• Geomechanics and Engineering
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• v.17 no.6
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• pp.565-572
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• 2019

Spatial variability is an inherent characteristic of soil, and auto-correlation length (ACL) is a very important parameter in the reliability or probabilistic analyses of geotechnical engineering that consider the spatial variability of soils. Current methods for estimating the ACL need a large amount of laboratory or in-situ experiments, which is a great obstacle to the application of random field theory to geotechnical reliability analysis and design. To estimate the ACL reasonably and efficiently, we propose a micro-structure based numerical simulation method. The quartet structure generation set algorithm is used to generate stochastic numerical micro-structure of soils, and scanning electron microscope test of soil samples combined with digital image processing technique is adopted to obtain parameters needed in the QSGS algorithm. Then, 2-point correlation function is adopted to calculate the ACL based on the generated numerical micro-structure of soils. Results of a case study shows that the ACL can be estimated efficiently using the proposed method. Sensitivity analysis demonstrates that the ACL will become stable with the increase of mesh density and model size. A model size of $300{\times}300$ with a grid size of $1{\times}1$ is suitable for the calculation of the ACL of clayey soils.

• Tunnel and Underground Space
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• v.16 no.2 s.61
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• pp.135-145
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• 2006

In this study, conditional simulation was conducted to estimate rock mass rating(RMR) in unsurveyed regions. Sequential Gaussian simulation(SGS) and sequential indicator simulation(SIS) were applied for estimating RMR from the bore hole logging data. The uncertainty of SGS and SIS was verified by sample cross validation. A subset composed of 5 bore hole logging data among the original 30 bore hole logging data was set aside as test data. The remainder was training data. The quality of SGS and SIS estimation on the testing data reflects how well it would perform in an unsupervised setting. SGS and SIS were useful stochastic methods to estimate the spatial distribution of rock mass classes correctly and assess the uncertainty of estimation quantitatively. The result of conditional simulation can offer useful information of rock mass classes such as RMR in unsurveyed regions.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.400-408
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• 2009

Simulation applications for analyzing the productivity of construction operations at operation level and project schedules at project level are crucial methods in project management. The application at two different levels should be very tightly linked to each other in practice. However, appropriate integration at the levels is not achieved in that existing systems do not support to integrate operation models into a schedule model. This paper presents a new approach named to Discrete Event Simulation-Nesting modeling approach, which supports not only productivity analysis at operation level but also schedule management at a project level. The system developed by the authors allows creating operation models at the operation level, maintaining them in operation model library, executing sensitivity analysis to find the behaviors of the operation models when different combination of resources are used as existing DES systems do. On top of the conventional functions, the new system facilitates to find the optimum solution of resource combinations which satisfy the user's interest by computing the hourly productivity and the hourly cost of the operation. By drag-and-dropping an operation model kept in the operation model library, the operation models are integrated into an activity of the schedule model. When a complete schedule model is established by nesting operation models into the schedule model, stochastic simulation based scheduling is executed. A case study is presented to demonstrate the new simulation system and verify the validity of the system.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.686-689
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• 2004

Genetic algorithm is well known for stochastic searching method in imitating natural phenomena. In recent times, studies have been conducted in improving conventional evolutionary computation speed and promoting precision. This paper presents an approach to optimize PID controller gains with the application of modified Genetic Algorithm using Support Vector Machine (SVMGA). That is, we aim to explore optimum parameters of PID controller using SVMGA. Simulation results are given to compare to those of tuning methods, based on Simple Genetic Algorithm and Ziegler-Nicholas tuning method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.2
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• pp.62-67
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• 2003

This paper deals with a First-Come, First-Served queueing model to analyze the behavior of heterogeneous finite source system with a single server Each sources and the processor are assumed to operate in independently Markovian environments, respectively. Each request is characterized by its own exponentially distributed source and service time with parameter depending on the state of the corresponding environment, that is, the arrival and service rates are subject to random fluctuations. Our aim is to get the usual stationary performance measures of the system, such as, utilizations, mean number of requests staying at the server, mean queue lengths, average waiting and sojourn times. In the case of fast arrivals or fast service asymptotic methods can be applied. In the intermediate situations stochastic simulation Is used. As applications of this model some problems in the field of telecommunications are treated.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.777-782
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• 2011

Neural network technique is widely employed in the fields of signal processing, control systems, pattern recognition, etc. Learning of neural networks is an important procedure to accomplish dynamic system modeling. This paper presents a novel learning approach for differential neural network models based on the Kalman-Bucy filter theory. We construct an augmented state vector including original neural state and parameter vectors and derive a state estimation rule avoiding gradient function terms which involve to the conventional neural learning methods such as a back-propagation approach. We carry out numerical simulation to evaluate the proposed learning approach in nonlinear system modeling. By comparing to the well-known back-propagation approach and Kalman-Bucy filtering, its superiority is additionally proved under stochastic system environments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.506-511
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• 1996

The performance evaluation of flexible manufacturing systems or cells at the stages of design and planning is one of important issues in manufacturing. For that reason, Guo has presented an approachbased on moment generating function and generalized stochastic PetriNets for performance analysis. In this paper, Buo's approach is extended tothe cases of flexible manufacturing cell including one machining center with a local buffer, AS/RS(Automatic Storage and Retrieval System), set-up station and AGV(Automated Guided Vehicle). Then the performance measures from this approach is compared with simulation. The major advantage ofthis method over existing performance evaluation methods is the ability to compute analytic solutions for performance measures.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3E
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• pp.79-84
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• 2004

In this paper, the AEC(acoustic echo canceller) is designed and implemented using VHDL(VHSIC hardware description language). The designed Echo Canceller employs the pipeline and the master-slave structure, and is realized with FPGA. As an adaptive algorithm, the Normalized LMS algorithm is used. For the coefficient adjustment, the Stochastic Iteration Algorithm(SIA) which uses only current residual values is used and the number of registers are evidently reduced and convergence speed is also much improved comparing to existing methods by using EAB of FPGA for FIR filter structure of transceiver. The designed Echo Canceller is verified with the test board implemented for this paper. From the timing simulation echo signals at about 1500 sampling data are converged and ERLE is improved by about 42-dB.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.249-261
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• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• Journal of The Korean Astronomical Society
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• v.34 no.4
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• pp.231-235
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• 2001

Cosmic-ray acceleration, although physically important in many astrophysical contexts, is difficult to incorporate into numerical models,. because it involves microphysics that is generally far from thermodynamic equilibrium, and also because the length and time scales for that physics typically range over many orders of magnitude, reflecting the huge range of particle rigidities that must be represented. The most common accelerator models are stochastic in nature and involve nonequilibrium plasma properties that are also often poorly understood. Still, nature clearly finds a way to produce simple, robust and almost scale-free energy distributions for the cosmic-rays. Their importance has inspired a number of approaches to examining the production and transport of cosmic-ray particles in numerical simulations. I offer here a brief comparison of some of the methods that have been introduced.