• 농업과학연구
• /
• 제16권1호
• /
• pp.84-101
• /
• 1989

본(本) 연구(硏究)의 공시(供試) 곡물(穀物)로서 벼 6 품종(品種)(Japonica 3 품종(品種), Indica ${\times}$ Japonica 3 품종(品種)), 콩 2 품종(品種), 밀 2 품종(品種), 보리 2 품종(品種)을 택(澤)하였다. 함수율(含水率)은 약(約) 13%~27%(w.b.)까지 4 수준(水準)으로 변화(變化)시키면서 곡립(穀粒)의 크기, 표면적(表面積) 및 체적(體積)을 각(各) 품종(品種) 및 함수율(含水率) 각(各) 수준(水準)에서 10~15반부(反復)으로 측정(測定)하여 함수율(含水率) 변화(變化)가 이들에 미치는 영향(影響)을 분석(分析)하고, 곡립(穀粒)의 주요(主要) 치수와 체적(體積) 및 표면적(表面積)과의 상호관계(相互關係), 체적(體積)을 인자(因子)로 하는 표면적(表面積)의 예측식(豫測式)에 관(關)한 연구(硏究) 결과(結果)를 요약(要約)하면 다음과 같다. 1. 본(本) 연구(硏究)에서 적용(適用)한 표면적(表面積) 및 체적(體積)의 측정(測定) 방법(方法)을 검정(檢定)하기 위(爲)하여 이론적(理論的)으로 계산(計算)이 가능(可能)한 0.0375m인 탁구(卓球)공으로 그 표면적(表面積)과 체적(體積)을 측정(測定)했던 결과, 회전각(回轉角) 증분(增分)을 $15^{\circ}$로 했을때 공식(公式)에 의한 계산치(計算値)와의 오차(誤差)가 각각(各各) 0.65% 및 0.77% 이었다. 2. 벼의 일반계(一般系)와 다수계(多收系) 사이와 콩, 밀의 품종간(品種間)에 본(本) 연구(硏究)에서 대상(對象)으로한 물리량(物理量)들에 대(對)하여 t-test한 결과(結果), 벼의 두 계통(系統)사이와 콩, 밀의 품종간(品種間)에는 5%의 유의수준(有意水準)에서 그 차이(差異)가 인정(認定)되었다. 3. 곡립(穀粒)의 길이, 폭, 두께는 함수율(含水率)의 증가(增加)에 따라 직선적(直線的)으로 증가(增加)하였으며, 길이와 두께의 비(比)(L/T)와 폭과 두께의 비(比)(W/T)는 벼의 모든 품종(品種)에서 함수율(含水率)의 증가(增加)에 따라 감소(減少)하는 반면(反面), 콩에서는 모두 증가(增加)했다. 그러나 밀, 보리에서는 품종(品種)에 따라 일률적(一律的)인 경향(傾向)이 나타나지 않았다. 4. 공시(供試) 곡립(穀粒)의 표면적(表面積)은 일반계(一般系) 벼 약(約) $45{\sim}51{\times}10^{-6}m^2$, 다수계(多收系) 벼 $42{\sim}47{\times}10^{-6}m^2$, 장엽콩 약(約) $188{\sim}200{\times}10^{-6}m^2$, 황금콩 약(約) $180{\sim}201{\times}10^{-6}m^2$, 대맥(大麥) 약(約) $60{\sim}69{\times}10^{-6}m^2$, 나맥(裸麥) 약(約) $47{\sim}60{\times}10^{-6}m^2$, 은파밀 약(約) $51{\sim}20{\times}10^{-6}m^2$, 그루밀은 약(約) $57{\sim}69{\times}10^{-6}m^2$ 이었으며, 체적(體積)은 일반계(一般系) 벼 약(約) $25{\sim}30{\times}10^{-9}m^3$ 다수계(多收系) 벼 약(約) $21{\sim}26{\times}10^{-9}m^3$, 장엽콩 약(約) $277{\sim}300{\times}10^{-9}m^3$, 황금콩 약(約) $190{\sim}253{\times}10^{-9}m^3$, 대맥(大麥) 약(約) $36{\sim}45{\times}10^{-9}m^3$, 나맥(裸麥) 약(約) $22{\sim}28{\times}10^{-9}m^3$, 은파일 약(約) $23{\sim}31{\times}10^{-9}m^3$, 그루밀 약(約) $27{\sim}34{\times}10^{-9}m^3$이었다. 5. 함수율(含水率)에 따른 표면적(表面積) 및 체적(體積)의 증가율(增加率)은 콩이 가장 컸고, 다음은 밀, 보리, 벼 순(順) 이였으며, 벼에서는 일반계(一般系) 벼가 다수계(多收系) 벼 보다 약간(若干) 높게 나타났다. 6. 함수율(含水率) 변화(變化)에 따른 곡립(穀粒)의 크기(결이, 폭, 두께), 표면적(表面積) 및 체적(體積)에 대한 1차(次) 회귀(回歸) 방정식(方程式), 곡립(穀粒)의 길이, 폭, 두께를 인자(因子)로 하는 표면적(表面積) 및 체적(體積)에 대한 지수(指數) 방정식(方程式)과 곡립(穀粒)의 체적(體積)을 인자(因子)로 하는 표면적(表面積)의 회귀(回歸) 방정식(方程式)을 공시(供試) 곡물(穀物) 및 품종별(品種別)로 각각(各各) 유도(誘導)하였다.

• 한국전통조경학회지
• /
• 제36권3호
• /
• pp.26-36
• /
• 2018

본 논문은 경주 옥산구곡의 위치와 경관 해석에 대한 연구이다. 옥산구곡은 회재(晦齋) 이언적(李彦迪)을 향사하는 옥산서원 앞을 흐르는 자계천(紫溪川, 자옥천(紫玉山)) 즉, 옥산천(玉山川)에 설정(設定)된 구곡으로 본 연구에서는 옥산구곡을 대상으로 문헌조사와 디지털 기기 분석을 통해 현장 실측분석을 수행하여 옥산구곡의 위치 및 설정상황을 확인하였다. 특히 Trimble Juno SB GPS로 측정한 구곡의 경위도와 같이 Google Earth Pro 및 지리정보원이 공개한 옥산구곡의 수치지형도를 이용해서 옥산구곡의 정확한 위치를 확정하였다. 문헌연구 및 현장조사를 통하여, 옥산구곡의 위치 비정 및 경관 해석 결과는 다음과 같다. 첫째, 퇴계 이황의 9세손 이야순(李野淳)은 이언적 사후 270년인 1823년 봄에 옥산서원을 방문하였다. 이때 이야순의 제안으로 이언적의 후손 이정엄(李鼎儼), 이정기(李鼎基), 이정병(李鼎秉) 등과 여러 선비들이 함께 옥산구곡을 처음 설정하고 함께 "옥산구곡가"를 창작했다. 이정엄의 "옥산동행기"는 옥산구곡의 설정할 때의 상황을 알려주는 결정적 자료이다. 둘째, 대부분의 구곡원림은 일반적인 시인묵객이 경영한 원림이 아니고 정통 성리학자들이 경영한 원림이다. 이언적과 이황의 후손이 "옥산구곡가"를 함께 창작한 것은 이언적이나 이황이 정통 주자학을 계승했다는 점에 대한 후손들의 자긍심의 한 표현이다. 셋째, 이정엄의 "옥산동행기"에는 "옥산구곡가"의 설곡 과정과 구곡가 창작 과정은 매우 구체적으로 기록되어 있는데, 구곡가의 설곡과정과 그때의 상황이 이처럼 구체적으로 드러난 것은 희귀한 사례라고 할 것이다. 넷째, 기존에 알려진 옥산구곡 제8곡과 제9곡의 위치를 새롭게 비정하였다. 확정된 제8곡 탁영대의 위치정보는 북위 $36^{\circ}01^{\prime}08.60^{{\prime}{\prime}}$,동경 $129^{\circ}09^{\prime}31.20^{{\prime}{\prime}}$이다. 9곡 사자암은 고문헌을 참고하여, 동서의 두 계곡이 모이는 아래쪽 바위로 비정하였다. 그 위치는 북위 $36^{\circ}01^{\prime}19.79^{{\prime}{\prime}}$, 동경 $129^{\circ}09^{\prime}30.26^{{\prime}{\prime}}$이다. 다섯째, 이정엄의 "옥산구곡가"에 나타난 경관요소와 경관현상은 형태요소, 의미요소, 풍토요소로 나누었다. 그 결과 이정엄의 조영관은 산수를 이상향으로 생각하는 점과 자연에 한가하게 노니는 심정과 아울러 무상감을 확인할 수 있었다. 여섯째, 경관요소와 경관현상들의 출현빈도를 살펴본 결과, 이정엄의 구곡가에서'물'과 '산'은 구곡원림을 조영하는 절대적인 요소였다. 따라서 이 구곡가에는 신선사상(神仙思想) 및 은거사상(隱居思想)이 내재되어 있는 것은 물론 산수간의 조화를 통해 자연과 하나가 되는 물아일체의 사상과 성리학적인 수행관을 살필 수 있었다.

• 한국농공학회지
• /
• 제7권1호
• /
• pp.861-876
• /
• 1965

During my stay in the Netherlands, I have studied the following, primarily in relation to the Mokpo Yong-san project which had been studied by the NEDECO for a feasibility report. 1. Unit hydrograph at Naju There are many ways to make unit hydrograph, but I want explain here to make unit hydrograph from the- actual run of curve at Naju. A discharge curve made from one rain storm depends on rainfall intensity per houre After finriing hydrograph every two hours, we will get two-hour unit hydrograph to devide each ordinate of the two-hour hydrograph by the rainfall intensity. I have used one storm from June 24 to June 26, 1963, recording a rainfall intensity of average 9. 4 mm per hour for 12 hours. If several rain gage stations had already been established in the catchment area. above Naju prior to this storm, I could have gathered accurate data on rainfall intensity throughout the catchment area. As it was, I used I the automatic rain gage record of the Mokpo I moteorological station to determine the rainfall lntensity. In order. to develop the unit ~Ydrograph at Naju, I subtracted the basic flow from the total runoff flow. I also tried to keed the difference between the calculated discharge amount and the measured discharge less than 1O~ The discharge period. of an unit graph depends on the length of the catchment area. 2. Determination of sluice dimension Acoording to principles of design presently used in our country, a one-day storm with a frequency of 20 years must be discharged in 8 hours. These design criteria are not adequate, and several dams have washed out in the past years. The design of the spillway and sluice dimensions must be based on the maximun peak discharge flowing into the reservoir to avoid crop and structure damages. The total flow into the reservoir is the summation of flow described by the Mokpo hydrograph, the basic flow from all the catchment areas and the rainfall on the reservoir area. To calculate the amount of water discharged through the sluiceCper half hour), the average head during that interval must be known. This can be calculated from the known water level outside the sluiceCdetermined by the tide) and from an estimated water level inside the reservoir at the end of each time interval. The total amount of water discharged through the sluice can be calculated from this average head, the time interval and the cross-sectional area of' the sluice. From the inflow into the .reservoir and the outflow through the sluice gates I calculated the change in the volume of water stored in the reservoir at half-hour intervals. From the stored volume of water and the known storage capacity of the reservoir, I was able to calculate the water level in the reservoir. The Calculated water level in the reservoir must be the same as the estimated water level. Mean stand tide will be adequate to use for determining the sluice dimension because spring tide is worse case and neap tide is best condition for the I result of the calculatio 3. Tidal computation for determination of the closure curve. During the construction of a dam, whether by building up of a succession of horizontael layers or by building in from both sides, the velocity of the water flowinii through the closing gapwill increase, because of the gradual decrease in the cross sectional area of the gap. 1 calculated the . velocities in the closing gap during flood and ebb for the first mentioned method of construction until the cross-sectional area has been reduced to about 25% of the original area, the change in tidal movement within the reservoir being negligible. Up to that point, the increase of the velocity is more or less hyperbolic. During the closing of the last 25 % of the gap, less water can flow out of the reservoir. This causes a rise of the mean water level of the reservoir. The difference in hydraulic head is then no longer negligible and must be taken into account. When, during the course of construction. the submerged weir become a free weir the critical flow occurs. The critical flow is that point, during either ebb or flood, at which the velocity reaches a maximum. When the dam is raised further. the velocity decreases because of the decrease\ulcorner in the height of the water above the weir. The calculation of the currents and velocities for a stage in the closure of the final gap is done in the following manner; Using an average tide with a neglible daily quantity, I estimated the water level on the pustream side of. the dam (inner water level). I determined the current through the gap for each hour by multiplying the storage area by the increment of the rise in water level. The velocity at a given moment can be determined from the calcalated current in m3/sec, and the cross-sectional area at that moment. At the same time from the difference between inner water level and tidal level (outer water level) the velocity can be calculated with the formula $h= \frac{V^2}{2g}$ and must be equal to the velocity detertnined from the current. If there is a difference in velocity, a new estimate of the inner water level must be made and entire procedure should be repeated. When the higher water level is equal to or more than 2/3 times the difference between the lower water level and the crest of the dam, we speak of a "free weir." The flow over the weir is then dependent upon the higher water level and not on the difference between high and low water levels. When the weir is "submerged", that is, the higher water level is less than 2/3 times the difference between the lower water and the crest of the dam, the difference between the high and low levels being decisive. The free weir normally occurs first during ebb, and is due to. the fact that mean level in the estuary is higher than the mean level of . the tide in building dams with barges the maximum velocity in the closing gap may not be more than 3m/sec. As the maximum velocities are higher than this limit we must use other construction methods in closing the gap. This can be done by dump-cars from each side or by using a cable way.e or by using a cable way.

• 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.