• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.36-39
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• 1996

In chemical processes, measurement errors reduce the credibility of information and cause inconsistency in material and energy balances. Because multicomposition flows and temperature measurements make material and energy balances nonlinear equations, data reconciliation becomes a nonlinear constrained optimization problem. In multicomposition processes, if we follow general optimization procedure, the number of measurement variables is so large that data reconciliation requires much computation time. We propose the decomposition procedure to reduce the computation time without the decrease of accuracy of data reconciliation. Decomposition procedure finds global variables, that can reduce the nonlinearity of constraints, and divides two sub-optimization problems. Once we optimize the global variables at upper level, we can easily optimize the remain variables at tower level, We can obtain the short computational time and the same accuracy as SQP optimization method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.560-562
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• 2005

The electronic transmission of VLBI data(dubbed e-VLBI) presents a special challenge to the use of high-speed global network. with long-term requirements for simultaneous or near-simultaneous Gbps data streams from antennas worldwide converging in a single processing center, e-VLBI is both a useful and highly synergetic application for global high-speed networksAs broband access to high speed research and education networks has become increasingly available to radio telescopes around the world the use of e-VLBI has also increased. High bandwidth e-VLBI experiments have been achieved across wide areas e-VLBI has also been used for the transfer of data from "production"exoeriments

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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• pp.284-287
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• 2012

GLOVE(GLObal Virtual reality visualization Environment for scientific simulation)는 컴퓨팅 자원의 성능 향상으로 데이터 양이 급속히 증가한 응용 과학과 전산 시뮬레이션 분야의 대용량 과학 데이터를 효율적으로 가시화하여 분석하기 위한 도구이다. GLOVE의 데이터 관리자인 GDM(GLOVE Data Manager)은 대용량 데이터의 분산 병렬 가시화를 위해 분산 공유 메모리를 제공하는 GA(Global Array)를 이용해 테라 바이트 단위의 데이터를 실시간으로 처리한다. 그러나 대용량 과학 데이터를 가시화 하는 과정에서 기존의 Data Locality를 고려하지 않은 데이터 접근 방식으로 인한 성능 저하를 확인했다. 본 논문은 기존 GLOVE에서 발견한 성능 저하 현상을 밝히고, 이에 대한 해결 방법을 제시한다.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• 한국지리정보학회지
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• 제17권1호
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• pp.107-118
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• 2014

본 연구는 전지구 위성 강우자료와 글로벌 지형자료를 이용하여 유출분석을 수행하여 계측자료가 부족한 지역에 대한 강우-유출 관계를 파악할 수 있는 방법을 제시하는 것을 목적으로 하였다. 사용된 위성 강우자료는 CMORPH와 GSMaP_NRT자료를 사용하였고, 글로벌 지형자료는 GTOPO30 및 GLCC자료를 이용하였다. 유출분석을 위한 도구는 IFAS를 이용하였다. 강우 정확도 평가에서 관측강우자료와의 상관계수는 CMORPH 및 GSMaP_NRT에서 0.37 및 0.30로 분석되었다. IFAS를 이용한 유출분석에서 매개변수를 보정하지 않은 경우에도 위성 강우자료를 이용한 모의 첨두유출량이 관측유출량과의 상대오차가 낮았다. 따라서 계측자료가 없는 유역에서의 적용성이 있는 것으로 사료된다. 향후 대표적인 비접근 지역인 북한지역에 대하여 위성강우 및 글로벌 지형자료를 이용하여 유출분석에 활용할 계획이다.

• 산업경영시스템학회지
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• 제40권2호
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• 한국의류산업학회지
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• 제24권3호
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• pp.289-302
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• 2022

This study aimed to provide information to establish a service strategy in cross-border e-commerce through an assessment of consumer satisfaction with a cross-border shopping channel and "amazon global store" managed by 11street, a domestic e-commerce corporation. The influence of brand familiarity with amazon as a global retail brand was tested. The mediating roles of the perceived value and risk of both cross-border shopping and amazon global store were investigated; the moderating role of the domestic e-commerce corporation was also studied. An empirical study was conducted on consumers who had experience using the amazon global store managed by 11street. To verify the hypothesis, data from 200 people was analyzed using PROCESS macro 4.0. The results indicated that familiarity with global brands did not have a direct effect on consumer satisfaction; the effect of global retail brand familiarity on consumer satisfaction was mediated only by the perceived value of cross-border shopping and amazon global store, not by the perceived risks. E-commerce corporate credibility showed a moderated mediation effect by mediating functional values of the amazon global store. For consumer groups with a credibility level of medium and above, the interaction effect of brand familiarity and corporate credibility was significant.

• International Journal of Knowledge Content Development & Technology
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• 제8권1호
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• pp.79-89
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• 2018

We collected and analyzed data from e3data.org, which is a global registry of data repository services. We analyzed data profile for three leading Asian economies-Korea, China, and Japan-against the reference data for other participating countries. In particular, we examined how individual countries contribute to the repository, organizational type, versioning and product quality management, and subject tagging. We come to the conclusion that all three Asian countries still fall short in terms of involvement. As for participating institutions, there are 7 from Korea, 64 from China, and 120 from Japan. Among Chinese organizations, 3 are profit, 61 non-profit, and 37 organizations (which yields 1.8%) are involved in repository building. In Japan, there is 1 is commercial and 119 non-profit organizations, of which 57 (3.0%) are involved in repository building. All 7 organizations from Korea are non-profit, and 6 of them (0.3%) are involved in repository building. As regards versioning and product quality management, Korea, China, and Japan are up to par with other countries. Subject analysis reveals that Korea contributes more to geosciences, Japan to physics and geosciences, while China, unlike Korea and Japan, is more active in life sciences. It is hoped that this study will help planning domestic infrastructure for research data repositories with proper consideration for specific research domains and national characteristics.

• 한국농공학회논문집
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• 제62권2호
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• pp.39-52
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• 2020

A high quality, long-term, high-resolution precipitation dataset is an essential in climate analyses and global water cycles. Rainfall data from station observations are inadequate over many parts of the world, especially North Korea, due to non-existent observation networks, or limited reporting of gauge observations. As a result, satellite-based rainfall estimates have been used as an alternative as a supplement to station observations. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and global coverage. CHIRPS is a global precipitation product and is made available at daily to seasonal time scales with a spatial resolution of 0.05° and a 1981 to near real-time period of record. In this study, we analyze the applicability of CHIRPS data on the Korean Peninsula by supplementing the lack of precipitation data of North Korea. We compared the daily precipitation estimates from CHIRPS with 81 rain gauges across Korea using several statistical metrics in the long-term period of 1981-2017. To summarize the results, the CHIRPS product for the Korean Peninsula was shown an acceptable performance when it is used for hydrological applications based on monthly rainfall amounts. Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and climate, hydrological application, for example, drought monitoring in this region.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2003년도 추계학술발표회
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• pp.219-225
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• 2003

The present study intends to assess the long-term steric sea-level change and its prediction, and potential impacts to the sea-level rise due to the 21st global warming in the coastal zone of the Korea in which much socioeconomic activities have been occurred. The analysis of the 23 tide-gauge data near Korea reveals the overall mean sea-level trend of 2.31 mm/yr.In the satellite altimeter data (Topex/Poseidon and ERS), the sea-level trend in the East Sea is 4.6mm/yr. Both are larger than those of the global average value. However, it is quite questionable that the sea-level trends with the tide-gauge data on the neighboring seas of Korea relate to global warming because of the relatively short observation period and large spatial variability. It is also not clear whether the high trend of altimeter data in the East Sea is related to the acceleration of sea level rise in the Sea, short response time of the Sea, natural variability such as decadal variability, short duration of the altimeter. The coastal zone of Korea appears to be quite vulnerable to the 21st sea level rise such that for the I-m sea level rise with high tide and storm surge, the inundation area is 2,643 km2, which is about $1.2\%$ of total area and the population in the risk areas of inundation is 1.255 million, about $2.6\%$ of total population. The coastal zone west of Korea is appeared to be the most vulnerable area compared to the east and south. In the west of the Korea, the North Korea appears to be more vulnerable than South Korea. In order to cope with the future possible impact of sea-level rise to the coastal zone of Korea effectively, it is essential to improve scientific information in the sea-level rise trend, regional prediction, and vulnerability assessment near Korean coast.