• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.2
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• pp.108-118
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• 2014

On the optimization design problem using surrogate model, it requires considerable number of sampling points to construct a surrogate model which retains the accuracy. As an alternative to reduce construction cost of the surrogate model, Variable-Fidelity Modeling(VFM) technique, where correct high fidelity model based on the low fidelity surrogate model is introduced. In this study, hierarchical kriging model for variable-fidelity surrogate modeling is used and an optimization framework with multi-objective genetic algorithm(MOGA) is presented. To prove the feasibility of this framework, airfoil design optimization process is performed for the transonic region. The parameters of PARSEC are used to design variables and the optimization process is performed in case of varying number of grid and varying fidelity. The results showed that pareto front of all variable-fidelity models are similar with its single-level of fidelity model and calculation time is considerably reduced. Based on computational results, it is shown that VFM is a more efficient way and has an accuracy as high as that single-level of fidelity model optimization.

• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.625-633
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• 2012

Extended Depth of Focus (EDF) algorithms for extracting three-dimensional (3D) information from a set of optical image slices are studied by many researches recently. Due to the limited depth of focus of the microscope, only a small portion of the image slices are in focus. Most of the EDF algorithms try to find the in-focus area to generate a single focused image and a 3D depth image. Inherent to most image processing algorithms, the EDF algorithms need parameters to be properly initialized to perform successfully. In this paper, we select three popular transform-based EDF algorithms which are each based on pyramid, wavelet transform, and complex wavelet transform, and study the performance of the algorithms according to the initialization of its parameters. The parameters we considered consist of the number of levels used in the transform, the selection of the lowest level image, the window size used in high frequency filter, the noise reduction method, etc. Through extended simulation, we find a good relationship between the initialization of the parameters and the properties of both the texture and 3D ground truth images. Typically, we find that a proper initialization of the parameters improve the algorithm performance 3dB ~ 19dB over a default initialization in recovering the 3D information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.47-54
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• 2010

Old archived film shows two major defects: line scratch and blobs. In this paper, we present a design and implementation of an automatic video restoration system for line scratches observed in archived film. We use autoregressive (AR) image model because we can make stochastic and specifically autoregressive image generation process with our PAST-PRESENT model and Sampling Pattern. We designed locality maximizing scanning pattern, which can generate nearly stationary time-like series of pixels, which is a strong requirement for a stochastic series to be autoregressive. The sampled pixel series undergoes filtering and model fitting using Durbin-Levinson algorithm before interpolation process. We designed three-stage film restoration system, which includes (1) film acquisition from VHS tapes, (2) simple line scratch detection and restoration, and (3) manual blob identification and sophisticated inpainting scheme. We implemented film acquisition and simple inpainting scheme on Texas Instruments DSP board TMS320DM642 EVM, and implemented our AR inpainting scheme on PC for sophisticated restoration. We experimented our scheme with two old Korean films: "Viva Freedom" and "Robot Tae-Kwon-V", and the experimental results show that our scheme improves Bertalmio's scheme for subjective quality (MOS), objective quality (PSNR), and especially restoration ratio (RR), which reflects how much similar to the manual inpainting results.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.11
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• pp.13-20
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• 2008

In this paper, an 1.8V 12-bit 10MSPS CMOS A/D converter (ADC) is described. The architecture of the proposed ADC is based on a folding and interpolation using an even folding technique. For the purpose of improving SNR, cascaded-folding cascaded-interpolation technique, distributed track and hold are adapted. Further, a digital encoder algorithm is proposed for efficient digital process. The chip has been fabricated with $0.18{\mu}m$ 1-poly 4-metal n-well CMOS technology. The effective chip area is $2000{\mu}m{\times}1100{\mu}m$ and it consumes about 250mW at 1.8V power supply. The measured SNDR is about 46dB at 10MHz sampling frequency.

• Journal of Preventive Medicine and Public Health
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• v.38 no.3
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• pp.351-358
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• 2005

Objectives : Dementia has rapidly increased with the prolongation of life expectancy and aging in Korea. This study was conducted to estimate the prevalence of, and find related factors for, dementia in an urban elderly population, using a newly developed screening method. Methods : Seven hundred and six people, aged over 65 years-old, in Dong district of Gwangju, Korea, were recruited using stratified cluster sampling, and completed Korean version of Geriatric Mental State Schedule B3 (GMS B3-K), the Korean version of the Community Screening Interview for Dementia (CSID-K) and modified 10 word list-learning from the Consortium to Establish a Registry of Alzheimer's Disease (CERAD). Dementia was diagnosed by an algorithm derived from all three of these measures. Results : The crude and age adjusted prevalence rates of dementia were 13.0 and 11.5%, respectively. Age, education, marital status and a history of cerebrovascular disease were identified as factors related with dementia. Conclusions : The new instrument, using the GMS B3-K, CSID-K and modified 10 word list-learning from the CERAD, was considered effective as a community screening and diagnostic tool for dementia. The results of this study can also be used to develop a community-based prevention and management system for dementia in the future.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.3
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• pp.644-653
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• 1998

In resistance spot weld, the assurance of weld quality has been a long-standing problem. Since the weld nuggets if resustance spot welding form between the workpieces, visual detection of defects in usually impossible. Welding quality of resistance spot welding can be verified by non destructive and destructive inspections such as X-Ray inspection and testing of weld strength. But these tests, in addition to being time-consuming and costly, can entail risks due to sampling basis. The purpose of this study is the development of the monitoring system based on fuzzy inference, aimed at diagonosis of quality in resistance spot welding. The fuzzy inference system consists of fuzzy input variables, fuzzy membership functions and fuzzy rules. For inferring the welding quality(strength), the experimental data of the spot welding were acquired in various welding conditions with the monitoring system designed. Some fuzzy input variables-maximum, slop and difference values of electrode movement signals-were extracted from the experimental data. It was confirmed that the fuzzy inference values of strength have a .${\pm}$5% error in comparison with actual values for the selected welding conditions(9-10.5KA, 10-14 cycle, 250-300 $kg_f$). This monitoring system can be useful in improving the quality assurance and reliability of the resistance spot welding process.

• Journal of Ecology and Environment
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• v.31 no.3
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• pp.233-241
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• 2008

Functional feeding groups (FFGs) of benthic macroinvertebrates are guilds of invertebrate taxa that obtain food in similar ways, regardless of their taxonomic affinities. They can represent a heterogeneous assemblage of benthic fauna and may indicate disturbances of their habitats. The proportion of different groups can change in response to disturbances that affect the food base of the system, thereby offering a means of assessing disruption of ecosystem functioning. In this study, we used benthic macroinvertebrate communities collected at 650 sites of 23 different water types in the province of Overijssel, The Netherlands. Physical and chemical environmental factors were measured at each sampling site. Each taxon was assigned to its corresponding FFG based on its food resources. A multilayer perceptron (MLP) using a backpropagation algorithm, a supervised artificial neural network, was applied to evaluate the influence of environmental variables to the FFGs of benthic macroinvertebrates through a sensitivity analysis. In the evaluation of input variables, the sensitivity analysis with partial derivatives demonstrates the relative importance of influential environmental variables on the FFG, showing that different variables influence the FFG in various ways. Collector-filterers and shredders were mainly influenced by $Ca^{2+}$ and width of the streams, and scrapers were influenced mostly with $Ca^{2+}$ and depth, and predators were by depth and pH. $Ca^{2+}$ and depth displayed relatively high influence on all four FFGs, while some variables such as pH, %gravel, %silt, and %bank affected specific groups. This approach can help to characterize community structure and to ecologically assess target ecosystems.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.511-526
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• 2020

Single-cell RNA-sequencing (scRNA-seq) data consists of each cell's RNA expression extracted from large populations of cells. One main purpose of using scRNA-seq data is to identify inter-cellular heterogeneity. However, scRNA-seq data pose statistical challenges when applying traditional clustering methods because they have many missing values and high level of noise due to technical and sampling issues. In this paper, motivated by analyzing scRNA-seq data, we propose a novel spectral-based clustering method by imposing different weights on genes when computing a similarity between cells. Assigning weights on genes and clustering cells are performed simultaneously in the proposed clustering framework. We solve the proposed non-convex optimization using an iterative algorithm. Both real data application and simulation study suggest that the proposed clustering method better identifies underlying clusters compared with existing clustering methods.