• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.107-114
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• 2022

In order to classify 3D film images as right or wrong, it is necessary to detect the pattern in a 3D film image. However, if the contrast of the pixels in the 3D film image is low, it is not easy to classify as the right and wrong 3D film images because the pattern in the image might not be clear. In this paper, we propose a method of classifying 3D film images as right or wrong by comparing the width at a specific frequency of each histogram after obtaining the histogram. Since, it is classified using the width of the histogram, the analysis process is not complicated. From the experiment, the histograms of right and wrong 3D film images were distinctly different, and the proposed algorithm reflects these features, and showed that all 3D film images were accurately classified at a specific frequency of the histogram. The performance of the proposed algorithm was verified to be the best through the comparison test with the other methods such as image subtraction, otsu thresholding, canny edge detection, morphological geodesic active contour, and support vector machines, and it was shown that excellent classification accuracy could be obtained without detecting the patterns in 3D film images.

• 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.

• The Journal of Korean Academy of Prosthodontics
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• v.48 no.4
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• pp.294-300
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• 2010

Purpose: The template-guided implant surgery offers several advantages over the traditional approach. The purpose of this study was to evaluate the accuracy of coordinate synchronization procedure with 5-axis milling machine for surgical template fabrication by means of reverse engineering through universal CAD software. Materials and methods: The study was performed on ten edentulous models with imbedded gutta percha stoppings which were hidden under silicon gingival form. The platform for synchordination was formed on the bottom side of models and these casts were imaged in Cone beam CT. Vectors of stoppings were extracted and transferred to those of planned implant on virtual planning software. Depth of milling process was set to the level of one half of stoppings and the coordinate of the data was synchronized to the model image. Synchronization of milling coordinate was done by the conversion process for the platform for the synchordination located on the bottom of the model. The models were fixed on the synchordination plate of 5-axis milling machine and drilling was done as the planned vector and depth based on the synchronized data with twist drill of the same diameter as GP stopping. For the 3D rendering and image merging, the impression tray was set on the conbeam CT and pre- and post- CT acquiring was done with the model fixed on the impression body. The accuracy analysis was done with Solidworks (Dassault systems, Concord, USA) by measuring vector of stopping’s top and bottom centers of experimental model through merging and reverse engineering the planned and post-drilling CT image. Correlations among the parameters were tested by means of Pearson correlation coefficient and calculated with SPSS (release 14.0, SPSS Inc. Chicago, USA) ($\alpha$ = 0.05). Results: Due to the declination, GP remnant on upper half of stoppings was observed for every drilled bores. The deviation between planned image and drilled bore that was reverse engineered was 0.31 (0.15 - 0.42) mm at the entrance, 0.36 (0.24 - 0.51) mm at the apex, and angular deviation was 1.62 (0.54 - 2.27)$^{\circ}$. There was positive correlation between the deviation at the entrance and that at the apex (Pearson Correlation Coefficient = 0.904, P = .013). Conclusion: The coordinate synchronization 5-axis milling procedure has adequate accuracy for the production of the guided surgical template.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.17-31
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• 2006

In this paper, passport recognition and face verification methods which can automatically recognize passport codes and discriminate forgery passports to improve efficiency and systematic control of immigration management are proposed. Adjusting the slant is very important for recognition of characters and face verification since slanted passport images can bring various unwanted effects to the recognition of individual codes and faces. Therefore, after smearing the passport image, the longest extracted string of characters is selected. The angle adjustment can be conducted by using the slant of the straight and horizontal line that connects the center of thickness between left and right parts of the string. Extracting passport codes is done by Sobel operator, horizontal smearing, and 8-neighborhood contour tracking algorithm. The string of codes can be transformed into binary format by applying repeating binary method to the area of the extracted passport code strings. The string codes are restored by applying CDM mask to the binary string area and individual codes are extracted by 8-neighborhood contour tracking algerian. The proposed RBF network is applied to the middle layer of RBF network by using the fuzzy logic connection operator and proposing the enhanced fuzzy ART algorithm that dynamically controls the vigilance parameter. The face is authenticated by measuring the similarity between the feature vector of the facial image from the passport and feature vector of the facial image from the database that is constructed with PCA algorithm. After several tests using a forged passport and the passport with slanted images, the proposed method was proven to be effective in recognizing passport codes and verifying facial images.

• Journal of Broadcast Engineering
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• v.10 no.1 s.26
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• pp.43-56
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• 2005

In the existing broadcasting environment, it is not easy to serve the bi-directional service between a broadcasting server and a TV audience. In the uni-directional broadcasting environments, almost TV programs are scheduled depending on the viewers' popular watching time, and the advertisement contents in these TV programs are mainly arranged by the popularity and the ages of the audience. The audiences make an effort to sort and select their favorite programs. However, the advertisement programs which support the TV program the audience want are not served to the appropriate audiences efficiently. This randomly provided advertisement contents can occur to the audiences' indifference and avoidance. In this paper, we propose the target advertisement service for the appropriate distribution of the advertisement contents. The proposed target advertisement service estimates the audience's profile without any issuing the private information and provides the target-advertised contents by using his/her estimated profile. For the experimental results, we used the real audiences' TV usage history such as the ages, fonder and time of the programs from AC Neilson Korea. And we show the accuracy of the proposed target advertisement service algorithm. NDS (Normalized Distance Sum) and the Vector correlation method, and implementation of our target advertisement service system.

• Science of Emotion and Sensibility
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• v.11 no.4
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• pp.461-480
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• 2008

There are innumerable studies on technology adoption and usage continuance; most examine cognitive factors while affective factors or the feelings of users are left relatively unexplored. Although attitude and user satisfaction are factors commonly considered in Information Systems(IS) research, they represent only some aspects of feelings. In contrast, researchers in diverse fields have begun to note the importance of feelings in understanding and predicting human behavior. Feelings are anticipated to be essential particularly in the context of modern applications, such as mobile internet(M-internet) services, where users are not simply technology users but also service consumers. Drawing on the support of consumer research, social psychology and computer science, this study proposes a balanced cognition-affect model of IS continuance. Prior works in relation to IS research have already considered the emotional factors. The common factors are enjoyment, anxiety, affect and satisfaction. The main difference in our study is that the factors that we used are the primary dimensions of affect according to Circumplex Model of Affect. The horizontal axis of the model represents the pleasure dimension and the vertical represents the arousal dimension. Other emotional factors such as enjoyment and anxiety can be viewed as a combination of these two dimensions, and they can be placed in the vector space formed by these two primary dimensions. Affect has been defined as the enjoyment a person derives from using computers. Satisfaction has different conceptualizations. It has been conceptualized as judgment based on the expectation disconfirmation theory. Thus, while prior works considered the direct and indirect effects of "feeling-related constructs"(enjoyment and anxiety) on usage behavior, our study proposes effects of "feeling-based constructs"(pleasure and arousal). The balanced cognition-affect model is tested in a survey of, M-internet service users. The results establish the validity of the model.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.88-95
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• 2011

An embedded mobile device mostly has lower computation power than a general purpose computer because of its relatively lower system specifications. Consequently, conventional face tracking and face detection methods, requiring complex algorithms for higher recognition rates, are unsuitable in a mobile environment aiming for real time detection. On the other hand, by applying a real-time tracking and detecting algorithm, we would be able to provide a two-way interactive multimedia service between an user and a mobile device thus providing a far better quality of service in comparison to a one-way service. Therefore it is necessary to develop a real-time face and eye tracking technique optimized to a mobile environment. For this reason, in this paper, we proposes a method of tracking horizontal face position of a user on a T-DMB device for enhancing the quality of 3D DMB content. The proposed method uses the orientation of edges to estimate the left and right boundary of the face, and by the color edge information, the horizontal position and size of face is determined finally to decide the horizontal face. The sobel gradient vector is projected vertically and candidates of face boundaries are selected, and we proposed a smoothing method and a peak-detection method for the precise decision. Because general face detection algorithms use multi-scale feature vectors, the detection time is too long on a mobile environment. However the proposed algorithm which uses the single-scale detection method can detect the face more faster than conventional face detection methods.