• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.68-73
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• 2008

전자 메일의 사용이 급증함에 따라 스팸메일의 양도 함께 증가하고 있다. 증가되는 스팸으로 인한 피해를 줄이기 위하여 여러 가지 기법들이 사용되고 있지만, 지능화되어가는 스패머들의 기술에 완전한 스팸메일의 차단은 불가능하며, 수신된 메일에 대해 사용자는 자신만의 기준으로 스팸메일 여부를 판단하고 있다. 본 논문에서는 스팸메일임에도 불구하고 수신되는 메일(FN)에 대해, 사용자의 반응 패턴을 통하여 이를 판단하고자 한다. 수신된 메일의 송신자와 제목, 보관 편지함 등에서 형태소 추출을 하고 이를 PN_DB(Positive형태소와 Negative형태소로 구성된 DB, 이하 PN_DB)로 구축한 뒤, Negative 형태소들을 Balcklist로 사용하여 FN 메일을 판단한다. FN 메일로 판단된 경우에 PN_DB에서 계산된 각각의 가중치 값을 적용하여 사용자의 과거 스팸 판단 성향이 반영된 FN_value를 시각적으로 표현함으로써 사용자의 판단을 용이하게 하는 시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1399-1402
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• 2011

본 논문에서는 조기 심실 수축과 조기 심방 수축을 검출함에 있어 정밀한 QRS 구간의 폭, 정확한 P파와 T파의 크기 및 위치를 크게 요구하지 않고, 데이터의 가공과 복잡한 알고리즘의 사용에 의해 발생하는 ECG 데이터의 변형과 손실을 최소화할 수 있으며, 또한 개인차 때문에 발생할 수 있는 오류를 최소화하기 위한 알고리즘을 제안한다. 이를 위해 ECG 신호를 각각의 단위 파형으로 분리한 후, 정상 R-R 간격을 가지는 파형을 기준으로 기준파형을 만들어, 각 파형과 기준파형사이의 패턴 대조 및 유사도 분석을 통해 조기 심실수축과 조기심방수축을 검출할 수 있도록 하였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.735-743
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• 2020

Secure coding is a technique that detects malicious attack or unexpected errors to make software systems resilient against such circumstances. In many cases secure coding relies on static analysis tools to find vulnerable patterns and contaminated data in advance. However, secure coding has the disadvantage of being dependent on rule-sets, and accurate diagnosis is difficult as the complexity of static analysis tools increases. In order to support secure coding, we apply machine learning techniques, such as DNN, CNN and RNN to investigate into finding major weakness patterns shown in secure development coding guides and present machine learning models and experimental results. We believe that machine learning techniques can support detecting security weakness along with static analysis techniques.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.161-170
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• 2017

Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.277-286
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• 2002

Most of the problems in dealing with the tunnel construction are the uncertainties and complexities of the stress conditions and rock strengths in ahead of the tunnel excavation. The limitations on the investigation technology, inaccessibility of borehole test in mountain area and public hatred also restrict our knowledge on the geologic conditions on the mountainous tunneling area. Nevertheless an extensive and superior geophysical exploration data is possibly acquired deep within the mountain area, with up to the tunnel locations in the case of alternative design or turn-key base projects. An appealing claim in the use of artificial neural networks (ANN) is that they give a more trustworthy results on our data based on identifying relevant input variables such as a little geotechnical information and biological learning principles. In this study, error back-propagation algorithm that is one of the teaching techniques of ANN is applied to presupposition on Rock Mass Ratings (RMR) for unknown tunnel area. In order to verify the applicability of this model, a 4km railway tunnel's field data are verified and used as input parameters for the prediction of RMR, with the learned pattern by error back propagation logics. ANN is one of basic methods in solving the geotechnical uncertainties and helpful in solving the problems with data consistency, but needs some modification on the technical problems and we hope our study to be developed in the future design work.

• Journal of IKEEE
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• v.23 no.4
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• pp.1373-1380
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• 2019

In order to increase energy efficiency, developments of the advanced metering infrastructure (AMI) in the smart grid technology have recently been actively conducted. An essential part of AMI is analyzing power consumption and forecasting consumption patterns. In this paper, we analyze the power consumption and summarized the data errors. Monthly power consumption patterns are also analyzed using the k-means clustering algorithm. Forecasting the consumption pattern by each household is difficult. Therefore, we first classify the data into 100 clusters and then predict the average of the next day as the daily average of the clusters based on the deep neural network. Using practically collected AMI data, we analyzed the data errors and could successfully conducted power forecasting based on a clustering technique.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.689-698
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• 2006

Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns. Color image consists of various color patterns. And most pattern recognition methods use the information of color which has been trained and extract the feature of the color. This thesis extracts adaptively specific color feature from images with several limited colors. Because the number of the color patterns is limited, the distribution of the color in the image is similar. But, when there are some noises and distortions in the image, its distribution can be various. Therefore we cannot extract specific color regions in the standard image that is well expressed in special color patterns to extract, and special color regions of the image to test. We suggest new method to reduce the error of recognition by extracting the specific color feature adaptively for images with the low distortion, and six test images with some degree of noises and distortion. We consequently found that proposed method shouws more accurate results than those of statistical pattern recognition.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.619-630
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• 2013

In this paper, we propose an edge directed color demosaicing algorithm considering color channel correlation. The proposed method consists of local region classification step and edge directional interpolation step. In the first step, each region of a given Bayer image is classified as normal edge, pattern edge, and flat regions by using intra channel and inter channel gradients. Especially, two criteria and verification process for the normal edge and pattern edge classification are used to reduce edge direction estimation error, respectively. In the second step, edge directional interpolation process is performed according to characteristics of the classified regions. For horizontal and vertical directional interpolations, missing color components are obtained from interpolation equations based on intra channel and inter channel correlations in order to improve the performance of the directional interpolations. The simulation results show that the proposed algorithm outperforms conventional approaches in both objective and subjective terms.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Journal of Korea Multimedia Society
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• v.11 no.7
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• pp.1035-1042
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• 2008

The support vector machine, as a binary classifier, is known to surpass the other classifiers only in binary classification problems through the various experiments. Even though its theory is based on the maximal margin classifier, the support vector machine approach cannot be easily extended to the multi-classification problems. In this paper, we review the extension techniques of the support vector machine toward the multi-classification and do the performance comparison. Depending on the data decomposition of the training data, the support vector machine is easily adapted for a multi-classification problem without modifying the intrinsic characteristics of the binary classifier. The performance is evaluated on a collection of the benchmark data sets and compared according to the selected teaming strategies, the training time, and the results of the neural network with the backpropagation teaming. The experiments suggest that the support vector machine is applicable and effective in the general multi-class classification problems when compared to the results of the neural network.