• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• 제48권1호
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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.

• 한국농림기상학회지
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• 제9권2호
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• pp.140-148
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• 2007

경관의 비균질성은 원격 탐사에 의한 지면 특성 관측에 필연적으로 영향을 준다. 본 연구에서는 주요 지면 모수의 경관 비균질성을 정량적으로 해석할 수 있는 지구통계기법을 소개한다. 연구지역은 두 곳의 KoFlux 연구지로서 (1) 복잡지형의 온대 혼합림으로 구성된 광릉 연구지와 (2) 비교적 평탄한 농경지인 해남 연구지이다. 복사 모수인 지면온도(LST)와 알베도의 공간적 비균질성을 특성화하기 위하여 세미베리오그램과 프랙털 분석을 수행하였다. 이 두 모수는 두 연구지의 상향 장파 및 단파 복사를 결정하는 중요한 인자들이다. 이 모수들은 2001년 9월 23일과 2002년 2월 14일의 두 지역의 고해상도 Landsat ETM+영상에서 추출하였다. 분석 결과, 광릉과 해남 연구지는 알베도의 특성 규모가 각각 1 km 이상 그리고 약 0.3 km 이었다. 지면온도의 경우, 특성 규모는 광릉이 1 km 이상 그리고 해남이 0.6-1.0 km 이상이었다. 두 지면 모수의 특성 규모는 두 지역에서 모두 계절 변화를 보이지 않았다.

• Geomechanics and Engineering
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• 제9권5호
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• pp.619-629
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• 2015

Influenced by various mining activities, fractures in rock masses have different densities, set numbers and lengths, which induce different mechanical properties and failure modes of rock masses. Therefore, precisely expressing the failure criterion of the fractured rock influenced by coal mining is significant for the support design, safety assessment and disaster prevention of underground mining engineering subjected to multiple mining activities. By adopting PFC2D particle flow simulation software, this study investigated the propagation and fractal evolution laws of the micro cracks occurring in two typical kinds of rocks under uniaxial compressive condition. Furthermore, it calculated compressive strengths of the rocks with different confining pressures and box-counting dimensions. Moreover, the quantitative relation between the box-counting dimension of the rocks and the empirical parameters m and s in Hoek-Brown strength criterion was established. Results showed that with the increase of the strain, the box-counting dimension of the rocks first increased slowly at the beginning and then exhibited an exponential increase approximately. In the case of small strains of same value, the box-counting dimensions of hard rocks were smaller than those of weak rocks, while the former increased rapidly and were larger than the latter under large strain. The results also presented that there was a negative correlation between the parameters m and s in Hoek-Brown strength criterion and the box-counting dimension of the rocks suffering from variable mining activities. In other words, as the box-counting dimensions increased, the parameters m and s decreased linearly, and their relationship could be described using first order polynomial function.

• 한국해양학회지:바다
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• 제18권3호
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• pp.122-130
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• 2013

점착성 퇴적물이 다른 광물입자 혹은 유기물과 결합하여 형성되는 플럭(floc)을 현장에서 관측하기 위한 플럭카메라 시스템을 제작하였다. 본 연구의 목적은 실험실에서 플럭카메라 시스템을 검증하고 영산강 점착성 해저 퇴적물의 플럭 특성을 규명하는 데 있다. 플럭카메라 시스템의 검증은 $88-125{\mu}m$와 $63-88{\mu}m$의 체에 걸러진 모래를 사용하였다. 플럭카메라 영상을 통해 분석한 평균입경은 각각 102와 $65.2{\mu}m$고, 침강속도는 각각 6.7과 5.9 mm/s이다. 카메라 심도에서 벗어난 입자는 실제 크기보다 크거나 작게 측정이 되는 현상으로 인해 체의 범위를 벗어난 입자가 관측되지만, 입경과 침강속도의 평균값에 대한 95% 신뢰수준 오차가 체의 범위에 속하므로 플럭카메라를 이용한 분석을 신뢰할 수 있었다. 영산강 하구의 해저 표층 퇴적물을 0 psu의 증류수에 0.1 g/L 퇴적물 농도로 관측한 평균 입경은 약 $40.6{\pm}0.66{\mu}m$, 침강속도는 1.4 mm/s 프랙탈 차원은 2.86이었다. 추가적으로 10과 34 psu의 염분과 0.1 및 0.3 g/L의 퇴적물 농도에서 관측한 평균입경과 침강속도는 서로 유사했고, 그 값들은 오차범위 내에 존재한다. 플럭카메라 관측을 통해서 얻은 플럭의 빠른 침강속도와 프랙탈 차원은 유기물 함량이 상대적으로 적은 표층퇴적물의 특성을 반영한다. 또한, 염분과 퇴적물 농도를 변화시켰음에도 플럭의 입경 변화가 거의 없는 것은 플럭을 형성에 충분한 난류 강도가 주어지지 못했기 때문이라고 판단된다. 향후에는 염분, 퇴적물 농도 및 외력의 변화에 따른 플럭의 특성을 밝히는 연구가 필요하다.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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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.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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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.

• Tribology and Lubricants
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• 제11권5호
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• pp.128-135
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• 1995

Atomic force microscopy/friction force microscopy (AFM/FFM) techniques are increasingly used for tribological studies of engineering surfaces at scales, ranging from atomic and molecular to microscales. These techniques have been used to study surface roughness, adhesion, friction, scratching/wear, indentation, detection of material transfer, and boundary lubrication and for nanofabrication/nanomachining purposes. Micro/nanotribological studies of single-crystal silicon, natural diamond, magnetic media (magnetic tapes and disks) and magnetic heads have been conducted. Commonly measured roughness parameters are found to be scale dependent, requiring the need of scale-independent fractal parameters to characterize surface roughness. Measurements of atomic-scale friction of a freshly-cleaved highly-oriented pyrolytic graphite exhibited the same periodicity as that of corresponding topography. However, the peaks in friction and those in corresponding topography were displaced relative to each other. Variations in atomic-scale friction and the observed displacement has been explained by the variations in interatomic forces in the normal and lateral directions. Local variation in microscale friction is found to correspond to the local slope suggesting that a ratchet mechanism is responsible for this variation. Directionality in the friction is observed on both micro- and macro scales which results from the surface preparation and anisotropy in surface roughness. Microscale friction is generally found to be smaller than the macrofriction as there is less ploughing contribution in microscale measurements. Microscale friction is load dependent and friction values increase with an increase in the normal load approaching to the macrofriction at contact stresses higher than the hardness of the softer material. Wear rate for single-crystal silicon is approximately constant for various loads and test durations. However, for magnetic disks with a multilayered thin-film structure, the wear of the diamond like carbon overcoat is catastrophic. Breakdown of thin films can be detected with AFM. Evolution of the wear has also been studied using AFM. Wear is found to be initiated at nono scratches. AFM has been modified to obtain load-displacement curves and for nanoindentation hardness measurements with depth of indentation as low as 1 mm. Scratching and indentation on nanoscales are the powerful ways to screen for adhesion and resistance to deformation of ultrathin fdms. Detection of material transfer on a nanoscale is possible with AFM. Boundary lubrication studies and measurement of lubricant-film thichness with a lateral resolution on a nanoscale have been conducted using AFM. Self-assembled monolyers and chemically-bonded lubricant films with a mobile fraction are superior in wear resistance. Finally, AFM has also shown to be useful for nanofabrication/nanomachining. Friction and wear on micro-and nanoscales have been found to be generally smaller compared to that at macroscales. Therefore, micro/nanotribological studies may help def'me the regimes for ultra-low friction and near zero wear.

• 자원환경지질
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• 제27권6호
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• pp.557-561
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• 1994

Quality factor Q 값은 처음 도착(倒着)한 P파(波)의 주기당(週期當) 에너지 손실(損失)을 주파수(周波數)의 함수(函數)로 직접(直接) 측정(測定)할 수 있다. 이때 관심(關心)의 대상(對象)이 되는 주파수대(周波數帶)(주로 1-100 Hz)내(內)에서 고유(固有) Q는 주파수(周波數)와 무관(無關)하고, 산란(散亂) Q는 주파수(周波數)와 밀접(密接)한 관계(關係)가 있다는 가정하(假定下)에 고유(固有) Q값과 산란(散亂) Q값의 전체(全體) Q값에 대(對)한 상대적(相對的)인 비솔(比率)을 계산(計算)할 수 있다. 이에 대(對)한 검증(檢證)은 탄성파(彈性波)가 점탄성(粘彈性)이고 부균질(不均質)한 매질(媒質)을 통과(通過)할 때의 합성탄성파(合成彈性波) 기록지(記錄紙)를 만들고 고유(固有) Q에 대(對)해서는 완화기구(緩和機具)(relaxation mechanism)가, 산란(散亂) Q에 대(對)해서는 산란(散亂)(satter)에 대(對)한 fractal 분포(分布)가 포함(包含)되는 pseudospectral 해(解)를 이용(利用)하여 실시(實施)될 수 있다. 대체로 S파(波)의 전체(全體) Q값이 P파(波)의 전체(全體) Q값보다 더 작다는 것이 정설(定說)로 되어있다. 역(逆)으로, 전체(全體) Q값은 합성탄성파(合成彈性波) 기록지(記錄紙)로 부터 최소자승법(最小自乘法)을 이용(利用)하여 구(求)할 수 있다. 이때 가정(假定)된 Q값의 절대값이 충분(充分)히 작아야만 P파(波)와 S파(波)의 고유(固有) Q값($Q_p$와 $Q_s$)의 가정(假定)은 신빙성(信憑性)이 높고 또한 유일(唯一)한 값을 가질 수 있다. 산란(散亂) Q값으로 부터 결정(決定)할 수 있는 매질(媒質)의 속도(速度)와 산란(散亂)의 크기에 대(對)한 표준편차(標准偏差)는 Blair의 수식(數式)에서 예측(豫測)할 수 있듯이 서로 상호보완관계(相互補完關係)에 있기 때문에 여러가지의 값을 가질 수 있다. 본(本) 연구결과에 의(依)하면, P파(波)에 있어서는 고유(固有) Q와 산란(散亂) Q가 모두 중요(重要)한 요소(要素)로 작용(作用)하며, S파(波)에 있어서는 고유(固有) Q가 산란(散亂) Q보다 더 중요(重要)한 요소(要素)로 작용(作用)한다.

• 한국운동역학회지
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• 제18권2호
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• pp.49-58
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• 2008

본 연구의 목적은 트레드밀 보행 시, gait dynamics 측면에서 보행의 속도 변화와 인지과제 수행 시 보행 변인의 가변성(variability)을 알아보고자 하는 것이다. 실험은 인지과제의 동시수행 유/무에 따른 5가지 속도(선호속도의 80%, 90%, 100%, 110% and 120%)에 의한 보행 실험으로 구성되었다. 인지과제의 종류는 학습기능이 없는 인지과제(2-back task)를 수행하였다. 인지과제는 피험자의 트레드밀 보행 시, 3m 앞에 놓여진 스크린에 주어지고 무선마우스를 통해 응답하는 형태로 구성되었다. 실험의 모든 과정은 3차원 동작분석기를 통해 동작데이터를 획득하였다. 이를 통해, 5가지 보행 시간 변인과 3가지 공간 변인을 추출하였다. gait dynamics 측면의 분석을 위해, 가변성의 크기를 살펴볼 수 있는 방법인 분산계수(coefficient of variance)와 변동량의 구조적 자기 유사성을 추론할 수 있는 detrended fluctuation analysis (DFA)를 사용하였다. 그 결과 보행 속도 변화에 따라 보행 변인의 평균값과 분산계수에서 통계적 유의한 차이가 발생하였고, 인지과제의 수행 유/무에 따라서는 DFA에서 통계적인 차이가 발생하였다. 이는 인지과제의 수행에 의해 보행의 발생과 조절 능력에 영향을 끼쳤다고 추론할 수 있다. 본 연구 결과를 명백히 하기 위해 더 많은 수의 피험자 실험과 추가 실험이 필요할 것이다.