• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.58-64
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• 2022

Breast ultrasonic reading is critical as a primary screening test for the early diagnosis of breast cancer. However, breast ultrasound examinations show significant differences in diagnosis based on the difference in image quality according to the ultrasonic equipment, experience, and proficiency of the examiner. Accordingly, studies are being actively conducted to analyze the texture characteristics of normal breast tissue, positive tumors, and malignant tumors using breast ultrasonography and to use them for computer-assisted diagnosis. In this study, breast ultrasonography was conducted to select 247 ultrasound images of 71 normal breast tissues, 87 fibroadenomas among benign tumors, and 89 malignant tumors. The selected images were calculated using a statistical method with 21 feature parameters extracted using the gray level co-occurrence matrix algorithm, and classified as normal breast tissue, benign tumor, and malignancy. In addition, we proposed five feature parameters that are available for computer-aided diagnosis of breast cancer classification. The average classification rate for normal breast tissue, benign tumors, and malignant tumors, using this feature parameter, was 82.8%.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.663-667
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• 2004

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.79-84
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• 2015

In a distribution of digital image, there is a serious problem that is a distribution of the altered image by a forger. For the problem solution, this paper proposes a median filtering (MF) image forensic decision algorithm using a feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value' gradients of original image then 1th~6th order coefficients to be six feature vector. And the reconstructed image is produced by the solution of Poisson's equation with the gradients. From the difference image between original and its reconstructed image, four feature vector (Average value, Max. value and the coordinate i,j of Max. value) is extracted. Subsequently, Two kinds of the feature vector combined to 10 Dim. feature vector that is used in the learning of a SVM (Support Vector Machine) classification for MF (Median Filtering) detector of the altered image. On the proposed algorithm of the median filtering detection, compare to MFR (Median Filter Residual) scheme that had the same 10 Dim. feature vectors, the performance is excellent at Unaltered, Averaging filtering ($3{\times}3$) and JPEG (QF=90) images, and less at Gaussian filtering ($3{\times}3$) image. However, in the measured performances of all items, AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.144-152
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• 2017

Among the technologies for implementing autonomous vehicles, advanced driver assistance system is a key technology to support driver's safe driving. In the technology using the vision sensor having a high utility, various preprocessing methods are used prior to feature extraction for lane detection. However, in the existing methods, the unnecessary lane candidates such as cars, lawns, and road separator in the road area are false positive. In addition, there are cases where the lane candidate itself can not be extracted in the area under the overpass, the lane within the dark shadow, the center lane of yellow, and weak lane. In this paper, we propose an efficient preprocessing method using k-means clustering for image division and the HSV color model. When the proposed preprocessing method is applied, the true positive region is maximally maintained during the lane detection and many false positive regions are removed.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1313-1322
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• 2018

This study has tested by positive analysis to investigate use intention of self service shop offered by franchisee. The independent variable of self service shop consisted of technology based self service and feature of shop largely. Convenience of technology based self service, speed, functionality of shop feature, suitability, and cost of self service shop had been selected. As parameter, expectation confirmation and satisfaction by using Expectation confirmation theory had been selected and a dependent variable had been selected use intention lastly. The study conducted a survey of self service shop customers and 181's answers out of them had been used. According to the analysis result, convenience, speed, functionality, and suitability had a positive effect on expectation and convenience and suitability variables only had positive effect on satisfaction. The cost did not have an effect on use intention and expectation confirmation and satisfaction had a positive effect on use intention lastly.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.201-212
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• 2011

In this paper, we suggest a system of detecting a vehicle with lane violation, which can detect the vehicle with lane violation, by using the feature point tracking. The whole algorism in the suggested system of detecting a vehicle with lane violation is composed of three stages such as feature extraction, register and tracking in feature for the tracking-targeted vehicle, and detecting a vehicle with lane violation. The feature is extracted from the morphological gradient image, which results in constructing robust detection system against shadows, weather conditions, head lights and illumination conditions without distinction day and night. The system shows excellent performance for the data captured at day time, night time, and rainy night time as much as 99.49% for positive recognition ratio and 0.51% for error ratio. Also the system is so fast as much as 91.34 frames per second in average that it may be possible for real-time processing.

• Women's Health Nursing
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• v.7 no.3
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• pp.359-372
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• 2001

The study was intended to investigate the bothersome level of premenstrual symptoms, their pattern and to examine the relationships between menstrual attitude and the premenstrual symptoms. Two hundred sixty eight female students were recruited from a college located in Kyungido from March 1, 2001 to July 1, 2001. A general characteristics questionnaires, the premenstrual assessment form(PAF) and the menstrual distress questionnaire(MDQ) were used to measure the bothersome level of the premenstrual symptoms and the menstrual attitude. The data were analyzed by SPSS-PC+ program. The results of this study were as follows ; 1. All subject who were participated in the research reported more than one symptom in premenstrual period and the mean score of total categories in PAF was low(1.89). The subject had more symptoms of fatigue, abdominal bloating and discomfort, backache and muscle stiffness and among the 21 categories fatigue feature, hysteroid feature, water retention feature and miscellaneous mood/behavior change feature were prevalent. On the other hand organic mental feature and increased well-being feature were rare that premenstrual symptom has negative aspect than positive. 2. Degree of discomfort in premenstrual symptom was related with dysmenorrhea but other general characteristics. 3. In Menstruation attitude, the student in college recognized menstruation as natural but bothersome and causes negatives effects on body and emotion. 4. There were significant correlation(r=.395, p<0.000) between premenstrual symptom and level of Menstrual attitude. 5. Menstrual attitude explained 15.3% variance of PMS and five categories of menstrual attitude, especially factor 1(menstruation is a phenomena that weakens women physically and psychologically) was most highly correlated with PMS and explained 21.1% variance of PMS.

• Korean Journal of Human Ecology
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• v.8 no.2
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• pp.255-273
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• 1999

The purpose of this study was to examine the relationship between characteristics of personality and respondent characteristics of Kinetic Family Drawing for young children. The subjects were 170 children(110 boys and 60 girls). The personal interview contained Personality Characteristic Test for young children(In-Sub Song, 1993) and Kinetic Family Drawing Test(Burns and Kaufman, 1982). Results of the test were analyzed by t-test and ANOVA by SAS program. Results are followings. First, chileren's sex and the general tendency of personal characteristic showed significant difference in the emotional personality among 4 personality characteristics. Girls show more positive tendency than boys in moral, physical, appearance and feature which expressed personal feeling and emotion. Also, girls showed more positive tendency than boys in personal characteristic which showed physical ability. Second, Children's sex and individual characteristic in Kinetic Family Drawing respondent characteristic showed significant difference in own's arm length. Also, Using a rare of paper and chapter 1 of the power among the family showed significant difference in styles and symbols. The boys drew lengther arms compared with their height than the girls. The girls were less complicative, anxious, comparative and aggressive for their family. Third, As a result of the study about the relationship between 4 personal characteristics of children and individual's behavior in Kinetic Family Drawing respondent characteristic, the significant difference is showed in academic personality and social personality had higher completion of their father's feature and drew bigger feet. In socal personality, negative behavior than positive children. Fourth, As a result of the study about the relationship between 4 personal characteristics of children and individual's characteristic, the significant difference were found in academic personality, social personality, family personality and emotional personality. Children with negative academic personality drew longer arms than children with positive academic personality, social personality and family personality. Also, Children with negative emotional personality drew more siblings than children with positive emotional personality. Fifth, The academic personality and the social personality had significant difference in the relationship between 4 personal characteristics of children and dynamics. In social personality, normal children were more tendencious to look at the important person with their mother's direction than positive children. Sixth, In terms of the relationship between 4 personal characteristics of children and mode, academic personality and family personality showed significant difference. Children with negative academic personality used more edge of papers than children with positive academic personality and children with positive academic personality and children with negative family personality fold more papers than children with positive family personality. At last, there were no significant difference between 4 personal characteristics of children and styles as well as symbols.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.385-395
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• 2018

With the development of Internet, cyber attack has become a major threat. To detect cyber attacks, intrusion detection system(IDS) has been widely deployed. But IDS has a critical weakness which is that it generates a large number of false alarms. One of the promising techniques that reduce the false alarms in real time is machine learning. However, there are problems that must be solved to use machine learning. So, many machine learning approaches have been applied to this field. But so far, researchers have not focused on features. Despite the features of IDS alerts are important for performance of model, the approach to feature is ignored. In this paper, we propose new feature set which can improve the performance of model and can be extracted from a single alarm. New features are motivated from security analyst's know-how. We trained and tested the proposed model applied new feature set with real IDS alerts. Experimental results indicate the proposed model can achieve better accuracy and false positive rate than SVM model with ordinary features.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.338-345
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• 2013

Induction motors play an increasing importance in industrial manufacturing. Therefore, the state monitoring systems also have been considering as the key in dealing with their negative effect by absorbing faulty symptoms in motors. There are numerous proposed systems in literature, in which, several kinds of signals are utilized as the input. To solve the multiple faults problem of induction motors, like the proposed system, the vibration signals is good candidate. In this study, a new signal processing scheme was utilized, which transforms the time domain vibration signal into the spatial domain as an image. Then the spatial features of converted image then have been extracted by applying the dominant neighbourhood structure (DNS) algorithm. In addition, these feature vectors were evaluated to obtain the fruitful dimensions, which support to discriminate between states of motors. Because of reliability, the conventional one-against-all (OAA) multi-class support vector machines (MCSVM) have been utilized in the proposed system as classifier module. Even though examined in severity levels of signal-to-noise ratio (SNR), up to 15dB, the proposed system still reliable in term of two criteria: true positive (TF) and false positive (FP). Furthermore, it also offers better performance than five state-of-the-art systems.