Development and Application of Human Resources Opportunity Index (HROI)

나일주(Rha Ilju),허유성(Heo Yusung),이예경(Lee Yekyung),홍영일(Young-il Hong) 한국방송통신대학교 미래원격교육연구원 2009 평생학습사회 Vol.5 No.2

With increased exchange of human resources (HR) among countries, there has emerged an urgent need to identify the quality of HR in each country and to compare it with that of other countries from a global perspective. Developing an index which can be used both as a tool to identify the quality of HR at a national level and to make cross‐national comparisons would be an effective way to address this need. In order to accurately assess a nation‘s current state of the quality of its HR, the index should cover not only intellectual abilities of its HR but also their communication skills, attitudes and values, and other aspects. At the same time, the index should reflect features of today’s globalized labor environment to promote cross‐national comparisons. The present study is an attempt to meet global needs for such an index. The study consists of two parts: the first part comprises the development of an index which can assess the quality of a nation’s HR from a global perspective and the second part applies the index in measuring the quality of HR in 55 nations in pursuit of cross‐national comparisons. The index is named as the Human Resource Opportunity Index (HROI). To develop the index, well‐established existing indexes including the Digital Opportunity Index, the Growth Competitiveness Index, the Human Development Index, and the World Competitiveness Yearbook Index were extensively analyzed with regard to conceptual frameworks, specific measurement indicators and development procedures. Based on the analysis, HROI indicators were created and reviewed by experts in human resource development and training. The revised HROI indicators were then sent to a group of researchers in the HR field for confirmation. As a result, nine indicators in three categories were identified. This index was then applied to identify the HR opportunities of 55 nations. Quantitative data on the indicators were collected from official documents of several international organizations and governments and used to calculate the HROI of 55 countries. Using these HROI indices, cluster analysis was performed to identify major factors influencing the index scores, and correlation analysis was conducted to identify the concurrent validity of the index. The results demonstrated that intellectual property rights and overall productivity were the most influential indicators for HROI values. The concurrent validity was secured by the high correlations between HROI and other indices; GCI(.78), HDI(.80), and WCYI(.80). It is concluded that the HROI is a reliable and valid tool for measuring a nation's quality of HR.

F-Index: 빠른 부분그래프 매칭을 위한 특징 인덱스

김송현(Song-Hyon Kim),송인철(Inchul Song),이윤준(Yoon-Joon Lee) 한국정보과학회 2013 정보과학회논문지 : 데이타베이스 Vol.40 No.1

본 논문에서는 대규모 데이터베이스 그래프에서 주어진 질의 그래프와 동형인 모든 부분그래프들을 찾는 부분그래프 문제를 다룬다. 최근 빠른 부분그래프 매칭을 위해서 특징 인덱스를 기반으로 하는 기법들이 제안되었다. 이 기법들은 데이터베이스 그래프 정점들과 질의 그래프 정점들을 정점 특징을 사용하여 비교한 후 부분그래프 매칭 작업에서 고려할 필요가 없는 데이터베이스 그래프 상의 정점들을 걸러냄으로써 부분그래프 매칭 비용을 줄인다. 기존 기법들에서는 라벨 분포와 부분구조를 정점 특징으로 사용한다. 하지만 가지치기 능력과 추출비용 사이의 교환 비용을 적절히 고려하지 않았다. 본 논문에서는 빠른 부분그래프 매칭을 위한 F-Index라고 부르는 특징 인덱스를 제안한다. F-Index는 정점 특징의 가지치기 능력과 추출비용 사이의 균형을 고려한다. 제안하는 기법에서는 라벨 분포와 함께 정점 주변의 연결정보를 정점 특징으로 사용한다. 데이터베이스 그래프 상에서 적합하지 않은 정점들을 빠르게 걸러내기 위해 정점 특징을 추출한 후 인덱스를 구축한다. 본 논문에서는 다양한 실험을 통해 제안하는 기법이 기존기법들에 비해서 질의 처리 시간과 인덱스 생성 시간 측면에서 우수함을 보인다. In this paper, we study the subgraph matching problem in a large database graph, which finds all subgraphs in the database graph that are isomorphic to a query graph. Recently, feature index-based methods have been proposed for fast subgraph matching in a large database graph. They adopt the concept of vertex features to easily compare the vertices from a database graph with those from a query graph and filter out vertices in the database graph not eligible for subgraph matching. Previous approaches use various kinds of vertex features such as label distribution and discriminative substructures. However, they do not carefully consider the tradeoff between pruning power and extraction cost of vertex features. In this paper we propose a feature index called F-Index for fast subgraph matching. F-Index strikes a balance between pruning power and extraction cost. It uses the combination of label distribution and connectivity information of neighbors as vertex features. F-Index is constructed over the extracted vertex features for fast filtering of unqualified vertices. Experimental results show that our method outperforms the existing methods in terms of query processing time with comparable index build time.

퍼지 K-평균 군집화의 재현성 평가

허명회,손은진 高麗大學校統計硏究所 2003 應用統計 Vol.18 No.-

Rand index는 군집화의 재현성을 평가하기 위한 자료 분할법에서 두 군집화 결과간의 일치도를 재는 지표이지만 (Rand, 1971) 개체가 1개 군집에 명확히 할당되는 군집화에만 적용될 수 있다. 따라서, 본 연구의 대상인 퍼지 K-평균 군집화(fuzzy K-means clustering)에서는 개체가 각 군집에 속할 소속도(membership)로 제시되므로 Rand index를 원형 그대로 사용할 수 없다. 본 연구의 목적은 퍼지 K-평균 군집화 결과 간 일치성 평가에 활용 가능하도록 Rand index를 확장하는 것이다. 제안 방법을 요약하면 다음과 같다. 1) 훈련 데이터로부터 얻은 퍼지 K-평균 군집화 규칙을 테스트 자료의 각 개체에 적용하여 K개 (=군집 수) 퍼지 소속도를 구한다. 독립적인 다른 훈련 데이터로부터 얻게 되는 퍼지 K-평균 군집화 규칙을 테스트 자료의 동일 개체에 적용하여 또 다른 K개 퍼지 소속도를 구한다. 2) 각 퍼지 군집화 규칙에 따른 군집 소속도에 비례하게 테스트 자료의 개체를 독립적으로 K개 군집 중 하나에 임의 할당하는 역 퍼지화 작업을 시행하여 명확한 분할(hard partition) 자료를 만든다. 3) 대응하는 두 개의 분할 군집화 결과로부터 통상적인 Rand index (또는 Hubert and Arabie (1985)의 C.(corrected) Rand index)를 산출한다. 4) 앞의 두 단계를 일정 수 반복하여 Rand index의 몬테칼로(Monte Carlo) 분포를 산출한다. 그 분포의 평균을 확장(extended) Rand index로 정의한다. 퍼지 K-평균 군집화에서 군집 수 K를 결정하는 문제에 확장 Rand index를 활용할 수 있다. 몇 개의 적용 사례를 제시하고 토의할 것이다. Rand index is an evaluation measure of consistency between two clustering rules (Rand. 1971). Hence it can be used to predict whether the clustering patterns are reproducible in the future. The index, however, cannot be applied to the fuzzy K-means clustering which has clear merits in dealing with overlapping clusters. The aim of this study is to extend Rand index or corrected Rand index of Hubert and Arabie (1985) for the use in fuzzy K-means clustering. The proposed method can be summarized as follows : Step 1: Partition the data into three parts - two training samples and one. test sample. Then, derive a K-means clustering rule from the first training sample and another rule from the second training sample. Then, apply both rules separately to the test sample units to obtain the list of cluster membership pairs. Step 2: Perform the inverse procedure opposite to make things fuzzy. In other words, generate a pair of hard partitions according to respective memberships of fuzzy partitions. Step 3: Compute Rand index or corrected Rand index of Hubert and Arabie (1985) from a pair of hard partitions. Step 4: Repeat Steps 3 and 4 for sufficient number of times. Then, one obtains a batch of Rand indices. Define Extended Rand Index by the average of Rand indices. We may use Extended Rand Index in determination of the number of clusters Kin fuzzy K-means clustering. Several examples are illustrated.

건강상태 평가를 위한 지수 개발: 헬스인덱스

문동주,이성일,이종선,김경철,강희정,양용주,Moon, Dong-Ju,Lee, Sung-Il,Lee, Chong-Sun,Kim, Gyeong-Cheol,Kang, Hee-Jung,Yang, Yong-Ju 대한의용생체공학회 2008 의공학회지 Vol.29 No.5

A health index was proposed that evaluates personal health state from both measured physiological variables and survey questions. Four health indices were defined such as cardiovascular index, stress index, obesity index, and management index. The total health index was calculated by summing these four health indices. Physiological variables such as blood pressure, heart rate variability(HRV), accelerated photoplethysmograph(APG), and body fat percentage were non-invasively measured and a survey questionnaire that asks personal health state, exercise intensity, and food preference was developed. The suggested health index was applied to thirty eight persons including 30 patients and 8 normal persons with an average age of 51.8. The average health index was estimated to be 75.1 out of 100 points. Young age group(below 50) and men group showed higher health indices than the aged(over 50) and women groups. The correlation coefficient between the cardiovascular index and stress index was found to be 0.513, which means stress is related to cardiovascular health state. The correlation coefficient between the measurements and survey questions was 0.385 for the cardiovascular index. It was as low as 0.182 for the stress index. More case studies may improve correlations between measurements and survey questions, and then, the current health index system may develop as an effective tool to evaluate personal health state.

초등학교 고학년 여학생의 형태지수에 따른 운동능력의 비교

김충현(ChungHyunKim),김기학(KiHackKim) 한국체육학회 2008 한국체육학회지 Vol.47 No.1

본 연구의 목적은 여자 아동의 형태지수 수준에 따른 운동능력을 알아보는데 있다. 연구 대상은 대구광역시 소재 초등학교 고학년 여학생 240명을 신체형태 10개를 측정하여 6개의 형태지수를 산출하였다. 이것을 다시 3구간별로 나누어 운동능력을 측정하였다. 이 자료로 일원분산분석을 실시한 후 Scheffe’법으로 사후검정을 하였고, 상관관계를 구하였다. 분석한 결과는 다음과 같다. 하지장좌고 지수, 상완위상지장 지수 및 흉위좌고 지수는 수준에 따라 하위 수준이 운동능력이 가장 좋은 것으로 나타났고, 견폭둔폭 지수는 상위 수준이 운동능력이 가장 좋은 것으로 나타났으며, 대퇴위하지장 지수와 BMI는 수준별로 차이가 있었다. 형태지수와 운동능력과의 관련성은 견폭둔폭 지수와 BMI가 근력에서, 흉위좌고 지수는 협응성에서 유의하게 나타났다. The purpose of this study was to compare motor abilities according to somatic index of elementary school girls. For this study, we selected 240 school girls from 5th grade to 6th grade in Daegu metropolitan city. We measured 10 items of physique and yielded 6 items of somatic index. The yielded somatic index was was divided into 3 levels by mean and standard deviation. In order to analyze the data, one way-ANOVA, Scheffe post hoc tests, correlation analysis were used. The result of this study was summarized as follows. In the skelic index, the upper limb index, and the stem index, motor abilities showed higher numerical value in the lower level. In the trunk width index, motor abilities showed higher numerical value in the high level. In the lower limb index and BMI, motor abilities showed different value in the levels. In the relationship between the somatic index and the motor ability, In the trunk width index and BMI showed the coefficient of correlation in muscular strength significantly, and the stem index showed the coefficient of correlation in coordnation significantly.

금융과 ESG 및 중소기업 간에 상호 미치는 영향에 관한 실증적 연구

임병진 한국무역보험학회 2023 무역금융보험연구 Vol.24 No.4

Purpose : This study investigates the effects among the ESG index, the SMEs and financial index in Korea. Research design, data, methodology : The data used in this study are the ESG index and the weekly time series data for SMEs and financial indexes, which are 581 weekly data from January 6, 2016 to February 23, 2023. By examining the causal relationship and mutual influence of the weekly time series data of the ESG index and the SME and financial index, the degree of influence between the ESG index and the weekly time series data of the SME and financial index is analyzed. Unit root test, cointegration test of weekly time series data of ESG index and SMEs and financial indexes, VAR model analysis of weekly time series data of ESG index and SMEs and financial indexes, and weekly time series data of ESG index and SMEs and financial indexes using VAR model analysis Prediction error variance decomposition analysis, impact response analysis, and Granger causality test were performed. Results : This research showed following main results. First, as a result of the stability test for the level variable of the weekly time series data of the ESG index and the SME index and financial index, it was found to be unstable at the 5% significance level. Second, it was found that the results of the stability test for the first difference data of the ESG index and the weekly data of the SME index and financial index were all stable at the 1% significance level. Third, as for level variables, it was found that there was no cointegration relationship at the 5% significance level between the ESG index and the weekly time series data of SMEs index and financial index. Conclusions : As a result of the Granger causality test, at the 5% significance level, the change in the ESG index was found to have a Granger causal relationship with the change in the SME index. Therefore, it means that small and medium-sized companies cannot lead and follow ESG, so small and medium-sized companies desperately need education on ESG and education on ESG.

특허정보의 효율적 활용을 위한 통합형 특허지표 설계

신한섭 한국경영과학회 2007 經營 科學 Vol.24 No.2

This paper presents a consolidated patent index to measure national technology innovation and science technology activation, as well as index for the main constituent such as corporation, research organization by comprehensive analysis of existing patent index. It is classified by macroscopic index and analytical index in the consolidated patent index, in which macroscopic index is to present a degree of innovation in national scientific innovation and is divided into the Consolidated Patent Index and Index for comparison between countries. The analytical index basically designed to measure R&D activity by the main constituent is divided to present by quantitative index utilizing bibliographical data in patent and other technical publication related therein, and qualitative index for analysis of bibliographical data. In this paper, the Consolidated Patent Index is presented by adding Creation Index representing for patent by developing excellent technology, Evaluation Index representing valuable technology thereof, and Utility Index representing applicability diffused.

3T 방식에 의한 국가 간 창의성 지수(Creativity Index) 비교연구

송치웅 ( Chi Ung Song ),장성일 ( Sung Il Jang ) 한국생산성학회 2014 生産性論集 Vol.28 No.4

This paper compares creativity capacities of 15 major OECD countries including Korea by measuring ‘Creativity Index’. Richard Florida has first developed Creativity Index. By comparing Creativity Indices of 15 countries, this paper aims to find the current status of Korea in knowledge-based or the so-called creative economy. To measure Creativity Index, the paper introduces almost same methodology used in Florida & Tinagli (2004), and improves it with recent data. In Florida & Tinagli (2004), Creativity Index was comprised of Talent Index, Technology Index, and Tolerance Index(3Ts) from 9 sub-indices. This paper modified the 9 sub-indices into 8 for more clarity in measuring. To measure one country`s relative position, the authors also employ ``15 points grading``. The grading is to give 15 (points) to the country which has the highest value. Talent Index is comprised of Creative Class Index, Human Capital Index, and Scientific Talent Index. Finland is on the top among other countries. Sweden, Norway, and Switzerland are just behind the first one. For Korea the country is ranked 10th, because the country`s creative class index is not high. Technology Index is based on Innovation Index, High Tech Innovation Index, and R&D Index. Switzerland is the highest country defeating other countries. Sweden, Finland, and Japan are the second group members. Korea is the 6th because of its heavy R&D investments. In Tolerance Index, Sweden is the very tolerant country to minorities and non-traditional behaviors. Norway, the Netherlands, and Slovenia are the 2nd group. Korea, on the contrary, shows no excellence in ‘tolerance’ ranking the bottom of the index. When the 3Ts are aggregated, Sweden is the top position in Creativity Index. In the 2nd group there are Switzerland, Finland, the Netherlands. Because of the country`s poor value of Tolerance Index. Korea is down there ranking the 11th. In conclusion, Korea`s Creativity Index is somewhat poor. In order for improving competitiveness in Creativity Index, authors recommend the following measures. First of all, society of Korea should be tolerant to minorities and non-traditional ideas & behaviors. Second, Korean government needs to pay more attention in promoting creative class in the longer term with large investments.

방향성매매를 위한 지능형 매매시스템의 투자성과분석

최흥식(Heung Sik Choi),김선웅(Sun Woong Kim),박성철(Sungcheol Park) 한국지능정보시스템학회 2011 지능정보연구 Vol.17 No.3

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

자연과학편 : 아동의 연령 증가에 따른 신체 형태지수의 변화

김충현(ChungHyunKim),김기학(KiHackKim) 한국체육학회 2007 한국체육학회지 Vol.46 No.4

본 연구의 목적은 아동의 연령 증가에 따른 신체 형태지수의 변화를 분석하여 상대적인 발육 경향을 알아보기 위한 것으로써, 연구의 대상은 대구광역시 소재 초등학교 6-11세까지의 아동을 대상으로 신체형태 7개 부분을 측정하여 형태지수로 산출하였다. 이 자료로 이원분산분석, 일원분산분석, t-test를 실시하여 분석한 결과는 다음과 같다. 좌고에 대한 하지장의 비는 약 80-90% 정도이고 연령 증가에 따라 지수값이 증가하며, 7세에서 여아가 남아보다 더 큰 경향을 보였다. 복위에 대한 흉위의 비는 약 104-112% 정도이고, 8, 10, 11에서 여아가 남아보다 더 큰 경향을 보였다. 하지장에 대한 하퇴위의 비는 43-47% 정도이고 연령 증가에 따라 비슷하며, 남아가 여아보다 더 큰 경향을 보였다. 상지장에 대한 상완위의 비는 32-36% 정도이고, 10, 11세에서 남아가 여아보다 더 큰 경향을 보였다. The purpose of this study was comparison and analysis of remarkable differences between male and female according to the aging in order to understand the tendency of relative growth in the Korean students using the formula of somatic index. The objects of this study were from six to eleven years old children in Dae-gu. The physique of the children was inspected in the seven items and yielded the somatic index. The results which were analyzed from two-way ANOVA, one-way ANOVA and t-test using these data were following. The skelic index showed about 80-90% and increased according to aging. It had a significant tendency that the index of girls was bigger than that of boys in the seven years. The trunk girth index showed about 104-112%. It had a significant tendency that the index of girls was bigger than that of boys in the eight, ten and eleven years. The lower limb index showed about 43-47% and continued the similarly although the aging. It had a significant tendency that the index of boys was bigger than that of girls. The upper limb index showed about 32-36% and had a significant tendency that the index of boys was bigger than that of girls in the ten and eleven years.