• Smart Structures and Systems
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• v.30 no.4
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• pp.421-432
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• 2022

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.121-130
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• 2024

Many foundation projects are built on red-bed soft rocks, and the damage evolution of this kind of rocks affects the safety of these projects. At present, there is insufficient research on the damage evolution of red-bed soft rocks, especially the progressive process from mesoscopic texture change to macroscopic elastoplastic deformation. Therefore, based on the dual-porosity characteristics of pores and fissures in soft rock, we adopted a cellular automata model to simulate the propagation of these voids in soft rocks under an external load. Further, we established a macro-mesoscopic damage model of red-bed soft rocks, and its reliability was verified by tests. The results indicate that the relationship between the number and voids size conformed to a quartic polynomial, whereas the relationship between the damage variable and damage porosity conformed to a logistic curve. The damage porosity was affected by dual-porosity parameters such as the fractal dimension of pores and fissures. We verified the reliability of the model by comparing the test results with an established damage model. Our research results described the progressive process from mesoscopic texture change to macroscopic elastoplastic deformation and provided a theoretical basis for the damage evolution of these rocks.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.57-62
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• 2008

In this paper, a folded helical monopole antenna for TDMB receiving and a trapezoidal fractal microstrip patch antenna for GPS were designed and fabricated for the vehicle shark antenna. To minimize null which is generating toward antenna axis direction and to receive both vertical polarization and horizontal polarization for TDMB antenna, we fold 90 degree helical monopole element. GPS antenna to get wide bandwidth and gain improvement was designed an air substrate trapezoidal microstrip patch antenna. Fabricated TDMB and GPS antenna were measured for S11 and radiation pattern, and compared with a commercialized antenna. TDMB antenna shows 3 dB higher antenna gain and receiving signal strength than the commercial one. GPS antenna shows the gain of 4.31 dBi at the resonant frequency, which is $3{\sim}5\;dB$ higher gain over whole operating band and 135MHz wide bandwidth at 2:1 VSWR than the conventional ceramic antenna.

• Journal of the Korean Society of Radiology
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• v.7 no.1
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• pp.25-30
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• 2013

In this work, we investigate the statistical properties of gamma exposure rates using well-known analysis methods, such as Autocorrelation Function Analysis(ACF), Rescaled Range Analysis(R/S Analysis), and Detrended Fluctuation Analysis(DFA). Especially, DFA is an important method to reliably detect long-range correlations in non-stationary time series. Our data are measured by Gangneung regional radiation monitoring station over the period of 1998 to 2011. First, we find a crossover indicating two different governing regimes in fluctuations of gamma exposure rates. Within a year, they show a strong long-ranged memory while this property vanishes over the range of time period longer than one year. Second, our finding is very securely supported by a variety of analysis tools. Those tools yield many relevant exponents which satisfies the well known relation between them.

• Journal of Science of Art and Design
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• v.1
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• pp.165-188
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• 1999

When we think about visual art, we can't pass over the matter of images and spaces. An image is fixed in a painting space. If the image were posited in the river of time, it would be changed as time flowed on. Its image might be created, rigged, transfigured or it might vanish or remain as traces. The image is changed through the process of expressing them on the painting surfaces. In paintings the changing of the image is caused not only by working processes but also recognizing the objects. In modernist painting, the idea of space was important, but in postmodernist view, the idea of time becomes essential. In this thesis I will focus on the matter of time and image of painting in the late 20th century. Studying the relationship of image and time is not familiar because we had considered the space as being more related to painting distinction than time. Presently, the concept of space can't be divided from that of time. In Physics, they recently called it as a united concept- time-space. Painting space also becomes one asking and recieve the concept of time actively. Now in painting, time is no more a supporting matter but becomes a major matter. In the late 20th century, we can find many trials to catch the change of images as time flowes on and express them as a trace through using verioty media. One of the way to drawing the change of images as time passes could be seen through various joint images in one painting space and another in the traces of one image. The other could be done by using new media such as computer in the example of fractal art. It shows the images chainging of practically as time changes. New understanding about time of this era has to be affirmatively accepted because it reflects the contemporary cultural situation and in art, the concern about the possiblity of it and finding new medias which has to go on.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.271-280
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• 1999

인공신경망을 시계열예측에 적용하는 경우에 고려되어야 할 문제중, 특히 모형에 적합한 입력변수의 생성이 중요시되고 있는데, 이러한 분야는 인공신경망의 모형생성과정에서 입력변수에 대한 전처리기법으로써 다양하게 제시되어 왔다. 가장 최근의 입력변수 전처리기법으로써 제시되고 있는 신호처리기법은 전통적 주기분할처리방법인 푸리에변환기법(Fourier transforms)을 비롯하여 이를 확장시킨 개념인 웨이블릿변환기법(wavelet transforms) 등으로 대별될 수 있다. 이는 기본적으로 시계열이 다수의 주기(cycle)들로 구성된 상이한 시계열들의 집합이라는 가정에서 출발하고 있다. 전통적으로 이러한 시계열은 전기 또는 전자공학에서 주파수영역분할, 즉 고주파 및 저주파수를 분할하기 위한 기법에 적용되어 왔다. 그러나, 최근에는 이러한 연구가 다양한 분야에 활발하게 응용되기 시작하였으며, 그 중의 대표적인 예가 바로 경영분야의 재무시계열에 대한 분석이다 전통적으로 재무시계열은 장, 단기의사결정을 가진 시장참여자들간의 거래특성이 시계열에 각기 달리 가격으로 반영되기 때문에 이러한 상이한 집단들의 고유한 거래움직임으로 말미암아 예를 들어, 주식시장이 프랙탈구조를 가지고 있다고 보기도 한다. 이처럼 재무시계열은 다양한 사회현상의 집합체라고 볼 수 있으며, 그만큼 예측모형을 구축하는데 어려움이 따른다. 본 연구는 이러한 시계열의 주기적 특성에 기반을 둔 신호처리분석으로서 기존의 시계열로부터 노이즈를 줄여 주면서 보다 의미 있는 정보로 변환시켜 줄 수 있는 웨이블릿분석 방법론을 새로운 필터링기법으로 사용하여 현재 많은 연구가 진행되고 있는 인공신경망과의 모형결합을 통해 기존연구와는 다른 새로운 통합예측방법론을 제시하고자 한다. 본 연구에서 제시하는 통합방법론은 크게 2단계 과정을 거쳐 예측모형으로 완성이 된다. 즉, 1차 모형단계에서 원시 재무시계열은 먼저 웨이블릿분석을 통해서 노이즈가 필터링 되는 동시에, 과거 재무시계열의 프랙탈 구조, 즉 비선형적인 움직임을 보다 잘 반영시켜 주는 다차원 주기요소를 가지는 시계열로 분해, 생성되며, 이렇게 주기에 따라 장단기로 분할된 시계열들은 2차 모형단계에서 신경망의 새로운 입력변수로서 사용되어 최종적인 인공 신경망모델을 구축하는 데 반영된다.

• The Korean Journal of Ecology
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• v.22 no.2
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• pp.69-77
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• 1999

Secondary vegetation. the holistically integrated system of nature and human being, is the complicated ecosystem that is composed of natural and man-created factors. Understanding the ecological function of secondary vegetation supplies us many important informations for sustainable landscape management and ecological restoration planning. In this research, we tried to examine the shape effect of vegetation patch on early structure of populations of pine and oaks. Moreover. we also tried to clarify the ecological functions of patch edge by exploring the patch effect on germination using patch index. In addition, we present the landscape structure of man -made vegetation of our study area, and setting experimental design of research. Vegetation landscape of study area is typical human disturbed landscape mainly composed of disturbance patches. Vegetation types of graveyard and managed pine forest were controlled by periodically repeated management. However, current seedlings of pine occurred well at both vegetation types. Presence of both saplings were more controlled in managed pine forest (PDM) and graveyard (G) than those of undergrowth (PD) and forest edge (FE) with canopy trees. The number of pine seedlings increased with patch size and patch perimeter. That of oak seedlings was, however, not significantly different. Larger graveyards provided higher light availability for germination of pine seedlings. We think, however, most seedlings of both species in the large sized graveyards without shade will die more easily than that of small sized ones before rainy summer. Relationships between patch shape and germination of two woody species cannot be exactly explained enough yet in these results. More informations on spatial interaction of the total species with differently sized patches are necessary to solve the concept of patch effect on species colonization.

• Archives of design research
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• v.16 no.3
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• pp.101-110
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• 2003

People are surrounded with numerous sounds. The sound is generated from nature and people. For example, the sound enables people or animal responsive to instinctive action. Music or fine arts are presented differently by its distinctive medium. On the other hand, those art forms are similar in a way that people uses right side of brain and intuitions for creative effects. Conjunctions between sounds and visual arts have been progressed to data. From art forms in subjective views to art forms using high technology such as the computer, experiments for sounds visualization are practiced constantly. For that reason, intrinsic attributes of sounds in design area and distinctive qualities are discussed in this study. With respect to existing category of studies and consideration of the tendency in recent researches, the object is to propose direction for the study in regards to methodology of design, which is reconstructing visualized expression.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.2
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• pp.97-104
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• 2007

An investigation has been made on the relationship between characteristics of Acoustic Emission (AE) signal in welding flaw and the stress corrosion defect in-service for the high pressure pipe steel. In order to tackle the problem of welding flaw in high pressure pipe, specimens were made by the aid of the application of both corrosion liquid usage and a quenching method after local heating. The amplitude of signal was $60{\sim}75\;dB$ in the territory which is suspected for defect, and the specimens which only have welding flaw showed gradients of 0.034, 0.034, 0.035. Moreover, there is a certain increase in gradient even though the differences are very slight. That is, corrosion specimens showed new gradients of 0.040, 0.039, 0.041 which put welding flaw and corrosion mechanism together. After pressurizing 3 minutes, AE signal has been detected from welding flaw easily in each part of the section. It is possible to predict the occurrence and also prevent the damage of stress corrosion crack which has characteristics of cleavage fracture.