• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.317-325
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• 2015

An adequate downscaling of synoptic forecasts is a prerequisite for improved agrometeorological service to rural areas in South Korea where complex terrains and small farms are common. In this study, geospatial schemes based on topoclimatology were used to scale down the Korea Meteorological Administration (KMA) temperature forecasts to the local scale (~30 m) across a rural catchment. Then, using these schemes, local temperatures were estimated at 14 validation sites at 0600 and 1500 LST in 2013/2014 and were compared with the observations. The estimation errors were substantially reduced for both 0600 and 1500 LST temperatures when compared against the uncorrected KMA products. The improvement was most notable at low lying locations for the 0600 temperature and at the locations on west- and south-facing slopes for the 1500 LST temperature. Using the downscaled real-time temperature data, a pilot service has started to provide the field-specific weather information tailored to meet the requirements of small-scale farms. For example, the service system makes a daily outlook on the phenology of crop species grown in a given field using the field-specific temperature data. When the temperature forecast is given for next morning, a frost risk index is calculated according to a known relationship of phenology and frost injury. If the calculated index is higher than a pre-defined threshold, a warning is issued and delivered to the grower's cellular phone with relevant countermeasures to help protect crops against frost damage.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.614-619
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• 2007

The importance of simulation of plastic flow in the injection mold is increasing as the parts are more complex, but the small-scale enterprises can't afford to invest for the infra individually. CAE (Computer Aided Engineering) service was naturally born based on these needs. This paper presents the engineering collaboration model between the large and the small/medium enterprises in the field of injection molds. Based on the engineering collaboration hub and CAE service, small-scale enterprises could research the necessary technology and develop the proper products. The analysis results of CAE are provided by the integrated visualization system on the web. This paper also deals with the delivery shorting process of CAE service for electric/electronic parts which should meet the needs of customers as soon as possible.

• Journal of Magnetics
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• v.5 no.2
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• pp.65-71
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• 2000

Although the electromagnetic launcher technology has been progressed significantly during the past two decades the number of firing test facilities are not many. This is prbably due to the large budget and man power required to build and maintain full scale electromagnetic launcher facilities. As the EM launcher technology's potential capabilities have been somewhat demonstrated with the full scale large systems the research is now headed more toward overcoming specific difficulties and answering questions experimentally with smaller, cost effective systems. The first half of this paper presents EM launcher's improved sub-scaling relationships based upon magnetic, thermal and momentum differential equations and EM launcher's basic equations. With the proposed scaling method the field variables can be matched or scaled linearly between the two geometrically scaled systems. The second half of the paper presents pulsed power system's circuit analysis and design technique, which is applied to the capacitor-powered small pulsed power system with crow-barring circuitry. The effect of the so-called speed volt is included. A sub-scaled small system's design is provided as an example.

• Wind and Structures
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• v.15 no.1
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• pp.43-64
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• 2012

Many potential small wind turbine locations are near obstacles such as buildings and shelterbelts, which can have a significant, detrimental effect on the local wind climate. A neural network-based model has been developed which predicts mean wind speed and turbulence intensity at points in an obstacle's region of influence, relative to unsheltered conditions. The neural network was trained using measurements collected in the wakes of 18 scale building models exposed to a simulated rural atmospheric boundary layer in a wind tunnel. The model obstacles covered a range of heights, widths, depths, and roof pitches typical of rural buildings. A field experiment was conducted using three unique full scale obstacles to validate model predictions and wind tunnel measurements. The accuracy of the neural network model varies with the quantity predicted and position in the obstacle wake. In general, predictions of mean velocity deficit in the far wake region are most accurate. The overall estimated mean uncertainties associated with model predictions of normalized mean wind speed and turbulence intensity are 4.9% and 12.8%, respectively.

• Environmental Engineering Research
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• v.15 no.2
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• pp.123-126
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• 2010

Modeling non-point pollution across multiple scales has become an important environmental issue. As a more representative and practical approach in quantifying and qualifying surface water, a modular neural network (MNN) was implemented in this study. Two different site-scales ($1.5\;{\times}\;10^5$ and $1.62\;{\times}\;10^6\;m^2$) with the same plants, soils, and paddy field management practices, were selected. Hydrologic data (rainfall, irrigation and surface discharge) and water quality data (time-series nutrient loadings) were continuously monitored and then used for the verification of MNN performance. Correlation coefficients (R) for the results predicted from the networks versus measured values were within the range of 0.41 to 0.95. The small block could be extrapolated to the large field for the rainfall-surface drainage process. Nutrient prediction produced less favorable results due to the complex phenomena of nutrients in the drainage water. However, the feasibility of using MNN to generate improved prediction accuracy was demonstrated if more hydrologic and environmental data are provided. The study findings confirmed the estimation accuracy of the upscaling from a small-segment block to large-scale paddy field, thereby contributing to the establishment of water quality management for sustainable agriculture.

• Journal of KSNVE
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• v.9 no.3
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• pp.466-471
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• 1999

The prediction methods of diffraction field in barrier has beenreported much about the infinite length barrier and it is very few work that reasonable sound source was used in experiment. This study, however, has worked about the several model barrier with acoustic scale model experiment. In the case of scale model experiment, it is difficult to use the kind of source with sufficiently characteristics. A spark discharge sound source with the high repeatability, broad band spectra, small size and omnidirectivity has veen used for the prediction of diffraction field. Several model barriers with different length on the ground were considered for the experiment and compared with the the results calculated by the approximation.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of the Korean earth science society
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• v.34 no.5
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• pp.393-401
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• 2013

Spherical harmonics power spectrum of the geopotential field of Gaussian-bell type on the sphere was investigated using integral formula that is associated with Legendre polynomials. The geopotential field of Gaussian-bell type is defined as a function of sine of angular distance from the bell's center in order to guarantee the continuity on the global domain. Since the integral-formula associated with the Legendre polynomials was represented with infinite series of polynomial, an estimation method was developed to make the procedure computationally efficient while preserving the accuracy. The spherical harmonics power spectrum was shown to vary significantly depending on the scale parameter of the Gaussian bell. Due to the accurate procedure of the new method, the power (degree variance) spanning over orders that were far higher than machine roundoff was well explored. When the scale parameter (or width) of the Gaussian bell is large, the spectrum drops sharply with the total wavenumber. On the other hand, in case of small scale parameter the spectrum tends to be flat, showing very slow decaying with the total wavenumber. The accuracy of the new method was compared with theoretical values for various scale parameters. The new method was found advantageous over discrete numerical methods, such as Gaussian quadrature and Fourier method, in that it can produce the power spectrum with accuracy and computational efficiency for all range of total wavenumber. The results of present study help to determine the allowable maximum scale parameter of the geopotential field when a Gaussian-bell type is adopted as a localized function.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.690-693
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• 2004

This paper proposes a multi-resolution electrostatic potential field (MREPF) based solution to the mobile robot path planning and collision avoidance problem in 2D dynamic environment. The MREPF is an environment method in calculation time and updating field map. The large scale resolution map is added to EPF and this resolution map interacts with the small scale resolution map to find an optimal solution in real time. This approach can be interpreted with Atlantis model. The simulation studies show the efficiency of the proposed algorithm.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.301-305
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• 2007

The performance of the railway bogie is classified into the stability and the steering performance. Testing for the bogie stability is conducted on the roller rig. But testing for the bogie steering performance on test facility is very difficult, so the testing for the vehicle curving performance is conducted on the real curve track. And it is desirable to test on the full scale test rig, but it caused many problems relating to test costs, test time. To overcome these problems, the small scale test rig is actively used in the field of bogie stability. Thus, in this paper, we have studied the scale track to test the bogie steering performance. For this, we designed the 1/5 scale test track equivalent to radius 200 curve and confirmed the validity of the testing for the bogie steering performance on the scale curve track through the testing using 1/5 scale bogie.