• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.685-690
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• 2008

In numerically evaluating the thermal performance of the heat exchanger, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table or diagram itself cannot be directly used without modelling. In this study the applicability of neural networks in modelling superheated water vapor was examined. The multi-layer neural networks consist of an input layer with 2 nodes, two hidden layers with 15 and 25 nodes respectively and an output layer with 3 nodes. Quadratic spline interpolation was also applied for comparison. Neural networks model revealed smaller percentage error compared with spline interpolation. From this result, it is confirmed that the neural networks could be a powerful method in modelling the superheated water vapor.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.87.2-87.2
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• 2010

The steady and dynamic process model for an internal reforming molten carbonate fuel cell power plant is discussed in this paper. The dominant thermal and chemical dynamic processes are modeled for the stack module and balance-of-plant, including cathode gas preparation, heat recovery, heat loss (Each heat loss amount for the stack and MBOP is obtained from real plant data) and fuel processing. Based on dynamic model and control demand, PID controllers are designed in the whole system. By applying these controllers we can obtain temperature balance of stack and control system depending on changing steam to carbon ratio, air feed amount, and transient condition.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.24-29
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• 2005

Optimal heat treatment process in martensitic stainless steel such as SUS416 is investigated. The approach is based on the combination of the interpolation and extrapolation method of a standard heat treatment technology with the principle of quenching and tempering temperature difference. The relationship of the macroscopic structure, fracture toughness and ductility as well as the hardness and strength are considered to induce a simple rule to apply with feasibility. Consequently, Optimal heat treatment condition in martensitic stainless steel is proposed and is shown the better quality. It was found that the smaller pain size of microstructure gives the enhanced fracture toughness and ductility.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.3-12
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• 2011

Understanding of water sediment trends is an important part of water quality monitoring. Water quality variables change over time and space, and cannot be modeled or explained clearly by either temporal or spatial analysis alone. This research analysed the trends of temperature, pH levels and dissolved oxygen levels based on the sediment records and spatial data obtained in Lower Mekong Basin (LMB) during 1985-2005. Our aim is to evaluate spatio-temporal trends and graphical analyses using an Inverse Distance Weighting (IDW) interpolation method. The main results from this research can be summarized as follows. The maximum temperature and pH have been stable during the study period and the maximum dissolved oxygen has been increasing gradually until 2002. The minimum pH and dissolved oxygen have been changing in an unsteady trend during the period. A spatial analysis shows that the water temperature in this region has been increasing over time. The pH trend shows that it is decreasing during 1993-2005. Dissolved oxygen concentration has been increasing from 1989 onwards and stays in that track.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.413-418
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• 2011

The thermodynamic properties of steam table are obtained by measurement or approximate calculation under appropriate assumptions. Therefore they are supposed to have basic measurement errors. And thermodynamic properties should be modeled through function approximation for using in numerical analysis. In order to make noised thermodynamic properties corresponding to measurement errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. Both neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure and temperature. In analysis spline interpolation method gives much less relative errors than neural networks at both ends of data. Excluding the both ends of data, the relative errors of neural networks is generally within ${\pm}0.2%$ and those of spline interpolation method within ${\pm}0.5$~1.5%. This means that the neural networks give smaller relative errors compared with quadratic spline interpolation method within range of use. From this fact it was confirmed that the neural networks trace the original values better than the quadratic interpolation method and neural networks are more appropriate method in modelling the saturated steam table.

• Journal of Food Hygiene and Safety
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• v.28 no.2
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• pp.168-173
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• 2013

This study was carried out to develop and validate predictive models of E. coli O157:H7 growth. Growth data of E. coli O157:H7 in Paprika were collected at 12, 24, 30 and $36^{\circ}C$. The population increased into 3.0 to 3.8 log10 CFU/g within 4 days, then continued to increase at a slower rate through 10 days of storage at $12^{\circ}C$. The lag time (LT) and maximum specific growth rate (SGR) obtained from each primary model was then modeled as a function of temperature using Davey and square root equations, respectively. For interpolation of performance evaluation, growth data for a mixture of E. coli O157:H7 were collected at time intervals in paprika incubated at the different temperatures, which was not used in model development. Results of model performance for interpolation data demonstrated that induced secondary models showed acceptable goodness of fit. Relative errors in the LT and SGR model for interpolation data (18 and $27^{\circ}C$) was 100%, which show acceptable goodness of fit and validated for interpolation. The primary and secondary models developed in this study can be used to establish tertiary models to quantify the effects of temperature on the growth of E. coli O157:H7 in paprika.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.1
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• pp.97-105
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• 2005

In order to reduce the uncertainties and improve the air flow field, objective analysis using asynoptic observational data is chosen as a method that enhances the reality of meteorology. In surficial data and their numerical interpolation for improving the interpretation of meteorological components, objective analysis scheme should perform a smooth interpolation, detect and remove the bad data and carry out internal consistency analysis. For objective analysis technique which related to data reliability and error suppression, we carried out two quality control methods. In site quality control, asynoptic observational data at urban area revealed low representation by the complex terrain and buildings. In case of wind field, it was more effective than temperature field when it were interpolated near waterbody data. Many roads, buildings, subways, vehicles are bring about artificial heat which left out of consideration on the simulation of air flow field. Therefore, in temperature field, objective analysis for more effective result was obtained when surficial data were interpolated as many as possible using value quality control rather than the selection of representative site.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.205-209
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• 2002

인체의 피부온도는 쾌적성과 감성에 크게 영향을 미치며 의류의 개발이나 건축환경의 설계 등에 활용되고 있다. 단순히 몇몇 측정점에서의 피부온도 데이터가 아니라 인체표면에 걸친 온도분포를 파악함으로써 다양한 정보를 이용하여 보다 광범위한 응용분야에 활용될 수 있을 것이다. 현재 인체표면의 온도분포를 측정하기 위하여 대부분 적외선 열화상 카메라를 활용하고 있다 그러나 열화상 카메라는 서미스터 등을 이용한 피부온 센서에 비하여 온도분해능이 떨어지며 특히 의복내의 피부온을 측정하는 것이 불가능하고 노출된 인체표면에 대해서만 측정이 가능하다. 따라서 본 연구에서는 피부온 센서를 이용한 인체표면 온도분포 측정시스템을 개발하기 위하여 각 센서의 위치와 간격, 그리고 인체 곡면을 따라서 보간법에 따라 온도분포 결과에 미치는 영향을 파악하고 적외선 화상 결과와 비교하고자 한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.3
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• pp.149-163
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• 2004

Evapotranspiration (ET) is a critical component of the hydrologic cycle which influences economic activities as well as the natural ecosystem. While there have been numerous studies on ET estimation for homogeneous areas using point measurements of meteorological variables, monitoring of spatial ET has not been possible at landscape - or watershed - scales. We propose a site-specific application of the land surface model, which is enabled by spatially interpolated input data at the desired resolution. Gyunggi Province of South Korea was divided into a regular grid of 10 million cells with 30m spacing and hourly temperature, humidity, wind, precipitation and solar irradiance were estimated for each grid cell by spatial interpolation of synoptic weather data. Topoclimatology models were used to accommodate effects of topography in a spatial interpolation procedure, including cold air drainage on nocturnal temperature and solar irradiance on daytime temperature. Satellite remote sensing data were used to classify the vegetation type of each grid cell, and corresponding spatial attributes including soil texture, canopy structure, and phenological features were identified. All data were fed into a standalone version of SiB2(Simple Biosphere Model 2) to simulate latent heat flux at each grid cell. A computer program was written for data management in the cell - based SiB2 operation such as extracting input data for SiB2 from grid matrices and recombining the output data back to the grid format. ET estimates at selected grid cells were validated against the actual measurement of latent heat fluxes by eddy covariance measurement. We applied this system to obtain the spatial ET of the study area on a continuous basis for the 2001-2003 period. The results showed a strong feasibility of using spatial - data driven land surface models for operational monitoring of regional ET.