scanners. In this paper, we propose an automatic hand measurement system using 2D image scanners for customized glove production. To develop the automatic hand measurement system, firstly hand scanning devices has been constructed. The devices are designed to block external lights and have user interface to guide hand posture during scanning. After hands are scanned, hand contour is extracted using binary image processing, noise elimination and outline tracing. And then, 19 hand landmarks are automatically detected using an automatic hand landmark detection algorithm based on geometric feature analysis. Then, automatic hand measurement program is executed based on the automatically extracted landmarks and measurement algorithms. The automatic hand measurement algorithms have been developed for 18 hand measurements required for custom-made glove pattern making. The program has been coded using the C++ programming language. We have implemented experiments to demonstrate the validity of the system using 11 subjects (8 males, 3 females) by comparing automatic 2D scan measurements with manual measurements. The result shows that the automatic 2D scan measurements are acceptable in the customized glove making industry. Our evaluation results confirm its effectiveness and robustness.

• Parasites, Hosts and Diseases
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• v.39 no.4
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• pp.301-306
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• 2001

A total of 827 malaria cases were reported in the Gangwon-do in the year 2000. There were 18.2 cases per 100,000 inhabitants. There were 283 cases among civilians and 544 cases among the military. 90.6% of cases were reported in Cheorwon-(531), Hwacheon- (152), and Goseong- (66) gun (county), which bordered the demilitarized zone (DMZ). A distinct feature pertaining to the malaria cases in Gangwon-do is that the number of cases has increased about two times over the last year. The mean time from the beginning of symptoms to malaria diagnosis was five days. Control systems for malaria by public health organizations and military organizations are well maintained , but were not able to reduce the malaria prevalence rate. The cause for the increase in pattern of the malaria cases in Gangwon-do may be caused by the spreading of prevalent areas of malaria to the east. Continuous endeavor such as early detection of cases, early treatment, education on clinical symptoms and prevention of mosquito bites with repellent and mosquito nets will help to reduce the infection rate of malaria in Gangwon-do.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.2
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• pp.1-9
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• 2018

In this paper, we propose a face recognition method which is not sensitive to illumination change and prevents false recognition of photographs. The proposed method uses infrared and depth images at the same time, solves sensitivity of illumination change by infrared image, and prevents false recognition of two - dimensional image such as photograph by depth image. Face detection method using infrared and depth images simultaneously and feature extraction and matching method for face recognition are realized. Simulation results show that accuracy of face recognition is increased compared to conventional methods.