• 정보화정책
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• 제30권4호
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• pp.113-137
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• 2023

본 연구는 플랫폼 정부의 개념이 다양하게 인지되고 있는 상황에서, 플랫폼 정부의 유형화를 수행하였다. 플랫폼 정부 개념의 포괄성 및 모호성이 실무자들의 정책추진에 장애가 되기 때문이다. 이에 관련 문헌 및 사례를 토대로 플랫폼 정부의 유형화 기준을 도출하였다. 기술적 측면은 데이터를 핵심 요인으로 하여, 공공부문의 정보시스템 및 웹사이트 등을 통합하는 통합시스템 플랫폼과 공공부문의 데이터를 단일 창구를 통해 제공 및 활용토록 하는 데이터 플랫폼 등의 플랫폼 정부 유형을 제시하였다. 거버넌스적 측면은 이해관계자를 핵심 요인으로, 공공부문과 민간부문의 교류를 목적으로 하는 소통 플랫폼과 공공부문과 민간부문이 협업하여 새로운 산출물을 도출하는 협업생산 플랫폼 등의 플랫폼 정부 유형을 제시하였다. 각 유형에 따른 플랫폼 정부 실제 사례를 제시하고, 이에 따른 함의를 제시하였다. 추가적으로 플랫폼 정부 유형 기준을 토대로 국내 디지털플랫폼정부의 추진현황에 대해 세부과제를 중심으로 어떠한 유형으로 분류되는지 내용분석을 진행했다. 분류결과 통합시스템 플랫폼 측면이 강조되고 있는 것으로 확인되었다. 플랫폼 정부 유형화를 토대로 국내 디지털플랫폼정부 구현의 정책적 제언은 다음과 같다. 첫째, 플랫폼 정부의 다양한 유형에 대한 균형 있는 구현이 요구된다. 둘째, 이해관계자의 참여와 협업을 촉진할 방안을 마련해야 한다. 셋째, 플랫폼 정부 구현을 위한 공공부문의 재구조 및 재창조의 필요성이다.

• 보존과학연구
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• 통권8호
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• pp.1-12
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• 1987

Fifty ancient Korean coins originated in Choson dynasty have been determined for 9 elements such as Sn, Fe, As, Ag, Co, Sb, Ir, Ru and Ni by instrumental neutron activation analysis and for 3 elements such as Cu, Pb, and Zn by atomicalsorption spectrometry. Bronze coins originated in early days of the dynasty contain as major constituents Cu, Pb and Sn approximately in the ratio 90 : 4 : 3, where as, those in latter days contain in the ratio 7 : 2 : 0. Brass coins which had begun in 17century contain as major constituents Cu, Zn and Pb approximately in the ratio 7 : 1: 1. The multivariate date have been analyzed for the relation among elemental contents through the variance-covariance matrix. The data have been fur theranalyzed by a principal component mapping method. As the results training set of 8class have been chosen, based on the spread of sample points in an eigenvector plotand archaeolgical data such as age and the office of minting.

• 한국생산제조학회지
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• 제9권2호
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• pp.144-150
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• 2000

Surface roughness is one of the most important parameters to estimate quality of products. As this reason so many studies were car-ried out through various attempts that were contact or non-contact using computer vision. Even through these efforts there were few good results in this research., however texture analysis making a important role to solve these problems in various fields including universe aviation living thing and fibers. In this study feature value of co-occurrence matrix was calculated by statistic method and roughness value of worked surface was classified, of it. Experiment was carried out using input vector of neural network with characteristic value of texture calculated from worked surface image. It's found that recognition rate of 74% was obtained when adapting texture features. In order to enhance recogni-tion rate combination type in characteristics value of texture was changed into input vector. As a result high recognition rate of 92.6% was obtained through these processes.

• International Journal of Precision Engineering and Manufacturing
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• 제8권3호
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• pp.45-49
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• 2007

A gearing system emits inconsistent noises from the impact of gear teeth when defects are present, but it is not easy for a noise inspector on a production line to distinguish defective products objectively. Since customers constantly complain about various noises from geared motors, it is desirable to devise an analytical technique to classify motors. However, it is difficult to separate inconsistent noises due to defective gears from the overall noise produced by a geared motor using a general signal processing method such as a FFT because low frequency impulse signals have a tendency not to appear in the frequency domain. In this paper, we propose a method that can be used to obtain more objective estimates and measurements of inconsistent noises from a gearing system. The proposed method makes use of the cepstrum domain with an applied autocorrelation and comb lifter, followed by a domain inversion.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.283-284
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• 2006

In this paper, we propose a novel segmentation and classification method using texture features for the document image. First, we extract the local entropy and then segment the document image to separate the background and the foreground using the Otsu's method. Finally, we classify the segmented regions into each component using PCA(principle component analysis) algorithm based on the texture features that are extracted from the co-occurrence matrix for the entropy image. The entropy-based segmentation is robust to not only noise and the change of light, but also skew and rotation. Texture features are not restricted from any form of the document image and have a superior discrimination for each component. In addition, PCA algorithm used for the classifier can classify the components more robustly than neural network.

• 한국물환경학회지
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• 제28권2호
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• pp.202-212
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• 2012

Due to the deterioration of water supply network and the deficiency of raw water, the water utility of local governments have performed various projects to improve their revenue water ratio. However, it is very difficult to estimate the cost for maintaining the revenue water ratio at higher level after completing the project, because local governments have different conditions affecting the operating and maintenance cost of water supply network. The purpose of this study is to present a procedure to estimate the operating and maintenance cost required to maintain the target revenue water ratio of the water supply network. For this purpose, we estimated the cost used only for operation and maintenance of water supply network of 164 local governments with the aid of K-Mean Clustering Analysis and the data from 40 representative local governments. Then, the regression analysis was performed to find relationship between revenue water ratio and the operating and maintenance cost with two different data sets generated by two classification methods; the first method classifies the local governments by means of k-means clustering, and the other classifies the local governments according to the index standardized by the operating and maintenance cost per unit length of water mains per revenue water ratio. The results shows that the method based on the index standardized by the cost and revenue water ratio of each government produces more reliable results for finding regression equations between revenue water ratio and the operating and maintenance cost only for water supply network. The estimated regression equations for each group can be used to estimate the cost required to keep the target revenue water ratio of the local government.

• 한국도서관정보학회지
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• 제48권1호
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• pp.191-206
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• 2017

본 연구는 국내 문헌정보학 분야의 연구협업의 특성을 기관 단위로 협업지수들을 활용하여 비교 분석 하였다. 학문 분야로서 문헌정보학 분야에서의 협업에 대한 연구는 국가 단위로 비교하는 연구는 몇몇 이루어졌으나, 단위 국가 내에서 일어나는 기관 단위 협업에 대한 연구는 많지 않다. 본 분석을 위해 KCI(Korea Citation Index) 웹사이트에서 주제별 분류를 통해 문헌정보학 분야에 해당하고 KCI에 등재된 8개의 저널들을 데이터로 선택하였다. CC(Collaborative Coefficient), CAI(Co-Authorship Index), Local Collaborative Index(LCI), Domestic Collaborative Index(DCI)의 협업지수를 통하여 문헌정보학 분야 기관 간 협업 패턴에 대해서 비교 분석하였다. 중앙대학교, 연세대학교, 이화여자대학교의 경우 대학교라는 기관 특성 상 공동 연구가 주로 교수와 대학원생 간의 협업, 교수들 간의 협업 형태로 많이 이루어지는 점이 반영되었다. 멀티, 메가 단위의 공동 저자 유형에서 매우 높은 지수를 나타낸 KISTI의 경우 연구소라는 기관의 특성이 반영된 것으로, 팀 단위로 프로젝트 성격의 공동 연구가 이루어지는 것이 보편적인 연구소의 특성이 영향요인으로 작용한 것으로 볼 수 있다.

• 대한화학회지
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• 제31권6호
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• pp.567-575
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• 1987

한국산 고대유리 시료 45종을 입수하여 그속에 함유된 19종의 원소(Ag, As, Br, Ce, Co, Cr, Eu, Fe, Hf, K, La, Lu, Na, Ru, Sb, Sc, Sm, Th, Zn)는 중성자방사화분석에 의하고, Pb는 원자흡수분광분석법에 의해 각각 정량하였다. 이들 20종 원소의 분석데이타를 사용하여 원소 상호간의 상관관계를 상관메트릭스법으로 검토하였다. 그리고 주성분분석법으로 각 시료의 농도분포를 평면에 나타내었으며, 측정된 제조년대 및 발굴위치가 같은 시료가 모이면 이들 시료를 SIMCA를 위한 참조시료로 삼았다. 이들 참조시료 및 시험시료를 SIMCA에 의해 분류하였더니 참조시료 전부와 시험시료중 3종이 주성분분석법에 의한 분류결과와 일치하였다.

• 한국안전학회지
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• 제38권6호
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• pp.50-59
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• 2023

As the diversity of disasters continues to increase, the concept of "complex disasters" has gained prominence in various policies and studies related to disaster management. However, there has been a certain limitation in the availability of the systematic statistics or data in advancing policies and research initiatives related to complex disasters. This study aims to analyze the macro-level characteristics of the complex disasters that have occurred domestically utilizing a 30-year span of a news data. Initially, we categorize the complex disasters into the three types: "Natural disaster-Natural disaster", "Natural disaster-Social disaster", and "Social disaster-Social disaster". As a result, the "natural diaster-social disaster" type is the most prevalent. It is noted that "natual disaster-natural disaster" type has increased significantly in recent 10 years (2011-2020). In terms of specific disaster types, "Storm and Flood", "Collapse", "Traffic Accident", "National Infrastructure Paralysis", and "Fire⋅Explosion" occur the most in conjunction with other disasters in a complex manner. It has been observed that the types of disasters co-ocuuring with others have become more diverse over time. Parcicularly, in recent 10 years (2011-2020), in addition to the aforementioned five types, "Heat Wave", "Heavy Snowfall⋅Cold Wave", "Earthquake", "Chemical Accident", "Infectious Disease", "Forest Fire", "Air Pollution", "Drought", and "Landslide" have been notable for their frequent co-occurrence with other disasters. These findings through the statistical analysis of the complex disasters using long-term news data are expected to serve as crucial data for future policy development and research on complex disaster management.

• 한국대기환경학회지
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• 제9권3호
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• pp.207-215
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• 1993

In order to statistically predict $O_3$ levels in Seoul, the study used the TMS (telemeted air monitoring system) data from the Department of Environment, which have monitored at 20 sites in 1989 and 1990. Each data in each site was characterized by 6 major criteria pollutants ($SO_2, TSP, CO, NO_2, THC, and O_3$) and 2 meteorological parameters, such as wind speed and wind direction. To select proper variables and to determine each pollutant's behavior, univariate statistical analyses were extensively studied in the beginning, and then various applied statistical techniques like cluster analysis, regression analysis, and expert system have been intensively examined. For the initial study of high level $O_3$ prediction, the raw data set in each site was separated into 2 group based on 60 ppb $O_3$ level. A hierarchical cluster analysis was applied to classify the group based on 60 ppb $O_3$ into small calsses. Each class in each site has its own pattern. Next, multiple regression for each class was repeatedly applied to determine an $O_3$ prediction submodel and to determine outliers in each class based on a certain level of standardized redisual. Thus, a prediction submodel for each homogeneous class could be obtained. The study was extended to model $O_3$ prediction for both on-time basis and 1-hr after basis. Finally, an expect system was used to build a unified classification rule based on examples of the homogenous classes for all of sites. Thus, a concept of high level $O_3$ prediction model was developed for one of $O_3$ alert systems.