• 방송공학회논문지
• /
• 제15권6호
• /
• pp.757-767
• /
• 2010

본 논문은 3차원 TV 시청에 있어서, 2차원에서와 같은 색감을 최대한 제공하기 위한 방법을 연구하였다. 이를 위하여 입력 RGB 색상 막대(color bar) 영상을 기준으로 2차원과 3차원 재생시 재현되는 RGB 강도의 입출력 관계를 모델링하였으며, 이를 근거로 2차원 대비 보정되어야 할 3차원 색상 사상표(mapping table)를 생성하였다. 생성된 사상표를 기존 3차원 TV 시스템의 출력부에 추가하여, 일반 2차원 재생시에는 입력 영상을 우회(bypass)하도록 하고, 3차원 재생시 색상 보정 과정을 수행하도록 하여 3차원 재생시에도 2차원에서 느낄 수 있는 색감을 재현할 수 있도록 하였다.

• 정보처리학회논문지D
• /
• 제12D권2호
• /
• pp.179-188
• /
• 2005

지난 수년 동안 공간 데이터의 조인 연산에 대한 많은 연구가 진행되어 왔다. 본 논문에서는 공간 조인연산 시 인덱스가 존재하지 않을 경우, 후보 객체의 여과 단계 처리에 중점을 둔다. 이 분야에 대한 여러 알고리즘들이 제안되었으며 대부분의 경우 공간 데이터의 조인 연산 시 우수한 성능을 나타내고 있다. 하지만, 조인을 위한 입력 테이블의 객체들이 편중되어 분포할 경우 조인 성능이 급격히 저하되는 문제점을 가지고 있으며 이 문제를 해결하려는 연구는 미흡한 실정이다. 따라서, 본 논문에서는 공간 데이터의 편중 문제를 개선하기 위해 기존의 공간 조인 알고리즘 중 Spatial Hash Join 알고리즘과 SSSJ 알고리즘의 장점을 결합한 Spatial Hash Sip Join 알고리즘을 제안한다. 이 알고리즘을 SHJ 알고리즘의 객체 분포에 기반한 공간 분할 특성과 공간 조인 시 SSSJ 알고리즘의 우수한 I/O 특성을 이용한다. 본 논문에서 제안한 SHSJ 알고리즘의 성능 평가를 위해 Tiger/line 데이터를 사용하여 기존 SHJ 알고리즘과 성능을 비교 평가 하였으며 평가 결과 인덱스가 존재하지 않는 입력 테이블에 대한 공간 조인 연산 시 모든 평가 파라미터에 대해 기존의 SHJ 알고리즘보다 우수함이 검증되었다.

• Spatial Information Research
• /
• 제21권1호
• /
• pp.37-44
• /
• 2013

본 연구에서는 U-City 전문가들에 대한 설문조사를 통해 U-City산업을 선정하고 이를 바탕으로 산업연관분석을 수행하였다. 그 결과로 U-City산업의 생산유발계수, 부가가치유발계수, 고용 및 취업유발계수, 영향력계수, 감응력 계수를 도출하였다. 이러한 계수들을 통해 U-City 산업의 전반적 특성과 파급효과를 확인하였다. U-City 산업은 전 산업 평균에 비해 부가가치유발효과, 고용유발효과가 큰 것으로 나타났으며 전방연쇄효과가 큰 산업이라는 특징을 확인하였다. 본 연구는 U-City산업에 대한 합리적 정의와 다양한 파급효과를 도출하여 정책 판단의 기초자료로 활용될 수 있다는 점에서 의미가 있다.

• 자원ㆍ환경경제연구
• /
• 제14권2호
• /
• pp.381-418
• /
• 2005

경제적 측면에서 보았을 때 '환경산업'이라는 개념과 분류 기준은 환경재화와 서비스에 대한 규정의 어려움과 견해 차이 등으로 인해서 국가별 혹은 기관별로 상이한 모습을 보이고 있으며 기초적인 경제적 효과 분석이 제대로 되지 못하고 있다. 본 논문에서는 환경산업에 대한 경제 효과를 분석해 보기 위해서 통계청의 특수 산업 분류를 근간으로 하여 투입 산출표 내에서 환경산업을 분리해 이를 부문 통합하고, 한국은행에서 발표하고 있는 1995년과 1998년의 투입 산출표를 재구성하였다. 다음으로 투입 산출표에서 얻을 수 있는 계수와 정보를 통해서 국내 환경산업의 특성을 파악해 보고 이를 통해 환경산업의 경제적 파급 효과를 분석하여 보았다. 연구 결과 우리나라 환경산업은 전체 경제 규모에 있어서 비중 면에서는 작은 수준에 있지만 전 후방 연쇄 효과가 전체 산업에 대비해 중 상위권에 위치하고 있고 중간재적인 성격을 지닌 재화를 생산한다는 것을 알 수 있었다. 또한 가격파급 효과 부문에 있어서도 산업 대비 중 상위권의 모습을 보인다는 것을 미루어 볼 때 환경산업이 국민경제 전체에 있어서 산업간 파급 효과의 매개체 역할을 수행해 내고 있음을 알 수 있었다.

• 한국가스학회지
• /
• 제20권6호
• /
• pp.102-114
• /
• 2016

파리 기후변화협약에 따라 온실가스 감축의 대안으로 도시가스가 각광받고 있다. 본 논문은 한국과 일본 도시가스산업의 경제적 파급효과를 한국은행의 산업연관표(2013)와 일본 경제산업성의 산업연관표(2011)를 활용하여 비교분석하였다. 분석의 결과, 생산유발효과, 부가가치유발효과, 취업유발효과 등은 모두 한국이 일본보다 크게 나타났다. 그러나 공급지장효과와 물가파급효과 역시 한국이 일본보다 크게 나타나는 것으로 분석되었다. 이에 따라 한국의 도시가스산업을 활성화하기 위해 생산유발, 부가가치유발 및 취업유발효과 등을 확대하고, 공급지장과 물가파급효과 등을 예측하여 국민경제를 안정화할 수 있는 정책이 함께 마련되어야 할 것으로 판단된다.

• 대한교통학회:학술대회논문집
• /
• 대한교통학회 1995년도 제27회 학술발표회
• /
• pp.101-113
• /
• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

• 역사민속학
• /
• 제33호
• /
• pp.31-72
• /
• 2010

이 글은 조선후기의 대표적 유서인 이수광(李晬光)의 『지봉유설(芝峯類說)』·홍만선(洪萬選)의 『산림경제(山林經濟)』·이익(李瀷)의 『성호사설(星湖僿說)』·이규경(李圭景)의 『오주연문장전산고(五洲衍文長箋散稿)』에 수록된 민속종교 관련 자료들을 살펴본 것으로, 먼저 4종의 유서에서 민속종교 관련 항목들을 추출하여, 그 내용을 표로 제시하였다. 다음으로 이들 유서류에서 언급된 민속종교 관련 자료들의 성격과 내용을 살펴보았다. ① 이들 유서들은 전대의 것이 후대의 것에 상당한 영향을 미치면서 많은 공통점을 가지게 되었지만, 한편으로는 저술 목적에 따라 차이가 있다. 즉 『지봉유설』·『성호사설』·『오주연문장전산고』는 백과전서식 저술답게 민속종교의 다양한 측면을 전하는데 비해 『산림경제(山林經濟)』는 실생활에서 발생할 수 있는 문제와 그 해결이란 실천적 내용이 중심을 이루고 있다. ② 이들 유서는 민속종교를 부정적인 것으로 인식했다. 그것은 이들 유서가 기본적으로 유교적 관념에 기초했기 때문이다. 그러나 완전한 부정에는 이르지 못하고, 영험성의 일부를 인정하기도 했다. ③ 조선시대 민속종교의 신앙대상들, 즉 성황신·업신·질병신·금부대왕신(金傅大王神)·정득양(鄭得揚)·관왕신(關王神)·부근신(付根神) 등에 대한 유서류의 내용을 살펴보았다. ④ 이들 유서류들은 귀신에 대해서도 공통적으로 관심을 보였는데, 그들의 귀신론은 기본적으로 성리학에 기초한 것이면서도 그 영험성으로 말미암아 민속종교의 귀신론을 일부 수용하고 있다. ⑤ 이들 유서류에서는 공통적으로 점복에 대해 상당한 관심을 가지고 있다. 그렇지만 개인의 운명에 관한 점복은 부정적으로 인식했고, 농사의 풍흉점에 대해서는 긍정적으로 이해하려 했다. ⑥ 이들 유서류에는 저주와 벽사에 관한 항목들도 상당수 있어, 이를 살펴보았다. ⑦ 무격은 민속종교의 성직자로서 민속종교의 핵심에 서 있는 존재이기 때문에 이들 유서류에서 공통의 관심사가 되었다. 이들 유서에서 무격은 부정적으로 묘사되고 있지만, 그 영험성의 일부는 인정하고 있었다. 그렇다고 한다면 이들 유서류들은 비록 민속종교에 대해 부정적인 입장을 취하고 있고 잘못된 정보를 제공하기도 하지만, 조선후기 민속종교의 다양한 사실들을 전한다는 점에서 자료적 가치를 간과할 수 없다. 따라서 이들 유서류들은 향후 민속종교 연구에서 반드시 심도 있게 검토되어야 할 자료라 하겠다.

• KDI Journal of Economic Policy
• /
• 제12권1호
• /
• pp.3-29
• /
• 1990

60년대 전반 이후의 수출주도형(輸出主導型) 공업화(工業化)을 통한 고도성장(高度成長)은 국내산업(國內産業) 또는 업종간(業種間) 성장율(成長率)의 차이로 인해서 상당한 산업구조변화(産業構造變化)를 수반했다. 따라서 본 논문에서는 산업연관표체계(産業聯關表體系)를 사용하여 우리나라의 장기적(長期的)인 산업성장(産業成長) 및 구조변화(構造變化)의 요인(要因)을 분석해 보고자 한다. 즉 과거(過去)의 장기적(長期的)인 성장과정(成長過程)에서 국내수요(國內需要), 수출(輸出), 수입대체(輸入代替)와 기술변화(技術變化)는 산업별 생산성장(生産成長)에 각각 얼마만큼 기여(寄與)했나 하는 것을 측정해 보고자 한다. 이러한 측정은 총량적(總量的) 경제수준(經濟水準)뿐만 아니라 세부산업별(細部産業別)로도 이루어질 수 있기 때문에 한국경제(韓國經濟) 전반(全般) 및 공업부문(工業部門)의 세부적(細部的) 변화과정(變化過程)에 관한 풍부한 분석자료(分析資料)를 제공할 것으로 기대한다.

• Journal of the Korean Wood Science and Technology
• /
• 제2권4호
• /
• pp.4-14
• /
• 1974

The total forest land area in Korea accounts for some 67 percent of the nation's land total. Its productivity, however, is very low. Consequently, forest production accounts for only about 2 percent of the gross national product and a minor proportion of no more than about 5 percent versus primary industry. In this case, however, only the direct income from forestry is taken into account, making no reference to the forestry output induced by other industrial sectors. The value added Or the induced forestry output in manufacturing the primary wood products into higher quality products, makes a larger contribution to the economy than direct contribution. So, this author has tried to analyze the structure of forestry and compute the repercussion effect and the induced output of primary forest products when utilized by other industries for their raw materials, Hsing the input-output table and attached tables for 1963 and 1966 issued by the Bank of Korea. 1. Analysis of forestry structure A. Changes in total output Durng the nine-year period, 1961-1969, the real gross national product in Korea increased 2.1 times, while that of primary industries went up about 1. 4 times. Forestry which was valued at 9,380 million won in 1961, was picked up about 2. 1 times to 20, 120 million won in 1969. The rate of the forestry income in the GNP, accordingly, was no more than 1.5 percent both in 1961 and 1962, whereas its rate in primary industries increased 3.5 to 5.4 percent. Such increase in forestry income is attributable to increased forest production and rise in timber prices. The rate of forestry income, nonetheless, was on the decrease on a gradual basis. B. Changes in input coefficient The input coefficient which indicates the inputs of the forest products into other sectors were up in general in 1966 over 1963. It is noted that the input coefficient indicating the amount of forest products supplied to such industries closely related with forestry as lumber and plywood, and wood products and furniture, showed a downward trend for the period 1963-1966. On the other hand, the forest input into other sectors was generally on the increase. Meanwhile, the input coefficient representing the yolume of the forest products supplied to the forestry sector itself showed an upward tendency, which meant more and more decrease in input from other sectors. Generally speaking, in direct proportion to the higher input coefficient in any industrial sector, the reinput coefficient which denotes the use of its products by the same sector becomes higher and higher. C. Changes in ratio of intermediate input The intermediate input ratio showing the dependency on raw materials went up to 15.43 percent m 1966 from 11. 37 percent in 1963. The dependency of forestry on raw materials was no more than 15.43 percent, accounting for a high 83.57 percent of value added. If the intermediate input ratio increases in any given sector, the input coefficient which represents the fe-use of its products by the same sector becomes large. D. Changes in the ratio of intermediate demand The ratio of the intermediate demand represents the characteristics of the intermediary production in each industry, the intermediate demand ratio in forestry which accunted for 69.7 percent in 1963 went up to 75.2 percent in 1966. In other words, forestry is a remarkable industry in that there is characteristics of the intermediary production. E. Changes in import coefficient The import coefficient which denotes the relation between the production activities and imports, recorded at 4.4 percent in 1963, decreased to 2.4 percent in 1966. The ratio of import to total output is not so high. F. Changes in market composition of imported goods One of the major imported goods in the forestry sector is lumber. The import value increased by 60 percent to 667 million won in 1966 from 407 million won in 1963. The sales of imported forest products to two major outlets-lumber and plywood, and wood products and furniture-increased to 343 million won and 31 million won in 1966 from 240million won and 30 million won in 1963 respectively. On the other hand, imported goods valued at 66 million won were sold to the paper products sector in 1963; however, no supply to this sector was recorded in 1963. Besides these major markets, primary industries such as the fishery, coal and agriculture sectors purchase materials from forestry. 2. Analysis of repercussion effect on production The repercussion effect of final demand in any given sector upon the expansion of the production of other sectors was analyzed, using the inverse matrix coefficient tables attached to the the I.O. Table. A. Changes in intra-sector transaction value of inverse matrix coefficient. The intra-sector transaction value of an inverse matrix coefficient represents the extent of an induced increase in the production of self-support products of the same sector, when it is generated directly and indirectly by one unit of final demand in any given sector. The intra-sector transaction value of the forestry sector rose from 1.04 in 1963 to 1, 11 in 1966. It may well be said, therefore, that forestry induces much more self-supporting products in the production of one unit of final demand for forest products. B. Changes in column total of inverse matrix coefficient It should be noted that the column total indicates the degree of effect of the output of the corresponding and related sectors generated by one unit of final demand in each sector. No changes in the column total of the forestry sector were recorded between the 1963 and 1966 figures, both being the same 1. 19. C. Changes in difference between column total and intra-sector transaction amount. The difference between the column total and intra-sector transaction amount by sector reveals the extent of effect of output of related industrial sector induced indirectly by one unit of final demand in corresponding sector. This change in forestry dropped remarkable to 0.08 in 1966 from 0.15 in 1963. Accordingly, the effect of inducement of indirect output of other forestry-related sectors has decreased; this is a really natural phenomenon, as compared with an increasing input coefficient generated by the re-use of forest products by the forestry sector. 3. Induced output of forestry A. Forest products, wood in particular, are supplied to other industries as their raw materials, increasng their value added. In this connection the primary dependency rate on forestry for 1963 and 1966 was compared, i. e., an increase or decrease in each sector, from 7.71 percent in 1963 to 11.91 percent in 1966 in agriculture, 10.32 to 6.11 in fishery, 16.24 to 19.90 in mining, 0.76 to 0.70 in the manufacturing sector and 2.79 to 4.77 percent in the construction sector. Generally speaking, on the average the dependency on forestry during the period 1963-1966 increased from 5.92 percent to 8.03 percent. Accordingly, it may easily be known that the primary forestry output induced by primary and secondary industries increased from 16, 109 million won in 1963 to 48, 842 million won in 1966. B. The forest products are supplied to other industries as their raw materials. The products are processed further into higher quality products. thus indirectly increasing the value of the forest products. The ratio of the increased value added or the secondary dependency on forestry for 1963 and 1966 showed an increase or decrease, from 5.98 percent to 7.87 percent in agriculture, 9.06 to 5.74 in fishery, 13.56 to 15.81 in mining, 0.68 to 0.61 in the manufacturing sector and 2.71 to 4.54 in the construction sector. The average ratio in this connection increased from 4.69 percent to 5.60 percent. In the meantime, the secondary forestry output induced by primary and secondary industries rose from 12,779 million Wall in 1963 to 34,084 million won in 1966. C. The dependency of tertiary industries on forestry showed very minor ratios of 0.46 percent and 0.04 percent in 1963 and 1966 respectively. The forestry output induced by tertiary industry also decreased from 685 million won to 123 million won during the same period. D. Generally speaking, the ratio of dependency on forestry increased from 17.68 percent in 1963 to 24.28 percent in 1966 in primary industries, from 4.69 percent to 5.70 percent in secondary industries, while, as mentioned above, the ratio in the case of tertiary industry decreased from 0.46 to 0.04 percent during the period 1963-66. The mining industry reveals the heaviest rate of dependency on forestry with 29.80 percent in 1963 and 35.71 percent in 1966. As it result, the direct forestry income, valued at 8,172 million won in 1963, shot up to 22,724 million won in 1966. Its composition ratio lo the national income rose from 1.9 percent in 1963 to 2.3 per cent in 1966. If the induced outcome is taken into account, the total forestry production which was estimated at 37,744 million won in 1963 picked up to 105,773 million won in 1966, about 4.5 times its direct income. It is further noted that the ratio of the gross forestry product to the gross national product. rose significantly from 8.8 percent in 1963 to 10.7 percent in 1966. E. In computing the above mentioned ratio not taken into consideration were such intangible, indirect effects as the drought and flood prevention, check of soil run-off, watershed and land conservation, improvement of the people's recreational and emotional living, and maintenance and increase in the national health and sanitation. F. In conclusion, I would like to emphasize that the forestry sector exercices an important effect upon the national economy and that the effect of induced forestry output is greater than its direct income.