• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.126-131
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• 2017

In this paper, we propose a method to extract meaningful motion among various kinds of hand gestures on giving commands to robots using hand gestures. On giving a command to the robot, the hand gestures of people can be divided into a preparation one, a main one, and a finishing one. The main motion is a meaningful one for transmitting a command to the robot in this process, and the other operation is a meaningless auxiliary operation to do the main motion. Therefore, it is necessary to extract only the main motion from the continuous hand gestures. In addition, people can move their hands unconsciously. These actions must also be judged by the robot with meaningless ones. In this study, we extract human skeleton data from a depth image obtained by using a Kinect v2 sensor and extract location data of hands data from them. By using the Kalman filter, we track the location of the hand and distinguish whether hand motion is meaningful or meaningless to recognize the hand gesture by using the hidden markov model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.2
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• pp.319-325
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• 2014

Since smart phones or tablet PCs have been widely used recently, the users can create and edit documents anywhere in real time. If the input and edit flows of documents can be traced, it can be used as evidence in digital forensic investigation. The typical document application is the MS(Microsoft) Office. As the MS Office applications consist of two file formats that Compound Document File Format which had been used from version 97 to 2003 and OOXML(Office Open XML) File Format which has been used from version 2007 to now. The studies on MS Office files were for making a decision whether the file has been tampered or not through detection of concealed items or analysis of documents properties so far. This paper analyzed the input order of text cells on MS Excel files and shows how to figure out what cell is the last edited in digital forensic perspective.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.338-338
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• 2023

최근 특정 지역에 짧은 시간동안 많은 강우가 내리는 국지성 집중호우가 빈번히 발생하고 있으나, 이에 대한 예측과 대비에도 불구하고 피해는 지속적으로 증가하고 있다. 지속적인 강우량 증가 추이로 시간최대 및 일최대 강우량 관측기록이 해마다 갱신되고, 도시, 하천 및 주요 홍수방어 시설의 설계용량을 초과하는 피해가 발생하고 있다. 다수의 인구가 거주하고 대규모 기반시설이 집중된 도시지역에서 발생하는 집중호우는 심각한 인명 및 재산피해로 이어질 수 있다. 따라서, 부처별 재난의 저감대책은 정량적인 피해규모의 피해금액 예측보다는 설계 빈도에 대한 규모의 크기로 대책을 마련하고 있다. 국내에서는 풍수해 피해를 저감시키기 위해 개발에 따르는 재해영향요인을 개발 사업 시행 이전에 예측·분석하고 적절한 저감대책안을 수립·시행하고 있으나 설계빈도에 대한 규모일 뿐 정량적인 저감대책으로 예방되는 피해금액은 알 수 없다. 본 연구에서는 재해연보를 기반으로 호우재해(호우, 태풍)에 대한 시군구-재해기간의 피해데이터를 1999년부터 2019년까지 총 20년의 빅데이터와 전국 68개 강우관측소를 대상으로 총 20년(1999년 ~ 2019년)의 강우자료를 구축하였다. 머신러닝의 학습별 알고리즘을 조사하여 호우재해 피해데이터의 적용성이 높고 다양한 분야에 적용이 가능한 Neural networks의 분석기술인 ANN기법을 선정하였다 피해데이터의 재해발생기간별 총강우량, 일최대강우량, 총피해금액에 대하여 1999년 ~ 2018년을 학습하고 2019년에 대하여 강우특성과 피해특성의 분석하였다. 분석결과 Neural Networks의 지도학습은 총 6,902개 중 2019년을 제외한 6,414개를 학습하였으며 분석 타깃은 호우재해의 피해규모를 분석할 수 있는 총강우량, 일최대강우량, 총피해금액에 대하여 은닉노드 5개씩 2계층에 대하여 분석하였다.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Journal of Veterinary Clinics
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• v.23 no.2
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• pp.105-110
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• 2006

A three-layer, feed-forward artificial neural network (ANN) with sixteen input neurons, three hidden neurons, and one output neuron was developed to identify the presence of infectious bronchitis (IB) infection as early as possible in laying hen flocks. Retrospective data from flocks that enrolled IB surveillance program between May 2003 and November 2005 were used to build the ANN. Data set of 86 flocks was divided randomly into two sets: 77 cases for training set and 9 cases for testing set. Input factors were 16 epidemiological findings including characteristics of the layer house, management practice, flock size, and the output was either presence or absence of IB. ANN was trained using training set with a back-propagation algorithm and test set was used to determine the network's capability to predict outcomes that it has never seen. Diagnostic performance of the trained network was evaluated by constructing receiver operating characteristic (ROC) curve with the area under the curve (AUC), which were also used to determine the best positivity criterion for the model. Several different ANNs with different structures were created. The best-fitted trained network, IBV_D1, was able to predict IB in 73 cases out of 77 (diagnostic accuracy 94.8%) in the training set. Sensitivity and specificity of the trained neural network was 95.5% (42/44, 95% CI, 84.5-99.4) and 93.9% (31/33, 95% CI, 79.8-99.3), respectively. For testing set, AVC of the ROC curve for the IBV_D1 network was 0.948 (SE=0.086, 95% CI 0.592-0.961) in recognizing IB infection status accurately. At a criterion of 0.7149, the diagnostic accuracy was the highest with a 88.9% with the highest sensitivity of 100%. With this value of sensitivity and specificity together with assumed 44% of IB prevalence, IBV_D1 network showed a PPV of 80% and an NPV of 100%. Based on these findings, the authors conclude that neural network can be successfully applied to the development of a screening model for identifying IB infection in laying hen flocks.

• Journal of the Korean Geotechnical Society
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• v.15 no.1
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• pp.99-112
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• 1999

By using 92 values of lateral earth pressure coefficient(K) measured in Korea, the tendency of K with varying depth is analyzed and compared with the range of K defined by Hoek and Brown. The horizontal stress is generally larger than the vertical stress in Korea : About 84 % of K values are above 1. In this study, the theory of elasto-plasticity is applied to analyze the variation of K values, and the results are compared with those of numerical analysis. This reveals that the erosion, sedimentation and weathering of earth crust are important factors in the determination of K values. Surface erosion, large lateral pressure and good rock mass increase the K values, but sedimentation decreases the K values. This study enable us to analyze the effects of geological processes on the K values, especially at shallow depth where underground excavation takes place. A neural network expert system using multi-layer back-propagation algorithm is developed to predict the K values. The neural network model has a correlation coefficient above 0.996 when it is compared with measured data. The comparison with 9 measured data which are not included in the back-propagation learning has shown an average inference error of 20% and the correlation coefficient above 0.95. The expert system developed in this study can be used for reliable determination of K values.

• Korean Security Journal
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• no.62
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• pp.347-367
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• 2020

Rapid advancement of technology in today's society has allowed for easy access and use of data, promoting the process of informationization. Along with the merits of such development, unintended consequences of security risks involving wiretapping have been increasing as well. The security threats posed by wiretapping technology must be addressed by every organization and individual, as it could be used to leak confidential information about the nation's security, military and diplomatic strategies, industrial technologies, and personal information. Despite increasing threats stemming from the surrounding nations using advanced wiretapping technology, there is a lack of awareness at the government level, and the existing security measures for detecting and counteracting the wiretapping equipment are ineffective. In this research, the authors offered technical suggestions for improving the security strategies against the threats of wiretapping and information leakage by conducting a content analysis. The authors suggested the units of an agency be assigned a security grade based on its importance, and that adequate security equipment should be operated according to the grade. For instance, around-the-clock surveillance is recommended for grade-1 facilities, and portable wiretapping equipment detectors should be used to protect conference rooms and other key sites.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.33-41
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• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.431-440
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• 2018

In this study, authors predicted probability of resignation of newly employed nurses using TensorFlow, an open source software library for numerical computation and machine learning developed by Google, and suggested strategic human resources management plan. Data of 1,018 nurses who resigned between 2010 and 2017 in single university hospital were collected. After the order of data were randomly shuffled, 80% of total data were used for machine leaning and the remaining data were used for testing purpose. We utilized multiple neural network with one input layer, one output layer and 3 hidden layers. The machine-learning algorithm correctly predicted for 88.7% of resignation of nursing staff with in one year of employment and 79.8% of that within 3 years of employment. Most of resigned nurses were in their late 20s and 30s. Leading causes of resignation were marriage, childbirth, childcare and personal affairs. However, the most common cause of resignation of nursing staff with in one year of employment were maladaptation to the work and problems in interpersonal relationship.

• The Journal of Engineering Geology
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• v.15 no.1
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• pp.67-76
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• 2005

This paper presents the application of a neural network for prediction of the unconfined compressive strength from physical properties and schmidt hardness number on rock samples. To investigate the suitability of this approach, the results of analysis using a neural network are compared to predictions obtained by statistical relations. The data sets containing 55 rock sample records which are composed of sandstone and shale were assembled in Daegu area. They were used to learn the neural network model with the back-propagation teaming algorithm. The rock characteristics as the teaming input of the neural network are: schmidt hardness number, specific gravity, absorption, porosity, p-wave velocity and S-wave velocity, while the corresponding unconfined compressive strength value functions as the teaming output of the neural network. A data set containing 45 test results was used to train the networks with the back-propagation teaming algorithm. Another data set of 10 test results was used to validate the generalization and prediction capabilities of the neural network.