• Journal of KIISE
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• v.44 no.10
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• pp.1026-1033
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• 2017

In this paper a Bayesian modeling and duration-based prediction method is proposed for health clinic time series data using the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). HDP-HMM is a Bayesian extension of HMM which can find the optimal number of health states, a number which is highly uncertain and even difficult to estimate under the context of health dynamics. Test results of HDP-HMM using simulated data and real health clinic data have shown interesting modeling behaviors and promising prediction performance over the span of up to five years. The future of health change is uncertain and its prediction is inherently difficult, but experimental results on health clinic data suggests that practical long-term prediction is possible and can be made useful if we present multiple hypotheses given dynamic contexts as defined by HMM states.

• Journal of the korean academy of Pediatric Dentistry
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• v.31 no.1
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• pp.66-78
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• 2004

The objective of this study was to apply the vibration technique to reduce the viscosity of bonding adhesives and thereby compare the bond strength and resin penetration in enamel and dentin achieved with those gained using the conventional technique and vibration technique. For enamel specimens, thirty teeth were sectioned mesio-distally. Sectioned two parts were assigned to same adhesive system but different treatment(vibration vs. non-vibration). Each specimen was embedded in 1-inch inner diameter PVC pipe with a acrylic resin. The buccal and lingual surfaces were placed so that the tooth and the embedding medium were at the same level. The samples were subsequently polished silicon carbide abrasive papers. Each adhesive system was applied according to its manufacture's instruction. Vibration groups were additionally vibrated for 15 seconds before curing. For dentin specimen, except removing the coronal part and placing occlusal surface at the mold level, the remaining procedures were same as enamel specimen. Resin composite(Z250. 3M. U.S.A.) was condensed on to the prepared surface in two increments using a mold kit(Ultradent Inc., U.S.A.). Each increments was light cured for 40 seconds. After 24 hours in tap water at room temperature, the specimens were thermocycled for 1000cycles. Shear bond strengths were measured with a universal testing machine(Instron 4465, England). To investigate infiltration patterns of adhesive materials, the surface of specimens was examined with scanning electron microscope. The results were as follows: 1. In enamel the mean values of shear bond strengths in vibration groups(group 2, 4, 6) were greater than those of non-vibration group(group 1, 3, 5). The differences were statistically significant except AQ bond group. 2. In dentin, the mean values of shear bond strengths in vibration groups(group 2, 4, 6) were greater than those of non-vibration groups(group 1, 3, 5). But the differences were not statistically significant except One-Up Bond F group. 3. The vibration group showed more mineral loss in enamel and longer resin tag and greater number of lateral branches in dentin under SEM examination.

• The Journal of Korean Philosophical History
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• no.42
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• pp.133-164
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• 2014

In "Lu's Spring and Autumn", the ideas of all the schools before Qin Dynasty were compiled and the diagram of the Trinity of heaven, earth and man using the category of Yin-yang and the five elements of the universe since the ancient times was established. This can be assessed to be the blueprint for a unified empire closely connecting time and space, and objects in heaven and on earth centered around human beings. In specific, Yin-yang and the five elements of the universe were combined to categorize and schematize all things in the universe, and connect them to human affairs at the same time. Its contents convert almost all academic fields including politics, economics, society, military, astronomy, geography, medical science, education and history. Particularly, the documents popular during the age of civil wars and the ideas of Jikha scholars were synthesized and specified. Yet, it went beyond simple collection of the thoughts and documents since the ancient times in terms of contents and forms, and the method of 12 months for 1 year was selected and prescript was expanded to the various fields of politics and the society. In the Twelve principle, Yin-yang and the five elements, and the ten celestial stems and the earthly ones were combined, the contradiction from the process was solved, and the Rules of Four Seasons Thought was completed. Therefore, even though some parts of the idea of Yin-yang and the five elements in "Lu's Spring and Autumn" is found here and there from other documents, the unificative systematization of the whole has an important meaning in the history of thought. In summary, it has been proved that the Rules of Four Seasons Thought in "Lu's Spring and Autumn" was not limited to the physical unity of Yin-yang and the five elements of the universe, but qualitatively specified particularly in the aspects of agriculture from the people's side and politics from a leader's position.

• Journal of Information Management
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• v.41 no.4
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• pp.227-249
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• 2010

Scholarly information has increased tremendously according to the development of IT, especially the Internet. However, simultaneously, people have to spend more time and exert more effort because of information overload. There have been many research efforts in the field of expert systems, data mining, and information retrieval, concerning a system that recommends user-expected information items through presumption. Recently, the hybrid system combining a content-based recommendation system and collaborative filtering or combining recommendation systems in other domains has been developed. In this paper we resolved the problem of the current recommendation system and suggested a new system combining collaborative filtering and Naive Bayes Classification. In this way, we resolved the over-specialization problem through collaborative filtering and lack of assessment information or recommendation of new contents through Naive Bayes Classification. For verification, we applied the new model in NDSL's paper service of KISTI, especially papers from journals about Sitology and Electronics, and witnessed high satisfaction from 4 experimental participants.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.19-32
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• 2007

This paper present a new face recognition method based on LVQ neural net to construct a real time face recognition system. The previous researches which used PCA, LDA combined neural net usually need much time in training neural net. The supervised LVQ neural net needs much less time in training and can maximize the separability between the classes. In this paper, the proposed method transforms the input face image by PCA and LDA sequentially into low-dimension feature vectors and recognizes the face through LVQ neural net. In order to make the system robust to external light variation, light compensation is performed on the detected face by max-min normalization method as preprocessing. PCA and LDA transformations are applied to the normalized face image to produce low-level feature vectors of the image. In order to determine the initial centers of LVQ and speed up the convergency of the LVQ neural net, the K-Means clustering algorithm is adopted. Subsequently, the class representative vectors can be produced by LVQ2 training using initial center vectors. The face recognition is achieved by using the euclidean distance measure between the center vector of classes and the feature vector of input image. From the experiments, we can prove that the proposed method is more effective in the recognition ratio for the cases of still images from ORL database and sequential images rather than using conventional PCA of a hybrid method with PCA and LDA.

• The Korean Journal of Zoology
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• v.37 no.4
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• pp.554-564
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• 1994

세균이 세포내 공생하는 xD strain과 모 세포주인 tD strain Amoeba proteus의 열충격 대응의 차이를 알아 보기 위하여 방사선 동위원소로 표지된 아미노산을 Ca2+_less Chalkley's 용액에서 음작용 경로를 통하여 90분 동안 흡수하게 하고, 저온 및 고온 스트레스에 대하여 새로 합성되는 스트레스 대응 단백질의 양상을 1, 2차원 전기영동 및 자기방사 사진법에 의해서 비교하였다 저온(10"C) 충격에 대응하여 아메바는 두 strain 모두 56.0 kDa, pl 6.0 단백질을 강하게 발현하였으며, xD strain에서는 tD strain과 달리 저온 충격 초기에 66 0 kDa, pl 5.5 단백질의 발현이 중단되었다. 한편 고온(33"C) 열충격에 대하여 두 strain 아메바에서 모두 10여종의 단백질이 새합성되는 것으로 확인되었으며, tD 아메바에는 이들 단백질의 새합성이 완만하게 이루어지는데 비하여 xD 아메바에서는 그중 66.0 kDa 단백질이 고온 대응 단백질로서 신속하게 새합성되는 것으로 나타났다. 이외에도 2차원 전기 영동 분석을 통하여 열충격에 의해서 발현이 촉진되는 다수의 단백질들을 탐지하였다 탐지된 아메바의 열충격 단백질은 분자량에 따라 hsp100군 2종, hsp90군, 3종, hsp70군 및 hsp60군 각 1종, 그리고 small csp군 4종으로 분류해 볼 수 있었다 두 분석의 결과를 종합해 보면 tD 아메바에는 저온 및 고온 충격에 대하여 열충격 단백질의 합성이 완만하게 상승하는 데 비하여 xD strain에서는 신속하게 이루어졌다. 이상의 결과로 보아 아메바의 세포내 공생 세균은 숙주의 열충격 대응기작에 변화를 야기한 것으로 판단된다한 것으로 판단된다. 10mg과 20mg의 estrogen 처리구 사이에 유두 직경, 길이 그리고 용적의 증가량에 있어서는 차이가 없었다. 10mg 및 20mg의 estrogen 처리는 초발정일령을 각각 20일 및 124일 단축시켰다. 전체적으로 이러한 결과는 송아지에 estradiol의 삽입은 성장과 유선 발달을 촉진시키고 초발정일령을 단축시킬수 있다는 것을 강력하게 지적한다. 일치하지 않으므로 더욱 정밀한 조사를 실시하여 분류학상의 위치를 정확히 밝혀 볼 필요가 있을 것으로 생각되었다.연한 도구이자 정신활동으로 보게함으로써, 주제 및 연구방법에서 획일성보다 다양성과 창조성이 강조되고 있다. 그리고 연구에 있어서 주제 의 다양성을 통해 보다 현실생활에 밀접하게 연결되어야 할 필요성은 학문이나 과학의 사회 성에 대한 새로운 인식을 가져다 주고 있다. 이러한 지리교육과정의 좌표의 변화된 측면들 을 고려하여, 지리교육과정의 새로운 방향은 다음의 세가지로 모색될 수 있다. 첫째, 爭點中 心 地理敎育課程이다. 사회쟁점에 대한 접근은 쟁점의 이해와 문제해결에의 지리적 관점의 활용을 통해 학습내용의 시사성과 사실성을 높힐 수 있다. 이때 문제해결능력을 통해 현대 시민의 자질 및 능력을 기를 수 있음은 물론, 다른 한편으로 실제세계 즉 학생의 실생활, 사 회, 국가, 세계에서 일어나는 일들과의 관련성을 갖게 함으로써, 내적 동기화와 외적인 자극 을 강력하게 결합할 수 있을 것이다. 이는 개인적 유관적합성과 사회적 유관적합성을 동시 에 확보하는 데 유리할 것이다. 둘째, 思考中心 地理敎育課程이다. 지리교육은 학생들을 지 식 및 기능의 숙달자가 되도록 할 것이 아니라 기본적 문장해독력의 수준을 넘어 능력있는 사고자로 길러내는 것을 목표로 하여야 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1283-1293
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• 2014

In ubiquitous environment, data are changing rapidly and new data is coming as times passes. And sometimes all of the past data will be lost if there is not sufficient space in memory. Therefore, there is a need to make rules and combine it with new data not to lose all the past data or to deal with large amounts of data. In making decision trees and extracting rules, the weight of each of rules is generally determined by the total number of the class at leaf. The computational problem of finding a minimum finite state acceptor compatible with given data is NP-hard. We assume that rules extracted are not correct and may have the loss of some information. Because of this precondition. this paper presents a new approach for refining rules. It controls their weight of rules of previous knowledge or data. In solving rule refinement, this paper tries to make a variety of rules with pruning method with majority and minority properties, control weight of each of rules and observe the change of performances. In this paper, the decision tree classifier with extended data expression having static weight is used for this proposed study. Experiments show that performances conducted with a new policy of refining rules may get better.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.1-8
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• 2019

In this paper, we propose an improved facial skin color extraction method to solve the problem that facial surface is lost due to shadow or illumination in skin color extraction process and skin color extraction is not possible. In the conventional HSV method, when facial surface is brightly illuminated by light, the skin color component is lost in the skin color extraction process, so that a loss area appears on the face surface. In order to solve these problems, we extract the skin color, determine the elements in the H channel value range of the skin color in the HSV color space among the lost skin elements, and combine the coordinates of the lost part with the coordinates of the original image, To minimize the number of In the face detection process, the face was detected using the LBP Cascade Classifier, which represents texture feature information in the extracted skin color image. Experimental results show that the proposed method improves the detection rate and accuracy by 5.8% and 9.6%, respectively, compared with conventional RGB and HSV skin color extraction and face detection using the LBP cascade classifier method.