• Asian Journal of Atmospheric Environment
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• v.12 no.3
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• pp.215-231
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• 2018

In order to understand the variation of air quality and the concentration of atmospheric particulates in Chengdu Second Ring Road renovation project, this paper starts to investigate the surrounding residents' opinions on the influenced environment and their daily lives via questionnaires. Then the study numerically simulates the change rule of atmospheric particulates in terms of time and space by using the Gaussian dispersion-deposition model and the compartment model. The optimized scientific scheme is selected by the improved fuzzy analytical hierarchy process(FAHP) to help decision making for the future urban reconstructions. Finally, the reduced emissions of atmospheric particulates are measured when the improvement scheme is provided. According to the study, it can be concluded that the concentration of atmospheric particulates increases rapidly in central Chengdu city during the renovation project, which results in worsening air quality in Chengdu during March 2012 to March 2013. Taking related measures on energy saving and emission reduction can effectively reduce the concentration of atmospheric particulates and promote economic, environmental and social coordination.

• ETRI Journal
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• v.27 no.2
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• pp.188-194
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• 2005

We report on the improvement of critical dimension (CD) linearity on a photomask by applying the concept of process proximity correction to a laser lithographic process used for the fabrication of photomasks. Rule-based laser process proximity correction (LPC) was performed using an automated optical proximity correction tool and we obtained dramatic improvement of CD linearity on a photomask. A study on model-based LPC was executed using a two-Gaussian kernel function and we extracted model parameters for the laser lithographic process by fitting the model-predicted CD linearity data with measured ones. Model-predicted bias values of isolated space (I/S), arrayed contact (A/C) and isolated contact (I/C) were in good agreement with those obtained by the nonlinear curve-fitting method used for the rule-based LPC.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.135-144
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• 2015

Fingerprint pores have proven to be useful features for fingerprint recognition and several pore-based fingerprint recognition systems have been reported recently. In order to recognize fingerprints using pore information, it is very important to extract pores reliably and accurately. Existing pore extraction methods utilize 2D model fitting to detect pore centers. This paper proposes a pore extraction method using 1D Gaussian model which is much simpler than 2D model. During model fitting process, 1D model requires less computational cost than 2D model. The proposed method first calculates local ridge orientation; then, ridge mask is generated. Since pore center is brighter than its neighboring pixels, pore candidates are extracted using a $3{\times}3$ filter and a $5{\times}5$ filter successively. Pore centers are extracted by fitting 1D Gaussian model on the pore candidates. Extensive experiments show that the proposed pore extraction method can extract pores more effectively and accurately than other existing methods, and pore matching results show the proposed pore extraction method could be used in fingerprint recognition.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.545-556
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• 2005

Hjort(1990) obtained the nonparametric Bayes estimator $\^{F}_{c,a}$ of $F_0$ with respect to beta processes in the random censorship model. Let $X_1,{\cdots},X_n$ be i.i.d. $F_0$ and let $C_1,{\cdot},\;C_n$ be i.i.d. G. Assume that $F_0$ and G are continuous. This paper shows that {$\^{F}_{c,a}$(u){\|}0 < u < T} converges weakly to a Gaussian process whenever T < $\infty$ and $\~{F}_0({\tau})\;<\;1$.

• Journal of the Korean Vacuum Society
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• v.10 no.2
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• pp.275-281
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• 2001

We used the discrete-sectional model to analyze the particle growth by coagulation of particles in silane plasma reactor, considering the Gaussian distribution function for particle charges. The effects of process conditions such as monomer size and mass generation rate of monomers on particle growth in plasma reactor were analyzed theoretically/ Based on the Gaussian distribution function of particle charges, the large particles of more than 40 nm in size are almost found to be charged negatively, but some fractions of small, tiny particles are in neutral state or even charged positively. As the particle size and surface area increase with time by particle coagulation, the number of charges per particle increases with time. As the large particles are generated by particle coagulation, the particle size distribution become bimodal. The results of discrete-sectional model for the particle growth in silane plasma reactor were in close agreement with the experimental results by Shiratani et al. [3] for the same plasma conditions. We believe the model equations for the particle charge distribution and coagulation between particles can be applied to understand the nano-sized particle growth in plasma reactor.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.57-64
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• 2015

In this paper, a model reference adaptive control (MRAC) scheme is applied for the precise and robust motion control of a pneumatic system with load variation. The reference model for MRAC is designed systematically using linear quadratic Gaussian control with loop transfer recovery (LQG/LTR). The sigmoid function of inverse velocity is used to compensate for the nonlinear friction force between the sliding parts. The experimental results show that MRAC effectively overcame the limit of the PID controller when there was unknown disturbance, including abrupt load variation and model uncertainty in the pneumatic control system.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.429-443
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• 1997

We consider the problem of obtaining several types of simultaneous confidence bands for the survival curve under the additive risk model. The derivation uses the weak convergence of normalized cumulative hazard estimator to a mean zero Gaussian process whose distribution can be easily approxomated through simulation. The bands are illustrated by applying them from two well-known clinicla studies. Finally, simulation studies are carried outo to compare the performance of the proposed bands for the survival function under the additive risk model.

• Journal of the Semiconductor & Display Technology
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• v.6 no.3
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• pp.25-33
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• 2007

A computationally efficient and accurate Monte Carlo (MC) simulator of electron beam lithography process, which is named SCNU-EBL, has been developed for semiconductor nanometer pattern design and fabrication. The simulator is composed of a MC simulation model of electron trajectory into solid targets, an Gaussian-beam exposure simulation model, and a development simulation model of photoresist using a string model. Especially for the trajectories of incident electrons into the solid targets, the inner-shell electron scattering of an target atom and its discrete energy loss with an incident electron is efficiently modeled for multi-layer resists and heterogeneous multi-layer targets. The simulator was newly applied to the development profile simulation of ZEP520 positive photoresist for NGL(Next-Generation Lithography). The simulation of ZEP520 for electron-beam nanolithography gave a reasonable agreement with the SEM experiments of ZEP520 photoresist.

• ETRI Journal
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• v.38 no.5
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• pp.1019-1029
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• 2016

The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.327-341
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• 2022

Cloud removal is often required to construct time-series sets of optical images for environmental monitoring. In regression-based cloud removal, the selection of an appropriate regression model and the impact analysis of the input images significantly affect the prediction performance. This study evaluates the potential of Gaussian process (GP) regression for cloud removal and also analyzes the effects of cloud-free optical images and spectral bands on prediction performance. Unlike other machine learning-based regression models, GP regression provides uncertainty information and automatically optimizes hyperparameters. An experiment using Sentinel-2 multi-spectral images was conducted for cloud removal in the two agricultural regions. The prediction performance of GP regression was compared with that of random forest (RF) regression. Various combinations of input images and multi-spectral bands were considered for quantitative evaluations. The experimental results showed that using multi-temporal images with multi-spectral bands as inputs achieved the best prediction accuracy. Highly correlated adjacent multi-spectral bands and temporally correlated multi-temporal images resulted in an improved prediction accuracy. The prediction performance of GP regression was significantly improved in predicting the near-infrared band compared to that of RF regression. Estimating the distribution function of input data in GP regression could reflect the variations in the considered spectral band with a broader range. In particular, GP regression was superior to RF regression for reproducing structural patterns at both sites in terms of structural similarity. In addition, uncertainty information provided by GP regression showed a reasonable similarity to prediction errors for some sub-areas, indicating that uncertainty estimates may be used to measure the prediction result quality. These findings suggest that GP regression could be beneficial for cloud removal and optical image reconstruction. In addition, the impact analysis results of the input images provide guidelines for selecting optimal images for regression-based cloud removal.