• Title/Summary/Keyword: Scheduling validation

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Parallel Processing of Airborne Laser Scanning Data Using a Hybrid Model Based on MPI and OpenMP (MPI와 OpenMP기반 하이브리드 모델을 이용한 항공 레이저 스캐닝 자료의 병렬 처리)

  • Han, Soo-Hee;Park, Il-Suk;Heo, Joon
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.30 no.2
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    • pp.135-142
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    • 2012
  • In the present study, a parallel processing method running on a multi-core PC-Cluster is introduced to produce digital surface model (DSM) and digital terrain model (DTM) from huge airborne laser scanning data. A hybrid model using both message passing interface (MPI) and OpenMP was devised by revising a conventional MPI model which utilizes only MPI, and tested on a multi-core PC-Cluster for performance validation. In the results, the hybrid model has not shown better performances in the interpolation process to produce DSM, but the overall performance has turned out to be better by the help of reduced MPI calls. Additionally, scheduling function of OpenMP has revealed its ability to enhance the performance by controlling inequal overloads charged on cores induced by irregular distribution of airborne laser scanning data.

A Model for Assessing Maximum Overtime Rate in Labor Subcontracting Practices

  • Nassar, Khaled;Hosny, Ossama
    • Journal of Construction Engineering and Project Management
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    • v.2 no.2
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    • pp.18-27
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    • 2012
  • Despite the rapid development in the construction industry due to the changing new technologies, many projects still fail to meet target deadlines. Shortage in manpower and skilled laborers is one of the main reasons for such delays. Markets with high economic growth and economic expansion (such as Gulf Countries in the Middle East) may have pronounced labor the shortages. Labor subcontracting practices are used sometimes to increase production rates and meet project deadlines. This paper explains and analyses labor subcontracting practices currently being used in many places around the world (and especially in the Gulf Countries) and in particular defines a maximum overtime rate for laborers in the laborer-subcontracting method ensuring that the contractor gains both the time saved during overtime and also reduces the cost per unit produced. The mathematical model used formalizes a closed-form equation for overtime pay in similar situations and as such can be applicable worldwide. Data was collected from representative projects that employed such practices from various trades. Validation of the model and formula has been tested successfully by analyzing historic data. The results prove that contractors often do not reach the optimum use of their practices resulting in a higher cost per unit. The presented model and the analysis should be of interest to many contractors currently involved in the practice or considering its use and to those who wish to find new methods that would help in eliminating as much wastes as possible by allocating their resources in the most efficient way.

Comparison of Artificial Neural Network and Empirical Models to Determine Daily Reference Evapotranspiration (기준 일증발산량 산정을 위한 인공신경망 모델과 경험모델의 적용 및 비교)

  • Choi, Yonghun;Kim, Minyoung;O'Shaughnessy, Susan;Jeon, Jonggil;Kim, Youngjin;Song, Weon Jung
    • Journal of The Korean Society of Agricultural Engineers
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    • v.60 no.6
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    • pp.43-54
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    • 2018
  • The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

Design of Experiment and Analysis Method for the Integrated Logistics System Using Orthogonal Array (직교배열을 이용한 통합물류시스템의 실험 설계 및 분석방법)

  • Park, Youl-Kee;Um, In-Sup;Lee, Hong-Chul
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.12 no.12
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    • pp.5622-5632
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    • 2011
  • This paper presents the simulation design and analysis of Integrated Logistics System(ILS) which is operated by using the AGV(Automated Guided Vehicle). To maximize the operation performances of ILS with AGV, many parameters should be considered such as the number, velocity, and dispatching rule of AGV, part types, scheduling, and buffer sizes. We established the design of experiment in a way of Orthogonal Array in order to consider (1)maximizing the throughput; (2)maximizing the vehicle utilization; (3)minimizing the congestion; and (4)maximizing the Automated Storage and Retrieval System(AS/RS) utilization among various critical factors. Furthermore, we performed the optimization by using the simulation-based analysis and Evolution Strategy(ES). As a result, Orthogonal Array which is conducted far fewer than ES significantly saved not only the time but the same outcome when compared after validation test on the result from the two methods. Therefore, this approach ensures the confidence and provides better process for quick analysis by specifying exact experiment outcome even though it provides small number of experiment.

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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