• Geomechanics and Engineering
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• v.30 no.5
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• pp.401-411
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• 2022

To obtain the dynamic mechanical properties, fracture modes, energy and brittleness characteristics of Furong Baijiao coal rock, the dynamic impact compression tests under 0, 4, 8 and 12 MPa confining pressure were carried out using the split Hopkinson pressure bar. The results show that failure mode of coal rock in uniaxial state is axial splitting failure, while it is mainly compression-shear failure with tensile failure in triaxial state. With strain rate and confining pressure increasing, compressive strength and peak strain increase, average fragmentation increases and fractal dimension decreases. Based on energy dissipation theory, the dissipated energy density of coal rock increases gradually with growing confining pressure, but it has little correlation with strain rate. Considering progressive destruction process of coal rock, damage variable was defined as the ratio of dissipated energy density to total absorbed energy density. The maximum damage rate was obtained by deriving damage variable to reflect its maximum failure severity, then a brittleness index BD was established based on the maximum damage rate. BD value declined gradually as confining pressure and strain rate increase, indicating the decrease of brittleness and destruction degree. When confining pressure rises to 12 MPa, brittleness index and average fragmentation gradually stabilize, which shows confining pressure growing cannot cause continuous damage. Finally, integrating dynamic deformation and destruction process of coal rock and according to its final failure characteristics under different confining pressures, BD value is used to classify the brittleness into four grades.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.142-142
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• 2023

Humans inevitably and continuously produce wastewater in daily life worldwide. To decrease the degradation of river water bodies and aquatic ecosystem therein, humans have built systems at different scales to collect, drain, and treat household-produced wastewater. Particularly, municipal wastewater treatment plants (WWTPs) with centralized controls have played a key role in reducing loads of nutrients in domestic wastewater for the last few decades. Notwithstanding such contributions, impaired rivers regarding water quality and habitat integrity still exist at the whole river basin scale. It is highly attributable to the absence of dilution capacity of receiving streams and/or the accumulation of the pollutant loads along flow paths. To improve the perspective for individual WWTPs assessment, the first crucial step is to achieve systematic understanding on spatial distribution characteristics of all WWTPs together in a given river basin. By taking the initiative, our former study showed spatial hierarchical distributions of WWTPs in three large urbanized river basins in Germany. In this study, we uncover how municipal WWTPs in the contiguous United States are distributed along river networks in a give river basin. The extended spatial scope allows to deal with wide ranges in geomorphological attributes, hydro-climatic conditions, and socio-economic status. Furthermore, we identify the relation of the findings with multiple factors related to human activities, such as the spatial distribution of human settlements, the degree of economy development, and the fraction of communities served by WWTPs. Generalizable patterns found in this study are expected to contribute to establishing viable management plans for recent water-environmental challenges caused by WWTP-discharges to river water bodies.

• Composites Research
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• v.36 no.3
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• pp.141-153
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• 2023

This review paper presents an introduction of contact mechanics and rubber friction theory for sliding friction of elastomer composites in contact with rough surfaces. Particularly, Klüppel & Heinrich theory considers the self-affine (or fractal) characteristic for rough surfaces to predict adhesion and hysteresis frictions of elastomers based on the contact mechanics of Greenwood & Williamson. Due to dynamic excitation process of elastomer composites while sliding in contact with multiscale surface roughness (or asperity), viscoelastic properties in a wide frequency range becomes major contributor to friction behaviors. A brief description and examples are provided to construct a viscoelastic master curve considering nonlinear viscoelasticity of elastomer composites. Finally, application of rubber friction theory to tire tread compounds in traction with road surfaces is discussed with several experimental and theoretical results.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.48 no.1
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• pp.41-49
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• 2022

Objectives: We compared changes in fractal dimension (FD) and grayscale value (GSV) of peri-implant alveolar bone on digital panoramic radiography (DPR) and cone-beam computed tomography (CBCT) immediately after implant surgery and 12 months postoperative. Materials and Methods: In this retrospective study, 16 patients who received posterior mandibular area dental implants with CBCT scans taken about 2 weeks after implantation and one year after implantation were analyzed. A region of interest was selected for each patient. FDs and GSVs were evaluated immediately after implant surgery and at 12-month follow-up to examine the functional loading of the implants. Results: There were no significant differences between DPR and CBCT measurements of FD values (P>0.05). No significant differences were observed between FD values and GSVs calculated after implant surgery and at the 12-month follow-up (P>0.05). GSVs were not correlated with FD values (P>0.05). Conclusion: The DPR and reconstructed panoramic CBCT images exhibit similar image quality for the assessment of FD. There were no changes in FD values or GSVs of the peri-implant trabecular bone structure at the 12-month postoperative evaluation of the functional loading of the implant in comparison to values immediately after implantation. GSVs representing bone mass do not align with FD values that predict bone microstructural parameters. Therefore, GSVs and FDs should be considered different parameters for assessing bone quality.

• Economic and Environmental Geology
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• v.27 no.6
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• pp.557-561
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• 1994

Q estimates are made by direct measurements of energy loss per cycle from primary P and S waves, as a function of frequency. Assuming that intrinsic Q is frequency independent and scattering Q is frequency dependent over the frequencies of interest, the relative contributions of each, to a total observed Q, may be estimated. Test examples are produced by computing viscoelastic synthetic seismograms using a pseudo spectral solution with inclusion of relaxation mechanisms (for intrinsic Q) and a fractal distribution of scatterers (for scattering Q). The composite theory implies that when the total Q for S-waves is smaller than that for P-waves (the usual situation), intrinsic Q is dominating; when it is larger, scattering Q is dominating. In the inverse problem, performed by a global least squares search, intrinsic $Q_p$ and $Q_s$ estimates are reliable and unique when their absolute values are sufficiently low that their effects are measurable in the data. Large $Q_p$ and $Q_s$ have no measurable effect and hence are not resolvable. Standard deviation of velocity $({\sigma})$ and scatterer size (A) are less unique as they exhibit a tradeoff as predicted by Blair's equation. For the P-waves, intrinsic and scattering contributions are of approximately the same importance, for S-waves, the intrinsic contributions dominate.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.49-58
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• 2008

The purpose of this study was to identify effects of walking speed and a cognitive task during treadmill walking on gait variability. Experiments consisted of 5 different walking speeds(80%, 90%, 100%, 110% and 120% of preferred walking speed) with/without a cognitive task. 3D motion analysis system was used to measure subject's kinematic data. Temporal/spatial variables were selected for this study; stride time, stance time, swing time, step time, double support time, stride length, step length and step width. Two parameters were used to compare stride-to-stride variability with/without cognitive task. One is the coefficient of variance which is used to describe the amount of variability. The other is the detrended fluctuation analysis which is used to infer self-similarity from fluctuation of aspects. Results showed that cognitive task may influence stride-to-stride variability during treadmill walking. Further study is necessary to clarify this result.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.95-126
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• 2003

This paper introduces multifractal processes and presents the empirical investigation of the multifractal asset pricing. The multifractal stock price process contains long-tails which focus on Levy-Stable distributions. The process also contains long-dependence, which is the characteristic feature of fractional Brownian motion. Multifractality introduces a new source of heterogeneity through time-varying local reqularity in the price path. This paper investigates multifractality in stock prices. After finding evidence of multifractal scaling, the multifractal spectrum is estimated via the Legendre transform. The distinguishing feature of the multifractal process is multiscaling of the return distribution's moments under time-resealing. More intensive study is required of estimation techniques and inference procedures.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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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 고요한 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 의미있는 정보로 변환시켜줄 수 있는 웨이블릿분석 방법론을 새로운 필터링기법으로 사용하여 현재 많은 연구가 진행되고 있는 인공신경망의 모형결합을 통해 기존연구과는 다른 새로운 통합예측방법론을 제시하고자 한다. 본 연구에서는 제시하는 통합방법론은 크게 2단계 과정을 거쳐 예측모형으로 완성이 된다. 즉, 1차 모형단계에서 원시 재무시계열은 먼저 웨이브릿분석을 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. 기존의 주기분할방법론은 모형개발자입장에서 여러 가지 통계기준치중에서 최적의 기준치를 합리적으로 선택해야 하는 문제가 추가적으로 발생하며, 본 연구에서는 이상의 제반 문제들을 개선시키기 위해 통합방법론으로서 기존의 인공신경망모형을 구조적으로 확장시켰다. 이 모형에서 기존의 입력층 이전단계에 새로운 층이 정의된다. 이렇게 해서 생성된 새로운 통합모형은 기존모형에서 생성되는 기본적인 학습파라미터와 더불어, 본 연구에서 새롭게 제시된 주기분할층의 파라미터들이 모형의 학습성과를 높이기 위해 함께 고려된다. 한편, 이러한 학습과정에서 추가적으로 고려해야 할 파라미터 갯수가 증가함에 따라서, 본 모델의 학습성과가 local minimum에 빠지는 문제점이 발생될 수 있다. 즉, 웨이블릿분석과 인공신경망모형을 모두 전역적으로 최적화시켜야 하는 문제가 발생한다. 본 연구에서는 이 문제를 해결하기 위해서, 최근 local minimum의 가능성을 최소화하여 전역적인 학습성과를 높여 주는 인공지능기법으로서 유전자알고리즘기법을 본 연구이 통합모델에 반영하였다. 이에 대한 실증사례 분석결과는 일일 환율예측문제를 적용하였을 경우, 기존의 방법론보다 더 나운 예측성과를 타나내었다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.