이 논문에서는 경기순환 및 변동과 주가수익률의 상관성에 대하여 분석함으로써, 경 기변동에 대한 기업들의 경기순응적 현상이 지속성을 갖는 지 알아보고 경기침체기에 있어서 향후 국내 금융시장에 미치는 시사점을 도출하고자 하였다. Granger 인과성 검 정 결과를 살펴보면 다음과 같다. 첫째, 코스피수익률 변수를 사용하였을 경우 산업생 산증가율과 코스피수익률 간에는 상호관계(interaction)를 보여 두드러진 외생성 (exogenous)은 발견하기가 어려웠지만, 상호 간에 인과관계가 있음을 나타내고 있다. 콜금리의 경우 물가상승률에 영향을 주고 이러한 물가상승률은 코스피수익률에 영향 을 주는 것을 알 수 있었다. 하지만 콜금리의 경우 직접적으로 코스피수익률에 영향을 주지는 못하는 것으로 나타났다. 둘째, 코스닥수익률 변수를 사용하였을 경우 자금사 정과 통화신용정책을 반영하는 콜금리와 경기변동을 반영하고 있는 산업생산증가율이 코스닥수익률에 외생성(exogenous)이 있음을 알 수 있다. 이는 산업생산 제고 등에 따 른 경기변동 요인이 신용등급이 상대적으로 낮은 기업들에 있어서도 중요함을 의미하 고 있다. 또한 콜금리가 직접적으로 그리고 물가상승률을 통하여 간접적으로 코스닥수 익률에 영향을 미치고 있음을 알 수 있다. 이는 코스닥과 관련된 업체들의 경우에 있 어서 유가증권시장에 있는 기업들에 비하여 자금사정에 보다 민감한 반응을 나타내기 때문으로 판단된다. 결론적으로 현재와 같은 경기침체 국면이 길어질수록 상대적으로 낮은 투자로 이어지고 경기순환상 수축기를 단축시키거나 확장기를 길게 만드는데 있 어서 부정적인 영향을 미칠 수 있음을 시사하고 있다.

With industrial development, energy demands continue to rise. Fossil fuels release more air pollutants to produce the same amount of energy compared with other types of fuel. The harmful exhaust gas exacerbated by the increasing uses of vehicles also makes a contribution to the worsening of air pollution. Thus there is a need for various processing methods and technologies to eliminate harmful gases such as sulfur oxides released into the air. Researches on the elimination of sulfur and nitrogen oxides with catalysts and absorbents reported many problems due to elimination efficiency and complex devices. In an attempt to supplement them, this study set out to increase the decomposition efficiency of harmful gases by moving them through the plasma generated in the SPCP reactor and the catalysis reactor specially designed and manufactured. The study used calcium hydroxide(Ca (OH)2),CaO,andTiO2 as catalysts. Harmful air polluting gases such as SO2 were measured for decomposition efficiency, power consumption, and voltage according to changes to the process variables including frequency, concentration, electrode material, thickness of electrode, number of electrode winding, and additives to obtain optimal process conditions and the highest decomposition efficiency. The standard sample was sulfur oxide(SO2). Harmful gases were eliminated by moving them through the plasma generated in the SPCP reactor and the Ca(OH)2 catalysis reactor. The elimination rate and products were analyzed with the gas analyzer (Ecom-AC,Germany), FT-IR(Nicolet, Magna-IR560), and GC-(Shimazu). The results of the experiment conducted to decompose and eliminate the harmful gas SO2with the Ca(OH)2 catalysis reactor and SPCP reactor show 96 decomposition efficiency at the frequency of 10kHz. The conductivity of the standard gas increased according to frequency at high voltage of 20 kHz or more. There was a partial flow of current along the surface. As a result, the decomposition efficiency decreased. The use of tungsten electrode resulted in the highest decomposition efficiency by the Ca(OH)2 catalysis reactor and SPCP reactor, and it was followed by the copper and aluminum electrode in the order. As for the impacts of thickness of electrode at electric discharge, the thicker the electrode was, the higher decomposition efficiency became. As for the number of electrode winding, the more it was wound, the higher decomposition efficiency became. The decomposition efficiency of harmful gas SO2 by the Ca(OH)2 catalysis reactor and SPCP reactor was 96.0% under the conditions of 300ppm concentration, 10kHz frequency, and decomposition power of 20W. It was higher than 92% when only the SPCP reactor was used. Decomposition efficiency was the highest at 98.0% when the concentration was 100ppm. As for the effects of additives to fit actual exhaust gases, the more methane (CH4) was added, the higher decomposition efficiency became over 99%. The higher the oxygen concentration was, the higher decomposition efficiency became, as well

As Internet has been wildly spreaded and it's technique is advanced, the use of computers has been routinized and almost data are stored in computers. Accordingly, many companies and researchers have tried to find the relations in these tremendous data and the one way is to use clustering algorithm which is used to find out similar data set in the entire data set and to discover the common properties. In early period, clustering algorithm was performed based on a main memory of a computer and PAM(Partitioning Around Medoids) was representative, which can be complemented k-means algorithm defeat. PAM performs clustering by using the medoid of data instead of means. PAM works well in small data set but it is difficult to apply it to large data set. Therefore, CLARA(Clutering LARge Application) shows up to be used in large data set. This algorithm samples data from large data set and applies PAM to the sample data. CLARA has limits caused by the fixed samples in each clustering stage and has a problem that if the good mediod is not sampled then the result of the clustering becomes not good. CLARANS(Clustering Large Application based upon Randomized Search) overcomes these problems by drawing a sample with some randomness. This algorithm executes clustering using k mediod set extracted in the processing of clustering in each stage. The main objective is to compare and analyze the algorithms which are popularly used for the clustering of big data.

This study is to review retirement function measures for estimating capital stocks. The measures include Retirement Probability Density Function(RPDF), Retirement Cumulative Distribution Function(RCDF), Retirement Survival Function(RSF) and Retirement Rate Function(RRF). The paper also provides the recommendations for using RRF as a distinct identification of the retirement curve functions for empirical service lives data.