• Journal of the Korean Dietetic Association
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• v.8 no.1
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• pp.9-18
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• 2002

This study was reviewed databases and outcomes of national/international off-line and on-line(Internet) nutrition softwares to identify the present conditions of nutrition softwares, and investigated user's needs and determine which component should be included in nutrition software. The most frequently used databases for the national programs were the food composition table provided from the National Rural Living Science Institution in Rural Development Administration and the food composition table and the nutrient contents of foods provided from the Korean Nutrition Society. For international programs, the food composition table from the USDA was commonly used. The analysed outcomes included the degree of obesity, nutrient analysis and nutrient intake compared with RDA, food intake from each by food group, food habits and the frequency of food consumption. As to the result of needs assessment for the Internet nutrition softwares, it was suggested that the needs of the Internet nutrition softwares were high because most of the respondents replied that 3-point('it is needed') or 4-point('it is necessary') on 4-points likert scale. As to the databases, the needs of 'food composition analysis' and 'the suggestion of the Korean RDA' were high. For the basic information for foods, the respondents replied that 'the classification of foods', 'foods codes', 'the amount of ingredients' and 'nutrient analysis' should be included. The needs of 'nutrient analysis of meal', 'diet therapy' and 'meal plan by caloric requirements' were high. As for utilizing the Internet meal planning programs, the respondents replied that 'it should be easy to use' most and demand for 'data saving and the saved data should be usable later' and 'meal planning education tools' were high. In conclusion, the Internet nutrition software that satisfies various needs of users should be developed for policy making that promote public health, nutritional care and self-supporting of foods.

• Journal of Advanced Navigation Technology
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• v.20 no.4
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• pp.285-291
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• 2016

Safety assessment is an essential activity for developing a system in the CNS/ATM domain. Up to now, there are many reference materials, but there is nothing that definitely specifies what to do and how to apply in the CNS/ATM. Another problem is that software assurance level has to be determined for a software under development. But there is nothing that defines a determination scheme of software assurance level. To solve these problems, this paper proposes a method to conduct a hazard assessment based on target level of safety defined in ICAO Doc 9689. To be applied generally in CNS/ATM domain, it mathematically defines procedures of hazard assessment. And it defines severity classification, probability, and safety objective of a system, which are necessary for hazard assessment, and it describes a method to apply event tree analysis process in order to conduct a hazard assessment.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.746-755
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• 2002

EXPRESS is product information description language. It is interpretable by human and software. Product data written in EXPRESS make it possible to exchange between heterogeneous systems. However, the number of software that can use EXPRESS is limited and it is expensive to use the software. XML makes it possible to update and manage data on the Web. Because the Web is easier to use and access than other tools comparatively, data represented by XML need not depend on specific applications or systems and it can be used for exchange of data. Therefore, if we represent EXPRESS-driven data in XML, there will be more active data exchange widely and easily In this work, a method of translation EXPRESS document to XML DTD and XML Schema is proposed. By classification all of EXPRESS syntax element and consideration complex cases caused by this syntax element, a translation rule that represent XML DTD and XML Schema is suggested. Also, a translator which is corresponding to this rule is implemented.

• ETRI Journal
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• v.38 no.6
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• pp.1064-1073
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• 2016

An adaptive speech streaming method to improve the perceived speech quality of a software-based multipoint control unit (SW-based MCU) over IP networks is proposed. First, the proposed method predicts whether the speech packet to be transmitted is lost. To this end, the proposed method learns the pattern of packet losses in the IP network, and then predicts the loss of the packet to be transmitted over that IP network. The proposed method classifies the speech signal into different classes of silence, unvoiced, speech onset, or voiced frame. Based on the results of packet loss prediction and speech classification, the proposed method determines the proper amount and bitrate of redundant speech data (RSD) that are sent with primary speech data (PSD) in order to assist the speech decoder to restore the speech signals of lost packets. Specifically, when a packet is predicted to be lost, the amount and bitrate of the RSD must be increased through a reduction in the bitrate of the PSD. The effectiveness of the proposed method for learning the packet loss pattern and assigning a different speech coding rate is then demonstrated using a support vector machine and adaptive multirate-narrowband, respectively. The results show that as compared with conventional methods that restore lost speech signals, the proposed method remarkably improves the perceived speech quality of an SW-based MCU under various packet loss conditions in an IP network.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.619-628
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• 2002

Reusable software component is an ultimate goal for the software development. Component based development is focused on advanced concepts rather than passive manipulation or class library with source codes. However, the primary component construction in component based development lead to an additional development cost and effort for reconstructing the new software component within a component model. Java application provides several features based on component model. But, we only have an opportunity to develop the smallest reuse units or the restricted set of GUI components. It cannot contributed as a component and only used in the specific domain component with high cost and efforts. In this paper, we apply java component model to the existing java application and extract javabeans through extending the component scalability. We also discuss the algorithm for transformation mechanism from legacy class to javabeans with a partial of business logic.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.39-42
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• 2023

최근 온라인 게임 산업이 급속도로 확장됨과 더불어 Gamebot과 같은 비정상적인 프로그램으로 인한 게임 서비스 피해사례가 급격하게 증가하고 있다. 특히, 대표적인 게임 장르 중 하나인 FPS(First-Person Shooter)에서 Aimbot의 사용은 정상적인 이용자들에게 재미 요소를 잃어버리게 하고 상대적 박탈감을 일으켜 게임의 수명을 줄이는 원인이 된다. 비정상 게임 이용자의 근절을 위해서 메모리 변조 및 불법 변조 프로그램 접근 차단 기법과 불법 프로그램 사용의 패턴 모니터링과 같은 기법들이 제안되었지만, 우회 프로그램 및 새로운 패턴을 이용한 비정상적인 프로그램의 개발에는 취약하다는 단점이 있다. 따라서, 본 논문에서는 정상적인 게임 이용자의 패턴만 학습함으로써 비정상 이용자 검출을 가능하게 하는 딥러닝 기반 단일 클래스 분류 기법을 제안하며, 가장 빈번하게 발생하는 치트(Cheat) 유형인 FPS 게임 내 Aimbot 사용 감지에 초점을 두었다. 제안된 비정상 게임 이용자 감지 시스템은 정상적인 사용자의 마우스 좌표를 데카르트 좌표계(Cartesian coordinates)와 극좌표계(Polar coordinates)의 형태로 패턴을 추출하는 과정과 정상적인 마우스 동작 기록으로 부터 학습된 LSTM 기반 Autoencoder의 복원 에러에 따른 검출 과정으로 구성된다. 실험에서 제안된 모델은 FPS 게임 내 마우스 동작을 기록한 공개 데이터셋인 CSGO 게임 데이터셋으로 부터 학습되었으며, 학습된 모델의 테스트 결과는 데카르트 좌표계로부터 훈련된 제안 모델이 비정상 게임 이용자를 분류하는데 적합함을 입증하였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.127-132
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• 2011

In this paper, we propose a novel method of adult image classification by combining skin color regions and edges in an input image. The proposed method consists of four steps. In the first step, initial skin color regions are detected by logical AND operation of all skin color regions detected by the existing methods of skin color detection. In the second step, a skin color probability map is created by modeling the distribution of skin color in the initial regions. Then, a binary image is generated by using threshold value from the skin color probability map. In the third step, after using the binary image and edge information, we detect final skin color regions using a region growing method. In the final step, adult image classification is performed by support vector machine(SVM). To this end, a feature vector is extracted by combining the final skin color regions and neighboring edges of them. As experimental results, the proposed method improves performance of the adult image classification by 9.6%, compared to the existing method.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.137-144
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• 2020

Delirium is among the most common mental disorders encountered in patients with a temporary cognitive impairment such as consciousness disorder, attention disorder, and poor speech, particularly among those who are older. Delirium is distressing for patients and families, can interfere with the management of symptoms such as pain, and is associated with increased elderly mortality. The purpose of this paper is to generate useful clinical knowledge that can be used to distinguish the outcomes of patients with delirium in long-term care facilities. For this purpose, we extracted the clinical classification knowledge associated with delirium using a local covering rule acquisition approach with the rough lower approximation region. The clinical applicability of the proposed method was verified using data collected from a prospective cohort study. From the results of this study, we found six useful clinical pieces of evidence that the duration of delirium could more than 12 days. Also, we confirmed eight factors such as BMI, Charlson Comorbidity Index, hospitalization path, nutrition deficiency, infection, sleep disturbance, bed scores, and diaper use are important in distinguishing the outcomes of delirium patients. The classification performance of the proposed method was verified by comparison with three benchmarking models, ANN, SVM with RBF kernel, and Random Forest, using a statistical five-fold cross-validation method. The proposed method showed an improved average performance of 0.6% and 2.7% in both accuracy and AUC criteria when compared with the SVM model with the highest classification performance of the three models respectively.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.6
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• pp.239-248
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• 2018

Many new technologies are studied with the arrival of the 4th industrial revolution. In particular, emotional intelligence is one of the popular issues. Researchers are focused on emotional analysis studies for music services, based on artificial intelligence and pattern recognition. However, they do not consider how we recommend proper music according to the specific emotion of the user. This is the practical issue for music-related IoT applications. Thus, in this paper, we propose an probability-based music emotion classification technique that makes it possible to classify music with high precision based on the range of emotion, when developing music related services. For user emotion recognition, one of the popular emotional model, Russell model, is referenced. For the features of music, the average amplitude, peak-average, the number of wavelength, average wavelength, and beats per minute were extracted. Multiple regressions were derived using regression analysis based on the collected data, and probability-based emotion classification was carried out. In our 2 different experiments, the emotion matching rate shows 70.94% and 86.21% by the proposed technique, and 66.83% and 76.85% by the survey participants. From the experiment, the proposed technique generates improved results for music classification.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.441-448
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• 2019

Previous researches on automatic spacing of Korean sentences has been researched to correct spacing errors by using n-gram based statistical techniques or morpheme analyzer to insert blanks in the word boundary. In this paper, we propose an end-to-end automatic word spacing by using deep neural network. Automatic word spacing problem could be defined as a tag classification problem in unit of syllable other than word. For contextual representation between syllables, Bi-LSTM encodes the dependency relationship between syllables into a fixed-length vector of continuous vector space using forward and backward LSTM cell. In order to conduct automatic word spacing of Korean sentences, after a fixed-length contextual vector by Bi-LSTM is classified into auto-spacing tag(B or I), the blank is inserted in the front of B tag. For tag classification method, we compose three types of classification neural networks. One is feedforward neural network, another is neural network language model and the other is linear-chain CRF. To compare our models, we measure the performance of automatic word spacing depending on the three of classification networks. linear-chain CRF of them used as classification neural network shows better performance than other models. We used KCC150 corpus as a training and testing data.