• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Food Science and Preservation
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• v.13 no.5
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• pp.581-588
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• 2006

Dynamic changes of free sugar and amino acid in the mixture of red bean paste sediment by corn syrup concentration and heating conditions were monitored. Glucose and fructose contents of red bean paste increased with an increasing blown color intensity. Amino acid content was affected by the heating temperature, increased with an increase in browning color intensify. Browning color intensity of each samples increased up to $95^{\circ}C$, but decreased above $95^{\circ}C$. This result was the same tend as changes of glucose and amino acid. The result of correlation coefficients among free sugar amino acid and browning color intensity show that increase in browning color intensity was not correlated directly with changes of free sugar and amino acid content. It seems that the contents of free sugar and amino acid resolved from saccharides and protein were much mote than contents nea for browning reaction.

• Smart Structures and Systems
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• v.15 no.1
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• pp.99-117
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• 2015

The Stonecutters Bridge (SCB) in Hong Kong is the third-longest cable-stayed bridge in the world with a main span stretching 1,018 m between two 298 m high single-leg tapering composite towers. A Wind and Structural Health Monitoring System (WASHMS) is being implemented on SCB by the Highways Department of The Hong Kong SAR Government, and the SCB-WASHMS is composed of more than 1,300 sensors in 15 types. In order to establish a linkage between structural health monitoring and maintenance management, a Structural Health Rating System (SHRS) with relevant rating tools and indices is devised. On the basis of a 3D space frame finite element model (FEM) of SCB and model updating, this paper presents the development of an SHR-oriented 3D multi-scale FEM for the purpose of load-resistance analysis and damage evaluation in structural element level, including modeling, refinement and validation of the multi-scale FEM. The refined 3D structural segments at deck and towers are established in critical segment positions corresponding to maximum cable forces. The components in the critical segment region are modeled as a full 3D FEM and fitted into the 3D space frame FEM. The boundary conditions between beam and shell elements are performed conforming to equivalent stiffness, effective mass and compatibility of deformation. The 3D multi-scale FEM is verified by the in-situ measured dynamic characteristics and static response. A good agreement between the FEM and measurement results indicates that the 3D multi-scale FEM is precise and efficient for WASHMS and SHRS of SCB. In addition, stress distribution and concentration of the critical segments in the 3D multi-scale FEM under temperature loads, static wind loads and equivalent seismic loads are investigated. Stress concentration elements under equivalent seismic loads exist in the anchor zone in steel/concrete beam and the anchor plate edge in steel anchor box of the towers.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.307-319
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• 2016

A Land-Atmosphere Modeling Package (LAMP) for supporting agricultural and forest management was developed at the National Center for AgroMeteorology (NCAM). The package is comprised of two components; one is the Weather Research and Forecasting modeling system (WRF) coupled with Noah-Multiparameterization options (Noah-MP) Land Surface Model (LSM) and the other is an offline one-dimensional LSM. The objective of this paper is to briefly describe the two components of the NCAM-LAMP and to evaluate their initial performance. The coupled WRF/Noah-MP system is configured with a parent domain over East Asia and three nested domains with a finest horizontal grid size of 810 m. The innermost domain covers two Gwangneung deciduous and coniferous KoFlux sites (GDK and GCK). The model is integrated for about 8 days with the initial and boundary conditions taken from the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data. The verification variables are 2-m air temperature, 10-m wind, 2-m humidity, and surface precipitation for the WRF/Noah-MP coupled system. Skill scores are calculated for each domain and two dynamic vegetation options using the difference between the observed data from the Korea Meteorological Administration (KMA) and the simulated data from the WRF/Noah-MP coupled system. The accuracy of precipitation simulation is examined using a contingency table that is made up of the Probability of Detection (POD) and the Equitable Threat Score (ETS). The standalone LSM simulation is conducted for one year with the original settings and is compared with the KoFlux site observation for net radiation, sensible heat flux, latent heat flux, and soil moisture variables. According to results, the innermost domain (810 m resolution) among all domains showed the minimum root mean square error for 2-m air temperature, 10-m wind, and 2-m humidity. Turning on the dynamic vegetation had a tendency of reducing 10-m wind simulation errors in all domains. The first nested domain (7,290 m resolution) showed the highest precipitation score, but showed little advantage compared with using the dynamic vegetation. On the other hand, the offline one-dimensional Noah-MP LSM simulation captured the site observed pattern and magnitude of radiative fluxes and soil moisture, and it left room for further improvement through supplementing the model input of leaf area index and finding a proper combination of model physics.

• Korean Journal of Ecology and Environment
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• v.50 no.2
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• pp.216-237
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• 2017

Thermal effluent of the hot spring has long been a field of interest in the relationship between temperature gradient and freshwater algae in geology, limnology and aquatic ecology throughout the world. On the other hand, many artificial hot springs have been developed in Korea, but the research on them has not been still active. This study was performed every month from December 2015 to September 2016, to elucidate the spatiotemporal effects of thermal wastewater effluent (TWE) on the ecosystem of benthic algal assemblage in four stations(BSU (upstream), HSW (hot spring wastewater outlet), BSD1~2 (downstream)) of the upstream reach of the Buso Stream, a tributary located in the Hantan River basin. During the survey, the influencing distance of temperature on TWE was <1.0 km, and it was the main source of N P nutrients at the same time. The effects of TWE were dominant at low temperature and dry season (December~March), but it was weak at high temperature and wet season (July~September), reflecting some seasonal characteristics. Under these circumstances, the attached algal communities were identified to 59 genera and 143 species. Of these, the major phylum included 21 genera 83 species of diatoms(58.0%), 9 genera 21 species of blue-green algae (14.7%) and 25 genera 32 species of green algae (22.4%), respectively. The spatiotemporal distribution of them was closely related to water temperature ($5^{\circ}C$ and $15^{\circ}C$) and current ($0.2m\;s^{-1}$ and $0.8m\;s^{-1}$). In the basic environment maintaining a high water temperature throughout the year round, the flora favoring high affinity to $PO_4$ in the water body or preferring stream habitat of abundant $NO_3-PO_4$ was dominant. As a result, when compared with the outcomes of previous algal ecology studies conducted in Korea, the Buso Stream was evaluated as a serious polluted state due to persistent excess nutrient supply and high thermal pollution throughout the year round by TWE. It can be regarded as a dynamic ecosystem in which homogeneity (Summer~Autumn) and heterogeneity (Winter~Spring) are repeated between upstream and downstream.

• Horticultural Science & Technology
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• v.28 no.5
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• pp.783-789
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• 2010

The effective method for heating root-zone during winter season was studied in the aspects of growth, yield and economics for tomato ($Solanum$ $lycopersicum$) in perlite bag culture. There were four root-zone heating treatments: two hours heating from one hour before to one hour after sunrise, four hours from two hours before to two hours after sunrise, 15 hours after sunset, and no heating. The growth characteristics of the upper parts of plants were not significantly different among the treatments, but root volume increased with longer heating of the root zone. The Plant Development Index, using stem diameter and the length between growing tip and the upper flowering truss, showed relation between yield per cluster and growth pattern. The treatment heating for four hours was the most economic in terms of growth and yield of tomato.

• Environmental and Resource Economics Review
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• v.26 no.4
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• pp.519-547
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• 2017

This paper explores whether it is better to forecast city gas demand in Korea using national level data directly or, alternatively, construct forecasts from regional demand models and then aggregate these regional forecasts. In the regional model, we consider gas demand for Seoul metropolitan and the other local areas. Our forecast evaluation exercise for 2013-2016 shows the regional forecast model generally outperforms the national forecasting model. This result comes from the fact that the dynamic properties of each region's gas demands can be better taken into account in the regional demand model. More specifically, the share of residential gas demand in the Seoul metropolitan area is above 50%, and subsequently this demand is heavily influenced by temperature fluctuations. Conversely, the dominant portion of regional gas demand is due to industrial gas consumption. Moreover, electricity is regarded as a substitute for city gas in the residential sector, and industrial gas competes with certain oil products. Our empirical results show that a regional demand forecast model can be an effective alternative to the demand model based on nation-wide gas consumption and that regional information about gas demand is also useful for analyzing sectoral gas consumption.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.1-11
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• 2011

Many researchers have studied on the methods to improve the processor performance. However, high integrated semiconductor technology for improving the processor performance causes many problems such as battery life, high power density, hotspot, etc. Especially, as hotspot has critical impact on the reliability of chip, thermal problems should be considered together with performance and power consumption when designing high-performance processors. To alleviate the thermal problems of processors, there have been various researches. In the past, mechanical cooling methods have been used to control the temperature of processors. However, up-to-date microprocessors causes severe thermal problems, resulting in increased cooling cost. Therefore, recent studies have focused on architecture-level thermal-aware design techniques than mechanical cooling methods. Even though architecture-level thermal-aware design techniques are efficient for reducing the temperature of processors, they cause performance degradation inevitably. Therefore, if the mechanical cooling methods can manage the thermal problems of processors efficiently, the performance can be improved by reducing the performance degradation due to architecture-level thermal-aware design techniques such as dynamic thermal management. In this paper, we analyze the cooling efficiency of high-performance multicore processors according to mechanical cooling methods. According to our experiments using air cooler and liquid cooler, the liquid cooler consumes more power than the air cooler whereas it reduces the temperature more efficiently. Especially, the cost for reducing $1^{\circ}C$ is varied by the environments. Therefore, if the mechanical cooling methods can be used appropriately, the temperature of high-performance processors can be managed more efficiently.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.148-161
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• 2012

The intensive observation (ProbeX-2010) with 6-hour launches of radiosonde was performed over Seoul metropolitan area (Dongducheon, Incheon Airport, and Yangpyeong) from 13 Aug. to 3 Sep. 2010. Five typical heavy rainfall patterns occurred consecutively which are squall line, stationary front, remote tropical cyclone (TC), tropical depression, and typhoon patterns. On 15 Aug. 03 KST, when squall line developed over Seoul metropolitan area, dry mid-level air was drawn over warm and moist low-level air, inducing strong convective instability. From 23 to 26 Aug and from 27 to 29 Aug. Rainfall event occurred influenced by stationary front and remote TC, respectively. During the stationary frontal rainy period, thermal instability was dominant in the beginning stage, but dynamic instability became strong in the latter stage. Especially, heavy rainfall occurred on 25 Aug. when southerly low level jet formed over the Yellow Sea. During the rainy period by the remote TC, thermal and dynamic instability sustained together. Especially, heavy rainfall event occurred on 29 Aug. when the tropical air with high equivalent potential temperature (>345 K) occupied the deep low-middle level. On 27 Aug. and 2 Sep. tropical depression and typhoon Kompasu affected Seoul metropolitan area, respectively. During these events, dynamic instability was very strong.