• Korean Journal of Agricultural Science
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• v.37 no.2
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• pp.315-321
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• 2010

Decision-makings or the related policies regarding domestic grape production heavily depends upon the known market price data and official statistics periodically announced by government, at national level. However, usual adaption of the 'simple means' from these data may bring seriously biased decision-makings when the original data are biased, especially when the data are not convinced to be normal distributions to decision makers. In this regards, this study employs Monte Carlo simulation technique to overcome the limitations, based on the decision makers' subjective assumptions on the known data, and, tries to come up with flexible range of business information regarding grape-producing farm income. The approach used in this study also provides possibility that it may be useful when adapting subjective assumptions from various statistical distributions.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.146-154
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• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.2
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• pp.169-178
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• 2005

A critical component of air pollution modeling is the representation of atmospheric flow fields within a model domain, since an accurate air quality simulation requires an accurate portrayal of the three-dimensional wind fields. The present study investigated data assimilation using surface observational data in the complex coastal regions to simulate a realistic atmospheric flow fields. Surface observational data were categorized into three groups (Near coastal region, Far coastal region 1, Far costal region 2) by the locations where the sites are. Experiments were designed according to the location of observational stations and MM5/CALPUFF was used. The results of numerical simulation of atmospheric flow fields are used as input data for CALPUFF which predicts dispersion fields of air pollutants. The result of this study indicated that data assimilation using data in the far coastal region 2 provided an attractive method for generating realistic meteorological fields and dispersion fields of air pollutants in Gwangyang area because data in the near coastal region are variable and narrow representation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.75-84
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• 2015

A data allocation technique is essential to improve the performance of data broadcast systems. This paper explores the issues for allocating data items on broadcast channels to process multiple-data queries in the environment where query profiles and query request rates are given, and proposes a new data allocation scheme named IDAS. The proposed scheme employs the strategy that the broadcast frequency of each data is determined by the square root value of its relative access probability. IDAS could enhance the performance of query response time since it can process queries of high request rate fast and show a resonable degree of query data adjacency. Simulation is performed to evaluate the performance of the proposed scheme. The simulation results show that IDAS outperforms other schemes in terms of the average response time.

• Journal of the Korea Safety Management & Science
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• v.11 no.2
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• pp.137-144
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• 2009

EPCIS(EPC Information Services) system is a core component of EPCglobal Architecture Framework offering information of the freights, the time of awareness and the location of awareness on the EPCglobal Network. The role of EPCIS is to exchange information based on EPC. There are four kinds of event data which are object event data, aggregation event data, quantity event data, and transaction event data. These EPCIS events data are stored and managed in EPCIS repository. This study suggest the quantitative modeling about total number of EPCIS event data under the assumption to aware the RFID tags of items, cases(boxes), vehicles(carriers, forklifts, auto guided vehicles, rolltainers) at a time on the reading points. We also estimate the number of created EPCIS event data by the suggested quantitative modeling under scenario of process in the integrated logistics center based on RFID system And this study compare the TO-BE model with the AS-IS model about the total sizes of created EPCIS event data using the simulation, in which we suggested the TO-BE model as the development of the repository by skipping the overlapped records.

• Korean Journal of Agricultural Science
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• v.51 no.3
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• pp.315-329
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• 2024

This study was conducted for simulation model development of an electric all-wheel-drive vehicle to adapt the agricultural machinery. Data measurement system was installed on a four-wheel electric driven vehicle using proximity sensor, torque-meter, global positioning system (GPS) and data acquisition (DAQ) device. Axle torque and rotational speed were measured using a torque-meter and a proximity sensor. Driving test was performed on an upland field at a speed of 7 km·h^-1. Simulation model was developed using a multi-body dynamics software, and tire properties were measured and calculated to reflect the similar road conditions. Measured and simulated data were compared to validate the developed simulation model performance, and axle rotational speed was selected as simulation input data and axle torque and power were selected as simulation output data. As a result of driving performance, an average axle rotational speed was 115 rpm for each wheel. Average axle torque and power were 4.50, 4.21, 4.04, and 3.22 Nm and 53.42, 50.56, 47.34, and 38.07 W on front left, front right, rear left, and rear right wheel, respectively. As a result of simulation driving, average axle torque and power were 4.51, 3.9, 4.16, and 3.32 Nm and 55.79, 48.11, 51.62, and 41.2 W on front left, front right, rear left, and rear right wheel, respectively. Absolute error of axle torque was calculated as 0.22, 7.36, 2.97, and 3.11% on front left, front right, rear left, rear right wheel, respectively, and absolute error of axle power was calculated as 4.44, 4.85, 9.04, and 8.22% on front left, front right, rear left, and rear right wheel, respectively. As a result of absolute error, it was shown that developed simulation model can be used for driving performance prediction of electric driven vehicle. Only straight driving was considered in this study, and various road and driving conditions would be considered in future study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.62-69
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• 2012

Traffic simulation models have been used for assessing various transportation strategies. Through comparing results from a simulation model and real field data, researchers try to show how close the model can reproduce the real world traffic. This model verification step is one of the most essential tasks in modeling procedure. Traffic counts and speeds have been frequently used for the verification or validation. Authors modeled severe PM peak bottleneck situation on the I-40 corridor in Raleigh, North Carolina using DYNASMART-P, a mesoscopic traffic simulation tool and verified the model. NCDOT has Traffic Information Management System which has archive capability for the traffic speeds on the I-40 corridor. However, the authors selected travel time as the field measure for model verification and collected the data using a GPS equipment because the speed data from NCDOT speed detectors are spot speeds which are not appropriate for comparison with link average speed from the simulation model. This paper describes the GPS field data collection procedure, the model verification method, and the results.

• Journal of Korean Neurosurgical Society
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• v.64 no.2
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• pp.229-237
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• 2021

Objective : Expansion in the spinal canal area (SCA) after laminoplasty is one of the critical factors to relieve the preoperative symptoms. No previous study has compared the increases in SCA achieved by open-door laminoplasty (ODL) and double door laminoplasty (DDL) according to the preoperative lamina angle (LA). This study was designed to clarify the relationship between the laminoplasty opening angle (OA)/laminoplasty opening size (OS) and increases in the SCA following ODL and DDL according to the preoperative LA using a simulation model. Methods : The simulation model was constructed and validated by comparing the clinical data of 64 patients who had undergone C3-C6 laminoplasty (43 patients with ODL and 21 patients with DDL). SCA expansion was predicted with a verified simulation model at various preoperative LAs (from 28° to 32°) with different OAs (40° to 44°) and OSs (10 mm to 14 mm) recruited from patient data. Results : The constructed simulation model was validated by comparing clinical data and revealed a very high degree of correlation (r=0.935, p<0.001). In this validated model, at the same OA, the increase in SCA was higher following ODL than following DDL in the usual LA (p<0.05). At the same OS, the increase in SCA was slightly larger following DDL than following ODL, but the difference was not significant (p>0.05). The difference was significant when the preoperative LA was narrower or much wider. Conclusion : Based on clinical data, a simulation model was constructed and verified that could predict increases in the SCA following ODL and DDL. When applying this model, prediction in SCA increase using the OS parameter was more practical and compatible with clinical data. Both laminoplasties achieved enough SCA, and there was no significant difference between them in the usual range.

• Journal of Institute of Convergence Technology
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• v.2 no.2
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• pp.35-39
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• 2012

This paper presents the effect of data-hold methods on stability of haptic system with a virtual wall. When a human operator interacts with virtual wall, the lager the stiffness of the virtual wall is, the more realistic the operator feels that the virtual wall is. However, if the stiffness of the virtual wall becomes extremely large, the system may be unstable. When a virtual wall is designed, it is necessary to analyze the maximum available stiffness to guarantee a stable haptic interaction. The simulation model in this paper is developed based on the haptic device model, sampler, a virtual wall model, and data hold methods to compute the maximum stiffness for stability. The effectiveness of the simulation is evaluated through comparing the results of previous studies with the results of this simulation. In addition, the effects of two data hold methods, that is, zero-order hold (ZOH) and first-order hold (FOH) on the stability are analyzed and the values of the maximum available stiffness are compared through the simulation.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.282-285
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• 2007

Satellite observed brightness temperature simulation using a radiative transfer model (here after, RTM) is useful for various fields, for example sensor design and channel selection by using theoretically calculated radiance data, development of satellite data processing algorithm and algorithm parameter determination before launch. This study is focused on elaborating the simulation procedure, and analyzing of difference between observed and modelled clear sky brightness temperatures. For the CMDPS (COMS Meteorological Data Processing System) development, the simulated clear sky brightness temperatures are used to determine whether the corresponding pixels are cloud-contaminated in cloud mask algorithm as a reference data. Also it provides important information for calibrating satellite observed radiances. Meanwhile, simulated brightness temperatures of COMS channels plan to be used for assessing the CMDPS performance test. For these applications, the RTM requires fast calculation and high accuracy. The simulated clear sky brightness temperatures are compared with those of MTSAT-1R observation to assess the model performance and the quality of the observation. The results show that there is good agreement in the ocean mostly, while in the land disagreement is partially found due to surface characteristics such as land surface temperature, surface vegetation, terrain effect, and so on.