• 대한기계학회논문집A
• /
• 제24권11호
• /
• pp.2828-2835
• /
• 2000

This paper presents the fatigue behavior of composite materials with impact-induced damage. The impact damage parameter is proposed to evaluate the effect of impact damage on fatigue life. Subsequently, a new model is developed to predict the fatigue life of impacted composite materials. Also, a stochastic model is proposed to describe the variation of fatigue life due to the material nonhomogeneity. For these models, the fatigue tests were performed on the unimpacted and impacted composite materials, The effect of impact damage on fatigue life can be characterized by the impact damage parameter. Additionally, the results by the present fatigue life prediction model agree will with experimental results regardless of applied impact energy. Also, the variation of fatigue life can be described by the present stochastic model and is reduced with applied impact energy.

• 한국신뢰성학회:학술대회논문집
• /
• 한국신뢰성학회 2000년도 춘계학술대회 발표논문집
• /
• pp.213-222
• /
• 2000

In present study, a stochastic model is developed for the low cycle fatigue life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. In the proposed model, fatigue phenomenon is considered as a Markov process, and damage vector and reliability are defined on every plane. Any low cycle fatigue damage evaluating method can be included in the proposed model. The model enables calculation of statistical reliability and crack initiation direction under variable multiaxial loading, which are generally not available. In present study, a critical plane method proposed by Kandil et al., maximum tensile strain range, and von Mises equivalent strain range are used to calculate fatigue damage. When the critical plane method is chosen, the effect of multiple critical planes is also included in the proposed model. Maximum tensile strain and von Mises strain methods are used for the demonstration of the generality of the proposed model. The material properties and the stochastic model parameters are obtained from uniaxial tests only. The stochastic model made of the parameters obtained from the uniaxial tests is applied to the life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. The predicted results show good accordance with experimental results.

• 대한수학회지
• /
• 제38권3호
• /
• pp.657-667
• /
• 2001

We propose a statistical interpolation approximate solution for a nonlinear stochastic integral equation of a stock price process. The proposed method has the order O(h$^2$) of local error under the weaker conditions of $\mu$ and $\sigma$ than those of Milstein' scheme.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2003년도 춘계학술대회
• /
• pp.1851-1856
• /
• 2003

A new Lagrangian stochastic dispersion model is developed by combining the GLM(generalized Langevin model) and the elliptic relaxation method. Under the physically plausible assumptions a simple analytical solution of elliptic relaxation is obtained. To compare the performance of our model with other model, the statistics of particle velocity as well as concentration are investigated. Numerical simulation results show good agreement with available experimental data.

• Asian Journal of Atmospheric Environment
• /
• 제10권4호
• /
• pp.197-207
• /
• 2016

Large quantities of air pollutants are released into the atmosphere and hence, must be monitored and routinely assessed for their health implications. This paper proposes a stochastic technique to predict unobserved hazardous air pollutants (HAPs), especially Benzo[a]pyrene (BaP), which can have negative effects on human health. The proposed approach constructs a nearest-neighbor structure by incorporating the linkage between BaP and meteorology and meteorological effects. This approach is adopted in order to predict unobserved BaP concentrations based on observed (or forecasted) meteorological conditions, including temperature, precipitation, wind speed, and air quality. The effects of BaP on human health are examined by characterizing the cancer risk. The efficient prediction provides useful information relating to the optimal monitoring period and projections of future BaP concentrations for both industrial and residential areas within Korea.

• 물과 미래
• /
• 제28권2호
• /
• pp.103-114
• /
• 1995

하천 수질변동의 예측을 위한 추계학적 모형 STO-RIV를 개발하였다. STO-RIV는 Streeter-Phelps 확장식의 해석적인 해와 Monte-Carlo 기법으로 구성하였다. 본 모형은 왜관에서 물금에 이르는 낙동강 유역에 적용하여 장래의 물금지점에서의 하천수질의 확률론적 특성이 정량적으로 계산될 수 있었다. 또한 금호강의 처리도 등을 고려한 여러 가지의 수질관리 대안에 대한 수질변동 특성의 해석이 수행되었다. 본 STO-RIV모형은 수질관련변수들의 변동성이 크게 나타나고 있는 국내하천 수질관리에 크게 활용될 수 있을 것으로 기대된다.

• 한국터널지하공간학회 논문집
• /
• 제12권3호
• /
• pp.265-274
• /
• 2010

터널에서 암반구조의 복잡성으로 인해 사전에 예측 할 수 없었던 암반의 붕락이 발생하여, 붕락대책에 막대한 비용과 시간을 낭비하는 사례가 많다. 암반 불연속면의 복잡성을 사전 조사 단계에서 충분히 파악하거나 대책을 수립하는 것은 어렵다. 최근 터널의 정보화 설계시공이 중요시 되어지고 있다. 본 연구에서는 터널의 굴착 전에 관찰된 정보를 최대한 활용하여 불안정한 암반블럭을 사전에 예측하기 위하여 신 하이브리드 추계학적-확정론적 암반블럭 해석기법을 제안하고, 현지에서 관찰한 불연속면 정보를 근거로 하여 터널현장에 적용했다. 터널현장에서의 해석결과를 비교 검토하여, 터널의 신 하이브리드 추계학적-확정론적 암반블럭 해석기법의 타당성과 적용성에 대한 검증을 하였다.

• 한국소음진동공학회논문집
• /
• 제13권1호
• /
• pp.48-55
• /
• 2003

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.7-10
• /
• 2003

In the field of financial technology, it is the U.S. initiative, and Japan is obliged to flattery in many respect. Currently Japan is in a too much defenseless situation that the economic structure is based on U.S. theory, In the conventional stochastic theory, it is also face that the prediction sometimes does not hit in the actual problem because it assumes a known probability distribution, none of which illustrates the real situation. A new research and development of management prediction support system is proposed based on fuzzy measures, that deals with the ambiguous, subjective evaluation by the people living in the real world well. Especially, the system will support venture, small and medium companies.

• Journal of Mechanical Science and Technology
• /
• 제19권8호
• /
• pp.1582-1596
• /
• 2005

Groundwater flow and behavior have to be investigated based on heterogeneous subsurface formation since the homogeneity assumption of this formation is not valid. Over the past twenty years, stochastic approach and Monte Carlo technique have been utilized very efficiently to understand the groundwater flow behavior. However, these techniques require lots of computational and numerical efforts according to the various researchers' comments. Therefore, utilizing new techniques with much less computational efforts such as Artificial Neural Network (ANN) in the prediction of the stochastic behavior for the groundwater based on heterogeneous subsurface formation is highly appreciated. The current paper introduces the ANN technique to investigate and predict the stochastic behavior of a well draw down in a confined aquifer based on subsurface heterogeneous hydraulic conductivity. Several ANN models are developed in this research to predict the unsteady two dimensional well draw down and its stochastic characteristics in a confined aquifer. The results of this study showed that ANN method with less computational efforts was very efficiently capable of simulating and predicting the stochastic behavior of the well draw down resulted from the continuous constant pumping in the middle of a confined aquifer with subsurface heterogeneous hydraulic conductivity.