• Journal of the Geological Society of Korea
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• v.54 no.5
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• pp.579-584
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• 2018

It is crucial to determine a mixing ratio using an end-member mixing analysis when there is seawater intrusion. In this study, an error from the calculation of the mixing ratio between seawater and freshwater based on the principles of uncertainty was determined. I present the errors in the calculated mixing ratios as a function of the chemical difference between the mean seawater concentrations and standard deviations. The error is caused by: (1) the mixing ratio between seawater and freshwater; (2) the difference between the mean concentration and the standard deviation; and (3) the difference of the tracer concentration between freshwater and seawater (inversely). In particular, the error may determine hydrogeochemical process (either precipitation or dissolution) when a value of ionic delta (difference between measured and theoretical concentration) is close to zero during cation exchange by seawater intrusion.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.3
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• pp.75-89
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• 1993

The present paper describes an application of UA(Uncertainty Analysis) to seakeeping model test, basically according to the Performance Test Code of ASME(American Society of Mechanical Engineers), in which all the possible error sources involved in the preparation of test, calibration of instruments, data acquisition and analysis are quantified, and summed up with error propagation coefficients to the final uncertainties. The differences between the static test such as resistance and propulsion test and the dynamic test like seakeeping test are clearly identified during all the procedures of UA and asymmetric bias errors are considered. The DRE(data reduction equation) subject to present UA are the heave and pitch response amplitude operator and nondimensionalized absolute frequency. The usefulness of UA in seakeeping test were confirmed not only for quantifying errors and improving measurement accuracy but also for the validation of various seakeeping analysis tools.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of the Korean Society for Railway
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• v.20 no.1
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• pp.156-163
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• 2017

In the major operation sections of the urban railway, there has been habitual delay, and delay propagation; another problem is the increase of crowds and of inconvenience to passengers. The urban railway has different characteristics from rural railways, such as uncertainty of demand and irregularity of train operation. In urban railways, recently, operators manage quality indicators of service using operation results, such as the delay of train operation and the congestion of trains. However, because the urban railway has characteristics in which demand, passenger behavior, and train operation mutually affect each other, it is difficult to express the quality of service that passengers actually feel. In this paper, we suggest a quality indicator of service from the viewpoint of passengers, and present a demand responsive multi-train simulation method to predict dynamic dwell time and train operation status; we also use simulation results to consider changes in the quality indicator of service.

• Smart Structures and Systems
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• v.26 no.2
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• pp.227-239
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• 2020

Despite proven effectiveness and accuracy in laboratories, the existing damage assessment based on guided ultrasonic waves (GUWs) or acoustic emission (AE) confronts challenges when extended to real-world structural health monitoring (SHM) for railway tracks. Central to the concerns are the extremely complex signal appearance due to highly dispersive and multimodal wave features, restriction on transducer installations, and severe contaminations of ambient noise. It remains a critical yet unsolved problem along with recent attempts to implement SHM in bourgeoning high-speed railway (HSR). By leveraging authors' continued endeavours, an SHM framework, based on actively generated diffuse ultrasonic waves (DUWs) and a benchmark-free condition contrast algorithm, has been developed and deployed via an all-in-one SHM system. Miniaturized lead zirconate titanate (PZT) wafers are utilized to generate and acquire DUWs in long-range railway tracks. Fatigue cracks in the tracks show unique contact behaviours under different conditions of external loads and further disturb DUW propagation. By contrast DUW propagation traits, fatigue cracks in railway tracks can be characterised quantitatively and the holistic health status of the tracks can be evaluated in a real-time manner. Compared with GUW- or AE-based methods, the DUW-driven inspection philosophy exhibits immunity to ambient noise and measurement uncertainty, less dependence on baseline signals, use of significantly reduced number of transducers, and high robustness in atrocious engineering conditions. Conformance tests are performed on HSR tracks, in which the evolution of fatigue damage is monitored continuously and quantitatively, demonstrating effectiveness, adaptability, reliability and robustness of DUW-driven SHM towards HSR applications.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.31-36
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• 2020

In recent years, increasing the variety of disasters and accidents that accompany large-scale damage. Disasters are accidents with uncertainty and have a direct impact on people's lives, safety and property protection. Therefore, it is necessary to establish and operate safety management systems such as prevention, response, and recovery for various disasters. Therefore, in this paper, a real-time disaster safety management solution in a smart environment was designed to systematically respond to disaster accidents. To this end, 1: 1 or 1: N situation propagation was performed to the situation room, related organizations, and experts through smart devices. Through this, the solution was configured to respond quickly and appropriately through multi-party information sharing and communication. In other words, we designed a solution that applied functions such as real-time and multi-party HD video transmission, mobile-type report management, voice / text situation propagation, location information sharing, recording and history management, and security.

• Journal of Korea Water Resources Association
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• v.39 no.10 s.171
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• pp.823-832
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• 2006

Precipitation is the most important component to the study of water and energy cycle in hydrology. In this study we investigate rainfall retrieval uncertainty from different sources of remotely sensed precipitation field and then probable error propagation in the simulation of hydrologic variables especially, runoff on different vegetation cover. Two remotely sensed rainfall retrievals (space-borne IR-only and ground radar rainfall) are explored and compared visually and statistically. Then, an offline Community Land Model (CLM) is forced with in situ meteorological data to simulate the amount of runoff and determine their impact on model predictions. A fundamental assumption made in this study is that CLM can adequately represent the physical land surface processes. Results show there are big differences between different sources of precipitation fields in terms of the magnitude and temporal variability. The study provides some intuitions on the uncertainty of hydrologic prediction via the interaction between the land surface and near atmosphere fluxes in the modelling approach. Eventually it will contribute to the understanding of water resources redistribution to the climate change in Korean Peninsula.

• Journal of the Korea Computer Industry Society
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• v.5 no.9
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• pp.993-1004
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• 2004

We propose the method for kinematic and dynamic modeling and Path-tracking of four-wheeled mobile robots with 2 d.o.f having the limited drive-torques. Controllability of wheeled-mobile robots is revealed by using the kinematic model. Instantaneously coincident coordinate system, force/torque propagation and Newton's equilibrium law are used to induce the dynamic model. When drive-torques generated by inverse dynamics exceed the limitation, we make wheeled-mobile robots follow the reference path by modifying the planned reference trajectory with time-scaling. The controller is introduced to compensate for error owing to modeling uncertainty and measurement noise. And simulation results prove that the method proposed by this paper is efficient.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.705-711
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• 2013

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

• Earthquakes and Structures
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• v.12 no.2
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• pp.153-163
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• 2017

The underlying goal of the present paper is to investigate soil and structural uncertainties on impedance functions and structural response of soil-shallow foundation-structure (SSFS) system using Monte Carlo simulations. The impedance functions of a rigid massless circular foundation resting on the surface of a random soil layer underlain by a homogeneous half-space are obtained using 1-D wave propagation in cones with reflection and refraction occurring at the layer-basement interface and free surface. Firstly, two distribution functions (lognormal and gamma) were used to generate random numbers of soil parameters (layer's thickness and shear wave velocity) for both horizontal and rocking modes of vibration with coefficients of variation ranging between 5 and 20%, for each distribution and each parameter. Secondly, the influence of uncertainties of soil parameters (layer's thickness, and shear wave velocity), as well as structural parameters (height of the superstructure, and radius of the foundation) on the response of the coupled system using lognormal distribution was investigated. This study illustrated that uncertainties on soil and structure properties, especially shear wave velocity and thickness of the layer, height of the structure and the foundation radius significantly affect the impedance functions, and in same time the response of the coupled system.