• The Journal of the Acoustical Society of Korea
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• v.37 no.3
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• pp.129-138
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• 2018

In port and coastal areas where ship traffic is frequent, ship noise dominantly influences underwater noise in low frequency band below 1 kHz. In this paper, we propose a modeling method to estimate the underwater shipping noise using the voyage information of ship observed in AIS (Automatic Identification System). For the purpose of ship noise modeling, the navigation information of the vessels operating in the southern part of Jeju was observed using AIS and underwater noise was measured by installing a hydrophone in the experimental area to verify the modeled ship noise. AIS data were used to model the noise level of ship and compared with measured underwater noise. The variation of noise level with time was found to be similar, and the cause of the error was discussed. Through this study, it was confirmed that the noise level of ship can be estimated within 5 dB error range using AIS data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.411-416
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• 2020

Field investigations of forests are carried out by writing measured data by hand, and it is a hassle to reorganize the results after a field survey. In this study, a method using object modeling and augmented reality (AR) was applied in a test forest to increase the efficiency of a field investigations. Using a 3D laser scanner, data on were acquired 387 trees within an area of 1 ha at the study site. The coordinates, height, and diameter were calculated through object extraction and modeling of a tree. The proposed can reduce the time required to acquire data in the field and can be used as basic data for building related systems. In addition, the modeling results of trees and a survey using GNSS and AR techniques can be used check coordinates, labor, and attribute information, such as the chest height diameter of the trees being surveyed in the field. The shortcomings of the survey method could be improved. In the future, the method could greatly improve the efficiency of tree surveys and monitoring by reducing the manpower and time required for field surveys.

• Journal of Industrial Convergence
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• v.20 no.1
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• pp.43-51
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• 2022

The purpose of this study is to provide basic information necessary for the establishment of mobile service marketing strategies, educational service development, and engineering education for Generation Z by analyzing the utilitization of various applications by Gen Z. To this end, 177 essays on mobile service usage experience were collected, major topics were analyzed using topic modeling, and these were visualized through word cloud analysis. As a result of the study, the main topics were related to 'transportation' such as movement and public transportation, 'personal management' such as schedule management, financial management, food management, 'transaction' such as checkout, meeting, purchase, 'leisure' such as eating out, travel, study, culture. Additionally, words such as time, thought, people, life, bus, information, confirmation, payment, KakaoTalk, and so on were found to have a high of frequency of use. Also, there was found to be a difference between topics by college. This study is meaningful in that it collected essays, which are unstructured data, and analyzed them through topic modeling.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.624-627
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• 2014

현재의 기업 정보시스템의 골격을 정의한 설계도라고 할 수 있는 데이터 모델은 정밀하게 작성되어야 한다. 데이터 모델의 핵심요소로는 엔터티, 속성, 관계가 있으며, 이 중에서도 속성은 실질적인 정보를 담는 가장 기본적인 단위라 할 수 있으므로 모든 정보의 근원이라 할 수 있을 것이다. 그래서 속성들을 제대로 도출하지 못하면 데이터 모델 전체가 무의미하게 될 수 있다. 기존의 속성을 도출하는 방법은 설계자의 경험에 많이 의존하고 실질적인 절차가 존재하지 않아 실무경험이 없는 초보자가 도출하기에는 너무나 어려운 것이 현실이다. 이를 해결하는데 도움이 될 수 있도록 본 논문에서는 데이터 모델 설계의 한 과정으로서, 선행연구에서 제시한 업무중심 엔터티 도출 방법을 이용하여 엔터티가 완전히 도출되어 있다고 가정하고 미리 도출되어 있는 엔터티를 바탕으로 속성을 도출하는 절차를 제안한다. 그리고 데이터 모델링 경험이 많이 없는 학부생 및 대학원생을 대상으로 본 논문에서 제안한 절차를 적용하도록 하였다. 기존에 속성을 도출하는 방법이 실질적으로 존재하지 않기 때문에 학생들이 도출한 속성과 전문 IT 컨설턴트로 멘토가 도출한 모법 답안 간의 유사도검사를 하였다. 최종 유사도 검사를 통하여 전문 IT 컨설턴트인 멘토가 도출한 모법 답안에 상당히 근접하게 속성을 도출할 수 있다는 것을 확인하였다. 따라서 본 논문에서 제안한 절차를 활용한다면 데이터모델링에 실무경험이 없는 초보자나 미숙련자가 적용하여도 속성을 도출할 수 있음을 보였다. 제안 절차에서 도출된 결과를 이용하여 데이터 모델 설계의 이후 과정인 관계도출 과정을 진행할 수 있을 것으로 기대한다.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.33-42
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• 2021

As smartphone-based tourism platforms have become active, policy establishment and service enhancement using review data are being made in various fields. In the case of the preceding studies using tourism review data, most of the studies centered on domestic tourists were conducted, and in the case of foreign tourist studies, studies were conducted only on data collected in some languages and text mining techniques. In this study, 3,515 review data written by foreigners were collected by designating the "Daegu attractions" keyword through the online review site. And LDA-based topic modeling was performed to derive tourism topics. The spatial approach through global and local spatial autocorrelation analysis for each topic can be said to be different from previous studies. As a result of the analysis, it was confirmed that there is a global spatial autocorrelation, and that tourist destinations mainly visited by foreigners are concentrated locally. In addition, hot spots have been drawn around Jung-gu in most of the topics. Based on the analysis results, it is expected to be used as a basic research for spatial analysis based on local government foreign tourism policy establishment and topic modeling. And The limitations of this study were also presented.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.147-159
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• 2023

This study is a study to develop a model for measurement and processing of experience data, which is emerging as a core value in quality management of medical services. In the theoretical background, a literature study was conducted on the importance of experience in medical service, measurement and processing of experience data, and realization of patient-centeredness. Based on these literature and theoretical background research results, operational definitions were performed for the following four research variables, and statistical tests were conducted. Hypothesis 1 is the effect of measuring experience data from the perspective of three factors on persona modeling, Hypothesis 2 is the effect of persona modeling on service blueprint visualization, Hypothesis 3 is the effect of service blueprint visualization on realization of patient-centeredness, and Hypothesis 4 is persona modeling This is the effect that modeling has on the realization of patient-centeredness. After data-based testing of factor analysis, reliability analysis, and correlation analysis, all four hypotheses were adopted as a result of verification using regression analysis. In conclusion, in an era where it is difficult to recognize the value of having only good medical staff and medical equipment in hospitals, it was possible to grasp the meaning that what kind of medical service experience is continuously obtained is more important to patients than the effectiveness of medical staff and medical equipment. In the era of the service economy, the core of hospital service competitiveness is providing attractive experiences, which is the real strength of hospitals, so the measurement and processing of experience data, which is the subject of this study, will have an important meaning in realizing patient-centeredness and realizing smart hospitals.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.280-282
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• 2005

초파리 초기 발생과정은 gap 유전자, pair-rule 유전자, polarity 유전자의 세 가지 유전자 그룹에 의해서 조직화 된다. Gap 유전자들에 의해 pair-rules 유전자들의 발현이 조절되며, 이들에 의해 결국 polarity 유전자들의 발현을 조절함으로써, 정확한 위치에서 각 기관의 형성을 유도한다. 특히 분열 14단계에서는 pair-rule 유전자 중의 하나인 eve 유전자의 발현이 조절되는데, eve 유전자는 배아의 분할의 줄무늬를 형성시키는 유전자에 해당된다. 본 논문에서는 eve 유전자의 발현조절자인 hunchback, giant, kruppel, bicoid의 gap 유전자들로 구성된 조절 네트워크를 S-system을 이용하여 모델링하였다. 이를 통해 각 유전자들의 발현 데이터로부터 파라미터들을 진화 연산을 통해 예측하고, 각 유전자들의 발현에 대한 시뮬레이션 결과를 보여준다. 예측된 결과와 실제 데이터의 비교는 전체적으로 패턴이 서로 유사함을 보여주고 있다.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2004.11a
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• pp.29-32
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• 2004

In this study, we had modelled Siemens type MLC using BEAM Monte Carlo code and tested the feasibility of the modelling. To model the Primus linac MLC, we had measured the actual dimensions of MLC and each leaves, then approximated the leaf shape. VARMLC component module was used for the modelling and leakage, tongue-and-groove effect were also considered. Simulation result showed the good agreement with the film measurement.

• Journal of KIISE:Information Networking
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• v.27 no.4
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• pp.465-475
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• 2000

초고속 통신망에서의 정보 침해는 짧은 시간에 많은 데이터의 손실을 초래할 수 있기 때문에 정보보호의 필요성이 제기되고 있다. ATM 망에서의 정보보안을 위해 보안 서비스를 ATM계층과 AAL 계층에 적용시킬 수도 있고 이 계층들 사이에도 적용시킬수 있으며 보안 계층을 따로 두어 보안 서비스를 하나의 계층에서 다양하고 투명하게 적용할 수 있다. 그러나 이러한 연구들의 결과가 이론에 그치고 있고 실제로 암호 알고리즘 등의 보안 서비스를 적용한 망에서의 성능을 평가하는 부분에 대해서는 연구가 부족한 상황이다. 본 논문에서는 사용자 평면에서 데이터의 비밀성, 무결성, 데이터 원천 인증 등의 보안 서비스를 적용한 보안 계층을 구현하고 SDL이라는 모델링 툴을 이용하여 망에서의 메시지 전달 지연 시간 등을 측정한후 그 성능을 비교.분석하였다 모델링하는 세가지 종류의 망은 첫 번째는 보안 서비스가 적용되지 않은 ATM망이고 두 번째는 보안계층이 CS 부계층과 SAR 부계층 사이에 삽입된 망이며 세 번째는 보안 계층이 ATM계층과 AAL계층 사이에 삽입된 망이다.