• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.535-542
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• 2002

최근 IT 산업이 급성장하면서 화상 회의, 게임, 채팅 등에서의 아바타(avatar) 제어를 위한 자연스러운 인터페이스 기술이 요구되고 있다. 본 논문에서는 동적 윤곽선 모델(active contour models; snakes)을 이용하여 복잡한 배경이 있는 컬러 CCD 카메라 영상에서 얼굴과 눈, 입, 눈썹, 코 등의 얼굴 요소에 대해 윤곽선을 추출하거나 위치를 파악하는 방법을 제안한다. 일반적으로 스네이크 알고리즘은 잡음에 민감하고 초기 모델을 어떻게 설정하는가에 따라 추출 성능이 크게 좌우되기 때문에 주로 단순한 배경의 영상에서 정면 얼굴의 추출에 사용되어왔다 본 연구에서는 이러한 단점을 파악하기 위해, 먼저 YIQ 색상 모델의 I 성분을 이용한 색상 정보와 차 영상 정보를 사용하여 얼굴의 최소 포함 사각형(minimum enclosing rectangle; MER)을 찾고, 이 얼굴 영역 내에서 기하학적인 위치 정보와 에지 정보를 이용하여 눈, 입, 눈썹, 코의 MER을 설정한다. 그런 다음, 각 요소의 MER 내에서 1차 미분과 2차 미분에 근거한 내부 에너지와 에지에 기반한 영상 에너지를 이용한 스네이크 알고리즘을 적용한다. 이때, 에지 영상에서 얼굴 주변의 복잡한 잡음을 제거하기 위하여 색상 정보 영상과 차 영상에 각각 모폴로지(morphology)의 팽창(dilation) 연산을 적용하고 이들의 AND 결합 영상에 팽창 연산을 다시 적용한 이진 영상을 필터로 사용한다. 총 7명으로부터 양 눈이 보이는 정면 유사 방향의 영상을 20장씩 취득하여 총 140장에 대해 실험한 결과, MER의 오차율은 얼굴, 눈, 입에 대해 각각 6.2%, 11.2%, 9.4%로 나타났다. 또한, 스네이크의 초기 제어점을 얼굴은 44개, 눈은 16개, 입은 24개로 지정하여 MER추출에 성공한 영상에 대해 스네이크 알고리즘을 수행한 결과, 추출된 영역의 오차율은 각각 2.2%, 2.6%, 2.5%로 나타났다.해서 Template-based reasoning 예를 보인다 본 방법론은 검색노력을 줄이고, 검색에 있어 Feasibility와 Admissibility를 보장한다.매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having the same condition of

• Journal of the Korean Society for Nondestructive Testing
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• v.13 no.4
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• pp.25-31
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• 1994

A system for EMAT, which generates ultrasound by electro-magnectic forces and performs nondestructive testing in noncontact, was established. By linking it with a 3 axis scanning system and a data acquisition and processing system the automation of EMAT testing was attempted. A EMAT sensor was fabricated and the directivity pattern of it was measured. To be suitable automation, it has a transmitter and a receiver in one case and the main beam direction of it can be controlled by the frequency of driving signal. A program which controls the EMAT system, the 3 axis scanner and the data acquisition and processing system was developed. It also processes acquired data and displays the processing results. IBM-PC/AT compatible PC was used as main controller and the stratage of the program is emulation of real devices on the PC monitor. To provide the performance of the established EMAT system, two aluminium blocks containing artificial flaws and a welded aluminium block were tested. The result of the tests were satisfactory.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.257-263
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• 2006

With the ever increasing demand for data communication methods, powerline communication has become in interesting alternative method for data communication in home networking. For the purpose of home safety, several sensors will be installed at door, windows, gas alarm, etc. When considering home networking, the sensor data as well as other types of data should be supported in powerline communication. Usually the sensor data do not have priority over isochronous traffics (voice, video traffic), but in the case of urgent situation at home, the data of sensor being aware of the situation should be transmitted earlier than others. The objective in this paper is to develop a method for supporting an urgent data in home networking using powerline communication. We propose a modified algorithm of HomePlug 1.0 and show the results of computer simulation.

• Journal of Fashion Business
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• v.7 no.5
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• pp.108-118
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• 2003

Surface properties, including the texture and the luster, of cotton fibers and yarns thereof play an important role in textile technology. The convolutions and the cross-sectional shape of the cotton fiber affect the fabric texture and the luster accordingly. Mercerization of the cotton fabric affects the luster, strength, and other properties of the fabric. In this study, the effect of mercerization was examined on the luster of the cotton fabric, together with the effect of polishing treatment. One of the traditional methods determining the fabric luster is the use of glossmeter or goniometric glossmeter. The use of glossmeter gives successful results in determining the gloss of rather flat and continuous surface such as plastic sheet, painted surface, or paper products. Since the textile fabrics have diverse surface structures and textures, these could be regarded as having three-dimensional surface. Such complexity imposes some difficulties for differentiating subtle surface luster properties of diverse textile fabrics. The advancement in the area of imaging technologies has enabled the micro-scale analysis of the surface textures and the fabric luster recently. Using a CCD camera, the surface luster images were taken at various incident illumination conditions. Microscale analysis, including the blob analysis, of the images could differentiate the subtle luster properties present in a group of cotton fabric samples comprising mercerized cotton fabric, non-mercerized cotton fabric, polished cotton fabric, and a 'standard' cotton fabric. The glossmeter measurement gave satisfactory but limited differentiation among the samples, whose luster differences are easily recognizable with visual observation, except for the mercerized cotton fabric sample and the non-mercerized cotton fabric. The microscale analysis of the fabric luster could, therefore, help understand the nature of diverse textile fabric luster.

• Journal of the Korea Concrete Institute
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• v.23 no.6
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• pp.701-709
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• 2011

One of the typical evaluation models of concrete air-void system is spacing factor (SF), which was suggested by Power. Power Spacing Factor (PSF) has a disadvantage of the result being different from the actual case due to the existence of entrapped air, because PSF uses average single spacing factor. Therefore, the Actual Measurement Spacing Factor (AMSF) using actually measured data of air void spacing was developed from this study. PSF and AMSF were compared and evaluated in this study by using the image analysis test result of concrete mixture. This study results showed that PSF and AMSF are generally similar, but AMSF had a larger value when PSF was greater than $400{\mu}m$. The results indicated a possibility of PSF giving false measurement estimation where the measurement is less than the actual value in the concrete mixture containing less air. Also, in the result of PSF and AMSF analysis according to the existence of entrapped air, AMSF showed a larger value in the analysis without entrapped air. But PSF showed a smaller value in the analysis without entrapped air, which was different from the actual case. Because PSF used average single spacing factor, it tended to give a false result. The study results showed that AMSF gave more accurate analysis results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.29-37
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• 2019

Textile quality management targets the physical properties of fabrics and the subjective discriminations of color and fitting. Color is the most representative quality factor that consumers can use to evaluate quality levels without any instruments. For this reason, quantification using a color discrimination device has been used for statistical quality management in the textile industry. However, small and medium-sized domestic textile manufacturers use only visual inspection for color discrimination. As a result, color discrimination is different based on the inspectors' individual tendencies and work procedures. In this research, we want to develop a textile industry-friendly quality management method, evaluating the possibility of rapid color discrimination using a digital imaging device, which is one of the office-automation instruments. The results show that an imaging process-based color discrimination method is highly correlated with conventional color discrimination instruments ($R^2=0.969$), and is also applicable to field discrimination of the manufacturing process, or for different lots. Moreover, it is possible to recognize quality management factors by analyzing color components, ${\Delta}L$, ${\Delta}a$, ${\Delta}b$. We hope that our rapid discrimination method will be a substitute technique for conventional color discrimination instruments via elaboration and optimization.

• Korean Journal of Cognitive Science
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• v.20 no.1
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• pp.79-107
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• 2009

Episodic memory, i.e. memorization of information within a spatiotemporal environment, is affected Alzheimer's disease(AD), but its impairment may also be occurred in the normal aging process. The purpose of this study is to analyze and evaluate memory in with Dementia of Alzheimer Type by examining their cognitive skills in episodic memory using the technique. This new method involves assessing the mental images the subject's own past in the mind like projected and movies. Three patients in the early stage of Dementia of Alzheimer Type, one with mild depression, and 2 healthy controls for comparison were asked to retrieve their episodic memory of the previous day, week, month, and a day testing day. The answers were then analyzed with regards to their specific features as emotional state, color, and time order. In the following day, the subjects were tasked to recall again the images they reproduced in the day's test order to observe of memory. Results showed that all 3 patients failed to arrange the retrieved images in time order and their images of the previous day were unclear in color and were stationary like photographs, even when they reproduced the mental images at much quantity as controls. patients could not remember particular events of yesterday, and only recalled the general occurrences of every day life. These results suggest that in the early stage of Dementia of Alzheimer Type, difficulties in the retrieval of recent episodic memory begin to primarily occur, and qualitative impairment happens earlier than quantitative.

• The korean journal of orthodontics
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• v.27 no.5 s.64
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• pp.815-824
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• 1997

The purpose of this study is to develop the basic algorithm for the finite element method modeling of individual malocclusions. Usually, a great deal of time is spent in preprocessing. To reduce the time required, we developed a standardized procedure for measuring the position of each tooth and a program to automatically preprocess. The following procedures were carried to complete this study. 1. Twenty-eight teeth morphologies were constructed three-dimensionally for the finite element analysis and saved as separate files. 2. Standard brackets were attached so that the FA points coincide with the center of the brackets. 3. The study model of a patient was made. 4. Using the study model, the crown inclination, angulation, and the vertical distance from the tip of a tooth was measured by using specially designed tools. 5. The arch form was determined from a picture of the model with an image processing technique. 6. The measured data were input as a rotational matrix. 7. The program provides an output file containing the necessary information about the three-dimensional position of teeth, which is applicable to several finite element programs commonly used. The program for a basic algorithm was made with Turbo-C and the subsequent outfile was applied to ANSYS. This standardized model measuring procedure and the program reduce the time required, especially for preprocessing and can be applied to other malocclusions easily.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.