• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.661-683
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• 2001

There is a rapid increase in the use of digital video information in recent years, it becomes more important to manage video databases efficiently. The development of high speed data network and digital techniques has emerged new multimedia applications such as internet broadcasting, Video On Demand(VOD) combined with video data processing and computer. Video database should be construct for searching fast, efficient video be extract the accurate feature information of video with more massive and more complex characteristics. Video database are essential differences between video databases and traditional databases. These differences lead to interesting new issues in searching of video, data modeling. So, cause us to consider new generation method of database, efficient retrieval method of video. In this paper, We propose the construction and generation method of the video database based on contents which is able to accumulate the meaningful structure of video and the prior production information. And by the proposed the construction and generation method of the video database implemented the video database which can produce the new contents for the internet broadcasting centralized on the video database. For this production, We proposed the video indexing method which integrates the annotation-based retrieval and the content-based retrieval in order to extract and retrieval the feature information of the video data using the relationship between the meaningful structure and the prior production information on the process of the video parsing and extracting the representative key frame. We can improve the performance of the video contents retrieval, because the integrated video indexing method is using the content-based metadata type represented in the low level of video and the annotation-based metadata type impressed in the high level which is difficult to extract the feature information of the video at he same time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3514-3523
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• 2009

Before 1990's, Korea had only few colleges that had film and film related majors. As Korean film industry started its marvelous improvement in both commercial and artistic phase, many colleges were interested in this new academic field. They hurried up to launch this new and profitable major; as a result, Korea now has more than hundred colleges and universities that has film related majors. Each college enumerates numbers of academic courses that may look reasonable; they have almost every course a fine film school should provide such as film theory, production, and performance in film. Lots of film schools offer lots of film courses; and they look alike. One unique thing in film major is its vast sub fields and categories. After you decide to study film, you have to select what specific field in film you want to study. Studying film theory and film production can be as different as majoring physics and physical education. The problem Korean colleges are dealing with is that there are too much film schools, and moreover those colleges have similar curriculums that just look like a department store that sells everything. One suggestion is specializing its curriculum in which the school can take advantages using their special conditions. San Francisco Art Institute is one of United States well known private film schools, but many people remember this school as a specialized film school in experimental film. San Francisco where this school is located has had many liberal and experimental artists as the city has been supporting and offering them an environment they can concentrate their work. Naturally, the school has world famous faculty members in experimental film, and students who want to study and make experimental film come to this school from all over the world because they know this school would be the best to study experimental film. There are many film schools in metro Los Angeles area; since its location near Hollywood, no wonder these schools concentrate on film producing and film production. They take advantage of their geographical location to hire film industry professional and to focus on commercial feature film productions. What we can do now to adjust the curriculum in film and film related major in Korean colleges is to adapt new standards in this changed film industry. One school can emphasize digital production while another school focuses on digital intermediate process. But if one school tries to both fields or all fields of film major just like we have done so far, the school could not take care of all the equipment and the faculty that the fields would need. Korean film schools should devide the field in film major and concentrate what they selected. Selection and concentration can be and should be applied in Korean film schools.

• Proceedings of the KSCN Conference
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• 2003.11a
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• pp.1058-1059
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• 2003

학동기(만 6-12세)에 좋은 영양 공급은 일생의 성장 발육의 기초를 조성하여 지적, 사회적, 정서적 능력을 향상시킬 수 있다. 이에 초등학생의 영양문제와 식습관을 해결하는 방안의 하나로 최근 대두되고 있는 아침결식률을 낮추기 위한 ‘아침 안 거르기’ 사업을 학교와 연계 개발하여 프로그램을 진행하고 그 효과를 평가하였다. 이 연구는 서울시 강북구 관내에 위치한 4개 초등학교 5학년 학생 1026명(실험군 451명, 대조군 675명)을 대상으로 4∼9월까지 6개월간 5단계로 실시되었다. 프로그램의 효과평가를 위해 사전(실험군-학생 428명, 부모 416명, 대조군-학생 656명, 부모 589명), 사후(실험군-학생 427명, 부모 368명, 대조군-학생 608명, 부모 558명) 설문조사를 2회 실시하여 행동과 인식률 변화를 측정하였다. 프로그램은 매월 1회 담임교사가 진행하였고, 교육자료 및 홍보물은 아침먹기 캐릭터 가방걸이와 학부모의 참여를 높이고자 가정통신문과 교육내용을 함께 삽입하여 제작한 알림장, 아침먹기와 일찍자기를 실천할 실천스티커를 배포하였고 프로그램 종료 후 아침 먹기 실천달력을 제공하였다. 또 아동들 자신의 식생활을 바르게 꾸려 나갈 수 있도록 가공식품의 ‘영양표시’에 관한 교육을 함께 교육하였고, 설문조사를 통해 영양표시제에 관한 인식변화와 행동변화율을 측정하였다. 학생들의 아침식사 실천율은 매일 먹는 그룹이 실험군은 62.9%에서 69.6%로 7% 상승하였고, 전혀 먹지 않는 그룹은 11.2%에서 6.4%로 4.8% 감소하였다(p<0.001) 대조군의 경우 매일 먹는 그룹은 70.4%에서 70.2%로, 전혀 먹지 않는 그룹은 6.9%에서 6.5%로 나타났다(p<0.001). 아침식사가 건강에 미치는 영향에 대한 인식률 변화는 큰 영향이 있다고 답한 그룹이 실험군은 60.1%에서 64.8% (p<0.001)로, 대조군은 64.8%에서 57.2% (p<0.001)로 각각 변화하였다. 일찍 자기가 건강에 미치는 영향에 대한 인식률은 큰 영향이 있다고 답한 그룹이 실험군은 52.5%에서 70.7%(p<0.001), 대조군은 55.3%에서 60.2%로(p<0.001), 일찍 자기 실천에 대한 의지 변화는 11시 이전에 잘 계획이라는 그룹이 실험군은 31.9%에서 43.9%(p<0.001), 대조군은 35.5%에서 36.8%로 나타났다(p<0.001). 영양 표시에 관한 문항에서는 식품구매시 영양표시를 보고 구매한 경험이 없다는 군이 실험군에서는 58.2%에서 40.4%로 감소하였고(p<0.001), 대조군은 64%에서 53.6% (P<0.001)로 감소하였다. 영양소에 관한 지식 평가에서 짜지 않은 간식을 선택할 때 유의할 성분을 나트륨이라고 답한 그룹은 실험군은 31.7%에서 56.3%로, 대조군은 15.9%에서 23.0%로 나타나 그룹간 큰 차이를 보였다. 아침식사로 좋아하는 음식은 김치, 김치찌개, 밥, 피자, 고기, 빵, 된장찌개, 김, 햄, 계란후라이 순이었으며 싫어하는 아침음식은 피망, 고추, 양파, 김치, 시금치, 마늘, 콩, 파, 피자, 나물순으로 나타났다. 아침식사로 혼자서도 준비할 수 있는 음식은 밥, 라면, 계란후라이, 김치, 반찬, 볶음밥, 계란, 국, 햄, 씨리얼 순으로 나타났다. 부모의 인식률 변화는 가정내에 컴퓨터 설치장소로 적합하다고 생각되는 장소로 실험군의 경우 학생 방은 28.0%에서 21.6%로 6.4%감소하였고, 마루나 거실은 61.3%에서 66.0%로 5%증가하였다(p<0.001). 반면 대조군의 경우 학생 방은 28.8%에서 24.9%로 3.9% 감소, 마루나 거실은 63.4%에서 65.9%로 2.5%증가하여 실험군과 큰 차이를 보였다(p<0.001) 자녀의 늦은 취침시간과 아침식사에 관한 인식률 변화는 아침식사에 영향이 있다는 군은 실험군의 경우 78.1%에서 82.7%로 4.2% 증가하였고(p<0.05), 대조군의 경우 82.2%에서 80.5%로 감소하여 나타났다.(P<0.001) 연구결과 실험군의 경우 일주일 동안 아침을 전혀 먹지 않는 학생이 감소하고 매일 아침을 먹는 학생이 유의적으로 증가하여 아동의 아침식사 행동변화와 인식률 변화에 도움이 된 것으로 나타났다.

• Proceedings of the Korea Society for Simulation Conference
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• 1996.05a
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• pp.1-3
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• 1996

시뮬레이션 기법을 이용한 시스템의 분석에 있어서 실험의 자동화는 현재 많은 연구와 개발이 진행 중인 분야이다. 컴퓨터와 정보통신 시스템에 대한 시뮬레이션의 예를 들어 보면, 수많은 모델을 대한 시뮬레이션을 수행할 경우 자동화된 실험의 제어가 요구되고 있다. 시뮬레이션 수행회수, 수행길이, 데이터 수집방법 등과 관련하여 시뮬레이션 실험방법이 자동화가 되지 않으면, 시뮬레이션 실험에 필요한 시간과 인적 자원이 상당히 커지게 되며 출력데이터에 대한 분석에 있어서도 어려움이 따르게 된다. 시뮬레이션 실험방법을 자동화하면서 효율적인 시뮬레이션 출력분석을 위해서는 시뮬레이션을 수행하는 경우에 항상 발생하는 초기편의 (initial bias)를 제거하는 문제가 선결되어야 한다. 시뮬레이션 출력분석에 사용되는 데이터들이 초기편의를 반영하지 않는 안정상태에서 수집된 것이어야만 실제 시스템에 대한 올바른 해석이 가능하다. 실제로 시뮬레이션 출력분석과 관련하여 가장 중요하면서도 어려운 문제는 시뮬레이션의 출력데이터가 이루는 추계적 과정 (stochastic process)의 안정상태 평균과 이 평균에 대한 신뢰구간(confidence interval: c. i.)을 구하는 것이다. 한 신뢰구간에 포함되어 있는 정보는 의사결정자에게 얼마나 정확하게 평균을 추정할 구 있는지 알려 준다. 그러나, 신뢰구간을 구성하는 일은 하나의 시뮬레이션으로부터 얻어진 출력데이터가 일반적으로 비정체상태(nonstationary)이고 자동상관(autocorrelated)되어 있기 때문에, 전통적인 통계적인 기법을 직접적으로 이용할 수 없다. 이러한 문제를 해결하기 위해 시뮬레이션 출력데이터 분석기법이 사용된다.본 논문에서는 초기편의를 제거하기 위해서 필요한 출력데이터의 제거시점을 찾는 새로운 기법으로, 유클리드 거리(Euclidean distance: ED)를 이용한 방법과 현재 패턴 분류(pattern classification) 문제에 널리 사용 중인 역전파 신경망(backpropagation neural networks: BNN) 알고리듬을 이용하는 방법을 제시한다. 이 기법들은 대다수의 기존의 기법과는 달리 시험수행(pilot run)이 필요 없으며, 시뮬레이션의 단일수행(single run) 중에 제거시점을 결정할 수 있다. 제거시점과 관련된 기존 연구는 다음과 같다. 콘웨이방법은 현재의 데이터가 이후 데이터의 최대값이나 최소값이 아니면 이 데이터를 제거시점으로 결정하는데, 알고기듬 구조상 온라인으로 제거시점 결정이 불가능하다. 콘웨이방법이 알고리듬의 성격상 온라인이 불가능한 반면, 수정콘웨이방법 (Modified Conway Rule: MCR)은 현재의 데이터가 이전 데이터와 비교했을 때 최대값이나 최소값이 아닌 경우 현재의 데이터를 제거시점으로 결정하기 때문에 온라인이 가능하다. 평균교차방법(Crossings-of-the-Mean Rule: CMR)은 누적평균을 이용하면서 이 평균을 중심으로 관측치가 위에서 아래로, 또는 아래서 위로 교차하는 회수로 결정한다. 이 기법을 사용하려면 교차회수를 결정해야 하는데, 일반적으로 결정된 교차회수가 시스템에 상관없이 일반적으로 적용가능하지 않다는 문제점이 있다. 누적평균방법(Cumulative-Mean Rule: CMR2)은 여러 번의 시험수행을 통해서 얻어진 출력데이터에 대한 총누적평균(grand cumulative mean)을 그래프로 그린 다음, 안정상태인 점을 육안으로 결정한다. 이 방법은 여러 번의 시뮬레이션을 수행에서 얻어진 데이터들의 평균들에 대한 누적평균을 사용하기 매문에 온라인 제거시점 결정이 불가능하며, 작업자가 그래프를 보고 임의로 결정해야 하는 단점이 있다. Welch방법(Welch's Method: WM)은 브라운 브리지(Brownian bridge) 통계량()을 사용하는데, n이 무한에 가까워질 때, 이 브라운 브리지 분포(Brownian bridge distribution)에 수렴하는 성질을 이용한다. 시뮬레이션 출력데이터를 가지고 배치를 구성한 후 하나의 배치를 표본으로 사용한다. 이 기법은 알고리듬이 복잡하고, 값을 추정해야 하는 단점이 있다. Law-Kelton방법(Law-Kelton's Method: LKM)은 회귀 (regression)이론에 기초하는데, 시뮬레이션이 종료된 후 누적평균데이터에 대해서 회귀직선을 적합(fitting)시킨다. 회귀직선의 기울기가 0이라는 귀무가설이 채택되면 그 시점을 제거시점으로 결정한다. 일단 시뮬레이션이 종료된 다음, 데이터가 모아진 순서의 반대 순서로 데이터를 이용하기 때문에 온라인이 불가능하다. Welch절차(Welch's Procedure: WP)는 5회이상의 시뮬레이션수행을 통해 수집한 데이터의 이동평균을 이용해서 시각적으로 제거시점을 결정해야 하며, 반복제거방법을 사용해야 하기 때문에 온라인 제거시점의 결정이 불가능하다. 또한, 한번에 이동할 데이터의 크기(window size)를 결정해야 한다. 지금까지 알아 본 것처럼, 기존의 방법들은 시뮬레이션의 단일 수행 중의 온라인 제거시점 결정의 관점에서는 미약한 면이 있다. 또한, 현재의 시뮬레이션 상용소프트웨어는 작업자로 하여금 제거시점을 임의로 결정하도록 하기 때문에, 실험중인 시스템에 대해서 정확하고도 정량적으로 제거시점을 결정할 수 없게 되어 있다. 사용자가 임의로 제거시점을 결정하게 되면, 초기편의 문제를 효과적으로 해결하기 어려울 뿐만 아니라, 필요 이상으로 너무 많은 양을 제거하거나 초기편의를 해결하지 못할 만큼 너무 적은 양을 제거할 가능성이 커지게 된다. 또한, 기존의 방법들의 대부분은 제거시점을 찾기 위해서 시험수행이 필요하다. 즉, 안정상태 시점만을 찾기 위한 시뮬레이션 수행이 필요하며, 이렇게 사용된 시뮬레이션은 출력분석에 사용되지 않기 때문에 시간적인 손실이 크게 된다.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.3
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• pp.143-150
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• 2012

A simulation methodology and corresponding program based on it is to be discussed for analyzing the effects of the networking operation of existing DHC system in connection with CHP system on-site. The practical simulation for arbitrary areas with various building compositions is carried out for the analysis of operational features in both systems, and the various aspects of thermal energy grids with connecting operation are highlighted through the detailed assessment of predicted results. The intrinsic operational features of CHP prime movers, gas engine, gas turbine etc., are effectively implemented by realizing the performance data, i.e. actual operation efficiency in the full and part loads range. For the sake of simplicity, a simple mathematical correlation model is proposed for simulating various aspects of change effectively on the existing DHC system side due to the connecting operation, instead of performing cycle simulations separately. The empirical correlations are developed using the hourly based annual operation data for a branch of the Korean District Heating Corporation (KDHC) and are implicit in relation between main operation parameters such as fuel consumption by use, heat and power production. In the simulation, a variety of system configurations are able to be considered according to any combination of the probable CHP prime-movers, absorption or turbo type cooling chillers of every kind and capacity. From the analysis of the thermal network operation simulations, it is found that the newly proposed methodology of mathematical correlation for modelling of the existing DHC system functions effectively in reflecting the operational variations due to thermal energy grids with connecting operation. The effects of intrinsic features of CHP prime-movers, e.g. the different ratio of heat and power production, various combinations of different types of chillers (i.e. absorption and turbo types) on the overall system operation are discussed in detail with the consideration of operation schemes and corresponding simulation algorithms.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.664-674
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• 2021

The relevance of this scientific research is determined by the negative impact of the COVID-19 pandemic on the current trends and dynamics of world tourism development. This article aims to identify patterns of development of the modern tourist market, analysis of problems and prospects of development in the context of the COVID-19 pandemic. Materials and methods. General scientific methods and methods of research are used in the work: analysis, synthesis, comparison, analysis of statistical data. The analysis of the viewpoints of foreign and domestic authors on the research of the international tourist market allowed us to substantiate the actual directions of tourism development due to the influence of negative factors connected with the spread of a new coronavirus infection COVID-19. Economic-statistical, abstract-logical, and economic-mathematical methods of research were used during the process of study and data processing. Results. The analysis of the current state of the tourist market by world regions was carried out. It was found that tourism is one of the most affected sectors from COVID-19, as, by the end of 2020, the total number of tourist arrivals in the world decreased by 74% compared to the same period in 2019. The consequence of this decline was a loss of total global tourism revenues by the end of 2020, which equaled $1.3 trillion. 27% of all destinations are completely closed to international tourism. At the end of 2020, the economy of international tourism has shrunk by about 80%. In 2020 the world traveled 98 million fewer people (-83%) relative to the same period last year. Tourism was hit hardest by the pandemic in the Asia-Pacific region, where travel restrictions are as strict as possible. International arrivals in this region fell by 84% (300 million). The Middle East and Africa recorded declines of 75 and 70 percent. Despite a small and short-lived recovery in the summer of 2020, Europe lost 71% of the tourist flow, with the European continent recording the largest drop in absolute terms compared with 2019, 500 million. In North and South America, foreign arrivals declined. It is revealed that a significant decrease in tourist flows leads to a massive loss of jobs, a sharp decline in foreign exchange earnings and taxes, which limits the ability of states to support the tourism industry. Three possible scenarios of exit of the tourist industry from the crisis, reflecting the most probable changes of monthly tourist flows, are considered. The characteristics of respondents from Ukraine, Germany, and the USA and their attitude to travel depending on gender, age, education level, professional status, and monthly income are presented. About 57% of respondents from Ukraine, Poland, and the United States were planning a tourist trip in 2021. Note that people with higher or secondary education were more willing to plan such a trip. The results of the empirical study confirm that interest in domestic tourism has increased significantly in 2021. The regression model of dependence of the number of domestic tourist trips on the example of Ukraine with time tendency (t) and seasonal variations (Turˆt = 7288,498 - 20,58t - 410,88∑5) it forecast for 2020, which allows stabilizing the process of tourist trips after the pandemic to use this model to forecast for any country. Discussion. We should emphasize the seriousness of the COVID-19 pandemic and the fact that many experts and scientists believe in the long-term recovery of the tourism industry. In our opinion, the governments of the countries need to refocus on domestic tourism and deal with infrastructure development, search for new niches, formats, formation of new package deals in new - domestic - segment (new products' development (tourist routes, exhibitions, sightseeing programs, special rehabilitation programs after COVID) -19 in sanatoriums, etc.); creation of individual offers for different target audiences). Conclusions. Thus, the identified trends are associated with a decrease in the number of tourist flows, the negative impact of the pandemic on employment and income from tourism activities. International tourism needs two to four years before it returns to the level of 2019.

• Journal of Korean Society of Forest Science
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• v.107 no.4
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• pp.456-477
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• 2018

We analyzed the data on medicinal plants and related traditional knowledge in Korea and China using data analysis method. There are 108 families 214 genera 542 species, and 202 families 660 genera 1,261 species were found in Korea and China respectively. Total of 86 families (79.6%) and 130 genera (60.7%) were in common both countries. More than one information were recorded in many species, however, there was only one information from some species (32.7% of genera in Korea, and 58.8% of genera in China). The most frequent appeared plant family was Compositae (Asteraceae) in both countries (8.4% in Korea and 10.7% in China) and followed by Rosecease and Leguminocae (Fabaceae). Plant parts were classified 11 parts and roots were mostly used in Korea and whole plants in China. Usages were described in different terms of ailments or symptoms. There are 120 usage in Korea and 230 in China. Plant uses for the ailment or symptoms are pain, digestive system disorder, cold and etc. in Korea. In China, plants were mostly used for clear heat, digestive system disorder, cough and etc. Relation between the plant and ailment(symptom) of the top 10 plants in Korea and China was different although from same plant family. We also analyzed the relations between plant species and part used, and plants parts and ailment(symptom). With the data analysis method, we were able to collect the medicinal plant resources data and found the differences in plant resources, usage, and plant part for use. The result provide important information of the plant resources and related traditional knowledge of Korea for use of plant resources in industry and facilitate to plan a strategy to cope with Nagoya Protocol in the future.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.261-268
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• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.