• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.1C
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• pp.26-35
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• 2006

In this paper, an automatic mobile robot system for a intelligent path planning using the detection scheme of the spatial coordinates based on stereo camera is proposed. In the proposed system, face area of a moving person is detected from a left image among the stereo image pairs by using the YCbCr color model and its center coordinates are computed by using the centroid method and then using these data, the stereo camera embedded on the mobile robot can be controlled for tracking the moving target in real-time. Moreover, using the disparity map obtained from the left and right images captured by the tracking-controlled stereo camera system and the perspective transformation between a 3-D scene and an image plane, depth information can be detected. Finally, based-on the analysis of these calculated coordinates, a mobile robot system is derived as a intelligent path planning and a estimation. From some experiments on robot driving with 240 frames of the stereo images, it is analyzed that error ratio between the calculated and measured values of the distance between the mobile robot and the objects, and relative distance between the other objects is found to be very low value of $2.19\%$ and $1.52\%$ on average, respectably.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.313-322
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• 2009

Recently, According to computer technology has been improving, Massive customer data has stored in database. Using this massive data, decision maker can extract the useful information to make a valuable plan with data mining. Data mining offers service providers great opportunities to get closer to customer. Data mining doesn't always require the latest technology, but it does require a magic eye that looks beyond the obvious to find and use the hidden knowledge to drive marketing strategies Automotive market face an explosion of data arising from customer but a rate of increasing customer is getting lower. therefore, we need to determine which customer are profitable clients whom you wish to hold. This paper builds model of customer loyalty detection and analyzes customer patterns in automotive market with data mining using association rule and basic statics methods. With 4he help of information technology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.155-163
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• 2014

To enhance network efficiency, content-centric networking (CCN) proposes that intermediated network nodes on a content-delivery path temporally cache transmitted contents. Then if an intermediated node receives a content request message (Interest) for previously cached content, the node directly transmits the cached content as a response message (Data) to requestors and finishes the transmission of the received Interest. Since Interest is performed by intermediated network nodes, it is possible to efficiently transmit contents and to effectively solve a network congestion problem caused around contents sources. For that, CCN utilizes both content store to temporarily cache content and pending Interest table (PIT) to record Interest incoming Face. However, it has mentioned the possibility of denial service attack using both the limitation of PIT resource and fake Interests. In this paper, we briefly describe the presented PIT flooding attack utilizing fake Interest. Then we introduce new attack possibility using fake Data and propose a countermeasure for the proposed attack. Also we evaluate the performance of our proposal.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.347-352
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• 2012

In this paper, an 2D spatial-map construction for workers identification and avoidance of AGV using the detection scheme of the spatial coordinates based on stereo camera is proposed. In the proposed system, face area of a moving person is detected from a left image among the stereo image pairs by using the YCbCr color model and its center coordinates are computed by using the centroid method and then using these data, the stereo camera embedded on the mobile robot can be controlled for tracking the moving target in real-time. Moreover, using the disparity map obtained from the left and right images captured by the tracking-controlled stereo camera system and the perspective transformation between a 3-D scene and an image plane, depth map can be detected. From some experiments on AGV driving with 240 frames of the stereo images, it is analyzed that error ratio between the calculated and measured values of the worker's width is found to be very low value of 2.19% and 1.52% on average.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4595-4599
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• 2014

Background: Colorectal cancer is a serious health problem. Early detection of colorectal cancer is crucial for treatment and reducing mortality. Beliefs related to colorectal cancer have been found to be a factor in a person's decision about colorectal cancer screening programs. To determine such beliefs, a valid and reliable instrument is necessary. Objective:The aim of this study was to adapt and determine the psychometric properties of the Persian version of Champion's Health Belief Model Scale of breast cancer screening in the measurement of beliefs toward colorectal cancer (CRC) screening. Materials and Methods: The 'forward-backward' procedure was applied to translate the instrument from English into Persian. This study was conducted in Iran from June 2012 to May 2013. A convenience sample of 200 individuals aged 50 years and older was recruited from the population at the outpatient clinics in the three teaching hospitals. Validity was assessed using content, face and construct validity. To test reliability, the internal consistency was assessed by using Cronbach's alpha coefficient and test-retest (intraclass correlation coefficient) analyses. Exploratory factor analysis was used to assess the construct validity and determine the factors of adapted Champion's Health Belief Model Scale. Results: The mean age of the participants were 62.5 years (SD=10.8 years) and the majority of them (75.5 percent) were female. The results of exploratory factor analysis indicated a six-factor solution for the questionnaire (benefits, motivation and confidence, seriousness, susceptibility, emotional barriers and background barriers) that jointly accounted for 55.52% of variance observed. Cronbach's alpha of the subscales ranged from 0.57 to 0.89 and test-retest reliability ranged from 0.81 to 0.93 indicating a good range of reliability. Conclusions: The findings of this study suggest that the Persian version of Champion's Health Belief Model Scale of CRC screening has good psychometric properties and could be an appropriate measure for health beliefs related to CRC screening in national and international studies.

• Journal of the Korea Society of Computer and Information
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• v.3 no.1
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• pp.81-94
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• 1998

In order to extract the contour of interesting object in the image, Kass suggested the Active Contour Model called "Snakes". Snakes is a model which defines the contour of image energy. It also can find the contour of object by minimizing these energy functions. The speed of this model is slow and this model is sensitive of initialization. In order to improve these problems, Gunn extracted the accurate contour by using two initialization. and operated to less sensitive of initialization. This method could extract more accurate contour than the existing method, but it had no effect in the speed and it was sensitive of noise. This paper applied to the Energy Minimization Algorithm about only the pixel within the window applying the window of 8$\times$8 size at each contour point consisting Snakes in order to solve these problems. The method offered in this paper is applied to extract the contour of original image and cup image added to gaussian noise. By tracking the face using this offered method, it is applied to virtual reality and motion tracking. tracking.

• Journal of Veterinary Clinics
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• v.8 no.2
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• pp.147-152
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• 1991

Trichophyton mentagrophytes was described as a primary cause of mycotic dermatitis in two young goats housed together in a humid, ill-ventilated and unhygienic byre. The diagnosis in both the cases was established on the detection of fungal element in the skin scrapings by potassium hydroxide technique and isolation of the pathogen in pure growth on mycological medium at 30$^{\circ}C$. The lesions were found on the face of one kid and on the neck and ear of another one. Two adult goats housed in the same enclosure were found to be free from this disease. Further, there was no evidence of ringworm in the goat owner and his family members. Genetic crossing of both the isolates on modified sunflower seed medium indicated that they belonged to (―) mating type. Hair performation test revealed the keratolytic activity of both the strains of T. mentagrophytes. The public health significance and chemotherapy are also discussed. The question of source of infection could not be emphatically established.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.215-224
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• 2011

There have been studying many researches to detect human body and to track one with increasing interest on human and computer interaction. In this paper, we propose the algorithm that automatically extracts joints, linked points of human body, using the ratio of human body under single camera and tracks object. The proposed method gets the difference images of the grayscale images and ones of the hue images between input image and background image. Then the proposed method composes the results, splits background and foreground, and extracts objects. Also we standardize the ratio of human body using face' length and the measurement of human body and automatically extract joints of the object using the ratio and the corner points of the silhouette of object. After then, we tract the joints' movement using block-matching algorithm. The proposed method is applied to test video to be acquired through a camera and the result shows that the proposed method automatically extracts joints and effectively tracks the detected joints.