• Journal of KIBIM
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• v.8 no.3
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• pp.31-42
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• 2018

This study focuses on the BIM request for information(RFI) processing performance and quantitatively analyzes the performance of the BIM coordinator and the loss due to the waiting of the project participants. For these purposes, a method to quantitatively evaluate the performance of the BIM coordinator was proposed using a queueing model. For the verification, two projects in which BIM was applied in the construction phase were selected, and the BIM RFI data were collected through the analysis of the BIM monthly report and BIM coordinator work log of each project. In addition, the BIM input personnel, labor cost, and productivity data were collected through interviews with the experts of the case projects. The analysis of the BIM RFI processing performance of the BIM coordinator using the queueing model exhibited on a probabilistic basis that the waiting status of the project participants could vary depending on the preliminary BIM application to the design verification as well as the input number and level of the BIM coordinator personnel. In addition, the loss cost due to the waiting of the project participants was analyzed using the number of BIM RFIs waiting to be processed in the queueing system. Finally, the economic feasibility analysis for the optimal BIM coordinator input was performed considering the loss cost. The results of this study can be used to make decisions about the optimal BIM coordinator input and can provide grounds for the BIM return on investment (ROI) analysis considering the waiting cost of the project participants.

• Convergence Security Journal
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• v.8 no.4
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• pp.173-181
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• 2008

Recently, Worm epidemic models have been developed in response to the cyber threats posed by worms in order to analyze their propagation and predict their spread. Some of the most important ones involve mathematical model techniques such as Epidemic(SI), KM (Kermack-MeKendrick), Two-Factor and AAWP(Analytical Active Worm Propagation). However, most models have several inherent limitations. For instance, they target worms that employ random scanning in the network such as CodeRed worm and it was able to be applied to the specified threats. Therefore, we propose the probabilistic of worm propagation based on the Markov Chain, which can be applied to cyber threats such as Mass SQL Injection worm. Using the proposed method in this paper, we can predict the occurrence probability and occurrence frequency for each threats in the entire system.

• Journal of Environmental Health Sciences
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• v.40 no.5
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• pp.397-406
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• 2014

Objectives: Perchloroethylene (PCE) is a volatile chemical widely used as a solvent in the dry-cleaning and textile processing industries. It was evaluated as Group 2 "probably carcinogenic to humans" by the Integrated Risk Information System (IRIS) of the United State Environmental Protection Agency (U.S. EPA) in 2012. In order to provide a scientific basis for establishing risk management measures for chemicals on the national priority substances list, aggregate risk assessment was conducted for PCE, included in the top-10 substances. Methods: We conducted the investigation and monitoring of PCE exposure (e.g., exposure scenario, detection levels, and exposure factors, etc.) and assessed its multi-media (e.g., outdoor air, indoor air, and ground water) exposure risk with a deterministic and probabilistic approach. Results: In human risk assessment (HRA), the level of human exposure was higher in the younger age group. The exposure level through inhalation at home was the highest among the exposure routes. Outdoor air or uptake of drinking water represented less than 1% of total contributions to PCE exposure. These findings suggested that the level of risk was negligible since the Hazard Index (HI) induced by HRA was below one among all age groups, with a maximum HI value of 0.17 when reasonable maximum exposure was applied. Conclusion: In conclusion, it was suggested that despite low exposure risk, further studies are needed considering main sources, including occupational exposure.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.18 no.3
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• pp.291-302
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• 2005

Due to the importance of the parameter in structural response, the uncertain elastic modulus was located at the center of stochastic analysis, where the response variability caused by the uncertain system parameters is pursued. However when we analyze the so-called stochastic systems, as many parameters as possible must be included in the analysis if we want to obtain the response variability that can reach a true one, even in an approximate sense. In this paper, a formulation to determine the statistical behavior of in-plane structures due to multiple uncertain material parameters, i.e., elastic modulus and Poisson's ratio, is suggested. To this end, the polynomial expansion on the coefficients of constitutive matrix is employed. In constructing the modified auto-and cross-correlation functions, use is made of the general equation for n-th moment. For the computational purpose, the infinite series of stochastic sub-stiffness matrices is truncated preserving required accuracy. To demons4rate the validity of the proposed formulation, an exemplary example is analyzed and the results are compared with those obtained by means of classical Monte Carlo simulation, which is based on the local averaging scheme.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.3
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• pp.43-52
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• 2004

This paper describes a method of evaluating seismic system performance of highway transportation network in California. The basic element that plays a crucial role in this study is the fragility information of highway bridges in Caltrans' (California Department of Transportation) freeway network. The bridge fragility information is expressed as a function of the ground motion intensity, such as peak ground acceleration (PGA) or peak ground velocity (PGV). Network damage was evaluated under the 1994 Northridge earthquake and scenario earthquakes. A probabilistic model was developed to determine the effect of repair of bridge damage on the improvement of the network performance as days passed after the event. As an example, the system performance degradation measured in terms of an index, “Drivers Delay”, is calculated for the Los Angeles area transportation system, and losses due to Drivers Delay with and without retrofit were estimated.

• Journal of the Earthquake Engineering Society of Korea
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• v.14 no.5
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• pp.53-64
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• 2010

Seismic fragility curves of structures represent the probability of exceeding the prescribed structural damage state for a given various levels of ground motion intensity, such as peak ground acceleration (PGA). This means that seismic fragility curves are essential to the evaluation of structural seismic performance and assessments of risk. Most of existing studies have not considered the near- and far-fault earthquake effect on the seismic fragility curves. In order to evaluate the effect of near- and far-fault earthquakes, seismic fragility curves for PSC box girder bridges subjected to near- and far-fault earthquakes are calculated and compared. The seismic fragility curves are strongly dependent on the earthquake characteristics such as fault distance. This paper suggests that the effect of near- and far-fault earthquakes on seismic fragility curves of PSC box girder bridge structure should be considered.

• Smart Structures and Systems
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• v.15 no.3
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• pp.717-733
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• 2015

Modal parameters of a structure are commonly used quantities for system identification and damage detection. With a limited number of studies on the statistics assessment of modal parameters, this paper presents procedures to properly account for the uncertainties present in the process of extracting modal parameters. Particularly, this paper focuses on how to deal with the measurement error in an ambient vibration test and the modeling error resulting from a modal parameter extraction process. A bootstrap approach is adopted, when an ensemble of a limited number of noised time-history response recordings is available. To estimate the modeling error associated with the extraction process, a model prediction expansion approach is adopted where the modeling error is considered as an "adjustment" to the prediction obtained from the extraction process. The proposed procedures can be further incorporated into the probabilistic analysis of applications where the modal parameters are used. This study considers the effects of the measurement and modeling errors and can provide guidance in allocating resources to improve the estimation accuracy of the modal data. As an illustration, the proposed procedures are applied to extract the modal data of a damaged beam, and the extracted modal data are used to detect potential damage locations using a damage detection method. It is shown that the variability in the modal parameters can be considered to be quite low due to the measurement and modeling errors; however, this low variability has a significant impact on the damage detection results for the studied beam.

• Proceedings of the Korea Society for Simulation Conference
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• 1997.04a
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• pp.93-99
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• 1997

Most of the past studies on FMS scheduling problems may be classified into two classes, namely off-line scheduling and on-line scheduling approach. The off-line scheduling methods are used mostly for FMS planning purposes and may not be useful real time control of FMSs, because it generates solutions only after a relatively long period of time. The on-line scheduling methods are used extensively for dynamic real-time control of FMSs although the performance of on-line scheduling algorithms tends vary dramatically depending on various configurations of FMS. Current study is about finding a better on-line scheduling rules for FMS operations. In this study, we propose a method to create priority functions that can be used in setting relative priorities among jobs or machines in on-line scheduling. The priority functions reflect the configuration of FMS and the user-defined objective functions. The priority functions are generated from diverse dispatching rules which may be considered a special priority functions by themselves, and used to determine the order of processing and transporting parts. Overall system of our work consists of two modules, the Priority Function Evolution Module (PFEM) and the FMS Simulation Module (FMSSM). The PFEM generates new priority functions using input variables from a terminal set and primitive functions from a function set by genetic programming. And the FMSSM evaluates each priority function by a simulation methodology. Based on these evaluated values, the PFEM creates new priority functions by using crossover, mutation operation and probabilistic selection. These processes are iteratively applied until the termination criteria are satisfied. We considered various configurations and objective functions of FMSs in our study, and we seek a workable solution rather than an optimum or near optimum solution in scheduling FMS operations in real time. To verify the viability of our approach, experimental results of our model on real FMS are included.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.457-464
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• 2009

The weak classifier of AdaBoost algorithm is a central classification element that uses a single criterion separating positive and negative learning candidates. Finding the best criterion to separate two feature distributions influences learning capacity of the algorithm. A common way to classify the distributions is to use the mean value of the features. However, positive and negative distributions of Haar-like feature as an image descriptor are hard to classify by a single threshold. The poor classification ability of the single threshold also increases the number of boosting operations, and finally results in a poor classifier. This paper proposes a weak classifier that uses multiple criterions by adding a probabilistic criterion of the positive candidate distribution with the conventional mean classifier: the positive distribution has low variation and the values are closer to the mean while the negative distribution has large variation and values are widely spread. The difference in the variance for the positive and negative distributions is used as an additional criterion. In the learning procedure, we use a new classifier that provides a better classifier between them by selective switching between the mean and standard deviation. We call this new type of combined classifier the "Mixed Weak Classifier". The proposed weak classifier is more robust than the mean classifier alone and decreases the number of boosting operations to be converged.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.181-190
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• 2017

Recently, safety accidents of daily necessities such as humidifier disinfectant, mobile phones, and infant diapers, have occurred frequently. To protect consumers from these accidents, product safety management is required, and a product risk assessment tool is needed to evaluate the degree of safety of the product. In this paper, we have constructed RAS, which is a system that can evaluate product risk based on injury information of product accident in Korea Consumer Agency. RAS consists of an injury information analysis system for analyzing accident-related information and a risk assessment system for assessing risk using information derived from the system. The Bayesian network - based probabilistic method is applied to reflect the causal relationships that affect product risk in the risk assessment process. We used RAS to evaluate 33 children's products and compared them with the results of EU RAPEX RAG. Subsequent tasks include reducing the subjectivity of the input of the accident impact scale, and linking the above two systems.