• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.329-337
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• 2003

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

• Journal of KSNVE
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• v.9 no.2
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• pp.377-386
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• 1999

This paper is a study on model method for estimating system inputs from vibration responses, which is one of indirect input identification methods in frequency domain. The method has advantages over direct inverse method especially when points of operational inputs are inaccessible so that artificial excitation forces cannot be applied to obtain frequency response functions of the complete system. Procedures of extended modal model method are proposed and checked by numerical experiment. Mechanisms of error propagation, i.e., how errors in modal parameters such as poles nad mode shape vectors affect estimation of the input forces, are illustrated. Then, in order to counteract the error propagation, discrete modal filter approach is taken in this paper to compute the inversion of modal matrix in which the most serious errors seem to be generated. Further, a Reduced form of Modified Reciprocal Modal Vector(RMRMV) is proposed for estimating multiple inputs. It is shown to have smaller orthogonality error than MRMV.

• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.277-286
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• 2002

Most of the problems in dealing with the tunnel construction are the uncertainties and complexities of the stress conditions and rock strengths in ahead of the tunnel excavation. The limitations on the investigation technology, inaccessibility of borehole test in mountain area and public hatred also restrict our knowledge on the geologic conditions on the mountainous tunneling area. Nevertheless an extensive and superior geophysical exploration data is possibly acquired deep within the mountain area, with up to the tunnel locations in the case of alternative design or turn-key base projects. An appealing claim in the use of artificial neural networks (ANN) is that they give a more trustworthy results on our data based on identifying relevant input variables such as a little geotechnical information and biological learning principles. In this study, error back-propagation algorithm that is one of the teaching techniques of ANN is applied to presupposition on Rock Mass Ratings (RMR) for unknown tunnel area. In order to verify the applicability of this model, a 4km railway tunnel's field data are verified and used as input parameters for the prediction of RMR, with the learned pattern by error back propagation logics. ANN is one of basic methods in solving the geotechnical uncertainties and helpful in solving the problems with data consistency, but needs some modification on the technical problems and we hope our study to be developed in the future design work.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.155-164
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• 2023

Sedirea japonica is one of the critically endangered species in South Korea mostly due to artificial harms such as illegal collection and habitat destruction. Therefore, artificial propagation through improving germination rate, increasing growth, and controlling flowering is meaningful for the conservation and reintroduction of S. japonica. It is suggested that cytokinins are one of the multi-factors that contribute to plant growth and floral responses. Especially, exogenous cytokinins have been known to induce or promote shoot growth or earlier flowering in orchids. Therefore, it was investigated how the application of 6-benzylaminopurine (BA) influenced the growth and inflorescence of S. japonica. A foliar spray containing BA at 100, 200, 300, and 400 ppm was applied from 1st July to 30th December 2021. Leaf length, leaf length growth rate, leaf width, and width and length ratio were measured as growth-related factors. Visible inflorescence rate, inflorescence length, the number of flowers per inflorescence, and the distance between the stalks were measured as flowering-related factors. Growth-related factors except for leaf growth rate were not affected by BA treatments, while leaf growth rate was significantly increased by 200 ppm of BA treatment. The visible inflorescence rate increased by 200 ppm of BA treatment, and there seems an optimal concentration and threshold of BA treatment. An iterative experiment with more seedlings and measurement factors would be helpful to figure out the effects of exogenous BA treatment on S. japonica, and it can be applied for mass propagation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.2
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• pp.133-144
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• 2006

In order to catch out such Bond Strength, the preceding researchers had ever examined the Bond Strength of FRP Plate through their experimentations by setting up of various fluent. However, since the experiment for research on such Bond Strength takes much of expenditure for equipment structure and time-consuming, also difficult to carry out, it is conducting limitedly. This Study purposes to develop the most suitable Artificial Neural Network Model by application of various Neural Network Model and Algorithm to the adhering experiment data of the preceding researchers. Output Layer of Artificial Neural Network Model, and Input Layer of Bond Strength were performed the learning by selection as the variable of the thickness, width, adhered length, the modulus of elasticity, tensile strength, and the compressive strength of concrete, tensile strength, width, respectively. The developed Artificial Neural Network Model has applied Back-Propagation, and its error was learnt to be converged within the range of 0.001. Besides, the process for generalization has dissolved the problem of Over-Fitting in the way of more generalized method by introduction of Bayesian Technique. The verification on the developed Model was executed by comparison with the resulted value of Bond Strength made by the other preceding researchers which was never been utilized to the learning as yet.

• Journal of Advanced Marine Engineering and Technology
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• v.41 no.1
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• pp.70-75
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• 2017

In this paper, we studied the RF characteristics of a voltage-controlled artificial transmission line employing periodically arrayed diodes for application to highly miniaturized wireless communication systems on an MMIC (monolithic microwave integrated circuit). According to the results, the novel voltage-controlled artificial transmission line employing periodically arrayed diodes exhibited a short wave length, which was only 35.2% that of the conventional transmission line, owing to increasing capacitance. In addition, it's effective permittivity and effective propagation constant exhibited considerably higher values than those of the conventional transmission line. Furthermore, attenuation constant of the voltage-controlled artificial transmission line was far higher than that of the conventional transmission line. Using the closed-form equation, we theoretically analyzed the equivalent circuit of the voltage-controlled artificial transmission line.

• Journal of Ocean Engineering and Technology
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• v.30 no.3
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• pp.169-176
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• 2016

Considering the artificial reef (AR) canyon intervals facilitated by flatly distributed placement models, the wake volumes of 25 AR sets were characterized through the following works. First, twenty-five different canyon intervals were established to investigate how the intervals affect the wake volumes of the AR placement models, each with nine cube-type ARs. Second, the element-based finite-volume method was used to facilitate flow analyses. Third, the so-called wake volume concept was adopted, and finally a reasonable placement interval was found based on the size of the wake volumes and the associated unit propagation indices. From the analysis results, it was found that a maximum wake volume of 25.18 m³ was generated when the longitudinal and transverse intervals were fixed at 6 m and 0 m, respectively. Thus, to magnify the wake volume, it is recommended that artificial reefs be placed at intervals of 6 m (3 times the reef length) in the flow direction, with no intervals in the normal direction, implicitly indicating that an intensively stacked placement model is a better option to efficiently secure a larger wake volume for the cube-type ARs.

• Journal of the Korean Society for Nondestructive Testing
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• v.5 no.2
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• pp.22-33
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• 1986

The application and development of on-line monitoring technology of AE to surveillance of crack propagation will contribute to the structural integrity of reactor pressure vessel and piping system. This research has been performed in order to obtain the evaluation technology for source location of AE and the analysis for the AE signal of the welded specimen. AE is detected by 4-channels AE system during pressurization in small pressure vessels. The cracking of artificial defects can be accurately located and categorized in real time. The welded specimens have more events rate and higher amplitude than the weldless less specimens, and the events rate have a peak around the yield point and just before the failure under tensile test.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.60-67
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• 2005

Compared with conventional welding, laser spot welding offers a unique combination of high speed, precision and low heat distortion. This combination of advantages is attractive for manufacturing industries including automotive and electronics companies. In this paper, a real time monitoring scheme fur a pulsed Nd:YAG laser spot welding was suggested. Acoustic emission (AE) signals were collected during welding and analyzed for given process conditions such as laser power and pulse duration. A back propagation artificial neural network, with AE frequency content inputs, was used to predict the weldability of stainless steel sheets.