• 한국구조물진단유지관리공학회 논문집
• /
• 제20권1호
• /
• pp.48-55
• /
• 2016

구조물의 수명관리는 일반적으로 불확실성의 효율적 고려를 위해 신뢰성 이론을 적용한다. 전체 구조시스템의 신뢰성 평가는 구조요소의 신뢰성 평가와 구조시스템의 모델링을 통해 이루어진다. 구조시스템은 신뢰도에 대한 구조요소의 역할과 기여도를 고려하여 모델링 된다. 따라서, 구조시스템의 신뢰도는 구조요소의 모델링과 구조요소의 상관관계에 따라 서로 다른 결과를 제시하게 된다. 최초 구조시스템의 신뢰도 평가와 열화요소를 반영한 구조시스템의 수명 평가를 바탕으로 생애주기 비용 최소화와 관련된 목적함수를 가지는 최적화 과정을 통해 수명관리가 이루어진다. 본 논문에서는 구조시스템을 구성하는 구조요소의 상관관계와 더불어 기존의 연구에서 고려된 바 없는 유지보수 간의 상관관계에 따른 수명평가 영향분석을 수행하며, 이를 통해 향후 좀 더 효율적인 수명관리 기법 개발에 활용하고자 한다. 또한, 예방유지보수와 필수유지보수를 모두 고려하며, 유지보수간 독립 상태와 완전상관 상태에 따른 수명 예측 및 최적화 유지보수 계획 수립을 비교 제시한다.

• 한국콘크리트학회:학술대회논문집
• /
• 한국콘크리트학회 2008년도 춘계 학술발표회 제20권1호
• /
• pp.1037-1040
• /
• 2008

해양 환경에 노출된 구조물의 잔존수명을 예측하기 위해서는 부식 개시기까지의 염화물 침투와 콘크리트 피복 균열과 같은 콘크리트 구조물의 열화현상에 대하여 시간과 공간적 요소를 고려한 분석적 접근 방법의 개발이 필요하다. 이를 위하여 본 연구에서는 유한요소해석 기법을 이용하여 염해에 노출되어 있는 콘크리트 구조물의 생애주기를 시뮬레이션하는 것을 목표로 한다. 내구성 예측을 위한 환경적 변수와 재료의 불확실성을 고려하기 위하여 신뢰성에 기반한 잔존수명의 예측을 위한 유한요소해석 모델링에 Monte Carlo Simulation 기법을 도입하였다. 본 논문에서는 콘크리트 구조물의 신뢰성에 기반한 잔존 내구수명에 대한 일반적 개념과 염화물 이온 침투, 부식 생성물의 팽창, 피복 균열 등에 대한 유한요소 모델에 대해 설명하고, 마지막으로 예제를 통하여 염화물 이온의 집중, 부식 생성물의 팽창등이 콘크리트 구조물의 잔존수명에 미치는 영향에 대해 논의하였다.

• Elastomers and Composites
• /
• 제55권2호
• /
• pp.81-87
• /
• 2020

Service life prediction and evaluation of rubber components is the foundational technology necessary for securing the safety and reliability of the product and to ensure an optimum design. Even though the domestic industry has recognized the importance thereof, technology for a systematic design and analysis of the same has not yet been established. In order to develop this technology, identifying the fatigue damage parameters that affect service life is imperative. Most anti-vibration rubber components had been damaged by repeated load and aging. Hence, the evaluation of the fatigue characteristics is indispensable. Therefore, in this paper, we propose a method that can predict the service life of rubber components relatively accurately in a short period of time. This method works even in the initial designing stage. We followed the service life prediction procedure of the proposed rubber components. The weak part of the rubber and the maximum strain were analyzed using finite element analysis of the rubber bushing for the tracked vehicles. In order to predict the service life of the rubber components that were in storage for a certain period of time, the fatigue test was performed on the three-dimensional dumbbell specimen, based on the results obtained by the rubber material acceleration test. The service life formula of the rubber bushing for tracked vehicles was derived using both finite element analysis and the fatigue test. The service life of the rubber bushing for tracked vehicles was estimated to be about 1.7 million cycles at room temperature (initial stage) and about 400,000 cycles when kept in storage for 3 years. Through this paper, the service life for various rubber parts is expected be predicted and evaluated. This will contribute to improving the durability and reliability of rubber components.

• International Journal of Computer Science & Network Security
• /
• 제22권2호
• /
• pp.1-8
• /
• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.

• Geomechanics and Engineering
• /
• 제31권2호
• /
• pp.129-147
• /
• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Structural Engineering and Mechanics
• /
• 제37권4호
• /
• pp.427-442
• /
• 2011

In fatigue life design of mechanical components, uncertainties arising from materials and manufacturing processes should be taken into account for ensuring reliability. A common practice is to apply a safety factor in conjunction with a physics model for evaluating the lifecycle, which most likely relies on the designer's experience. Due to conservative design, predictions are often in disagreement with field observations, which makes it difficult to schedule maintenance. In this paper, the Bayesian technique, which incorporates the field failure data into prior knowledge, is used to obtain a more dependable prediction of fatigue life. The effects of prior knowledge, noise in data, and bias in measurements on the distribution of fatigue life are discussed in detail. By assuming a distribution type of fatigue life, its parameters are identified first, followed by estimating the distribution of fatigue life, which represents the degree of belief of the fatigue life conditional to the observed data. As more data are provided, the values will be updated to reduce the credible interval. The results can be used in various needs such as a risk analysis, reliability based design optimization, maintenance scheduling, or validation of reliability analysis codes. In order to obtain the posterior distribution, the Markov Chain Monte Carlo technique is employed, which is a modern statistical computational method which effectively draws the samples of the given distribution. Field data of turbine components are exploited to illustrate our approach, which counts as a regular inspection of the number of failed blades in a turbine disk.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2007년도 춘계학술대회A
• /
• pp.1740-1743
• /
• 2007

In this study, two types of fatigue tests were conducted. First, cyclic bending tests were performed using the micro-bending tester. A four-point bending test method was adopted, because it induces uniform stress fields within a loading span. Second, thermal fatigue tests were conducted using a pseudo power cycling machine which was newly developed for a realistic testing condition. The pseudo-power cycling method makes up for the weak points in a power cycling and a chamber cycling method. Two compositions of solder are tested in all test condition, one is lead-free solder (95.5Sn4.0Ag0.5Cu) and the other is eutectic lead-contained solder (63Sn37Pb). In the cyclic bending test, the solder that exhibits a good reliability can be reversed depending on the load conditions. The lead-contained solders have a longer fatigue life in the region where the applied load is high. On the contrary, the lead-free solder sustained more cyclic loads in the small load region. A similar trend was detected at the thermal cycling test. A three-dimensional finite element analysis model was constructed. A finite element analysis using ABAQUS was performed to extract the applied stress and strain in the solder joints. A constitutive model which includes both creep and plasticity was employed. Thermal fatigue was occurred due to the creep. And plastic deformation is main damage for bending failure. From the inelastic energy dissipation per cycle versus fatigue life curve, it can be found that the bending fatigue life is longer than the thermal fatigue life.

• 한국통신학회논문지
• /
• 제32권6B호
• /
• pp.375-381
• /
• 2007

본 연구는 상황 인지 서비스 구현의 다양한 기술 요소 중, 추론 및 예측 기술에 초점을 둔다. 대표적인 예측 알고리즘에는 베이시안 네트워크가 있으나 상황 인지 시스템을 구현할 때 그 구조를 실제로 구현하는 것은 매우 복잡한 일이며 실시간 환경에서 트레이닝 데이터 처리에서 오는 시간 지연 문제 등이 발생하게 된다. 또한 특정 목적의 상황 인지 시스템에서 이 알고리즘이 어느 정도 예측 정확도와 신뢰도를 가지고 상황 정보와 부합하는지 역시 미지수이다. 본 논문에서는 가장 간단한 알고리즘인 순차적 매칭 알고리즘에 캐시 기법을 이용한 위치 예측 알고리즘을 제안한다. 이러한 접근 방식을 통해 알고리즘 수행 시 처리 시간을 캐시 기법을 사용하지 않았을 때 보다 평균적으로 48.7%를 줄이게 된다. 이는 사용자의 습관이나 행동 양식을 고려함으로써 상황 인지 시스템의 상황 정보와 부합하기 때문이라 할 수 있다.

• 융합정보논문지
• /
• 제11권11호
• /
• pp.44-50
• /
• 2021

최근 빅데이터를 수용하기 위한 대용량 저장 장치가 필요한 엔터프라이즈 저장 시스템에서는 비용과 크기 대비 직접도가 높은 대용량의 플래시 메모리 기반 저장 장치를 많이 사용하고 있다. 본 논문에서는 엔터프라이즈 대용량 저장 장치의 신뢰도와 이용성에 직접적인 영향을 주는 플래시 메모리 미디어의 수명을 극대화 하기 위해 경사하강법을 적용한 고효율 수명 예측 방법을 제안한다. 이를 위해 본 논문에서는 불량 발생 빈도를 학습하기 위한 메타 데이터를 저장하는 매트릭스의 구조를 제안하고 메타데이터를 이용한 비용 모델을 제안한다. 또한 학습된 범위를 벗어난 불량이 발생 했을 때 예외 상황에서의 수명 예측 정책을 제안한다. 마지막으로 시뮬레이션을 통해 본 논문에서 제안하는 방법이 이전까지 플래시 메모리의 수명 예측을 위해 사용되어 온 고정 횟수 기반 수명 예측 방법과 예비 블록의 남은 비율을 기반으로 하는 수명 예측 방법 대비 수명을 극대화 할 수 있음을 증명하여 우수성을 확인했다.

• 한국지구과학회지
• /
• 제43권4호
• /
• pp.511-519
• /
• 2022

Given the significant social and economic impact caused by heat waves, there is a pressing need to predict them with high accuracy and reliability. In this study, we analyzed the real-time forecast data from six models constituting the Subseasonal-to-Seasonal (S2S) prediction project, to elucidate the key mechanisms contributing to the prediction of the recent record-breaking Korean heat wave event in 2018. Weekly anomalies were first obtained by subtracting the 2017-2020 mean values for both S2S model simulations and observations. By comparing four Korean heat-wave-related indices from S2S models to the observed data, we aimed to identify key climate processes affecting prediction accuracy. The results showed that superior performance at predicting the 2018 Korean heat wave was achieved when the model showed better prediction performance for the anomalous anticyclonic activity in the upper troposphere of Eastern Europe and the cyclonic circulation over the Western North Pacific (WNP) region compared to the observed data. Furthermore, the development of upper-tropospheric anticyclones in Eastern Europe was closely related to global warming and the occurrence of La Niña events. The anomalous cyclonic flow in the WNP region coincided with enhancements in Madden-Julian oscillation phases 4-6. Our results indicate that, for the accurate prediction of heat waves, such as the 2018 Korean heat wave, it is imperative for the S2S models to realistically reproduce the variabilities over the Eastern Europe and WNP regions.