• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1249-1263
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• 2013

In order to minimize the damages caused by long-term drought, appropriate drought management plans of the basin should be established with the drought forecasting technology. Further, in order to build reasonable adaptive measurement for future drought, the duration and severity of drought must be predicted quantitatively in advance. Thus, this study, attempts to forecast drought in Korea by using an Artificial Neural Network Model, and drought index, which are the representative statistical approach most frequently used for hydrological time series forecasting. SPI (Standardized Precipitation Index) for major weather stations in Korea, estimated using observed historical precipitation, was used as input variables to the MLP (Multi Layer Perceptron) Neural Network model. Data set from 1976 to 2000 was selected as the training period for the parameter calibration and data from 2001 to 2010 was set as the validation period for the drought forecast. The optimal model for drought forecast determined by training process was applied to drought forecast using SPI (3), SPI (6) and SPI (12) over different forecasting lead time (1 to 6 months). Drought forecast with SPI (3) shows good result only in case of 1 month forecast lead time, SPI (6) shows good accordance with observed data for 1-3 months forecast lead time and SPI (12) shows relatively good results in case of up to 1~5 months forecast lead time. The analysis of this study shows that SPI (3) can be used for only 1-month short-term drought forecast. SPI (6) and SPI (12) have advantage over long-term drought forecast for 3~5 months lead time.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.2
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• pp.121-131
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• 2007

Despite the close linkage in changes between the ecological and hydrological processes in forest ecosystems, an integrative approach has not been incorporated successfully. In this study, based on the vegetation and hydrologic data of the Gwangneung headwater catchment with the Geographic Information System, we attempted such an integrated approach by employing the Regional Hydro-Ecologic Simulation System (RHESSys). To accomplish this, we have (1) constructed the input data for RHESSys, (2) developed an integrated calibration system that enables to consider both ecological and hydrological processes simultaneously, and (3) performed sensitivity analysis to estimate the optimum parameters. Our sensitivity analyses on six soil parameters that affect streamflow patterns and peak flow show that the decay parameter of horizontal saturated hydraulic conductivity $(s_1)$ and porosity decay by depth (PD) had the highest sensitivity. The optimization of these two parameters to estimate the optimum streamflow variation resulted in a prediction accuracy of 0.75 in terms of Nash-Sutcliffe efficiency (NSec). These results provide an important basis for future evaluation and mapping of the watershed-scale soil moisture and evapotranspiration in forest ecosystems of Korea.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.1
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• pp.90-98
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• 1998

This paper is to describe the improved vector control which can control the induction motor robustly in low speed. When the induction motor is drived with low speed, below 10 percent of the rated speed, an algorithm which can compensate the error of unit vector angle generated by the harmonics is proposed. Another algorithm which can be tuned to the rotor time constant so that nay be robust to the rotor parameter change in low speed and transient state was proposed. The ripple of flux and torque was reduced by the proposed vector control and then the stable output characteristics was obtained in low speed. When the input and output is sinusoidal, the proposed vector control, the direct vector control and the indirect vector control were analyzed and compared in the low speed characteristics. And each control characteristics is compared and analyzed in state of containing harmonics. The estimation and tunning performance of rotor time constant is confirmed with simulation. The whole control system is implemented by real hardware and experimented to compare the proposed vector control with the direct vector control. As a result of the experiment with two control methods in low speed, the torque ripple of the proposed vector control is improved by 45 percent than the direct vector control. And it is confirmed that the flux current ripple is reduced in 0.2 p.u. and torque current ripple is reduced in 0.6 p.u. It is confirmed that the rotor time constant by the estimation and the tunning algorithm is tunned by the real rotor time constant. Finally, it was confirmed that the validity and robustness for the proposed vector control in low speed existed.

• Journal of Environmental Science International
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• v.10 no.2
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• pp.119-127
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• 2001

The Keum river is one of the important river in Korea and has a drainage area of 9,873$\textrm{km}^2$. The Keum river is deepening pollution state due to development of the lower city and construction of a industrial complex. The water quality of the Keum river come to eutrophication state and belong to III grade of water quality standard. The concentration BOD in river is affected by the organic loading from a tributary and the algae biomass that largely happen to under eutrophication state. In the eutrophic water mass such as the Keum river, the autochthonous BOD was very important part for making a decision of water quality management, because it was accounted for majority of the total BOD. The purpose of this study was to survey the chatacteristics of water quality in summer and to estimate reaction coefficient. Also, we studied to correlationship between chlorophyll a and BOD(COD) for estimation of the autochthonous BOD. The correlationship between chlorophyll a and BOD(COD) were obtained through the culture experiment of phytoplankton in the laboratory. The results of this study may be summarized as follows ; The characteristics of water quality in summer were belong to III~IV grade of water quality standard as BOD and nutritive condition is very high. The BOD, ammonia nitrogen and phosphate loadings in Miho stream which inflowing untreated sewage from Chungju city was occupied with 64.07%, 26.36%, 46.08%, respectively. Maximum nutrient uptake (Vmax) was 0.4400$\mu$M/hr as substrate of ammonia nitrogen, 0.1652$\mu$M/hr as substrate of phosphate. Maximum specific growth rate ($\mu$max) was 1.2525$hr^{-1}$ as substrate of ammonia nitrogen, 1.5177$hr^{-1}$ as substrate of phosphate. The correlation coefficient between chlorophyll a and BOD by the culture experiment were found to be 0.911~0.935 and 0.942~0.947 in the case adding nutrient and no adding nutrient, respectively. The correlation coefficient between chlorophyll a and COD through the culture experiment were found to be 0.918~0.977 and 0.880~0.931 in the case adding nutrient and no adding nutrient, respectively. The autochthonous BOD(COD) was estimated to the relationship between BOD(COD) and chlorophyll a. The regression equation were found to be autochthonous BOD=(0.045~0.073)${\times}chlorophyll$ a and autochthonous $COD=(0.137~0.182){\times}chlorophyll$ a.

• Journal of the Korean GEO-environmental Society
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• v.14 no.9
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• pp.31-37
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• 2013

In this study installation diameter, interval, area replacement ratio and ground hardness of applicable ground in C.G.S method should be mastered through surrounding ground by conducting modeling. Optimum artificial neural network was selected through the study of the parameter of artificial neural network and prediction model was developed by the relationship with numerical analysis and artificial neural network. As this result, C.G.S pile settlement and ground settlement were found to be equal in terms of diameter, interval, area replacement ratio and ground hardness, presented in a single curve, which means that the behavior pattern of applied ground in C.G.S method was presented as some form, and based on such a result, learning the artificial neural network for 3D behavior was found to be possible. As the study results of artificial neural network internal factor, when using the number of neural in hidden layer 10, momentum constant 0.2 and learning rate 0.2, relationship between input and output was expressed properly. As a result of evaluating the ground behavior of C.G.S method which was applied to using such optimum structure of artificial neural network model, is that determination coefficient in case of C.G.S pile settlement was 0.8737, in case of ground settlement was 0.7339 and in case of ground heaving was 0.7212, sufficient reliability was known.

• Journal of IKEEE
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• v.5 no.1 s.8
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• pp.100-110
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• 2001

In this paper, we investigated several buffer management schemes for the design of shared-memory type ATM switches, which can enhance the utilization of switch resources and can support quality-of-service (QoS) functionalities. Our results show that dynamic threshold (DT) scheme demonstrate a moderate degree of robustness close to pushout(PO) scheme, which is known to be impractical in the perspective of hardware implementation, under various traffic conditions such as traffic loads, burstyness of incoming traffic, and load non-uniformity across output ports. Next, we considered buffer management strategies to support QoS functions, which utilize parameter values obtained via connection admission control (CAC) procedures to set tile threshold values. Through simulations, we showed that the buffer management schemes adopted behave well in the sense that they can protect regulated traffic from unregulated cell traffic in allocating buffer space. In particular, it was observed that dynamic partitioning is superior in terms of QoS support than virtual partitioning.

• Smart Structures and Systems
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• v.20 no.5
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• pp.549-562
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• 2017

The US railroad network carries 40% of the nation's total freight. Railroad bridges are the most critical part of the network infrastructure and, therefore, must be properly maintained for the operational safety. Railroad managers inspect bridges by measuring displacements under train crossing events to assess their structural condition and prioritize bridge management and safety decisions accordingly. The displacement of a railroad bridge under train crossings is one parameter of interest to railroad bridge owners, as it quantifies a bridge's ability to perform safely and addresses its serviceability. Railroad bridges with poor track conditions will have amplified displacements under heavy loads due to impacts between the wheels and rail joints. Under these circumstances, vehicle-track-bridge interactions could cause excessive bridge displacements, and hence, unsafe train crossings. If displacements during train crossings could be measured objectively, owners could repair or replace less safe bridges first. However, data on bridge displacements is difficult to collect in the field as a fixed point of reference is required for measurement. Accelerations can be used to estimate dynamic displacements, but to date, the pseudo-static displacements cannot be measured using reference-free sensors. This study proposes a method to estimate total transverse displacements of a railroad bridge under live train loads using acceleration and tilt data at the top of the exterior pile bent of a standard timber trestle, where train derailment due to excessive lateral movement is the main concern. Researchers used real bridge transverse displacement data under train traffic from varying bridge serviceability levels. This study explores the design of a new bridge deck-pier experimental model that simulates the vibrations of railroad bridges under traffic using a shake table for the input of train crossing data collected from the field into a laboratory model of a standard timber railroad pile bent. Reference-free sensors measured both the inclination angle and accelerations of the pile cap. Various readings are used to estimate the total displacements of the bridge using data filtering. The estimated displacements are then compared to the true responses of the model measured with displacement sensors. An average peak error of 10% and a root mean square error average of 5% resulted, concluding that this method can cost-effectively measure the total displacement of railroad bridges without a fixed reference.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.60-69
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• 1998

This paper proposes a speech synthesis method using Fuzzy VQ, and then study how to make choice of fuzziness value which optimizes (controls) the performance of FVQ in order to obtain the synthesized speech which is closer to the original speech. When FVQ is used to synthesize a speech, analysis stage generates membership function values which represents the degree to which an input speech pattern matches each speech patterns in codebook, and synthesis stage reproduces a synthesized speech, using membership function values which is obtained in analysis stage, fuzziness value, and fuzzy-c-means operation. By comparsion of the performance of the FVQ and VQ synthesizer with simmulation, we show that, although the FVQ codebook size is half of a VQ codebook size, the performance of FVQ is almost equal to that of VQ. This results imply that, when Fuzzy VQ is used to obtain the same performance with that of VQ in speech synthesis, we can reduce by half of memory size at a codebook storage. And then we have found that, for the optimized FVQ with maximum SQNR in synthesized speech, the fuzziness value should be small when the variance of analysis frame is relatively large, while fuzziness value should be large, when it is small. As a results of comparsion of the speeches synthesized by VQ and FVQ in their spectrogram of frequency domain, we have found that spectrum bands(formant frequency and pitch frequency) of FVQ synthesized speech are closer to the original speech than those using VQ.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.4
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• pp.136-145
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• 2009

Site characteristic is an important input parameter in the geologic hazard assessments including, but not limited to, earthquakes, liquefaction and landslides. Although it is a routine to use data collected by boreholes or seismic prospecting for site classifications, we used indirect methods using the geologic and the topographic maps. A site classification map in the Gyeongsang Province has been produced by GIS tools based on geologic age, rock types, and elevations from the geologic map and the topographic map of Korea. Site B (rock site) is dominant in the study area, although softer soils are observed along rivers and in reclaimed lands. We have found that 73% of the site classification results in the study are in concordance with those obtained from borehole data. Observed discrepancies are attributed to errors in the geologic and the topographic maps. For some sites, the origin of the differences is not clear, which requires a further field study or a drilling. Site classification from this study provides essential information for reliable hazard assessments of earthquakes, floods, landslides and liquefaction. Results obtained in the study also play a crucial role in land use planning for developing areas.

• Journal of dental hygiene science
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• v.9 no.2
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• pp.181-187
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• 2009

Oral diseases may hinder people from living a healthy life by causing obstacles in the nutrition supply of the human body. This study aims at the found out the eating habits and recognition factors of people who are currently suffering from denial pain, and made a predictive modeling using neural network, which is a data mining. The oral health condition for maintaining and improving oral health has been examined and analyzed through a survey and the groups were divided based on the presence and the absence of dental pain. This study observed on eating habits, exercise and oral habits. The study results of neural network modeling input parameter was selected significant survival factors. As a result of making a predictive modeling using the neural network, the fitness of the predictive modeling of dental pain factors was 88.7%. As for the people who are likely to experience dental pain predicted by the neural network model, preventive measures including proper eating habits, education on oral hygiene, and stress release must precede any dental treatment.