• Science of Emotion and Sensibility
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• v.24 no.3
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• pp.3-16
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• 2021

Studies have found that people with higher social status pay little attention to other people's emotions and facial expressions. However, only a few studies have made similar observations on adolescents with high cyberspace social status. Therefore, this study sought to identify how adolescents with different online game character social statuses interpreted the smile emoticons in negative and positive situations, that is, did they perceive the emoticon to be positive (smile, encouragement, and consolation) or negative (derision, ridicule, and sarcasm). In Experiment 1, the participants were separated into three groups; those who had a lower than global average online game character status, those who had the same as the global average, and those who had higher than the global average. The participants were then asked to judge the meaning of the smile emoticon received in various positive or negative situations. In Experiment 2, the game character levels of the participants were set to be either higher or lower than the others' characters, and they were again asked to judge the meaning of the smile emoticon received in the positive or negative situations. In Experiment 3, the participants were separated into four groups; lower level than the average game character status (no information on the level of acquaintance's game character), lower than the average but higher than the character of the other, higher than the average status (no information on the other's character level), and higher than the average but lower than the character of the other, and asked to judge the meaning of the smile emoticon in positive or negative situations. It was found that when participants had a lower-level character compared to the average, had a lower-level character than the other, and had higher than the average but lower than the other's character, they interpreted the smile emoticon as derision, ridicule, or sarcasm. However, participants with higher level characters, higher than that of the other, and lower than the average but higher than the other interpreted the emoticon as a smile or consolation. This study was significant because it demonstrated the impact of an adolescent's social cyberspace status on their online communication.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1057-1068
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• 2022

This paper compares the combination performance of feature point-based matching algorithms as a study to confirm the matching possibility between image taken by a user and a virtual texture image with the goal of developing mobile-based real-time image positioning technology. The feature based matching algorithm includes process of extracting features, calculating descriptors, matching features from both images, and finally eliminating mismatched features. At this time, for matching algorithm combination, we combined the process of extracting features and the process of calculating descriptors in the same or different matching algorithm respectively. V-World 3D desktop was used for the virtual indoor texture image. Currently, V-World 3D desktop is reinforced with details such as vertical and horizontal protrusions and dents. In addition, levels with real image textures. Using this, we constructed dataset with virtual indoor texture data as a reference image, and real image shooting at the same location as a target image. After constructing dataset, matching success rate and matching processing time were measured, and based on this, matching algorithm combination was determined for matching real image with virtual image. In this study, based on the characteristics of each matching technique, the matching algorithm was combined and applied to the constructed dataset to confirm the applicability, and performance comparison was also performed when the rotation was additionally considered. As a result of study, it was confirmed that the combination of Scale Invariant Feature Transform (SIFT)'s feature and descriptor detection had the highest matching success rate, but matching processing time was longest. And in the case of Features from Accelerated Segment Test (FAST)'s feature detector and Oriented FAST and Rotated BRIEF (ORB)'s descriptor calculation, the matching success rate was similar to that of SIFT-SIFT combination, while matching processing time was short. Furthermore, in case of FAST-ORB, it was confirmed that the matching performance was superior even when 10° rotation was applied to the dataset. Therefore, it was confirmed that the matching algorithm of FAST-ORB combination could be suitable for matching between virtual texture image and real image.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.

• Journal of the Korean Society of Radiology
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• v.12 no.3
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• pp.403-407
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• 2018

Currently, the highest cancer death rate in Korea is lung cancer, which is a typical cancer that is difficult to detect early. Low-dose chest CT is being used for early detection, which has a greater lung cancer diagnosis rate of about three times than regular chest x-ray images. However, low-dose chest CT not only significantly reduces image resolution but also has a weak signal and is sensitive to noise. Also, air filled lungs are low-density organs and the presence of noise can significantly affect early diagnosis of cancer. This study used Visual C++ to set a circle inside a large circle with a density of 2.0, with a density of 1.0, which is the density of water, in which five small circle of mathematics have different densities. Gaussian noise was generated by 1%, 2%, 3%, and 4% respectively to determine the effect of noise on the mean value, the standard deviation value, and the relative noise ratio(SNR). In areas where the density difference between the large and small circles was greatest in the event of 1 % noise, the SNR in the area with the greatest variation in noise was 4.669, and in areas with the lowest density difference, the SNR was 1.183. In addition, the SNR values can be seen to be high if the same results are obtained for both positive and negative densities. Quality was also clearly visible when the density difference was large, and if the noise level was increased, the SNR was reduced to significantly affect the noise. Low-density organs or organs in areas of similar density to cancers, will have significant noise effects, and the effects of density differences on the probability of noise will affect diagnosis.

• Journal of Energy Engineering
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• v.25 no.4
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• pp.103-109
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• 2016

Polyethylene pipes has useful benefits which are anti-corrosive and flexible material, so it is used to gas pipes but also class 3 water pipes of nuclear power plant, process pipes of petrochemical plant and chemical plant. So the usage of polyethylene pipes is widely increased. But it has been limited for the usage of polyethylene, because it can not be directly detected to fusion joints by using non destructive evaluation. Polyethylene pipes are connected by two methods, one is butt fusion and the other is electrofusion. Butt fusion is widely used to connecting the pipes. It is proposed to method for determining the reliability of joints in this study that is detection of the melt flow zone at fusion joints. In this study, middle density polyethylene is used, outside diameter of the test specimen is 225mm and thickness is 20.5mm. Speed of ultrasonic of this test specimen is 2,200m/s. Test specimens were fabricated by varying the heating time which means from 0% to 130% applying time through heating plate to polyethylene for detecting melt flow zone. Also 4 additional test specimens were made, one was made that not scrapping attached surface of pipes but applying 100% of the proper heating time and the others were made to include of soil, gravel and vinly tape paper at fusion joints, that were also applied 100% of proper heating time. Ultrasonic testing to measure the melt flow zone of 20 test specimens was conducted by using 3.5MHz and 5.0MHz ultrasonic probes and melt flow zone measuring was conducted to three times at different point to one specimen. To differentiate the melt flow zone signal, post image processing was equally conducted to all test results and image levels, contrast, sharpen, threshold were adopted to all teat results and the test results were displayed gray scale. From the results, for the shorter heating times the reflection area of multiple echo have been increased, so the data was obtained from the position where it can be eliminated as much as possible. At 80% of proper heating time(168 sec.), the signal of melt flow zone was obtained clearly, so measuring could be conducted. From 7% of proper heating time(15 sec.) to shorter heating times. we could not obtain the signal because test specimen was not fused. From the result, we can verify that measuring of melt flow zone by using phased array ultrasonic imaging method is possible. And we can verify to complete and incomplete butt fusion by measuring the melt flow zone.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.

• Progress in Medical Physics
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• v.25 no.3
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• pp.151-156
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• 2014

The purpose of this study was to confirm the feasibility of imaging of therapy region from the boron neutron capture therapy (BNCT) using the measurement of the prompt gamma ray depending on the neutron flux. Through the Monte Carlo simulation, we performed the verification of physical phenomena from the BNCT; (1) the effects of neutron according to the existence of boron uptake region (BUR), (2) the internal and external measurement of prompt gamma ray dose, (3) the energy spectrum by the prompt gamma ray. All simulation results were deducted using the Monte Carlo n-particle extended (MCNPX, Ver.2.6.0, Los Alamos National Laboratory, Los Alamos, NM, USA) simulation tool. The virtual water phantom, thermal neutron source, and BURs were simulated using the MCNPX. The energy of the thermal neutron source was defined as below 1 eV with 2,000,000 n/sec flux. The prompt gamma ray was measured with the direction of beam path in the water phantom. The detector material was defined as the lutetium-yttrium oxyorthosilicate (Lu0,6Y1,4Si0,5:Ce; LYSO) scintillator with lead shielding for the collimation. The BUR's height was 5 cm with the 28 frames (bin: 0.18 cm) for the dose calculation. The neutron flux was decreased dramatically at the shallow region of BUR. In addition, the dose of prompt gamma ray was confirmed at the 9 cm depth from water surface, which is the start point of the BUR. In the energy spectrum, the prompt gamma ray peak of the 478 keV was appeared clearly with full width at half maximum (FWHM) of the 41 keV (energy resolution: 8.5%). In conclusion, the therapy region can be monitored by the gamma camera and single photon emission computed tomography (SPECT) using the measurement of the prompt gamma ray during the BNCT.

• The Korean Journal of Malacology
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• v.24 no.1
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• pp.73-80
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• 2008

Numerous morphological studies on N. samarangae have been well conducted over the last ten years. In this context, we have attemtped to do molecular phylogenetic analysis by using metallothionein (MT) gene from N. samarangae. To this end, we cloned the full length cDNA of MT from cDNA library of N. samarangae. The complete cDNA sequences were obtained from the expressed sequence tag (EST) sequencing project of N. samarangae, The coding region of 195 bp gives an amino acid sequence of 65 residues including methionine. There are 5' (61 bp) and 3' (48 bp) untranslated region at both ends of the Ns-MT cDNA sequence. The combined results from BLAST analyses, multiple sequence alignment and molecular phylogenetic study of Ns-MT cDNA indicate that N. samarangae has similarity to land snails such as Helix pomatia, Helix aspersa and Arianta arbustorum.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.17-18
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• 2007

데이터베이스 튜닝은 일반적으로 데이터베이스 어플리케이션을 "좀 더 빠르게" 실행하게 하는 일련의 활동을 뜻한다[1]. 데이터베이스 관리자가 튜닝에 필요한 주먹구구식 룰(Rule of thumb)들을 모두 파악 하고 상황에 맞추어 적용하는 것은 비싼 비용과 오랜 시간을 요구한다. 그렇게 때문에 서로 다른 어플 리케이션들이 맞물려 있는 복잡한 서비스는 필수적으로 자동화된 데이터베이스 성능 관리와 튜닝을 필 요로 한다. 본 논문에서는 이를 해결하기 위하여 지식 도매인(Knowledge Domain)을 기초로 한 자동화 된 데이터베이스 튜닝 원칙(Tuning Principle)을 제시하는 시스템을 제안한다. 각각의 데이터베이스 튜닝 이론들은 지식 도매인의 지식으로 활용되며, 성능에 영향을 미치는 요소들을 개체(Object)와 콘셉트 (Concept)로 구성하고 추론 시스템을 통해 튜닝 원칙을 추론하여 쉽고 빠르게 현재 상황에 맞는 튜닝 방법론을 적용시킬 수 있다. 자동화된 데이터베이스 튜닝에 대해 여러 분야에 걸쳐 학문적인 연구가 이루어지고 있다. 그 예로써 Microsoft의 AutoAdmin Project[2], Oracle의 SQL 튜닝 아키텍처[3], COLT[4], DBA Companion[5], SQUASH[6] 등을 들 수 있다. 이러한 최적화 기법들을 각각의 기능적인 방법론에 따라 다시 분류하면 크게 Design Tuning, Logical Structure Tuning, Sentence Tuning, SQL Tuning, Server Tuning, System/Network Tuning으로 나누어 볼 수 있다. 이 중 SQL Tuning 등은 수치적으로 결정되어 이미 존재하는 정보를 이용하기 때문에 구조화된 모델로 표현하기 쉽고 사용자의 다양한 요구에 의해 변화하는 조건들을 수용하기 쉽기 때문에 이에 중점을 두고 성능 문제를 해결하는 데 초점을 맞추었다. 데이터베이스 시스템의 일련의 처리 과정에 따라 DBMS를 구성하는 개체들과 속성, 그리고 연관 관계들이 모델링된다. 데이터베이스 시스템은 Application / Query / DBMS Level의 3개 레벨에 따라 구조화되며, 본 논문에서는 개체, 속성, 연관 관계 및 데이터베이스 튜닝에 사용되는 Rule of thumb들을 분석하여 튜닝 원칙을 포함한 지식의 형태로 변환하였다. 튜닝 원칙은 데이터베이스 시스템에서 발생하는 문제를 해결할 수 있게 하는 일종의 황금률로써 지식 도매인의 바탕이 되는 사실(Fact)과 룰(Rule) 로써 표현된다. Fact는 모델링된 시스템을 지식 도매인의 하나의 지식 개체로 표현하는 방식이고, Rule 은 Fact에 기반을 두어 튜닝 원칙을 지식의 형태로 표현한 것이다. Rule은 다시 시스템 모델링을 통해 사전에 정의되는 Rule와 튜닝 원칙을 추론하기 위해 사용되는 Rule의 두 가지 타업으로 나뉘며, 대부분의 Rule은 입력되는 값에 따라 다른 솔루션을 취하게 하는 분기의 역할을 수행한다. 사용자는 제한적으로 자동 생성된 Fact와 Rule을 통해 튜닝 원칙을 추론하여 데이터베이스 시스템에 적용할 수 있으며, 요구나 필요에 따라 GUI를 통해 상황에 맞는 Fact와 Rule을 수동으로 추가할 수도 었다. 지식 도매인에서 튜닝 원칙을 추론하기 위해 JAVA 기반의 추론 엔진인 JESS가 사용된다. JESS는 스크립트 언어를 사용하는 전문가 시스템[7]으로 선언적 룰(Declarative Rule)을 이용하여 지식을 표현 하고 추론을 수행하는 추론 엔진의 한 종류이다. JESS의 지식 표현 방식은 튜닝 원칙을 쉽게 표현하고 수용할 수 있는 구조를 가지고 있으며 작은 크기와 빠른 추론 성능을 가지기 때문에 실시간으로 처리 되는 어플리케이션 튜닝에 적합하다. 지식 기반 모률의 가장 큰 역할은 주어진 데이터베이스 시스템의 모델을 통하여 필요한 새로운 지식을 생성하고 저장하는 것이다. 이를 위하여 Fact와 Rule은 지식 표현 의 기본 단위인 트리플(Triple)의 형태로 표현된다, 트리플은 Subject, Property, Object의 3가지 요소로 구성되며, 대부분의 Fact와 Rule들은 트리플의 기본 형태 또는 트리플의 조합으로 이루어진 C Condition과 Action의 두 부분의 결합으로 구성된다. 이와 같이 데이터베이스 시스템 모델의 개체들과 속성, 그리고 연관 관계들을 표현함으로써 지식들이 추론 엔진의 Fact와 Rule로 기능할 수 있다. 본 시스템에서는 이를 구현 및 실험하기 위하여 웹 기반 서버-클라이언트 시스템을 가정하였다. 서버는 Process Controller, Parser, Rule Database, JESS Reasoning Engine으로 구성 되 어 있으며, 클라이 언트는 Rule Manager Interface와 Result Viewer로 구성되어 었다. 실험을 통해 얻어지는 튜닝 원칙 적용 전후의 실행 시간 측정 등 데이터베이스 시스템 성능 척도를 비교함으로써 시스템의 효용을 판단하였으며, 실험 결과 적용 전에 비하여 튜닝 원칙을 적용한 경우 최대 1초 미만의 전처리에 따른 부하 시간 추가와 최소 약 1.5배에서 최대 약 3배까지의 처리 시간 개선을 확인하였다. 본 논문에서 제안하는 시스템은 튜닝 원칙을 자동으로 생성하고 지식 형태로 변형시킴으로써 새로운 튜닝 원칙을 파생하여 제공하고, 성능에 영향을 미치는 요소와 함께 직접 Fact과 Rule을 추가함으로써 커스터마이정된 튜닝을 수행할 수 있게 하는 장점을 가진다. 추후 쿼리 자체의 튜닝 및 인텍스 최적화 등의 프로세스 자동화와 Rule을 효율적으로 정의하고 추가하는 방법 그리고 시스템 모델링을 효과적으로 구성하는 방법에 대한 연구를 통해 본 연구를 더욱 개선시킬 수 있을 것이다.

• The Korean Journal of Malacology
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• v.25 no.2
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• pp.153-160
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• 2009

Pisidium (Neopisidium) coreanum is a freshwater snail and lives in spring water near mountain areas. Interestingly, this snail has been traditionally regarded as medicinal food, and thus has been used as folk remedies for healing broken bones. Recently, alpha classification on Pisidium (Neopisidium) coreanum through redescription has been conducted. However, not much attention has been made in beta classification. In this study, we performed the beta classification based on metallothionein (MT) genes found from various organisms. To this end, the complete cDNA sequences were obtained from the Expressed Sequence Tag (EST) sequencing project of Pisidium (Neopisidium) coreanum. The coding region (315 bp) encoded an amino acid sequence of 105 residues. The combined results from BLAST analyses, multiple sequence alignment and molecular phylogenetic study of Pc-MT gene indicate that Pisidium (Neopisidium) coreanum has similarity to freshwater bivalve such as Dreissena polymorpha (zebra mussel), Unio tumidus (swollen river mussel) and Crassostrea ariakensis (suminoe oyster).