• KIPS Transactions on Software and Data Engineering
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• v.9 no.9
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• pp.267-280
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• 2020

Mirror is a physical reflective surface, typically of glass coated with a metal amalgam, and it is to reflect an image clearly. They are available everywhere anytime and become an essential tool for us to observe our faces and appearances. With the advent of modern software technology, we are motivated to enhance the reflection capability of mirrors with the convenience and intelligence of realtime processing, microservices, and machine learning. In this paper, we present a development of Digital Mirror System that provides the realtime reflection functionality as mirror while providing additional convenience and intelligence including personal information retrieval, public information retrieval, appearance age detection, and emotion detection. Moreover, it provides a multi-model user interface of touch-based, voice-based, and gesture-based. We present our design and discuss how it can be implemented with current technology to deliver the realtime mirror reflection while providing useful information and machine learning intelligence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4508-4515
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• 2014

Real-time body detection has been researched actively. On the other hand, the detection rate of color distorted images is low because most existing detection methods use static skin color model. Therefore, this paper proposes a new method for detecting the skin color region using a gradient map and saturation features. The basic procedure of the proposed method sequentially consists of creating a gradient map, extracting a gradient feature of skin regions, noise removal using the saturation features of skin, creating a cluster for extraction regions, detecting skin regions using cluster information, and verifying the results. This method uses features other than the color to strengthen skin detection not affected by light, race, age, individual features, etc. The results of the detection rate showed that the proposed method is 10% or more higher than the traditional methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.350-356
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• 2017

With increasing attention to real-time body detection, active research is being conducted on human body detection based on skin color. Despite this, most existing skin detection methods utilize static skin color models and have detection rates in images, in which colors are distorted. This study proposed a method of detecting the skin region using a fuzzy classification of the gradient map, saturation, and Cb and Cr in the YCbCr space. The proposed method, first, creates a gradient map, followed by a saturation map, CbCR map, fuzzy classification, and skin region binarization in that order. The focus of this method is to rigorously detect human skin regardless of the lighting, race, age, and individual differences, using features other than color. On the other hand,the borders between these features and non-skin regions are unclear. To solve this problem, the membership functions were defined by analyzing the relationship between the gradient, saturation, and color features and generate 108 fuzzy rules. The detection accuracy of the proposed method was 86.35%, which is 2~5% better than the conventional method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.25-38
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• 2022

With the diversification of payment methods and games, related financial accidents are causing serious problems for users and game companies. Recently, game companies have introduced an Fraud Detection System (FDS) for game payment systems to prevent financial incident. However, FDS is ineffective and cannot provide major evidence based on judgment results, as it requires constant change of detection patterns. In this paper, we analyze abnormal transactions among payment log data of real game companies to generate related features. One of the unsupervised learning models, Autoencoder, was used to build a model to detect abnormal transactions, which resulted in over 85% accuracy. Using X-FDS (Explainable FDS) with XAI-SHAP, we could understand that the variables with the highest explanation for anomaly detection were the amount of transaction, transaction medium, and the age of users. Based on X-FDS, we derive an improved detection model with an accuracy of 94% was finally derived by fine-tuning the importance of features that adversely affect the proposed model.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.484-492
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• 2018

This study examined the effects of field complexity and type of target objects on the performance in baggage screening task. A total of 62 participants(male: 45.2%, female: 54.8%) participated and their mean age was 22.88. The simulated baggage screening task was developed for this study and after the orientation and task exercises, main experiment session was conducted. Participants performed a total of 200 tasks and 40(20%) contained target object. The complexity was set at three levels: high, middle, and low levels and the number of background items contained 20, 14. and 8 respectively. The type of target was set as gun, knife, liquid, and righter. The dependent variables were hit ratio and reaction time. The results showed that the hit ratio decreased and reaction time increased significantly as field complexity increased, and they varied depending on the type of target. The hit ratio of the knife was the highest and liquid lowest and reaction time of the knife was the fastest and liquid slowest. In addition, the interaction effect was also significant. Knife was not affected by complexity, however, small item such as lighter was most affected by complexity.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.27-28
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• 2019

수술 시 시행되는 마취 과정에서 저혈압, 빈맥 등의 합병증이 다양한 정도로 발생한다. 이는 환자의 수술 후 심근경색이나 급성 신장 손상과 같은 심각한 합병증을 야기할 수 있으며 이러한 합병증들은 환자를 사망에 이르게 하는 원인이 되기도 한다. 본 연구에서는 머신러닝 기법을 활용해 전신마취 유도 중 저혈압 발생 환자를 예측하고자 한다. 순천향대학교 부천병원에서 수집된 207명 환자의 데이터를 이용하여 저혈압 발생 환자를 탐지하는 모델을 구축하였다. 의무 기록정보에 나타난 성별, 나이, 몸무게, 키, 신체적 상태 정보와 마취 유도 단계의 생체 신호 정보를 이용하였다. 신체적 상태 정보를 제외한 전체 피쳐를 모두 사용하였을 때, 탐지 정확도 68.06%, 관련 논문을 바탕으로 중요 피쳐만을 사용하여 실험하였을 때, 정확도 71.53%였으며, 환자의 신체적 상태 피쳐를 포함하여 실험하였을 때, 정확도 75%로 가장 우수한 결과를 얻었다.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.86-96
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• 2020

Based on the use cases of image recognition technology, this study looked at how artificial intelligence plays a role in image recognition technology. Through image recognition technology, satellite images can be analyzed with artificial intelligence to reveal the calculation of oil storage tanks in certain countries. And image recognition technology makes it possible for searching images or products similar to images taken or downloaded by users, as well as arranging fruit yields, or detecting plant diseases. Based on deep learning and neural network algorithms, we can recognize people's age, gender, and mood, confirming that image recognition technology is being applied in various industries. In this study, we can look at the use cases of domestic and overseas image recognition technology, as well as see which methods are being applied to the industry. In addition, through this study, the direction of future research was presented, focusing on various successful cases in which image recognition technology was implemented and applied in various industries. At the conclusion, it can be considered that the direction in which domestic image recognition technology should move forward in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.847-857
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• 2019

Recently, various cyber threats such as Ransomware and APT attack are increasing by e-mail. The characteristic of such an attack is that it is important to take administrative measures by improving personal perception of security because it bypasses technological measures such as past pattern-based detection The purpose of this study is to investigate the human factors of employees who are vulnerable to spam mail attacks through field experiments and to establish future improvement plans. As a result of sending 7times spam mails to employees of a company and analyzing training report, It was confirmed that factors such as the number of training and the recipient 's gender, age, and workplace were related to the reading rate. Based on the results of this analysis, we suggest ways to improve the training and to improve the ability of each organization to carry out effective simulation training and improve the ability to respond to spam mail by awareness improvement.

• The Journal of the Acoustical Society of Korea
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• v.26 no.2
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• pp.87-94
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• 2007

In active sonar system, aspect angle and length of a target can be estimated by calculating the cross-correlation between left and right split-beams of a LFM(Linear Frequency Modulated) signal. However, high-resolution performances in bearing and range are required to estimate the information of a remote target. Because a certain higher sampling frequency than the Nyquist sampling frequency is required in this performance, an over-sampling process through interpolation method should be required. However, real-time implementation of split-beam processing with over-sampled split-beam outputs on a COTS(commercial off-the-shelf) DSP platform limits its performance because of given throughput and memory capacity. This paper proposes a cumulative processing algorithm for split-beam processing to solve the problems. The performance of the proposed method was verified through some simulation tests. Also, the proposed method was implemented as a real-time system using an ADSP-TS101.

• Journal of Animal Science and Technology
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• v.50 no.2
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• pp.273-278
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• 2008

There have been studies about dog’s personality and behavior, which is helpful to breed dogs as guide or companion. In this study, a questionnaire was developed using 54 Jindo dogs, which considered ten items about aggressiveness and sociability. The scores were analyzed by principle component analysis (PCA), after accounting for four variables: age, gender, growing place, and coat-colors. Our results from the PCA indicated three principle components, which classified ‘aggressiveness’, ‘sociability’ and unknown factor. The four variables did not significantly affect aggressiveness(P>0.05). However, there was a relationship between coat-color and sociability, i.e., the Jindo dogs with fawn color were more sociable than the white ones(P<0.1).