• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.31-39
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• 2012

In this paper, we propose a microprocessor-based automatic voltage regulator(AVR) to reduce consumers' electric energy consumption and to help controlling peak demanding power. Hybrid Switching Automatic Voltage Regulator (HS-AVR) consist of a toroidal core, several tap control switches, display and command control parts. The coil forms an autotransformer which has a serial main winding and four parallel auxiliary windings. It controls the output voltage by changing the combination of the coils and the switches. Relays are adopted as the link switches of the coils to minimize the loss. To make connecting and disconnecting time accurate, relays of the circuit have parallel TRIACs. A software phase locked loop(PLL) has been used to synchronize the timings of the switches to the voltage waveform. The software PLL informs the input voltage zero-crossing and positive/negative peak timing. The traditional voltage transformers and AVRs have a disadvantage of having a large mandatory capacity to accommodate maximum inrush current to avoid the switch contact damage. But we propose a suitable AVR for every purpose in smart grid with reduced size and increased efficiency.

• Journal of the Korea Concrete Institute
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• v.17 no.4 s.88
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• pp.543-550
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• 2005

In the present work, modulus of rupture (MOR), flexural toughness properties $(I_{30}\;and\;W_{2.0})$ and workability (slump) of high performance hybrid fiber reinforced concrete (HPHFRC) mixed with micro-fiber (carbon fiber) and macro-fiber (steel fiber), and replaced with a fine mineral admixture such as silica fume (SF) are characterized through the analysis of variance (ANOVA). Data of MOR, $I_{30}(or W_{2.0})$ and slump are used as the characteristic values to estimate flexural performance and workable property of HPHFRC. Specially, an experimental design was Planned according to the fractional orthogoanl nay method to reduce experimental number of times. The experimental results show that steel fiber is a considerable significant factor in MOR and I30 $(W_{2.0})$. Based on the significance of experimental factors about each characteristic factors, the following evaluation can be used: Experiment factors which reduce slump most remarkably are carbon fiber, steel fiber, silica fume order.; Those that improve MOR most significantly are silica fume $({\fallingdotseq}\;carbon\;fiber)$, steel fiber order; Those that increase flexural toughness most distinctly are silica fume, carbon fiber, steel fiber order. It is obtained that the combination of steel fiber $1.0\%$, carbon fiber $0.25\%$ and silica fume $5.0\%$ is the experimental condition that improve MOR and flexural toughness excellently with workability ensured within the experiment.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.681-695
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• 2019

A Transient Storage Model (TSM) is one of the most widely used model accounting for complex solute transport in natural river to understanding natural river properties with four TSM key parameters. The TSM parameters are estimated via inverse modeling. Parameter estimation of the TSM is carried out by solving optimization problem about finding best fitted simulation curve with measured curve obtained from tracer test. Several studies have reported uncertainty in parameter estimation from non-convexity of the problem. In this study, we assessed best combination of optimization method and objective function for TSM parameter estimation using Cheong-mi Creek tracer test data. In order to find best optimization setting guaranteeing convergence and speed, Evolutionary Algorithm (EA) based global optimization methods, such as CCE of SCE-UA and MCCE of SP-UCI, and error based objective functions were compared, using Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL). Overall results showed that multi-EA SC-SAHEL with Percent Mean Squared Error (PMSE) objective function is the best optimization setting which is fastest and stable method in convergence.

• Clinical and Experimental Pediatrics
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• v.49 no.9
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• pp.917-929
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• 2006

Over the last several decades there has been a remarkable change in the therapeutic strategy of congenital heart disease. Development of new tools and devices, accumulations of experience, technical refinement have positively affected the outcome of interventional treatment. Many procedures including atrial septostomy, balloon valvuloplasty, balloon dilation of stenotic vessel with or without stent implantation, transcatheter occlusion of abnormal vascular structure, transcatheter closure of patent arterial duct and atrial septal defect, are now performed as routine interventional procedures in many institutes. In diverse conditions, transcatheter techniques also provide complementary and additive role in combination with surgery. Intraoperative stent implantation on stenotic vessels, perventricular device insertion, and hybrid stage 1 palliative procedure for hypoplastic left heart syndrome have been employed in high risk patients for cardiac surgery with encouraging results. Transcatheter closure of ventricular septal defect has been performed safely showing comparable result with surgery. Investigational procedures such as percutaneous valve insertion and valve repair are expected to replace the role of surgery in certain group of patients in the near future. Continuous evolvement in this field will contribute to reduce the risk and suffering from congenital heart disease, while surgery will be still remained as a gold standard for significant portion of congenital heart disease.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.4
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• pp.429-436
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• 2005

A coagulation/ultrafiltration membrane hybrid system was operated to treat river water with capacity of $0.06m^3/d$. The impact on membrane fouling by flux and linear velocity was investigated. It is known that pressure increase is proportional to flux increase. However, pressure increase was much faster than theoretical value in the pilot plant test. So it was suggested that flux was on important factor in ultrafiltration of continuous operation. Membrane fouling was decreased when linear velocity was increased. This phenomenon was found more obviously without coagulation. With the combination of coagulation and sedimentation, membrane fouling was not reduced conspicuously. Big particles formed during coagulation and sedimentation were destroyed by feed and circulation pumping, which resulted in little effect on membrane fouling reduction. The degree of destruction was similar at various linear velocities. In this study, the hollow fiber membrane was used and the system was operated in pressure type module. In case of the system used in this study, membrane fouling has been affected lightly by linear velocity variation when coagulation pretreatment was applied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.99-118
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• 2013

Electrocardiograms (ECGs) are widely used by clinicians to identify the functional status of the heart. Thus, there is considerable interest in automated systems for real-time monitoring of arrhythmia. However, intra- and inter-patient variability as well as the computational limits of real-time monitoring poses significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is therefore a promising new intelligent diagnostic tool.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.2
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• pp.74-80
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• 2013

In this study, a novel and flexible recommender system was developed, based on product taxonomy and usage patterns of users. The proposed system consists of the following four steps : (i) estimation of the product-preference matrix, (ii) construction of the product-preference matrix, (iii) estimation of the popularity and similarity levels for sought-after products, and (iv) recommendation of a products for the user. The product-preference matrix for each user is estimated through a linear combination of clicks, basket placements, and purchase statuses. Then the preference matrix of a particular genre is constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product with respect to the total. The popularity and similarity levels of a user's clicked product are estimated with an entropy index. Based on this information, collaborative and content-based filtering is used to recommend a product to the user. To assess the effectiveness of the proposed approach, an empirical study was conducted by constructing an experimental e-commerce site. Our results clearly showed that the proposed hybrid method is superior to conventional methods.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.50-53
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• 2002

There are increasing needs to deliver the multimedia streaming over heterogeneous networks. When considering network environments and equipment accessed by user, delivery of video streaming must be scalable. There are many kinds of scalable video coding: spatial, temporal, SNR, and hybrid. The SNR scalable and spatial resolution, but different SNR quality with respect to layers. The 1-layer SNR scalable encoder produces SNR scalable video streams with ease. But, there is drift problem. Modified 1-layer approach does not have this problem but coding inefficiency, and is not MPEG-compliant. The present MPEG-compliant 2-layer encoder comes out to reduce coding rate. But it still use only base layer to encode whole layer. In this paper, we propose adaptive MPEG-compliant 2-layer encoder. Using linear combination algorithm, encoder use 1 motion vector to encode the sequences efficiently. By dong this, we can achieve the coding efficiency of SNR scalable coding.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.307-312
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• 2007

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of induction motor using FLC-FNN and estimation of speed using ANN controller The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.