• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.489-498
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• 2012

This paper consists of two parts: In the first part, we describe our work to build hierarchical knowledge base of digital library patron's research interests and learning topics in various scholarly areas through analyzing well classified Electronic Theses and Dissertations (ETDs) of NDLTD Union catalog. Journal articles from ACM Transactions and conference web sites of computing areas also are added in the analysis to specialize computing fields. This hierarchical knowledge base would be a useful tool for many social computing and information service applications, such as personalization, recommender system, text mining, technology opportunity mining, information visualization, and so on. In the second part, we compare four grouping algorithms to select best one for our data mining researches by testing each one with the hierarchical knowledge base we described in the first part. From these two studies, we intent to show traditional verification methods for social community miming researches, based on interviewing and answering questionnaires, which are expensive, slow, and privacy threatening, can be replaced with systematic, consistent, fast, and privacy protecting methods by using our suggested hierarchical knowledge base.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.253-257
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• 2007

최근 웹 상에 정보가 폭발적으로 증가함에 따라, 사용자의 취향에 맞는 정보를 선별하여 제공하는 추천 시스템에 대한 연구가 활발히 진행되고 있다. 추천시스템은 사용자의 관심정보를 기술한 사용자 프로파일을 기반으로 동작하기 때문에 정확한 사용자 프로파일의 생성은 매우 중요하다. 사용자의 암시적인 행동정보를 기반으로 취향을 분석하는 대표적인 연구로 사용자가 이용한 웹 문서를 분석하는 방법이 있다. 이는 사용자가 이용하는 웹 문서에 빈번하게 등장하는 단어를 기반으로 사용자의 프로파일을 생성하는 것이다. 그러나 최근 웹 문서는 사용자 취향과 관련 없는 많은 구성요소들(로고, 저작권정보 등)을 포함하고 있다. 따라서 이러한 내용들을 모두 포함하여 웹 문서를 분석한다면 생성되는 프로파일의 정확도는 낮아질 것이다. 따라서 본 논문에서는 사용자 기기에서 사용자의 웹 문서 이용내역을 분석하고, 동일한 사이트로부터 얻어진 문서들에서 반복적으로 등장하는 블록을 제거한 후, 정보블럭을 식별하여 사용자의 관심단어를 추출하는 새로운 프로파일 생성방법을 제안한다. 이를 통해 보다 정확하고 빠른 프로파일 생성이 가능해진다. 본 논문에서는 제안방법의 평가를 위해, 최근 구매활동이 있었던 사용자들이 이용한 웹 문서 데이터를 수집하였으며, TF-IDF 방법과 제안방법을 이용하여 사용자 프로파일을 각각 추출하였다. 그리고 생성된 사용자 프로파일과 구매데이터와의 연관성을 비교하였으며, 보다 정확한 프로파일이 추출되는 결과와 프로파일 분석시간이 단축되는 결과를 통해 제안방법의 유효성을 입증하였다.)으로 높은 점수를 보였으며 내장첨가량에 따른 관능특성에서는 온쌀죽은 내장 $2{\sim}5%$ 첨가, 반쌀죽은 내장 $3{\sim}5%$ 첨가구에서 유의적(p<0.05)으로 높은 점수를 보였으나 쌀가루죽은 내장 $1{\sim}2%$ 첨가구에서 유의적(p<0.05)으로 낮은 점수를 보였다. 이상의 연구 결과를 통해 온쌀은 2%, 반쌀은 3%, 쌀가루는 4%의 내장을 첨가하여 제조한 전복죽이 이화학적, 물성적 및 관능적으로 우수한 것으로 나타났다.n)방법의 결과와 비교하였다.다. 유비스크립트에서는 모바일 코드의 개념을 통해서 앞서 언급한 유비쿼터스 컴퓨팅 환경에서의 문제점을 해결하고자 하였다. 모바일 코드에서는 프로그램 코드가 네트워크를 통해서 컴퓨터를 이동하면서 수행되는 개념인데, 이는 물리적으로 떨어져있으면서 네트워크로 연결되어 있는 다양한 컴퓨팅 장치가 서로 연동하기 위한 모델에 가장 적합하다. 이는 기본적으로 배포(deploy)라는 단계가 필요 없게 되고, 새로운 버전의 프로그램이 작성될지라도 런타임에 코드가 직접 이동하게 되므로 버전 관리의 문제도 해결된다. 게다가 원격 함수를 매번 호출하지 않고 한번 이동된 코드가 원격지에서 모두 수행을 하게 되므로 성능향상에도 도움이 된다. 장소 객체(Place Object)와 원격 스코프(Remote Scope)는 앞서 설명한 특징을 직접적으로 지원하는 언어 요소이다. 장소 객체는 모바일 코드가 이동해서 수행될 계산 환경(computational environment

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.4
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• pp.95-103
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• 2009

In this paper, a generation method of transmit and receive beamforming vectors based on base station cooperation is proposed which maximizes the user SINR in mobile cellular multi-user MIMO systems. There are two main sources of interference which deteriorate the performance of the system, i.e. the inter-user interference caused by the usage of the same radio resource for multiple users in the system, and the inter-cluster interference from neighboring base stations which are not participating in cooperative transmission. The proposed scheme cancels out the inter-user interference by using the block diagonalization(BD) method, and mitigate the inter-cluster interference by using optimal transmit and receive beamforming vectors based on optimal combining(OC) with the statistic information of inter-cluster interference. We perform computer simulations to verify the performance of the proposed scheme, and compare the result to the conventional performance obtained from utilizing the receiver side information only or utilizing the information from neither sides. The performance evaluations are conducted not only over the independent MIMO channels, but over correlated MIMO channels to demonstrate the robustness of the proposed scheme over the channels with correlation among antennas.

• Restorative Dentistry and Endodontics
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• v.30 no.2
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• pp.112-120
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• 2005

This study was done to evaluate whether there were any differences in microleakage of class V composite restorations according to restoration site and cavity size. Total sixty-four restorations were made in molar teeth using Esthet-X. Small ($2\;{\times}\;2\;{\times}\;1.5\;mm$) and large ($4{\times}2{\times}1.5\;mm$) restorations were made at the buccal/lingual surface and the proximal surface each. After 1,000 times of thermocycling ($5^{\circ}\;-\;55^{\circ}C$), resin replica was made and the percentage of marginal gap to the whole periphery of the restoration was estimated from SEM evaluation. Thermocycled tooth was dye penetrated with $50\%$ silver nitrate solution. After imbedding in an auto-curing resin, it was serially ground with a thickness of 0.25 mm. Volumetric microleakage was estimated after reconstructing three dimensionally. Two-way ANOVA and independent T-test for dye volume, Mann-Whitney U test for the percentage of marginal gap, Spearman's rho test for the relationship between two techniques were used, The results were as follows : 1. The site and size of the restoration affected on the microleakage of restoration. Namely, much more leakage was seen in the proximal and the large restorations rather than the buccal/lingual and the small restorations. 2. Close relationship was found between two techniques (Correlation coefficient = 0.614/ P = 0.000). Within the limits of this study, it was noted that proximal and the large restorations leaked more than buccal/lingual and the small restorations. Therefore, it should be strictly recommended large exposure of margins should be avoided by reducing unnecessary tooth reduction.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• Progress in Medical Physics
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• v.25 no.4
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• pp.271-280
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• 2014

Recently, digital breast tomosynthesis (DBT) has been investigated to overcome the limitation of conventional mammography for overlapping anatomical structures and high patient dose with cone-beam computed tomography (CBCT). However incomplete sampling due to limited angle leads to interference on the neighboring slices. Many studies have investigated to reduce artifacts such as interference. Moreover, appropriate filters for tomosynthesis have been researched to solve artifacts resulted from incomplete sampling. The primary purpose of this study is finding appropriate filter scheme with FBP reconstruction for DBT system to reduce artifacts. In this study, we investigated characteristics of various filter schemes with simulation and prototype digital breast tomosynthesis under same acquisition parameters and conditions. We evaluated artifacts and noise with profiles and COV (coefficinet of variation) to study characteristic of filter. As a result, the noise with parameter 0.25 of Spectral filter reduced by 10% in comparison to that with only Ramp-lak filter. Because unbalance of information reduced with decreasing B of Slice thickness filter, artifacts caused by incomplete sampling reduced. In conclusion, we confirmed basic characteristics of filter operations and improvement of image quality by appropriate filter scheme. The results of this study can be utilized as base in research and development of DBT system by providing information that is about noise and artifacts depend on various filter schemes.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• Journal of Korea Multimedia Society
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• v.9 no.7
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• pp.872-893
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• 2006

In this study, I classify the elements of physiognomic judgment of character with regard to form and meaning from a visual perspective based on physiognomic judgment of character study in 'the cosmic dual forces and the Five Elements theory'. Individual characters for each type are designed using graphic data. Based on that, design system of individual characters for each personality type is investigated using Neural Network system. Faces with O-Haeng (Five Elements) shapes are shown to constitute the system with ${\pm}0.3%$ degree of error tolerance for the non-loaming input data. For the shapes of Chinese characters 'tree, fire, soil, gold and water', their MSE(Mean Square Error) are 0.3, 0.3, 0.2, 0.5, 0.2. It seems to be the best regarding the scoring system which ranges from 0 to 5. Therefore, this system might be regarded to produce the most accurate facial shape of character automatically when we input character's personality we desire to make.

• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.1
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• pp.55-63
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• 2001

N fertilizer recommendations to optimize with consideration to maximum crop yields, maximum profits, and minimum N losses to ground or runoff water, an advisory system. Nitrogen Expert System (NES), was developed. The system was to estimate the optimal rate of N fertilizer application cornfields in Illinois. NES was constructed using Smart Elements, a knowledge-based system that manages the expertise of human experts. NES was reinforced by addition of the effect of a productivity index (PI), soil organic matter content (SOM), and pre-sidedressing of nitrate concentration (PSNT) to the optimal N fertilizer recommendation. NES contains 49 rules, 1 class, 14 objects, and 2 properties. NES was successfully operated, showing N recommendations with inputs of three soil properties including PI, SOM, and PSNT. NES can reduce N loss to the environment, but adherence to the recommendations may also reduce farmers income. Therefore, NES will be more effective by evaluating both environmental damage assessment and other economic agricultural management parameters and other soil physico-chemical parameters.