• Journal of Korean Society of Steel Construction
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• v.11 no.4 s.41
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• pp.329-338
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• 1999

A systematic procedure to evaluate fatigue damages and to predict remaining fatigue lives is introduced for a steel railway bridge. Fatigue damages are evaluated by using the currently available fatigue damage theory. Fatigue lives with the condition of fatigue crack initiation are estimated by the probabilistic approach based on the reliability theory as well as the simplified procedure. A equivalent deterministic procedure is also suggested to assess the remaining fatigue life under various traffic conditions. Numerical simulations are used to assess dynamic stress histories with correction factors. Loading models are obtained from the passenger volume data. Train coincidences are also considered. Based on the results, the fatigue life is found to be underestimated by without considering the coincidence of trains on the bridge. The simplified method proposed in this study are found to yield approximately the same results as the systematic procedure.

• Journal of Korean Society of Transportation
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• v.18 no.2
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• pp.7-16
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• 2000

This study Presents an guideline of the exclusive bus and/or truck lane on 8 lanes freeways using FREFLO model and INTEGRATiON model. Four alternatives : do nothing, bus exclusive lane, truck exclusive lane, bus and truck exclusive lane, were evaluated using average daily vehicle travel time as measures of effectiveness. It is found that bus and truck exclusive lane is the most effective alternative reducing total travel time by 7% when ADT is below 80,000 vehicle/day. However, when ADT is above 80,000 vehicle/day and bus Proportion is above 12%, bus exclusive lane is the most effective alternative reducing total travel time by more than 5.6%. It is further found that INTEGRATION, the stochastic and microscopic model, is more sensitive than FREFLO, the deterministic and macroscopic model, in evaluating the effectiveness of four alternatives.

• Smart Structures and Systems
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• v.3 no.2
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• pp.189-200
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• 2007

As a functional material, shape memory alloy (SMA) has attracted much attention and research effort to explore its unique properties and its applications in the past few decades. Some of its properties, in particular the electrical resistance (ER) based self-sensing property of SMA, have not been fully studied. Electrical resistance of an SMA wire varies during its phase transformation. This variation is an inherent property of the SMA wire, although it is highly nonlinear with hysteresis. The relationship between the displacement and the electrical resistance of an SMA wire is deterministic and repeatable to some degree, therefore enabling the self-sensing ability of the SMA. The potential of this self-sensing ability has not received sufficient exploration so far, and even the previous studies in literature lack generality. This paper concerns the utilization of the self-sensing property of a spring-biased Nickel-Titanium (Nitinol) SMA actuator for two applications: ER feedback position control of an SMA actuator without a position sensor, and estimation of the opening of a SMA actuated valve. The use of the self-sensing property eliminates the need for a position sensor, therefore reducing the cost and size of an SMA actuator assembly. Two experimental apparatuses are fabricated to facilitate the two proposed applications, respectively. Based on open-loop testing results, the curve fitting technique is used to represent the nonlinear relationships between the displacement and the electrical resistance of the two SMA wire actuators. Using the mathematical models of the two SMA actuators, respectively, a proportional plus derivative controller is designed for control of the SMA wire actuator using only electrical resistance feedback. Consequently, the opening of the SMA actuated valve can be estimated without using an extra sensor.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.183-193
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• 2019

Process mining is an analytical technique aimed at obtaining useful information about a process by extracting a process model from events log. However, most existing process models are deterministic because they do not include stochastic elements such as the occurrence probabilities or execution times of activities. Therefore, available information is limited, resulting in the limitations on analyzing and understanding the process. Furthermore, it is also important to develop an efficient methodology to discover the process model. Although genetic process mining algorithm is one of the methods that can handle data with noises, it has a limitation of large computation time when it is applied to data with large capacity. To resolve these issues, in this paper, we define a stochastic process tree and propose a tabu search-genetic process mining (TS-GPM) algorithm for a stochastic process tree. Specifically, we define a two-dimensional array as a chromosome to represent a stochastic process tree, fitness function, a procedure for generating stochastic process tree and a model trace as a string of activities generated from the process tree. Furthermore, by storing and comparing model traces with low fitness values in the tabu list, we can prevent duplicated searches for process trees with low fitness value being performed. In order to verify the performance of the proposed algorithm, we performed a numerical experiment by using two kinds of event log data used in the previous research. The results showed that the suggested TS-GPM algorithm outperformed the GPM algorithm in terms of fitness and computation time.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.3
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• pp.139-151
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• 1992

A reservoir operation model was established under the varying restricted water level(r.w.l.) subject to the inflow distributions in flood period. The optimization model consists of 2 sub-models. One model minimizes deviations of releases from the expected release and the other minimizes capacity requirement for flood control. In order to make deterministic equivalents, the inflow distribution of reservoir is assumed to be 2-parameter Lognormal, and its parameters are estimated by the maximum likelihood method. The model is applied to joint operation of Soyang and Chungju dam. The results show that Soyang was designed for larger flood event than that for Chungju. The operation under the varying r.w.l. turns out to be more effective than one under the uniform r.w.l. Such effect is more obvious at Chungju compared with Soyang. Release pattern shows diminishing and delaying effect in a period of high inflows and larger discharges than actual in a period of low inflows.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.353-363
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• 2020

Hydrologic models can be classified into two types: those for understanding physical processes and those for predicting hydrologic quantities. This study deals with how to use the model to predict today's stream flow based on the system's knowledge of yesterday's state and the model parameters. In this regard, for the model to generate accurate predictions, the uncertainty of the parameters and appropriate estimates of the state variables are required. In this study, a relatively simple hydrologic partitioning model is proposed that can explicitly implement the hydrologic partitioning process, and the posterior distribution of the parameters of the proposed model is estimated using the Markov chain Monte Carlo approach. Further, the application method of the ensemble Kalman filter is proposed for updating the normalized soil moisture, which is the state variable of the model, by linking the information on the posterior distribution of the parameters and by assimilating the observed steam flow data. The stochastically and recursively estimated stream flows using the data assimilation technique revealed better representation of the observed data than the stream flows predicted using the deterministic model. Therefore, the ensemble Kalman filter in conjunction with the Markov chain Monte Carlo approach could be a reliable and effective method for forecasting daily stream flow, and it could also be a suitable method for routinely updating and monitoring the watershed-averaged soil moisture.

• Korean Journal of Construction Engineering and Management
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• v.8 no.5
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• pp.71-79
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• 2007

It is common that the analysis of VE/LCC is performed in design phase of quay wall structures. The analysis is mainly executed based on experience and engineering sense of expert considering the selection of construction method, construction and maintenance cost. Recently there are increasing demands on the analysis that includes uncertainty and vulnerability of input parameters, for this purpose, fuzzy reliability based probabilistic VE/LCC analysis model for quay wall structures is suggested. In VE/LCC analysis for quay wall structures, the application of probabilistic analysis method give very similar results compare with those of deterministic analysis method. It is anticipated that the methodology proposed in this paper can also be utilized in the design and maintenance phase of other facilities where decision making is made for the probabilistic life cycle cost and value analysis.

• Computers and Concrete
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• v.15 no.2
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• pp.259-277
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• 2015

The lack of experimental studies on the mechanical behavior of reinforced concrete (RC) haunched beams leads to difficulties in statistical and reliability analyses. This study performs stochastic and reliability analyses of the ultimate shear capacity of RC haunched beams based on nonlinear finite element analysis. The main aim of this study is to investigate the influence of uncertainty in material properties and geometry parameters on the mechanical performance and shear capacity of RC haunched beams. Firstly, 65 experimentally tested RC haunched beams and prismatic beams are analyzed via deterministic nonlinear finite element method by a special program (ATENA) to verify the efficiency of utilized numerical models, the shear capacity and the crack pattern. The accuracy of nonlinear finite element analyses is verified by comparing the results of nonlinear finite element and experiments and both results are found to be in a good agreement. Afterwards, stochastic analyses are performed for each beam where the RC material properties and geometry parameters are assigned to take probabilistic values using an advanced simulating procedure. As a result of stochastic analysis, statistical parameters are determined. The statistical parameters are obtained for resistance bias factor and the coefficient of variation which were found to be equal to 1.053 and 0.137 respectively. Finally, reliability analyses are accomplished using the limit state functions of ACI-318 and ASCE-7 depending on the calculated statistical parameters. The results show that the RC haunched beams have higher sensitivity and riskiness than the RC prismatic beams.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.953-961
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• 2009

The objective of this study is to develop a stochastic continuous storage function model for enhancement of an event-oriented watershed and channel storage function models which have been used as an official flood forecast model in Korea. For this study, soil moisture accounting component is added to the original storage function model and each hydrologic component, such as surface flow, subsurface flow, groundwater flow and actual evaportranspiration, is simulated as a function of soil water content. And also, ensemble Kalman filtering technique is used for real-time assimilation of measured streamflow from various stream locations in the watershed. Therefore the enhanced model will be able to simulate hydrologic components for long-term period without additional estimation of model parameters and to give more accurate and reliable results than those from the existing deterministic model due to the assimilation of measured streamflow data.