• 농업과학연구
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• 제27권1호
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• pp.25-32
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• 2000

Monensin의 첨가 급여가 한우의 육생산 효율 및 제1위내 발효성상에 미치는 효과를 구명하고자 본 실험을 실시하였다. 거세우 72두(평균체중 266.9kg)를 0, 22, 및 33 ppm의 monensm 처리구에 처리당 3반복, 반복당 8두씩 체중이 비슷하도록 배치하여 옥외 사육장에서 140일간 사양시험을 실시하였다. Monensin은 농후사료에 0, 22, 및 33 ppm 수준으로 첨가하였으며, 농후사료와 조사료(15cm 정도로 절단한 볏짚)는 자유채식 시켰다. 본 실험에서 얻어진 결과는 다음과 같다. 1. 일당증체량은 각 처리구 간에 유의한 차이를 나타내지 않았다. 2. 총사료(농후사료+조사료)섭취량은 처리구간에 유의성은 없었으나 monensin 급여수준의 증가와 함께 감소하는 경향이어서 33ppm구에서 약 5% 의 감소를 나타내었다. 3. 총사료효율(총사료섭취량/증체량)에 있어서는 처리구간에 유의성은 없었으나 0ppm 구에 비하여 22ppm구와 33ppm구가 각각 5.2%와 5.1%의 거의 통일한 개선을 나타냈다. 4. 제1위내 total VFA와 acetic acid의 농도는 monensin에 의한 일정한 경향이 없었고, propionic acid 농도는 처리간에 유의성은 없었으나 무첨가구에 비하여 22 및 33ppm 구에서 각각 13.8%와 19.3%의 증가를 보였다. Butyric acid 농도는 22ppm구에서도 감소하는 경향이었지만, 33ppm구에서는 유의한 ($p{\leq}0.054$) 감소가 인정되었다. A/P비는 monensin 무첨가구에 비하여 22ppm구와 33ppm구에서 각각 12.5 %의 감소를 나타내었다. 결론적으로 monensin은 propionic acid 의 증가와 butyric acid 의 감소 및 A/P비의 감소를 가져오는 효과와 함께 사료섭취량을 감소시켜서 비육우의 사료효율을 개선하는 효과가 있었다. 따라서 monensin은 축우의 비육시 육생산 효율을 높이기 위한 사료첨가제로서 충분한 사용가치가 있다고 사료되며, 22ppm과 33ppm 수준이 거의 통일한 사료효율을 보인 것으로 보아 22ppm 수준에서도 기대하는 효과를 얻을 수 있을 것으로 생각된다.

• 한국농림기상학회지
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• 제18권4호
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• pp.233-241
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• 2016

기상청에서 제공하는 넓은 지역의 수평면 일조시간 정보를 복잡한 산간집수역의 지형특성을 반영한 실제 일조시간 분포도로 변환하기 위해 지형효과를 정량화하기 위한 실험을 수행하였다. 경남 하동군 악양면 단일 집수역을 대상으로 정밀 DEM을 이용하여 그림자모형화 및 공제선분석 기법을 적용하여 일중 시간대별 음영기복도 1년 자료를 제작하였다. 2015년 5월 15일부터 2016년 5월 14일까지 1년 간 지형조건이 서로 다른 3지점에서 바이메탈식 일조계로 측정한 일조시간자료에 음영기복도 상 해당 지점의 휘도값을 추출하여 회귀시킴으로써 맑은 날의 휘도-일조시간 반응곡선을 얻었다. 이 곡선식을 하늘상태(운량)에 따라 보정할 수 있는 방법을 고안함으로써 일조시간 상세화 모형을 도출하였다. 이 모형의 신뢰도를 기존 수평면 일조시간 추정기법과 비교한 결과 추정값의 편의가 크게 개선된 것은 물론, 일적산일조시간 기준 RMSE가 1.7시간으로 지형효과를 반영하기 전보다 37% 이상 개선되었다. 어떤 지역을 대상으로 일조시간을 상세화 하기 위해서는 먼저 대상 지역의 매 시간 음영기복도의 격자점 휘도를 모형에 입력시켜 해당 시간대의 청천 일조시간을 추정한다. 다음에 같은 시간대의 기상청 동네예보(하늘상태)에 의해 구름 효과를 보정한다. 이렇게 추정된 매 시간 일조시간을 하루 단위로 적산하여 그 날의 누적 일조시간을 얻는다. 이 과정을 연구대상 집수역에 적용하여 수평 해상도 3m의 정밀한 일조시간 분포도를 얻을 수 있었다.

• 한국터널지하공간학회 논문집
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• 제21권1호
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• pp.201-210
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• 2019

최근 국내외 도심지역의 교통정체 완화와 공간의 효율성 증대를 위해 대도시내 도로의 지하화 시행빈도가 증가하고 있다. 도시부 터널은 극심한 정체상황 중 화재가 발생할 경우 대형 참사를 유발할 가능성이 산악터널보다 높기 때문에 소단면으로된 소형차전용터널로 시공이 될 가능성이 높다. 소형차 전용의 터널로 시공이 될 경우, 소형차 전용터널은 차량에 의한 설계화재강도를 감소할 수 있는 반면, 터널 높이 감소 등에 따른 단면적 축소에 따라 유해가스농도가 증가하여 위험도는 증가하는 상반된 특징이 있다. 본 연구에서는 수치해석을 통해 대배기구의 설치간격과 형태에 따른 화재양상을 터널 내 온도 및 CO농도를 분석하여 비교 검토하였으며, 다음과 같은 결론을 얻었다. 설치간격에 따른 연기확산거리는 큰 차이가 없는 것으로 분석되었으나, 본 연구에서는 간격이 100 m인 경우가 가장 효과적으로 분석되었다. 대배기구 형상에 따른 연기확산거리는 $4m{\times}3m$, $6m{\times}2m$, $3m{\times}2m$ (2열) 순으로 배연성능이 우수한 것으로 분석되었으며, 대배기구 형상에 따른 연기확산거리는 플래시오버 이후에는 큰 차이가 없지만 화재 성장과정에서 $3m{\times}2m$인 경우 다른 경우보다 확산거리가 큰 것으로 분석되었고, 대배기구 종횡비에 따른 연기확산거리는 횡방향으로 긴 경우보다 종방향으로 긴 경우가 화재연기의 확산거리가 더 짧은 것으로 분석되었다.

• 전기전자학회논문지
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• 제27권4호
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• pp.464-471
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• 2023

본 논문에서는 StyleGAN Encoder를 활용한 표정 이미지 생성에서의 연령 왜곡을 감소시키는 방법을 제안한다. 표정 이미지 생성 과정은 StyleGAN Encoder를 사용하여 얼굴 이미지를 생성하고, SVM을 이용하여 학습된 boundary를 잠재 벡터에 적용하여 표정을 변화시킨다. 그러나 웃는 표정의 boundary를 학습할 때 표정 변화에 따른 연령 왜곡이 발생한다. 웃는 표정에 대한 SVM 학습에서 생성된 smile boundary는 표정 변화로 인해 생긴 주름이 학습 요소로 포함되어 있으며 연령에 대한 특성도 함께 학습된 것으로 판단한다. 이를 해결하기 위해, 제안된 방법에서는 smile boundary와 age boundary의 상관계수를 계산하고, 이를 이용하여 smile boundary에서 age boundary를 상관계수에 비례하여 조절하는 방식을 도입한다. 제안된 방법의 효과를 확인하기 위해 공개된 표준 얼굴 데이터셋인 FFHQ 데이터셋을 사용하고 FID score를 측정하여 실험한 결과는 다음과 같다. Smile 이미지에서는 기존 방법에 비하여, Ground Truth와 제안된 방법으로 생성된 smile 이미지의 FID score가 약 0.46 향상되었다. 또한, Smile 이미지에서 기존 방법에 비하여, StyleGAN Encoder로 생성된 이미지와 제안된 방법으로 생성된 smile 이미지의 FID score가 약 1.031 향상되었다. Non-smile 이미지에서는 기존 방법에 비하여, Ground Truth와 본 논문에서 제안된 방법으로 생성된 non-smile 이미지의 FID score가 약 2.25 향상되었다. 또한, Non-smile 이미지에서 기존 방법에 비하여, StyleGAN Encoder로 생성된 이미지와 제안된 방법으로 생성된 non-smile 이미지의 FID score가 약 약 1.908 향상됨을 확인하였다. 한편, 각 생성된 표정 이미지의 연령을 추정하여 StyleGAN Encoder로 생성된 이미지의 추정된 연령과 MSE를 측정한 결과, 기존방법 대비 제안하는 방법이 smile 이미지에서 약 1.5, non-smile 이미지에서 약 1.63의 성능 향상되어 제안한 방법에 대한 성능의 효율성이 입증되었다.

• Journal of Biosystems Engineering
• /
• 제1권1호
• /
• pp.15-15
• /
• 1976

In analyzing the operational characteristics of a rice whitening machine, the internal radial pressure of the machine was measured using strain gage equipment. Changes in cylinder and feed screw configurations, screen type, cylinder speed and counter-pressure levels were examined to determine their impact on the quality and quantity of milled rice and the performance of the machine. The results are summarized as follows: 1. The internal radial pressure in the whitening chamber varied with the surface condition of the grain being processed. During the first or second pass through the machine, pressure was relatively low, reached a maximum after two to three passes with combinations I and II, three to six with combination III and then began to fall. 2. The pitch of the feed screw and the size of the feed gate opening which determine the rate of entry of grain into the whitening chamber, appeared to be the most important factor aff-::cting the degree of radial pressure, quality and quantity of milled rice and the efficiency of the machine. Using a feed screw with a wide pitch (4.8cm), radial pressure was relatively high and head rice recovery ratio \vere quite low. In this case capacity and machine effic?iency were much higher than obtained when using a feed screw with a narrow pitch (2.3cm). Very significant responses in radial pressure, head rice recovery rates and machine capacity were observed with changes in cylinder speed and counter-pressure levels when using the wide pitch feed screw. 3. The characteristics of the screen which surrounds the whitening chamber had an important effect on whitening efficiency. The existence of small protuberances on the original screen resulted in significant increases in both machine capacity and efficiency but without a significant decrease in head rice recovery or development of excessive radial pressure. Further work is required to determine the effects of screen surface conditions and the shape of the cylinderical steel roller on the rate of bran removal, machine efficiency and recovery rates. The size of the slotted perforations 0:1 the screen affects total milled rice recovery. The opening size on the original screen was fabricated to accommodate the round shape of Japonica rice varieties but was not suitable for the more slender Indica type. Milling Indica varieties with this screen resulted in a reduction in total milled rice recovery. 4. An increase in cylinder speed from 380 to 820 rpm produced a positive effect on head rice recovery for all machine combinations at every level of counter-pressure used in the tests. Head rice recovery was considerably lower at 380rpm using a wide screw pitch when compared to the results obtained at speeds from 600 to 820 r.p.m. The effects of cylinder speed On radial pressure, capacity and machine efficiency showed contrasting results, depending on the width of the feed screw pitch. With a narrow feed screw pitch (2.3cm), a direct proportional relationship was observed bet?ween cylinder speed and both radial pressure and machine efficiency. In contrast, using a 4.8 centimeter pitch feed roller produced a series of inverse relationships between the above variables. Based on the results of this study it is recommended when milling Indica type long grain rice varieties that the cylinder speed of the original machine be increased from 500-600 rmp up to a minimum of 800 rpm to obtain a greater abrasive effect between the grain and the screen. The pitch of the feed screw should be also reduced to decr?ease the level of internal radial pressure and to obtain higher machine efficiency and increased quality of milled rice with increased cylinder speeds. Further study on the interaction between cylinder speed and feed screw pitch is recommended. 5. An increase in the counter pressure level produced a negative effect On the head rice recovery with an increase in radial pressure, capacity, and machine efficiency over all combinations and at every level of cylinder speed. 6. Head rice recovery rates were conditioned primarily by the pressure inside the whitening chamber. According to the empirical cha racteristics curve developed in this study, the relationships of head rice recovery ($Y_h$) and machine capacity ($Y_c$/TEX>) to internal radial pressure ($X_p$) followed an inverse quadratic function and a linear function respectively: $$Y_h^\Delta=\frac{1}{{1.4383-0.2951X_p^\ast+0.1425X_p^{\ast\ast}}^2} , (R^2=0.98)$$$$Y_c^\Delta=-305.83+374.37X_p^{\ast\ast}, (R^2=0.88)$$The correlation between capacity and power consumption per unit of brown rice expressed in the following exponential function: $$Y_c^\Delta=1.63Y_c^{-0.7786^\{\ast\ast}, (R^2=0.94)$$These relationships indicate that when radial pressure increases above a certain range (1. 6 to 2.0 kg/$cm^2$ based On the results of the experiment) head ricerecovery decrea?ses in a quadratic relation with a inear increase in capacity but without any decrease in power consump tion per unit of brown rice. On the other hand, if radial pressure is below the range shown above, power consumption increases dramatically with a lin?ear decrease in capacity but without significant increases in head rice recovery. During the operation of a given whitening machine, the optimum radial pressure range or the correct capacity range should be selected by controlling the feed rate and/or counter-pressure keeping in mind the condition of the grain, particulary the hardness. It was observed that the total number of passes is related to radial pessure level, feed rate and counter-pressure level. The higher theradial pressure the fewer num?ber of pass required but with decreased head rice recovery. In particular, when using high feed rates, the total number of passes should be increased to more than three by reducing the counter-pressure level to avoid decreaseases in head rice recovery (less than 65 percent head rice recovery on the basis of brown rice) at every cylinder speed. 7. A rapid rise in grain temperature seemed to have a close relationship with the pressure generated inside the whitening chamber and, subsequently with head rice reco?very rates. The higher the rate of increase, the lower were the resulting head rice recoveries.

• Journal of Biosystems Engineering
• /
• 제1권1호
• /
• pp.17-48
• /
• 1976

In analyzing the operational characteristics of a rice whitening machine, the internal radial pressure of the machine was measured using strain gage equipment. Changes in cylinder and feed screw configurations, screen type, cylinder speed and counter-pressure levels were examined to determine their impact on the quality and quantity of milled rice and the performance of the machine. The results are summarized as follows: 1. The internal radial pressure in the whitening chamber varied with the surface condition of the grain being processed. During the first or second pass through the machine, pressure was relatively low, reached a maximum after two to three passes with combinations I and II, three to six with combination III and then began to fall. 2. The pitch of the feed screw and the size of the feed gate opening which determine the rate of entry of grain into the whitening chamber, appeared to be the most important factor aff-::cting the degree of radial pressure, quality and quantity of milled rice and the efficiency of the machine. Using a feed screw with a wide pitch (4.8cm), radial pressure was relatively high and head rice recovery ratio \vere quite low. In this case capacity and machine effic\ulcorneriency were much higher than obtained when using a feed screw with a narrow pitch (2.3cm). Very significant responses in radial pressure, head rice recovery rates and machine capacity were observed with changes in cylinder speed and counter-pressure levels when using the wide pitch feed screw. 3. The characteristics of the screen which surrounds the whitening chamber had an important effect on whitening efficiency. The existence of small protuberances on the original screen resulted in significant increases in both machine capacity and efficiency but without a significant decrease in head rice recovery or development of excessive radial pressure. Further work is required to determine the effects of screen surface conditions and the shape of the cylinderical steel roller on the rate of bran removal, machine efficiency and recovery rates. The size of the slotted perforations 0:1 the screen affects total milled rice recovery. The opening size on the original screen was fabricated to accommodate the round shape of Japonica rice varieties but was not suitable for the more slender Indica type. Milling Indica varieties with this screen resulted in a reduction in total milled rice recovery. 4. An increase in cylinder speed from 380 to 820 rpm produced a positive effect on head rice recovery for all machine combinations at every level of counter-pressure used in the tests. Head rice recovery was considerably lower at 380rpm using a wide screw pitch when compared to the results obtained at speeds from 600 to 820 r.p.m. The effects of cylinder speed On radial pressure, capacity and machine efficiency showed contrasting results, depending on the width of the feed screw pitch. With a narrow feed screw pitch (2.3cm), a direct proportional relationship was observed bet\ulcornerween cylinder speed and both radial pressure and machine efficiency. In contrast, using a 4.8 centimeter pitch feed roller produced a series of inverse relationships between the above variables. Based on the results of this study it is recommended when milling Indica type long grain rice varieties that the cylinder speed of the original machine be increased from 500-600 rmp up to a minimum of 800 rpm to obtain a greater abrasive effect between the grain and the screen. The pitch of the feed screw should be also reduced to decr\ulcornerease the level of internal radial pressure and to obtain higher machine efficiency and increased quality of milled rice with increased cylinder speeds. Further study on the interaction between cylinder speed and feed screw pitch is recommended. 5. An increase in the counter pressure level produced a negative effect On the head rice recovery with an increase in radial pressure, capacity, and machine efficiency over all combinations and at every level of cylinder speed. 6. Head rice recovery rates were conditioned primarily by the pressure inside the whitening chamber. According to the empirical cha racteristics curve developed in this study, the relationships of head rice recovery ($Y_h$) and machine capacity ($Y_c$/TEX>) to internal radial pressure ($X_p$) followed an inverse quadratic function and a linear function respectively: $$Y_h^\Delta=\frac{1}{{1.4383-0.2951X_p^\ast+0.1425X_p^{\ast\ast}}^2} , (R^2=0.98)$$ $$Y_c^\Delta=-305.83+374.37X_p^{\ast\ast}, (R^2=0.88)$$ The correlation between capacity and power consumption per unit of brown rice expressed in the following exponential function: $$Y_c^\Delta=1.63Y_c^{-0.7786^\{\ast\ast}, (R^2=0.94)$$ These relationships indicate that when radial pressure increases above a certain range (1. 6 to 2.0 kg/$cm^2$ based On the results of the experiment) head ricerecovery decrea\ulcornerses in a quadratic relation with a inear increase in capacity but without any decrease in power consump tion per unit of brown rice. On the other hand, if radial pressure is below the range shown above, power consumption increases dramatically with a lin\ulcornerear decrease in capacity but without significant increases in head rice recovery. During the operation of a given whitening machine, the optimum radial pressure range or the correct capacity range should be selected by controlling the feed rate and/or counter-pressure keeping in mind the condition of the grain, particulary the hardness. It was observed that the total number of passes is related to radial pessure level, feed rate and counter-pressure level. The higher theradial pressure the fewer num\ulcornerber of pass required but with decreased head rice recovery. In particular, when using high feed rates, the total number of passes should be increased to more than three by reducing the counter-pressure level to avoid decreaseases in head rice recovery (less than 65 percent head rice recovery on the basis of brown rice) at every cylinder speed. 7. A rapid rise in grain temperature seemed to have a close relationship with the pressure generated inside the whitening chamber and, subsequently with head rice reco\ulcornervery rates. The higher the rate of increase, the lower were the resulting head rice recoveries.

• 지능정보연구
• /
• 제24권1호
• /
• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet)은 시각적 특징의 계층 구조를 분석하고 학습할 수 있는 대표적인 심층 신경망이다. 첫 번째 신경망 모델인 Neocognitron은 80 년대에 처음 소개되었다. 당시 신경망은 대규모 데이터 집합과 계산 능력이 부족하여 학계와 산업계에서 널리 사용되지 않았다. 그러나 2012년 Krizhevsky는 ImageNet ILSVRC (Large Scale Visual Recognition Challenge) 에서 심층 신경망을 사용하여 시각적 인식 문제를 획기적으로 해결하였고 그로 인해 신경망에 대한 사람들의 관심을 다시 불러 일으켰다. 이미지넷 첼린지에서 제공하는 다양한 이미지 데이터와 병렬 컴퓨팅 하드웨어 (GPU)의 발전이 Krizhevsky의 승리의 주요 요인이었다. 그러므로 최근의 딥 컨볼루션 신경망의 성공을 병렬계산을 위한 GPU의 출현과 더불어 ImageNet과 같은 대규모 이미지 데이터의 가용성으로 정의 할 수 있다. 그러나 이러한 요소는 많은 도메인에서 병목 현상이 될 수 있다. 대부분의 도메인에서 ConvNet을 교육하기 위해 대규모 데이터를 수집하려면 많은 노력이 필요하다. 대규모 데이터를 보유하고 있어도 처음부터 ConvNet을 교육하려면 많은 자원과 시간이 소요된다. 이와 같은 문제점은 전이 학습을 사용하면 해결할 수 있다. 전이 학습은 지식을 원본 도메인에서 새 도메인으로 전이하는 방법이다. 전이학습에는 주요한 두 가지 케이스가 있다. 첫 번째는 고정된 특징점 추출기로서의 ConvNet이고, 두번째는 새 데이터에서 ConvNet을 fine-tuning 하는 것이다. 첫 번째 경우, 사전 훈련 된 ConvNet (예: ImageNet)을 사용하여 ConvNet을 통해 이미지의 피드포워드 활성화를 계산하고 특정 레이어에서 활성화 특징점을 추출한다. 두 번째 경우에는 새 데이터에서 ConvNet 분류기를 교체하고 재교육을 한 후에 사전 훈련된 네트워크의 가중치를 백프로퍼게이션으로 fine-tuning 한다. 이 논문에서는 고정된 특징점 추출기를 여러 개의 ConvNet 레이어를 사용하는 것에 중점을 두었다. 그러나 여러 ConvNet 레이어에서 직접 추출된 차원적 복잡성을 가진 특징점을 적용하는 것은 여전히 어려운 문제이다. 우리는 여러 ConvNet 레이어에서 추출한 특징점이 이미지의 다른 특성을 처리한다는 것을 발견했다. 즉, 여러 ConvNet 레이어의 최적의 조합을 찾으면 더 나은 특징점을 얻을 수 있다. 위의 발견을 토대로 이 논문에서는 단일 ConvNet 계층의 특징점 대신에 전이 학습을 위해 여러 ConvNet 계층의 특징점을 사용하도록 제안한다. 본 논문에서 제안하는 방법은 크게 세단계로 이루어져 있다. 먼저 이미지 데이터셋의 이미지를 ConvNet의 입력으로 넣으면 해당 이미지가 사전 훈련된 AlexNet으로 피드포워드 되고 3개의 fully-connected 레이어의 활성화 틀징점이 추출된다. 둘째, 3개의 ConvNet 레이어의 활성화 특징점을 연결하여 여러 개의 ConvNet 레이어의 특징점을 얻는다. 레이어의 활성화 특징점을 연결을 하는 이유는 더 많은 이미지 정보를 얻기 위해서이다. 동일한 이미지를 사용한 3개의 fully-connected 레이어의 특징점이 연결되면 결과 이미지의 특징점의 차원은 4096 + 4096 + 1000이 된다. 그러나 여러 ConvNet 레이어에서 추출 된 특징점은 동일한 ConvNet에서 추출되므로 특징점이 중복되거나 노이즈를 갖는다. 따라서 세 번째 단계로 PCA (Principal Component Analysis)를 사용하여 교육 단계 전에 주요 특징점을 선택한다. 뚜렷한 특징이 얻어지면, 분류기는 이미지를 보다 정확하게 분류 할 수 있고, 전이 학습의 성능을 향상시킬 수 있다. 제안된 방법을 평가하기 위해 특징점 선택 및 차원축소를 위해 PCA를 사용하여 여러 ConvNet 레이어의 특징점과 단일 ConvNet 레이어의 특징점을 비교하고 3개의 표준 데이터 (Caltech-256, VOC07 및 SUN397)로 실험을 수행했다. 실험결과 제안된 방법은 Caltech-256 데이터의 FC7 레이어로 73.9 %의 정확도를 얻었을 때와 비교하여 75.6 %의 정확도를 보였고 VOC07 데이터의 FC8 레이어로 얻은 69.2 %의 정확도와 비교하여 73.1 %의 정확도를 보였으며 SUN397 데이터의 FC7 레이어로 48.7%의 정확도를 얻었을 때와 비교하여 52.2%의 정확도를 보였다. 본 논문에 제안된 방법은 Caltech-256, VOC07 및 SUN397 데이터에서 각각 기존에 제안된 방법과 비교하여 2.8 %, 2.1 % 및 3.1 %의 성능 향상을 보였다.

• 대한교통학회:학술대회논문집
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• 대한교통학회 1995년도 제27회 학술발표회
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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.