• Nuclear Engineering and Technology
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• 제50권4호
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• pp.553-561
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• 2018

To easily understand and systematically express the behaviors of the industrial systems, various system modeling techniques have been developed. Particularly, the importance of system modeling has been greatly emphasized in recent years since modern industrial systems have become larger and more complex. Multilevel flow modeling (MFM) is one of the qualitative modeling techniques, applied for the representation and reasoning of target system characteristics and phenomena. MFM can be applied to industrial systems without additional domain-specific assumptions or detailed knowledge, and qualitative reasoning regarding event causes and consequences can be conducted with high speed and fidelity. However, current MFM techniques have a limitation, i.e., the dynamic features of a target system are not considered because time-related concepts are not involved. The applicability of MFM has been restricted since time-related information is essential for the modeling of dynamic systems. Specifically, the results from the reasoning processes include relatively less information because they did not utilize time-related data. In this article, the concepts of time-to-detect and time-to-effect were adopted from the system failure model to incorporate time-related issues into MFM, and a methodology for enhancing MFM-based reasoning with time-series data was suggested.

• 방사성폐기물학회지
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• 제11권3호
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• pp.179-192
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• 2013

본 연구는 경상북도 경주시 양북면 지역의 결정질 암반의 지하수위 모니터링 자료를 이용하여 지하수 유동 특성을 평가하였다. 시계열분석 결과, 수리 지질 특성과 강우 사건을 반영한 4가지 변동 유형으로 분류할 수 있다. 유형 1(DB1-1, DB1-2)은 지하수위가 강우 사건의 영향을 가장 크게 받고 있다. 유형 2(DB1-3, DB1-7, KB-1, KB-2, KB-3, KB-7, KB-14, KB-15)는 공간적으로 단열대 주변에 분포하여 지하수투수특성대 발달이 우수하기 때문에, 강우의 직접적인 침투와 주단열대를 통한 지하수 유동이 동시에 나타난다. 유형 3(DB-5, DB1-6, DB2-2, KB-10, KB-11, KB-13)은 주단열대와 원거리에 분포한 결정 질 암반에 위치되기 때문에 강우의 직접적인 침투 영향보다는 고지대의 함양지역에서 소규모의 단열대를 통한 지하수 유동이 우세하게 일어난다. 유형 4(DB1-8, KB-9)는 결정질암반의 다양한 불균질성에 의해 지하수위 변동이 영향을 받고 있다.

• 지능정보연구
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• 제4권1호
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• 한국ITS학회 논문지
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• 제20권6호
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• pp.1-13
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• 2021

본 연구에서는 도로교통분야의 계획, 설계, 유지관리, 연구 등 다양한 목적으로 활용되고 있는 교통량 데이터의 정확도 확보를 위해 시계열 분석 기법을 적용하여 교통량 데이터의 보정 및 예측을 수행하였다. 기존 알고리즘의 경우 주기성 및 계절성이 강하거나 불규칙한 데이터에 한계를 보이고 있어 교통량 데이터와 같은 자료에 적용하기에는 한계가 있다. 이러한 한계점을 극복하고 보완하기 위해 ARIMA 모형에 자기상관 모형인 SAR(Seasonal Auto Regressive)과 계절 이동평균 모형인 SMA(Seasonal Moving Average)가 결합된 분석 기법인 SARIMA 모형을 적용하였다. 분석결과 최적 파라미터 조합인 SARIMA(4,1,3)(4,0,3) 12 모형을 활용한 교통량 예측 결과 평균 85% 정도의 우수한 성능을 보였다. 본 연구를 통해서 교통량 데이터의 결측 발생 시 교통량 보정 및 예측의 정확도를 높일 수 있으며, 교통량 데이터 외에도 계절성에 영향을 받는 시계열 데이터에 적용이 가능하다.

• 대한전자공학회논문지SD
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• 제41권6호
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• pp.25-31
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• 2004

본 논문에서는 나노 스케일 확산 공정 모사를 위한 방법으로 동력학적 몬테칼로(kinetic Monte Carlo)를 소개하고자 한다. 먼저 동력학적 몬테칼로의 이론과 배경을 살펴보고 실제적인 이해를 돕기 위하여 실리콘 기판에 이온(전자) 주입 후 열처리과정에서 일어나는 점결함의 확산을 동력학적 몬테칼로를 이용하여 모사하는 간단한 예를 보여주고 있다. 동력학적 몬테칼로는 몬테칼로의 일종이지만 기존의 몬테칼로에서 구현하지 못하였던 물리적인 시간을 포아송 확률 과정을 통하여 구현하였다. 동력학적 몬테칼로 확산 공정 모사에서는 연속 확산 미분 방정식의 해를 구하는 기존의 유한 요소 수치 해석적 방법과 달리원자 상호간 혹은 원자와 결함 또는 결함들 간의 화학적 반응과 입자들의 확산 과정을 포아송 확률 과정에 따라 일어나는 화학적 반응, 입자들의 확산 사건의 연속으로 본다. 사건마다 고유의 사건 발생 확률을 갖고 이 사건 발생 확률에 따라 일어나는 확률적 사건의 연속적 발생으로 실제의 반도체 확산 공정을 시간에 따라 직접적으로 모사할 수 있다. 입자들 간의 화학적 반응 사건 확률과 입자들의 확산 공정에 필요한 확률적 인자들은 분자 동역학, 양자 역학적 계산, 흑은 실험으로 얻어진다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1853-1856
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• 2003

This paper considers the problem of longitudinal control of a platoon of automotive vehicles on a straight lane of a highway and proposes control laws in the event of loss of communication between the lead vehicle and the other vehicles in the platoon. Since safety plays a key role in the development of an Automated Highway System, fault-tolerant control is vital. In this paper, we develop a control algorithm in vehicle platooning and prove that this control algorithm is stable for certain class of faults such as parameter uncertainties. The performance of the controller is demonstrated through a series of simulations incorporating various vehicles and AHS faults. Results of simulation shows that the vehicles have good performance in spite of simple automotive and AHS failure, such as actuator failure,that is to say, engine input failure, communication failure between lead vehicle and the another vehicles.

• 대한약학회:학술대회논문집
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• 대한약학회 2002년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2
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• pp.351.3-353
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• 2002

Oxygen radicals are produced in many pathophysiologic states whether the event is a causal factor of illness or is a result of their progression. Oxygen radicals including superoxide anions. hydrogen peroxide are thought to be involved in a number of type of acute. and chronic pathologic condition in the brain and neural tissue. So the antioxidants have recently received much attention as therapeutic agent for the treatment of neurodegenerative disease. In this study. we describe synthesis of a series of chromenone derivatives as antioxidant agents. The target compounds are designed to include the structural frature of caffeic acid. flavoniod. and focopherol known as antioxidants.

• 한국물환경학회지
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• 제26권2호
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• pp.243-251
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• 2010

Evapotranspiration and rainfall-runoff are the major components of hydrological cycle and thereby the changes of them can directly affect the wetness/dryness or runoff characteristics of basins. In this study the wetness/dryness in Geum river basin are classified by dint of cumulative probability density function of monthly moisture index and the long term changes of them are analyzed based on climatic water balance concept. The drought events in Geum river basin are selected through evaluation of monthly moisture index and the various hydrological properties of them are investigated in detail. Also the trends of time-series of climatic water balance components are examined by Seasonal Kendall test and the variability of hydrological cycle in Geum river basin during the recent decade is inquired. It is judged that the results of this study can be contributed to establishment of the counter plan against the future drought events as the fundamental information.

• Journal of Astronomy and Space Sciences
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• 제20권2호
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• pp.101-108
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• 2003

This study reports one approach for the classification of magnetic storms into recurrent patterns. A storm event is defined as a local minimum of Dst index. The analysis of Dst index for the period of year 1957 through year 2000 has demonstrated that a large portion of the storm events can be classified into a set of recurrent patterns. In our approach, the classification is performed by seeking a categorization that minimizes thermodynamic free energy which is defined as the sum of classification errors and entropy. The error is calculated as the squared sum of the value differences between events. The classification depends on the noise parameter T that represents the strength of the intrinsic error in the observation and classification process. The classification results would be applicable in space weather forecasting.

• 산업기술연구
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• 제19권
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• pp.197-208
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• 1999

The process of localization of cracks and movement of the fracture process zone(FPZ) was studied using the acoustic-emission(AE) techniques. The rate of AE events and sources of AE activity were studied for mortar and rock specimens loaded in uniaxial compression. A series of transducers could be used to detect and AE activity. Based on the time differences between detection of the event at different transducers, source of AE activity could be detected. The rate of AE events increased sharply before peak load. The highest rate occurred just after peak load was attained. The effective crack length estimated from the modified linear-elastic fracture mechanics seemed consistent with the optical and AE measurements.