• Korean Journal of Heritage: History & Science
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• v.54 no.4
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• pp.152-173
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• 2021

The Large Banner was introduced during the Japanese Invasions of Korea with a new military system. It was a flag that controlled the movement of soldiers in military training. In addition, it was used in other ways, such as a symbol when receiving a king in a military camp, a flag raised on the front of a royal procession, at the reception and dispatch of envoys, and at a local official's procession. The Large Banner was recognized as a symbol of military dignity and training rites. The Large Banner was analyzed in the present study in the context of two different types of decorations. Type I includes chungdogi, gakgi and moongi. Type II includes grand, medium, and small obangi, geumgogi and pyomigi. Each type is decorated differently for each purpose. The size of the flag is estimated to be a square of over 4 ja long in length. Flame edges were attached to one side and run up and down The Large Banner used the Five Direction Colors based on the traditional principles of Yin-Yang and Five Elements. The pattern of the Large Banner is largely distinguished by four. The pattern of large obangi consists of divine beasts symbolizing the Five Directions and a Taoism amulet letter. The pattern of medium obangi features spiritual generals that escort the Five Directions. The pattern of small obangi has the Eight Trigrams. The pattern of moongi consists of a tiger with wings that keeps a tight watch on the army's doors. As for historical sources of coloring for Large Banner production, the color-written copy named Gije, from the collection of the Osaka Prefect Library, was confirmed as the style of the Yongho Camp in the mid to late 18th century, and it was also used for this essay and visualization work. We used Cloud-patterned Satin Damask as the background material for Large Banner production, to reveal the dignity of the military. The size of the 4 ja flag was determined to be 170 cm long and 145 cm wide, and the 5 ja flag was 200 cm long and 175 cm wide. The conversion formula used for this work was Youngjochuck (1 ja =30cm). In addition, the order of hierarchy in the Flag of the King was discovered within all flags of the late Joseon Dynasty. In the above historical study, the two types of Large Banner were visualized. The visualization considered the size of the flag, the decoration of the flagpole, and the patterns described in this essay to restore them to their original shape laid out the 18th century relics on the background. By presenting color, size, material patterns, and auxiliary items together, it was possible not only to produce 3D content, but also to produce real products.

• Journal of the Korean Society of International Agriculture
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• v.23 no.5
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• pp.546-551
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• 2011

Chrysanthemums, often called mums or chrysanths, belong to the genus Chrysanthemum, which includes about 30 species of perennial flowering plants in the family Asteraceae. We extracted DNA from Dendranthema grandiflorum ('Smileball') to construct a simple sequence repeat (SSR)-enriched library, using a modified biotin-streptavidin capture method. GS FLX (Genome Sequencer FLX System which provides the flexibility to perform the broad range of applications) sequencing (at the 1/8 run specification) resulted in 18.83 mega base pairs (Mbp) with an average read length of 280.06 bp. Sequence analyses of all SSR-containing clones revealed a predominance of di-nucleotide motifs (16,375, 61.5%) followed by tri-nucleotide motifs (6,616, 24.8%), tetra-nucleotide motifs (1,674, 6.3%), penta-nucleotide motifs (1,283, 4.8%), and hexa-nucleotide motifs (693, 2.6%). Among the di-nucleotide motifs, the AC/CA class was the most frequently identified (93.5% of all di-nucleotide types), followed by the GA/AG class (6.1%), the AT/TA class (0.4%), and the CG/GC class (0.03%). When we analyzed the distribution of different repeat motifs and their respective numbers of repeats, regardless of the motif class, of 100 SSR markers, we found a higher number of di-nucleotide motifs with 70 to 80 repeats; we also found two di-nucleotide motifs with 83 and 89 repeats, respectively, but their product lengths were within optimum size (297 and 300 bp). In future work, we will screen for polymorphisms of possible primer pairs. The results will provide a useful tool for assessing molecular diversity and investigating the population structure among and within Chrysanthemum species.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.185-196
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• 2023

Purpose This study aimed to investigate radiomics analysis of ultrasonographic images to develop a potential biomarker for predicting lymph node metastasis in papillary thyroid carcinoma (PTC) patients. Materials and Methods This study included 431 PTC patients from August 2013 to May 2014 and classified them into the training and validation sets. A total of 730 radiomics features, including texture matrices of gray-level co-occurrence matrix and gray-level run-length matrix and single-level discrete two-dimensional wavelet transform and other functions, were obtained. The least absolute shrinkage and selection operator method was used for selecting the most predictive features in the training data set. Results Lymph node metastasis was associated with the radiomics score (p < 0.001). It was also associated with other clinical variables such as young age (p = 0.007) and large tumor size (p = 0.007). The area under the receiver operating characteristic curve was 0.687 (95% confidence interval: 0.616-0.759) for the training set and 0.650 (95% confidence interval: 0.575-0.726) for the validation set. Conclusion This study showed the potential of ultrasonography-based radiomics to predict cervical lymph node metastasis in patients with PTC; thus, ultrasonography-based radiomics can act as a biomarker for PTC.

• Korean Journal of Agricultural Science
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• v.16 no.1
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• pp.84-101
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• 1989

An accurate measurement of size, surface area and volume of agricultural products is essential in many engineering operations such as handling and sorting, and in heat transfer studies on heating and cooling processes. Little information is available on these properties due to their irregular shape, and moreover very little information on the rough rice, soybean, barley, and wheat has been published. Physical dimensions of grain, such as length, width, thickness, surface area, and volume vary according to the variety, environmental conditions, temperature, and moisture content. Especially, recent research has emphasized on the variation of these properties with the important factors such as moisture content. The objectives of this study were to determine physical dimensions such as length, width and thickness, surface area and volume of the rough rice, soybean, barley, and wheat as a function of moisture content, to investigate the effect of moisture content on the properties, and to develop exponential equations to predict the surface area and the volume of the grains as a function of physical dimensions. The varieties of the rough rice used in this study were Akibare, Milyang 15, Seomjin, Samkang, Chilseong, and Yongmun, as a soybean sample Jangyeobkong and Hwangkeumkong, as a barley sample Olbori and Salbori, and as a wheat sample Eunpa and Guru were selected, respectively. The physical properties of the grain samples were determined at four levels of moisture content and ten or fifteen replications were run at each moisture content level and each variety. The results of this study are summarized as follows; 1. In comparison of the surface area and the volume of the 0.0375m diameter-sphere measured in this study with the calculated values by the formula the percent error between them showed least values of 0.65% and 0.77% at the rotational degree interval of 15 degree respectively. 2. The statistical test(t-test) results of the physical properties between the types of rough rice, and between the varieties of soybean and wheat indicated that there were significant difference at the 5% level between them. 3. The physical dimensions varied linearly with the moisture content, and the ratios of length to thickness (L/T) and of width to thickness (W/T) in rough rice decreased with increase of moisture content, while increased in soybean, but uniform tendency of the ratios in barley and wheat was not shown. In all of the sample grains except Olbori, sphericity decreased with increase of moisture content. 4. Over the experimental moisture levels, the surface area and the volume were in the ranges of about $45{\sim}51{\times}10^{-6}m^2$, $25{\sim}30{\times}10^{-9}m^3$ for Japonica-type rough rice, about $42{\sim}47{\times}10^{-6}m^2$, $21{\sim}26{\times}10^{-9}m^3$ for Indica${\times}$Japonica type rough rice, about $188{\sim}200{\times}10^{-6}m^2$, $277{\sim}300{\times}10^{-9}m^3$ for Jangyeobkong, about $180{\sim}201{\times}10^{-6}m^2$, $190{\sim}253{\times}10^{-9}m^3$ for Hwangkeumkong, about $60{\sim}69{\times}10^{-6}m^2$, $36{\sim}45{\times}10^{-9}m^3$ for Covered barley, about $47{\sim}60{\times}10^{-6}m^2$, $22{\sim}28{\times}10^{-9}m^3$ for Naked barley, about $51{\sim}20{\times}10^{-6}m^2$, $23{\sim}31{\times}10^{-9}m^3$ for Eunpamill, and about $57{\sim}69{\times}10^{-6}m^2$, $27{\sim}34{\times}10^{-9}m^3$ for Gurumill, respectively. 5. The increasing rate of surface area and volume with increase of moisture content was higher in soybean than other sample grains, and that of Japonica-type was slightly higher than Indica${\times}$Japonica type in rough rice. 6. The regression equations of physical dimensions, surface area and volume were developed as a function of moisture content, the exponential equations of surface area and volume were also developed as a function of physical dimensions, and the regression equations of surface area were also developed as a function of volume in all grain samples.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.3
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• pp.26-36
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• 2018

This study aims to examine the characteristics of landscape through the analysis of location and the landscape of Gugok while also conducting the empirical study through the literature review, field study, and digital analysis of the Okgung Gugok. Oksan Gugok is a set of songs set in Ogsan Creek(玉山川)or Jagyese Creek(紫溪川, 紫玉山), which flows in front of the Oksan Memorial Hall(李彦迪), which is dedicated to the Lee Eong-jeok (李彦迪). We first ascertained the location and configuration of Oksan Gogok. Second, we confirmed the accurate location of Oksan Gogok by utilizing the digital topographic map of Oksan Gogok which was submitted by Google Earth Pro and Geographic Information Center as well as the length of the longitude of the gravel measured by the Trimble Juno SB GPS. Through the study of the literature and the field investigation, The results of the study are as follows. First, Yi Eonjeok was not a direct composer of Oksan Gugok, nor did he produce "Oksan Gugokha(Music)". Lee Ia-sung(李野淳), the ninth Youngest Son of Tweo-Kye, Hwang Lee, visited the "Oksan Gugokha" in the spring of 1823(Sunjo 23), which was the 270th years after the reign of Yi Eonjeok. At this time, receiving the proposal of Ian Sung, Lee Jung-eom(李鼎儼), Lee Jung-gi(李鼎基), and Lee Jung-byeong(李鼎秉), the descendants of Ian Sung set up a song and created Oksan Gugok Music. And the Essay of Oksan Travel Companions writted by Lee Jung-gi turns out being a crucial data to describe the situation when setting up the Ok-San Gugok. Second, In the majority of cases, Gogok Forest is a forest managed by a Confucian Scholar, not run by ordinary people. The creation of "Oksan Bugok Music" can be regarded as an expression of pride that the descendants of Yi Eonjeok and Lee Hwang, and next generation of several Confucian scholars had inherited traditional Neo-Confucian. Third, Lee Jung-eom's "Oksan Donghaengki" contains a detailed description of the "Oksan Gugokha" process and the process of creating a song. Fourth, We examined the location of one to nine Oksan songs again. In particular, eight songs and nine songs were located at irregular intervals, and eight songs were identified as $36^{\circ}01^{\prime}08.60^{{\prime}{\prime}}N$, $129^{\circ}09^{\prime}31.20^{{\prime}{\prime}}E$. Referring to the ancient kingdom of Taojam, the nine-stringed Sainam was unbiased as a lower rock where the two valleys of the East West congregate. The location was estimated at $36^{\circ}01^{\prime}19.79^{{\prime}{\prime}}N$, $129^{\circ}09^{\prime}30.26^{{\prime}{\prime}}E$. Fifth, The landscape elements and landscapes presented in Lee Jung-eom's "Oksan Gugokha" were divided into form, semantic and climatic elements. As a result, Lee Jung-eom's Cho Young-gwan was able to see the ideal of mountain water and the feeling of being idle in nature as well as the sense of freedom. Sixth, After examining the appearance of the elements and the frequency of the appearance of the landscape, 'water' and 'mountain' were the absolute factors that emphasized the original curved environment at the mouth of Lee Jung-eom. Therefore, there was gugokga can gauge the fresh ideas(神仙思想)and retreat ever(隱居思想). This inherent harmony between the landscape as well as through the mulah any ideas that one with nature and meditation, Confucian tube.

• Magazine of the Korean Society of Agricultural Engineers
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• v.7 no.1
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• pp.861-876
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• 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.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 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.