• Title/Summary/Keyword: Range correction table

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Position error estimation of sub-array in passive ranging sonar based on a genetic algorithm (유전자 알고리즘 기반의 수동측거소나 부배열 위치오차 추정)

  • Eom, Min-Jeong;Kim, Do-Young;Park, Gyu-Tae;Shin, Kee-Cheol;Oh, Se-Hyun
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.6
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    • pp.630-636
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    • 2019
  • Passive Ranging Sonar (PRS) is a type of passive sonar consisting of three sub-array on the port and starboard, and has a characteristic of detecting a target and calculating a bearing and a distance. The bearing and distance calculation requires physical sub-array position information, and the bearing and distance accuracy performance are deteriorated when the position information of the sub-array is inaccurate. In particular, it has a greater impact on distance accuracy performance using plus value of two time-delay than a bearing using average value of two time-delay. In order to improve this, a study on sub-array position error estimation and error compensation is needed. In this paper, We estimate the sub-array position error based on enetic algorithm, an optimization search technique, and propose a method to improve the performance of distance accuracy by compensating the time delay error caused by the position error. In addition, we will verify the proposed algorithm and its performance using the sea-going data.

A Study on the Development of a Simulation Model for Predicting Soil Moisture Content and Scheduling Irrigation (토양수분함량 예측 및 계획관개 모의 모형 개발에 관한 연구(I))

  • 김철회;고재군
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.19 no.1
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    • pp.4279-4295
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    • 1977
  • Two types of model were established in order to product the soil moisture content by which information on irrigation could be obtained. Model-I was to represent the soil moisture depletion and was established based on the concept of water balance in a given soil profile. Model-II was a mathematical model derived from the analysis of soil moisture variation curves which were drawn from the observed data. In establishing the Model-I, the method and procedure to estimate parameters for the determination of the variables such as evapotranspirations, effective rainfalls, and drainage amounts were discussed. Empirical equations representing soil moisture variation curves were derived from the observed data as the Model-II. The procedure for forecasting timing and amounts of irrigation under the given soil moisture content was discussed. The established models were checked by comparing the observed data with those predicted by the model. Obtained results are summarized as follows: 1. As a water balance model of a given soil profile, the soil moisture depletion D, could be represented as the equation(2). 2. Among the various empirical formulae for potential evapotranspiration (Etp), Penman's formula was best fit to the data observed with the evaporation pans and tanks in Suweon area. High degree of positive correlation between Penman's predicted data and observed data with a large evaporation pan was confirmed. and the regression enquation was Y=0.7436X+17.2918, where Y represents evaporation rate from large evaporation pan, in mm/10days, and X represents potential evapotranspiration rate estimated by use of Penman's formula. 3. Evapotranspiration, Et, could be estimated from the potential evapotranspiration, Etp, by introducing the consumptive use coefficient, Kc, which was repre sensed by the following relationship: Kc=Kco$.$Ka+Ks‥‥‥(Eq. 6) where Kco : crop coefficient Ka : coefficient depending on the soil moisture content Ks : correction coefficient a. Crop coefficient. Kco. Crop coefficients of barley, bean, and wheat for each growth stage were found to be dependent on the crop. b. Coefficient depending on the soil moisture content, Ka. The values of Ka for clay loam, sandy loam, and loamy sand revealed a similar tendency to those of Pierce type. c. Correction coefficent, Ks. Following relationships were established to estimate Ks values: Ks=Kc-Kco$.$Ka, where Ks=0 if Kc,=Kco$.$K0$\geq$1.0, otherwise Ks=1-Kco$.$Ka 4. Effective rainfall, Re, was estimated by using following relationships : Re=D, if R-D$\geq$0, otherwise, Re=R 5. The difference between rainfall, R, and the soil moisture depletion D, was taken as drainage amount, Wd. {{{{D= SUM from { {i }=1} to n (Et-Re-I+Wd)}}}} if Wd=0, otherwise, {{{{D= SUM from { {i }=tf} to n (Et-Re-I+Wd)}}}} where tf=2∼3 days. 6. The curves and their corresponding empirical equations for the variation of soil moisture depending on the soil types, soil depths are shown on Fig. 8 (a,b.c,d). The general mathematical model on soil moisture variation depending on seasons, weather, and soil types were as follow: {{{{SMC= SUM ( { C}_{i }Exp( { - lambda }_{i } { t}_{i } )+ { Re}_{i } - { Excess}_{i } )}}}} where SMC : soil moisture content C : constant depending on an initial soil moisture content $\lambda$ : constant depending on season t : time Re : effective rainfall Excess : drainage and excess soil moisture other than drainage. The values of $\lambda$ are shown on Table 1. 7. The timing and amount of irrigation could be predicted by the equation (9-a) and (9-b,c), respectively. 8. Under the given conditions, the model for scheduling irrigation was completed. Fig. 9 show computer flow charts of the model. a. To estimate a potential evapotranspiration, Penman's equation was used if a complete observed meteorological data were available, and Jensen-Haise's equation was used if a forecasted meteorological data were available, However none of the observed or forecasted data were available, the equation (15) was used. b. As an input time data, a crop carlender was used, which was made based on the time when the growth stage of the crop shows it's maximum effective leaf coverage. 9. For the purpose of validation of the models, observed data of soil moiture content under various conditions from May, 1975 to July, 1975 were compared to the data predicted by Model-I and Model-II. Model-I shows the relative error of 4.6 to 14.3 percent which is an acceptable range of error in view of engineering purpose. Model-II shows 3 to 16.7 percent of relative error which is a little larger than the one from the Model-I. 10. Comparing two models, the followings are concluded: Model-I established on the theoretical background can predict with a satisfiable reliability far practical use provided that forecasted meteorological data are available. On the other hand, Model-II was superior to Model-I in it's simplicity, but it needs long period and wide scope of observed data to predict acceptable soil moisture content. Further studies are needed on the Model-II to make it acceptable in practical use.

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Experiences of the First 130 Patients in Gangnam Severance Hospital (강남세브란스병원 토모테라피를 이용한 치료환자의 130예 통계분석 및 경험)

  • Ha, Jin-Sook;Jeon, Mi-Jin;Kim, Sei-Joon;Kim, Jong-Dae;Shin, Dong-Bong
    • The Journal of Korean Society for Radiation Therapy
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    • v.20 no.1
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    • pp.45-53
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    • 2008
  • Purpose: We are trying to analyze 130 patients' conditions by using our Helical Tomotherapy, which was installed in our center in Oct. 2007. We will be statistically approach this examination and analyze so that we will be able to figure out adaptive plans according to the change in place of the tumor, GTV (gross tumor volume), total amount of time it took, vector (${\upsilon}=\surd$x2+y2+z2) and the change in size of the tumor. Materials and Methods: Objectives were the patients who were medicated with Tomotherapy in our medical center since Oct. 2007 August 2008. The Average age of the patients were 53 years old (Minimum 25 years old, Maximum 83 years old). The parts of the body we operated were could be categorized as Head&neck (n=22), Chest (n=47), Abdomen (n=25), Pelvis (n=11), Bone (n=25). MVCT had acted on 2702 times, and also had acted on our adaptive plan toward patients who showed big difference in the size of tumor. Also, after equalizing our gained MVCT and kv-CT we checked up on the range of possible mistake, using x, y, z, roll and vector. We've also investigated on Set-up, MVCT, average time of operation and target volume. Results: Mean time on table was 22.8 minutes. Mean treatment time was 13.26 minutes. Mean correction (mm) was X=-0.7, Y=-1.4, Z=5.77, roll=0.29, vector=8.66 Head&neck patients had 2.96 mm less vector value in movement than patients of Chest, Abdomen, Bone. In increasing order, Head&neck, Bone, Abdomen, Chest, Pelvis showed the vector value in movement. Also, there were 27 patients for adaptive plan, 39 patients, who had long or multiple tumor. We could know that When medical treatment is one cure plan, it takes 32 minutes, and when medical treatment is two cure plan, it takes 40 minutes that one medical treatment takes 21 minutes, and the other medical treatment takes 19 minutes. Conclusion:With our basic tools, we could bring more accurate IMRT with MVCT. Also, through our daily image, we checked up on the change in tumor so that adaptive plan could work. It was made it possible to take the cure of long or multiple tumor, the cure in a nearby OAR, and the complicated cure that should make changes of gradient dose distribution.

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