• 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.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1031-1031
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• 2001

The mammary gland is made up of remarkably sensitive tissue, which has the capability of producing a large volume of secretion, milk, under normal or healthy conditions. When bacteria enter the gland and establish an infection (mastitis), inflammation is initiated accompanied by an influx of white cells from the blood stream, by altered secretory function, and changes in the volume and composition of secretion. Cell numbers in milk are closely associated with inflammation and udder health. These somatic cell counts (SCC) are accepted as the international standard measurement of milk quality in dairy and for mastitis diagnosis. NIR Spectra of unhomogenized composite milk samples from 14 cows (healthy and mastitic), 7days after parturition and during the next 30 days of lactation were measured. Different multivariate analysis techniques were used to diagnose the disease at very early stage and determine how the spectral properties of milk vary with its composition and animal health. PLS model for prediction of somatic cell count (SCC) based on NIR milk spectra was made. The best accuracy of determination for the 1100-2500nm range was found using smoothed absorbance data and 10 PLS factors. The standard error of prediction for independent validation set of samples was 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. It has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. From the spectral changes, we learned that when mastitis occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk. It was consistent with the results we obtained further when applied 2DCOS. Two-dimensional correlation analysis of NIR milk spectra was done to assess the changes in milk composition, which occur when somatic cell count (SCC) levels vary. The synchronous correlation map revealed that when SCC increases, protein levels increase while water and lactose levels decrease. Results from the analysis of the asynchronous plot indicated that changes in water and fat absorptions occur before other milk components. In addition, the technique was used to assess the changes in milk during a period when SCC levels do not vary appreciably. Results indicated that milk components are in equilibrium and no appreciable change in a given component was seen with respect to another. This was found in both healthy and mastitic animals. However, milk components were found to vary with SCC content regardless of the range considered. This important finding demonstrates that 2-D correlation analysis may be used to track even subtle changes in milk composition in individual cows. To find out the right threshold for SCC when used for mastitis diagnosis at cow level, classification of milk samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two levels of SCC - 200 000 cells/$m\ell$ and 300 000 cells/$m\ell$, respectively, were set up and compared as thresholds to discriminate between healthy and mastitic cows. The best detection accuracy was found with 200 000 cells/$m\ell$ as threshold for mastitis and smoothed absorbance data: - 98% of the milk samples in the calibration set and 87% of the samples in the independent test set were correctly classified. When the spectral information was studied it was found that the successful mastitis diagnosis was based on reviling the spectral changes related to the corresponding changes in milk composition. NIRS combined with different ways of spectral data ruining can provide faster and nondestructive alternative to current methods for mastitis diagnosis and a new inside into disease understanding at molecular level.

• 분석과학
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• 제9권4호
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• pp.336-344
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• 1996

뇨시료 중 혼적량의 코발트, 구리, 니켈, 카드뮴, 납 및 아연을 흑연로 원자흡수분광 광도법으로 정량하기 위한 dithizone이 포함된 chloroform으로의 용매추출에 관하여 연구하였다. 실험조건인 시료의 전처리 과정, 추출용액의 pH, 킬레이트제인 dithizone의 농도, 역추출할 때 사용하는 산의 종류와 농도에 관하여 최적화하였다. 유기물의 방해를 제거하고자 뇨시료 100.0mL에 진한 질산 30mL를 가하고 30% 과산화수소 50mL를 5.0mL씩 단계적으로 가하면서 가열하여 유기물질을 분해하였다. 삭힌 뇨시료를 100mL로 만들어 분별 깔때기에 넣고 시판용 완충용액으로 pH가 8이 되게 조절한 다음 0.1% dithizone을 포함하는 chloroform 15.0mL를 가했다. 진탕기(shaker)를 이용하여 90분 동안 흔들어 준 후 상분리시켜 용매층을 분리하였다. 카드뮴, 납, 아연은 0.2M 질산용액 10.0mL로 역추출하여 직접 정량하였고, 이런 조건으로 역추출되지 않은 코발트, 구리, 니켈은 유기 용매를 증발 건고시킨 다음 잔류물을 $HNO_3$ $H_2O_2$로 녹이고, 정확히 10.0mL가 되게 탈염수로 묽혀서 정량하였다. 최적의 추출조건을 찾기 위하여 인공 뇨시료를 제조하여 검토하였고, 얻은 최적조건으로 검정곡선을 작성하였다. 삭힌 각 시료에 일정량 첨가된 원소를 정량하여 얻은 회수율은 77 내지 109%였고, 검출한계는 Cd(II) 0.09, Pb(II) 0.59, Zn(II) 0.18, Co(II) 0.24, Cu(II) 1.3, Ni(II) 1.7ng/mL였다. 이로써 본 방법이 과량의 유기물과 알칼리 및 알칼리 토금속이 포함된 뇨시료에서 혼적량 원소들을 정량적으로 분리 분석할 수 있음 을 알았다.

• 공업화학
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• 제34권2호
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• pp.111-120
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• 2023

이산화탄소 순환 물질 중 대표적인 광물인 탄산칼슘의 형성 과정을 관찰하고, 대표적 조절 변수인 마그네슘-칼슘 이온의 혼합 비율(Mg/Ca 비)과 온도가 pre-nucleation cluster (PNC) 및 탄산칼슘 형성에 미치는 영향을 분석하고자 실험과정에서 칼슘 이온 선택성 전극(calcium ion selective electrode, Ca ISE)을 이용하여 핵형성 과정을 연구하였다. 실험결과 미량의 결정이 형성되었으며 표면 원소 분석을 위해 에너지 분산 X선 분석법(energy dispersive X-ray spectroscopy, EDS)을 사용하였고, 형상 분석을 위해 주사 전자 현미경(field emission scanning electron microscope, FE-SEM)을 사용하였다. Mg/Ca 비와 온도 조건에 따라 다양한 형상의 결정질 탄산칼슘(방해석, 아라고나이트 등)을 확인하였으며 Ca ISE로부터 얻은 칼슘 이온 농도 그래프는 탄산칼슘 형성 과정을 보여주었다. 칼슘 이온 농도 그래프 분석을 통해 마그네슘 이온은 칼슘 이온과 탄산 이온의 결합을 방해하고 PNC 간 응집을 지연시켜 핵형성 및 탄산칼슘의 형성을 지연시킴을 확인하였다. 반면 온도는 이와 반대되는 효과를 보였으며, 본 실험 조건에서는 마그네슘 이온보다 더 큰 영향을 미쳤다. 또한 Mg/Ca 비와 온도에 따라 탄산칼슘의 형상이 뚜렷하게 변화하여 두 인자는 탄산칼슘 형성 과정에 전반적으로 영향을 미치는 중요 조절 변수임을 확인하였다.

• 분석과학
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• 제22권5호
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• pp.432-438
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• 2009

Ranitidine HCl을 함유하는 의약품 정제에 대한 네모파 전압전류법(SWV) 분석방법을 개발하고자 다양한 pH의 인산염 완충용액을 지지전해질로 하여 $5.00{\times}10^{-5}M$ ranitidine HCl용액의 SWV를 실행한 결과 ranitidine의 구조 내 $-NO_2$기의 전기화학적 환원에 기인한 주 봉우리는 Ep가 -70 mV/pH로 이동하여 수소가 관여함을 나타내었다. Ranitidine HCl용액 $1.00{\times}10^{-7}{\sim}1.00{\times}10^{-5}M$에 대하여 봉우리 전류(Ip)를 도시하여 검량곡선을 작성 시 좋은 직선성을 나타내었으며 기울기는 $232,530{\mu}A/M$ (pH 6.14), $289,015{\mu}A/M$ (pH 7.07)과 $232,843{\mu}A/M$ (pH 8.01)이었다. 의약품 정제 1정을 단순히 pH 6.14 인산염 완충용액에 용해시켜 표준물 첨가법에 의해 SWV로 정량분석 할 때 하루 중 정밀도 검사(n=4)는 큐란$^{(R)}$의 경우 1정 중 $171{\pm}2.1mg$(규정된 함량의 $102{\pm}1.3%$)의 ranitidine HCl이 함유되어 1.2% RSD를 보였으며, 5일에 걸쳐 날짜 간 정밀도 검사를 행하였을 때에도 1.1%의 RSD를 나타내었다. 잔탁$^{(R)}$ 역시 하루 중 정밀도 검사(n=4)를 하였을 때 1정 중 $167{\pm}0.8mg$(규정된 함량의 $99{\pm}0.5%$)의 ranitidine HCl이 함유되어 0.5%의 RSD를, 날짜 간 정밀도 검사도 0.3%의 RSD로 좋은 정밀도를 나타내었다.