• Journal of Korean Society on Water Environment
• /
• v.28 no.6
• /
• pp.866-881
• /
• 2012

Han River is a complex water system consisting of many lakes. The water quality of Lake Paldang is significantly affected by incoming flows, which are the South and North branches of the Han River, and the Kyungan Stream. In order to manage the water quality of the Lake Paldang, we should consider the entire water body where the incoming flows are included. The objectives of this study are to develop an integrated river and lake modeling system for Han River system using a multidimensional dynamic model and evaluate the model's performance against field measurement data. The integrated model was calibrated and verified using field measurement data obtained in 2007 and 2008. The model showed satisfactory performance in predicting temporal variations of water level, flow rate and temperature. The Root Mean Square Error (RMSE) for water temperature simulation were $0.88{\sim}2.13^{\circ}C$ (calibration period) and $1.05{\sim}2.00^{\circ}C$ (verification period) respectively. And Nash-Sutcliffe Efficiency (NSE) for water temperature simulation were 1089~0.98 (calibration period) and 0.90~0.98 (verification period). Utilizing the validated model, we analyzed the spatial and temporal distributions of temperature within Han River system. The variations of temperature along the river reaches and vertical thermal profiles for each lakes were effectively simulated with developed model. The suggested modeling system can be effectively used for integrated water quality management of water system consisting of many rivers and lakes.

• Journal of Climate Change Research
• /
• v.9 no.1
• /
• pp.47-58
• /
• 2018

In this study, we evaluated the uncertainty in the process of selecting GCM and downscaling method for assessing the impact of climate change, and influence of user-centered climate change information on reproducibility of Chungju Dam inflow was analyzed. First, we selected the top 16 GCMs through the evaluation of spatio-temporal reproducibility of 29 raw GCMs using 30-year average of 10-day precipitation without any bias-correction. The climate extreme indices including annual total precipitation and annual maximum 1-day precipitation were selected as the relevant indices to the dam inflow. The Simple Quantile Mapping (SQM) downscaling method was selected through the evaluation of reproducibility of selected indices and spatial correlation among weather stations. SWAT simulation results for the past 30 years period by considering limitations in weather input showed the satisfactory results with monthly model efficiency of 0.92. The error in average dam inflow according to selection of GCMs and downscaling method showed the bests result when 16 GCMs selected raw GCM analysi were used. It was found that selection of downscaling method rather than selection of GCM is more is important in overall uncertainties. The average inflow for the future period increased in all RCP scenarios as time goes on from near-future to far-future periods. Also, it was predicted that the inflow volume will be higher in the RCP 8.5 scenario than in the RCP 4.5 scenario in all future periods. Maximum daily inflow, which is important for flood control, showed a high changing rate more than twice as much as the average inflow amount. It is also important to understand the seasonal fluctuation of the inflow for the dam management purpose. Both average inflow and maximum inflow showed a tendency to increase mainly in July and August during near-future period while average and maximum inflows increased through the whole period of months in both mid-future and far-future periods.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.3
• /
• pp.205-210
• /
• 2022

A novel OLED pixel circuit is proposed in this paper that uses only n-type thin-film transistors(TFTs) to improve the luminance non-uniformity of the AMOLED display caused by the threshold voltage variation of an OLED driving TFT. The proposed OLED pixel circuit is composed of 6 n-channel TFTs and 2 capacitors. The operation of the proposed OLED pixel circuit consists of the capacitor initializing period, threshold voltage sensing period of an OLED·driving TFT, image data voltage writing period, and OLED·emitting period. As a result of SmartSpice simulation, when the threshold voltage of·OLED·driving TFT varies from 1.2 V to 1.8 V, the proposed OLED pixel circuit has a maximum current error of 5.18 % at IOLED = 1 nA. And, when the OLED cathode voltage rises by 0.1 V, the proposed OLED pixel circuit has very little change in the OLED current compared to the conventional OLED pixel circuit. Therefore, the proposed pixel circuit exhibits superior compensation characteristics for the threshold voltage variation of an OLED driving TFT and the rise of the OLED cathode voltage compared to the conventional OLED pixel circuit.

• The Journal of Information Systems
• /
• v.31 no.3
• /
• pp.197-220
• /
• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.5
• /
• pp.409-415
• /
• 2013

In this paper, we propose a correlation function using newly designed local signals for cosine-phased binary offset carrier (BOC) signal tracking. First, we divide a sub-carrier pulse over one pseudo random noise code period into multiple rectangular pulses, and subsequently, design novel local signals. Then, we obtain a correlation function with no side-peak based on a combination of correlations between the newly generated local signals and received cosine-phased BOC signal. From numerical results, it is confirmed that the proposed correlation function provides a tracking performance improvement over the conventional correlation functions in terms of the tracking error standard deviation.

• Membrane and Water Treatment
• /
• v.9 no.6
• /
• pp.455-462
• /
• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.11
• /
• pp.163-169
• /
• 1994

We propose a new learning method of recurrent neural networks as an effort to solve local minima problem. In this method the network with fixed connection weights is run for a given period time under given time-variant external inputs and initial conditions. The weights are changed in the direction that the total error is maximally decreased by using the steepest gradient method. If the obtained error is not sufficiently small even after iterating this procedure, additional feedback connections are introduced. Then, the external input signal is redefined. And we execute experiments on the recognition of Korean spoken digits as an application of the proposed network.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.53 no.3
• /
• pp.188-195
• /
• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of The Korean Digital Architecture Interior Association
• /
• v.12 no.1
• /
• pp.61-70
• /
• 2012

A construction project is very costly and takes a long time to make investment and yield profit. For this reason, financial institutions are cautious about financing construction projects. Meanwhile, a construction company needs financing from financial institutions to cover a large expense of a construction project. Thus, there is likely to be a close correlation between financing conditions and business operation of a construction company. To examine the relationship, variables were identified that are related to insolvency of a construction company and changes in financing conditions. The analysis period is between the second quarter of 2001 and the fourth quarter of 2010. Data was retrieved from TS2000 established by Korea Listed Companies Association (KLCA), Statistics Office, and Construction Economy Research Institute of Korea (CERIK). In terms of methodology, VECM (Vector Error Correction Model) was used to analyze dynamic relationship between changes in financing conditions and insolvency of a construction company based on the identified variables. The hypothesis was that changes in financing conditions would significantly affect business of a construction company, but, the analysis did not find a close relation between the two factors. However, it was shown that poor business of a construction company affects financing conditions adversely.

• Journal of the Korean earth science society
• /
• v.24 no.1
• /
• pp.7-21
• /
• 2003

The state-of-art third generation wave prediction model WAM was applied to the Korean seas for a winter monsoon period of January 1997. The wind field used in the present study is the global NSCAT-ERS/NCEP blended winds, which was further interpolated using a bi-cubic spline interpolator to fine grid limited area shallow water regime surrounding the Korean seas. To evaluate and investigate the accuracy of WAM, the hindcasted wave heights are compared with observed data from two shallow water buoys off Chil-Bal and Duk-Juk. A detailed study has been carried with the various meteorological parameters in observed buoy data and its inter-dependency on model computed wave fields was also investigated. The RMS error between the observation and model computed wave heights results to 0.489 for Chil-Bal and 0.417 for Duk-Juk. A similar comparison between the observation and interpolated winds off Duk-Juk show RMS error of 2.28 which suggest a good estimate for wave modelling studies.