한국 경제에서 창업·벤처기업의 중요성이 커지고 있다. 이 연구는 창업·벤처기업의 성장을 포함하여 창업·벤처 생태계가 성장하고 있는지 측정하였다. 창업·벤처 생태계는'생태계'의 주요 행위자인 창업·벤처기업, 투자기관, 정부로 구성하고, 이들의 주요 활동을 정량적 지표 25개로 측정하였다. 창업·벤처 생태계 지수는 25개 지표의 2010~2018년의 시계열 원자료를 토대로 종합주가지수 방식과 AHP를 통한 가중치를 적용하여 산출하였다. 2018년 창업·벤처 생태계는 2010년에 비해 2.1배 성장하였으며, 정부 지수의 증가가 성장에 큰 영향을 미쳤다. 2018년 각각의 지수를 구성하는 개별지표를 보면, 기업 지수는 천억 벤처기업의 수, 투자 지수는 회수금액, 정부 지수는 모태펀드 출자금액이 성장에 가장 큰 영향을 주었다. 원자료를 토대로 창업·벤처 생태계 지수를 생태계별(창업생태계와 벤처생태계), 업종별(전업종과 제조업), 지역별(전국과 부산)로 구분하여 분석하였다. 그 결과, 지난 8년간 창업생태계의 성장이 벤처생태계의 성장 보다 근소한 차이로 컸다. 제조업 창업·벤처 생태계는 전업종 보다 낮게 나타났으며, 예시로 살펴본 부산의 창업·벤처 생태계 지수는 전국 보다 낮게 나타났다. 이 연구는 창업·벤처 생태계 지수를 개발 및 측정하여 모니터링 함으로써 지원 정책의 수립 및 시행에 활용하고자 했다. 이 지수는 주요 행위자 간의 상호관계를 파악해 볼 수 있으며, 공식적인 통계조사 결과를 활용하여 누구라도 지수를 산출할 수 있는 장점이 있다. 향후에도 지수를 지속적으로 모니터링하여 경제사회적 사건이나 정책적 지원이 창업·벤처 생태계에 어떤 영향을 미쳤는지 파악할 필요가 있다.
본 연구는 산림을 기반으로 서식하는 야생조류의 효율적인 조사시간을 계절별로 제시하기 위하여 남해군 삼동면 물건리 방조어부림을 대상으로 수행하였다. 적합성을 규명하기 위해 대상지 및 일대의 토지이용현황, 현존식생, 식생구조 등을 파악하였으며, 산림성 야생조류의 적정 조사시간을 제안하고자 계절별 3일간 일출 몰 시간을 기준하여 1시간 간격으로 반복 조사하였다. 방조어부림은 주변 산림과 연결되어 산림성 야생조류의 유입이 가능하고 숲 자체의 층위구조 발달, 대경목의 느티나무와 푸조나무가 분포하는 등 자연림과 유사하여 다양한 서식처 및 채이장소를 제공할 수 있어 야생조류를 연구하기에 적합한 장소이다. 관찰된 105종을 유형화하여 산새류를 구분하였고 각 계절별로 시간대별 최고값을 선정한 후 총 출현 종수 및 개체수에 대해 시간대별 종풍부도, 종다양도, 유사도지수를 분석하였다. 그 결과 봄철에는 8~9시까지가 최적의 조사시간이었고 여름철에는 전체 출현종과의 종구성이 유사한 6~9시의 시간대가 적정 조사시간이었다. 가을철에는 일출 후 30~60분 이후부터인 오전 7시부터 11시까지의 시간대에 야생조류의 움직임이 활발하여 관찰이 용이한 것으로 분석되었으며 그 중 8~9시에 종풍부도가 가장 높아 최적의 조사시간이었다. 겨울철은 7~12시의 시간대가 효율적이며 10~11시가 최적 시간이나 일몰 전 1시간을 제외하면 시간대별 편차가 크지 않아 유사한 결과를 도출할 수 있을 것이다. 4계절 모두 일출 후 30~60분 후부터 조사를 실시하는 것이 대상지역의 야생조류 군집을 파악하기에 바람직한 것으로 판단되었다.
고성오광대 제1과장 문둥북춤은 양반의 자손으로서 조상들의 죄업으로 문둥병에 걸려 고통으로 괴로워하다 신명을 통해 극복한다는 내용을 대사 없이 춤으로만 표현한다. 고성오광대 문둥북춤의 문화재 지정 이후 현재까지 변화양상을 살펴본 결과 시간이 지남에 따라 문둥북춤의 춤사위가 더 많아지고 동작이 구체화된 것을 확인할 수 있었다. 1965년 문둥북춤 춤사위부터 2000년 문둥북춤까지 춤사위가 5개에서 20개로 확장되고 이전보다 구체화된 것을 확인할 수 있었다. 또한 문둥북춤에서 사용하는 소도구 북의 표현하는 방식이 65년의 경우 북을 치고 어르는 춤사위만 있었다면, 1988년의 경우 현재와 같이 북과 북채를 무대에 두고 등장하고 춤사위도 14개로 늘어난 것을 확인할 수 있었다. 2000년 문둥북춤의 경우 문둥이가 북을 잡고 난 이후부터 신명나게 춤을 추며 자진모리장단으로 발전되는 형태는 이전보다 춤사위가 더 다양해지고 이야기 구조를 좀 더 명확하게 표현하는데 사용된 것을 알 수 있었다. 마지막으로 고성오광대의 대표적인 춤사위 배김새가 1965년엔 보이지 않고 1969년엔 한 방향으로만 배김사위를 하는데 1988년과 2000년 문둥북춤에서 보여지는 배김사위는 좌우로 대칭하여 한 번씩 연행해 현재의 배김사위와 같은 것을 알 수 있었다.
오일샌드는 비재래형(unconventional) 석유자원의 하나로서 비투멘(bitumen), 물, 점토, 모래의 혼합물이다. 오일샌드 비투멘은 API 비중이
지난 10여 년간 딥러닝(Deep Learning)은 다양한 기계학습 알고리즘 중에서 많은 주목을 받아 왔다. 특히 이미지를 인식하고 분류하는데 효과적인 알고리즘으로 알려져 있는 합성곱 신경망(Convolutional Neural Network, CNN)은 여러 분야의 분류 및 예측 문제에 널리 응용되고 있다. 본 연구에서는 기계학습 연구에서 가장 어려운 예측 문제 중 하나인 주식시장 예측에 합성곱 신경망을 적용하고자 한다. 구체적으로 본 연구에서는 그래프를 입력값으로 사용하여 주식시장의 방향(상승 또는 하락)을 예측하는 이진분류기로써 합성곱 신경망을 적용하였다. 이는 그래프를 보고 주가지수가 오를 것인지 내릴 것인지에 대해 경향을 예측하는 이른바 기술적 분석가를 모방하는 기계학습 알고리즘을 개발하는 과제라 할 수 있다. 본 연구는 크게 다음의 네 단계로 수행된다. 첫 번째 단계에서는 데이터 세트를 5일 단위로 나눈다. 두 번째 단계에서는 5일 단위로 나눈 데이터에 대하여 그래프를 만든다. 세 번째 단계에서는 이전 단계에서 생성된 그래프를 사용하여 학습용과 검증용 데이터 세트를 나누고 합성곱 신경망 분류기를 학습시킨다. 네 번째 단계에서는 검증용 데이터 세트를 사용하여 다른 분류 모형들과 성과를 비교한다. 제안한 모델의 유효성을 검증하기 위해 2009년 1월부터 2017년 2월까지의 약 8년간의 KOSPI200 데이터 2,026건의 실험 데이터를 사용하였다. 실험 데이터 세트는 CCI, 모멘텀, ROC 등 한국 주식시장에서 사용하는 대표적인 기술지표 12개로 구성되었다. 결과적으로 실험 데이터 세트에 합성곱 신경망 알고리즘을 적용하였을 때 로지스틱회귀모형, 단일계층신경망, SVM과 비교하여 제안모형인 CNN이 통계적으로 유의한 수준의 예측 정확도를 나타냈다.
본 연구에서는 경호, 경비현장에서 이루어지는 대치상황에서 경호, 경비원의 장비 중 삼단봉을 선택하여 머리치기 동작의 운동학적 분석을 실시하였다. H대학교 경호학과 학생 출신10명을 대상으로 연구를 실시하였다. 숙련자 그룹에서 삼단봉 교육 프로그램 실시하였고, 숙련자그룹과 비숙련자 그룹으로 연구 하였다. 구간별 소요시간은 숙련자 (e2) 0.428sec 비숙련자 0.435 sec, (e3) 에서는 숙련지 0.230 sec 비숙련자 0.232 sec 소요되었으며, 비숙련자 그룹에서 소요시간이 많이 소요된 것으로 나타났다. 신체중심 이동 변위는 좌, 우 숙련자
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
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.