• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.405-416
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• 2018

In this research, the distribution of Platinum Group Elements (PGEs) at roadside dust in Daejeon, Korea was examined using an ICP-MS (Inductively Coupled Plasma-Mass Spectrometry) technique. For the quality assurance of the determination, method validation based on its accuracy and precision was conducted using SRM (Standard Reference Material). It was found that the relative errors of Pt, Pd, and Rh against each SRM value were -0.7%, -10.0%, and -20.4%, respectively, while relative standard deviations for three elements were less than 10%. The concentrations of Pt, Pd and Rh in roadside dust averaged as $17.4{\pm}9.2{\mu}g/kg$, $283.6{\pm}20.5{\mu}g/kg$, and $7.3{\pm}2.8{\mu}g/kg$, respectively. The concentrations of Pt and Rh have significantly higher distribution patterns in the dust at roadside and underground parking lot than those in soil of the background or other urban area. The correlation analysis between concentrations of PGEs in roadside dust indicates that the distribution of Pt and Rh concentration were strongly affected by automobile sources.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.605-612
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• 2008

In this study, we estimate solar surface insolation (SSI) by using physical methods with MTSAT-1R data. SSI is regarded as crucial parameter when interpreting solar-earth energy system, climate change, and agricultural production predict application. Most of SSI estimation model mainly uses ground based-measurement such as pyranometer to tune the constructed model and to validate retrieved SSI data from optical channels. When compared estimated SSI with pyranometer measurements, there are some systemic differences between those instruments. The pyranometer data observed upward-looking hemispherical solid angle and distributed hourly measurements data which are averaged every 2 minute instantaneous observation. Whereas MTSAT-1R channels data are taken instantaneously images at fixed measurement time over scan area, and are pixel-based observation with a much smaller solid angle view. Those temporal discrepancies result from systemic differences can induce validation error. In this study, we adjust hour when estimate SSI to improve the retrieved accurate SSI.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1025-1032
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• 2018

Snow Cover is a form of precipitation that is defined by snow on the surface and is the single largest component of the cryosphere that plays an important role in maintaining the energy balance between the earth's surface and the atmosphere. It affects the regulation of the Earth's surface temperature. However, since snow cover is mainly distributed in area where human access is difficult, snow cover detection using satellites is actively performed, and snow cover detection in forest area is an important process as well as distinguishing between cloud and snow. In this study, we applied the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to the geostationary satellites for the snow detection of forest area in existing polar orbit satellites. On the rest of the forest area, the snow cover detection using $R_{1.61{\mu}m}$ anomaly technique and NDSI was performed. As a result of the indirect validation using the snow cover data and the Visible Infrared Imaging Radiometer (VIIRS) snow cover data, the probability of detection (POD) was 99.95 % and the False Alarm Ratio (FAR) was 16.63 %. We also performed qualitative validation using the Himawari-8 Advanced Himawari Imager (AHI) RGB image. The result showed that the areas detected by the VIIRS Snow Cover miss pixel are mixed with the area detected by the research false pixel.

• Journal of the Korean Geotechnical Society
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• v.38 no.5
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• pp.35-46
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• 2022

The in-situ resource utilization (ISRU) for sustainable lunar surface and deep space explorations has recently gained attention. Also, research on the development of construction material preparation technology using lunar regolith is in progress. Microwave sintering technology for construction material preparation does not require a binder and is energy efficient. This study applies microwave sintering technology to KLS-1, a Korean lunar simulant. It is crucial to secure the homogeneity to produce a sintered specimen for construction material. Therefore, understanding the interactions between microwaves, cavities, and raw materials is required. Using a numerical model in terms of efficient assessment of several cases and establishment of equipment operating conditions is a very efficient approach. Therefore, this study also proposes and verifies a coupled electric-thermal numerical model through cross-validation and comparison with experimental results. The numerical model proposed in this study will be used to present an efficient method for producing construction material using microwave sintering technology.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.1
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• pp.27-35
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• 2015

The material properties of crack growth models at an elevated temperature were derived from the results of numerous crack growth tests for Mod.9Cr-1Mo (ASME Grade 91) steel specimens under fatigue loading and creep loading at an elevated temperature. These crack growth models were needed for defect assessment under creep-fatigue loading. The mathematical crack growth rate models for fatigue crack growth (FCG) and creep crack growth (CCG) were determined based on the test results, and the models were compared with those of the French design code RCC-MRx to investigate the conservatism of the code. The French design code RCC-MRx provides an FCG model and a CCG model for Grade 91 steel in Section III Tome 6. It was shown that the FCG model of RCC-MRx is conservative, while the CCG model is non-conservative compared with the present test data. Thus, it was shown that further validation of the property was required. Mechanical strength tests and creep tests were also conducted, and the test results were compared with those of RCC-MRx.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.10
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• pp.89-100
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• 2007

Consumer Portal is defined as "a combination of hardware and software that enables two-way communication between energy service provider(ESP, like KEPCO) and equipment within the consumer's premises". The portal provides both a physical link(between wires, radio waves, and other media) and a logical link(translating among language-like codes and etiquette-like protocols) between in-building and wide-area access networks. Thus, the consumer portal is an important, open public shared infrastructure in the future vision of energy services. In this paper, we describe a new methodology for local communication and computer network capacity design of consumer portal, and also presents capacity calculation method using a network system limitation factors. By the approach, we can check into the limitations of existing methods, and propose an improved data processing algorithm that can expand the maximum number of the networked end-use devices up to $30{\sim}40$ times. For validation, we applies the proposed methode to our real system design. Our contribution will help electrical power information network design.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.10
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• pp.985-993
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• 2015

Free-piston Stirling engines (FPSEs) have attracted much attention in the renewable energy field as a key device in the conversion from thermal to mechanical energy, and in the recycling of waste energy. Traditional Stirling engines consist of two pistons that are connected by a mechanical link, while FPSEs are formed as a vibration system by connecting each piston to a spring without a physical link. To ensure the correct design and control of operations, this requires elaborate dynamic-performance predictions. In this paper, we present the performance-prediction methodology using a linear and nonlinear dynamic analytical model considering the external load of FPSEs. We perform linear analyses to predict the operating point of the engine using the root locus technique. Using nonlinear analysis, we also predict the amplitude of pistons by performing numerical integration considering both the linear and nonlinear damping terms of the external load. We utilize the predicted dynamic behavior to predict the engine performance. In addition, we compare the experiment results and existing model predictions for RE-1000 to verify the reliability of the analytical model.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.527-536
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• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.471-482
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• 2000

Surface energy balance components were evaluated by Landsat TM data and GIS with meteorological data. Calibration and validation for the applicability of this methodology were made through the estimating of the large-scale evapotranspiration (ET). In addition, sensitivity and error analysis was conducted to see the effects of the surface energy balance components on ET and the accuracy of each components. Bochong-chon located on the upper part of Guem River basin was selected as the case study area. Spatial distribution map of ET were produced for five dates: Jan. 1, Apr. 3, May. 10, and Nov. 27, 1995. The study results showed tat ET was greatly varied with the aspect and theland use type on the surface. In the case of having northeast and southeast in the aspect, ET was linearly increased depending on growing net radiation. While surface temperature has a high value, NDVI(Normalized Difference Vegetation Index) has a low value in the vegetated area. Therefore, ground heat flux was increased but ET was relatively decreased. The results of sensitivity and error analysis showed that net radiation is most sensitive and effective, ranging from 12.5% to 23.6% of sensitivity. Furthermore, the surface temperature, air temperature, and wind speed have the significant effects on ET estimation using remotely sensed data.