• Environmental and Resource Economics Review
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• v.17 no.1
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• pp.147-164
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• 2008

When measuring the value of environmental attributes of housing, the conventional Hedonic Pricing Method assumes market equilibrium. Thus each attribute is believed to be implicitly valued based on the market price. The revealed preference is the basic logic in this approach. However, if the participants in the housing market are not perfectly informed or feel vagueness regarding the attributes of the housing, the conventional Hedonic Pricing Approach could not provide relevant value of the attribute in question. A Fuzzy Regression Method is suggested to handle with the lack of information or preference uncertainty problem m the Hedonic Pricing Approach. In this paper, our main concern IS given to the fuzziness effect on the airport noise in the metropolitan areas of South Korea.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.259-264
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• 2006

It is well known that the uncertainty of the covariance parameters of the process noise (Q) and the observation errors (R) has a significant impact on Kalman filtering performance. Q and R influence the weight that the filter applies between the existing process information and the latest measurements. Errors in any of them may result in the filter being suboptimal or even cause it to diverge. The conventional way of determining Q and R requires good a priori knowledge of the process noises and measurement errors, which normally comes from intensive empirical analysis. Many adaptive methods have been developed to overcome the conventional Kalman filter's limitations. Starting from covariance matching principles, an innovative adaptive process noise scaling algorithm has been proposed in this paper. Without artificial or empirical parameters to be set, the proposed adaptive mechanism drives the filter autonomously to the optimal mode. The proposed algorithm has been tested using road test data, showing significant improvements to filtering performance.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.37.3-38
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• 2021

Radiation pressure noise of photon and photon shot noise are quantum noise limitation in interferometric gravita-tional wave detectors. Since relationship between the two noises is position and momentum of the Heisenberg uncertainty principle, quantum non-demolition (QND) technique is required to reduce the two noises at the same time. Frequency dependent squeezing using a filter cavity is one of realistic solutions for QND measurement and experimental results show that its cutting-edge performance is sufficient to apply to the current gravitational wave detectors. A 300m filter cavity is under construction at adv-LIGO. KAGRA (gravitational wave detector in Japan) has also started international collaboration to build a filter cavity. Recently we joined the filter cavity project for KAGRA. Current status of squeezing and filter cavity research at KASI and details of the KAGRA filter cavity project will be presented.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.135-143
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• 2014

In this study, Chungju inflow was simulated for climate change considering the uncertainties of GCMs and a stochastic model. TFN (Transfer Function Noise) model and 4 different GCMs (CNRM, CSIRO, CONS, UKMO) based on IPCC AR4 A2 scenario were used. In order to evaluate uncertainty of TFN model, 100 cases of noises are applied to the TFN model. Thus, 400 cases of inflow results are simulated. Future inflows according to the GCMs show different rates of changes for the future 3 periods relative to the past 30-years reference period. As the results, the summer inflow shows increasing trend and the spring inflow shows decreasing trend based on AR4 A2 scenario.

• Plant Journal
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• v.17 no.4
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• pp.53-60
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• 2021

The purpose of this study was to develop a portable vibration analyzer that is effective for acquiring and analyzing vibration data of rotating equipment of a power plant and a domestic vibration monitoring system manufacturer Nada Co., Ltd. The hardware of the developed portable vibration analyzer minimizes measurement errors by calibrating the measured values obtained through measurement uncertainty for calibration of the measuring devices in the system, and is composed of a signal processing device with high resolution through high speed data processing. The software structure implements a variety of vibration plots to execute a detailed analysis program, and applies algorithms to measure and remove noise caused by disturbances while operating a rotating machine. The developed product contributed greatly to increase the user's mobility and performance, as well as to reduce the purchase cost due to localization.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.98-105
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• 2024

This paper proposes a new approach with a distance-based regularization to the entropy applied to the NBV (Next-Best-View) selection with NeRF (Neural Radiance Fields). 3D reconstruction requires images from various viewpoints, and selecting where to capture these images is a highly complex problem. In a recent work, image acquisition was derived using NeRF's ray-based uncertainty. While this work was effective for evaluating candidate viewpoints at fixed distances from a camera to an object, it is limited when dealing with a range of candidate viewpoints at various distances, because it tends to favor selecting viewpoints at closer distances. Acquiring images from nearby viewpoints is beneficial for capturing surface details. However, with the limited number of images, its image selection is less overlapped and less frequently observed, so its reconstructed result is sensitive to noise and contains undesired artifacts. We propose a method that incorporates distance-based regularization into entropy, allowing us to acquire images at distances conducive to capturing both surface details without undesired noise and artifacts. Our experiments with synthetic images demonstrated that NeRF models with the proposed distance and entropy-based criteria achieved around 50 percent fewer reconstruction errors than the recent work.

• Smart Structures and Systems
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• v.4 no.1
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• pp.19-34
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• 2008

Nowadays developments in the field of laminated composite structures with piezoelectric have attracted significant attention of researchers due to their wide range of applications in engineering such as sensors, actuators, vibration suppression, shape control, noise attenuation and precision positioning. Due to large number of parameters associated with its manufacturing and fabrication, composite structures with piezoelectric display a considerable amount of uncertainty in their material properties. The present work investigates the effect of the uncertainty on the free vibration response of piezoelectric laminated composite plate. The lamina material properties have been modeled as independent random variables for accurate prediction of the system behavior. System equations have been derived using higher order shear deformation theory. A finite element method in conjunction with Monte Carlo simulation is employed to obtain the secondorder statistics of the natural frequencies. Typical results are presented for all edges simply supported piezoelectric laminated composite plates to show the influence of scattering in material properties on the second order statistics of the natural frequencies. The results have been compared with those available in literature.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.27-30
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• 2012

One of the most fundamental functions required for an intelligent agent is to estimate its current position based upon uncertain sensor data. In this paper, we explain the implementation of a robot localization system using Particle filters, which are the most effective one of the probabilistic localization methods, and then present the result of experiments for evaluating the performance of our system. Through conducting experiments to compare the effect of the noise-free model with that of the noisy state transition model considering inherent errors of robot actions, we show that it can help improve the performance of the Particle filter localization to apply a state transition model closely approximating the uncertainty of real robot actions.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.2
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• pp.145-154
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• 2023

In this study, to control the heading angle of a ship, which is constantly subjected to various internal and external disturbances during the voyage, an LADRC (linear active disturbance rejection control) design that focuses more on improving the disturbance removal performance was proposed. The speed rate of change of the ship's heading angle due to the turn of the rudder angle was selected as a significant factor, and the nonlinear model of the ship's maneuvering equation, including the steering gear, was treated as a total disturbance. It is the similar process with an LADRC design for the first-order transfer function model. At this time, the gains of the controller included in LADRC and the gains of the extended state observer were tuned to RCGAs (real-coded genetic algorithms) to minimize the integral time-weighted absolute error as an evaluation function. The simulation was performed by applying the proposed GA-LADRC controller to the heading angle control of the Mariner class vessel. In particular, it was confirmed that the proposed controller satisfactorily maintains and follows the set course even when the disturbances such as nonlinearity, modelling error, uncertainty and noise of the measurement sensor are considered.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.99-106
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• 2016

In this paper, we will discuss about correction method of missing data on noisy observation matrix and uncertainty analysis for the potential noise. In situations without missing data in an observation matrix, this solution is known to be accurately induced by SVD (Singular Value Decomposition). However, usually the several entries of observation matrix have not been observed and other entries have been perturbed by the influence of noise. In this case, it is difficult to find the solution as well as cause the 3D reconstruction error. Therefore, in order to minimize the 3D reconstruction error, above all things, it is necessary to correct reliably the missing data under noise distribution and to give a quantitative evaluation for the corrected results. This paper focuses on a method for correcting missing data using geometrical properties between 2D projected object and 3D reconstructed shape and for estimating a noise level of the observation matrix using ranks of SVD in order to quantitatively evaluate the performance of the correction algorithm.