• 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.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1510-1519
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• 2005

We proposed a digital watermarking method that it is possible to identify the copyright because the watermark is detected in the first print-scan and to protect a forgery because the watermark is not detected in the second print-scan. The proposed algorithm uses LPM and DFT transform for the robustness to the distortion of pixel value and geometrical distortion. This methods could improve watermark detection performance and image quality by selecting maximum sampling radius in LPM transform. After analyzing the characteristics of print-scan process, we inserted the watermark in the experimentally selected frequency bands that survives robustly to the first print-scan and is not detected in the second print-scan, using the characteristic of relatively large distortion in high frequency bands of DFT As the experimental result, the original proof is possible because average similarity degree 5.13 is more than the critical value 4.0 in the first print-scan. And the detection of forgery image is also possible because average similarity degree 2.76 is less than the critical value 4.0 in the second print-scan.

• Current Optics and Photonics
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• v.4 no.1
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• pp.1-8
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• 2020

We research a system of compressed-sensing computational ghost imaging (CSCGI) based on the intensity fluctuation brought by turbulence. In this system, we used the gamma-gamma intensity-fluctuation model, which is commonly used in transmission systems, to simulate the CSCGI system. By setting proper values of the parameters such as transmission distance, refractive-index structure parameter, and sampling rates, the peak signal-to-noise ratio (PSNR) performance and bit-error rate (BER) performance are obtained to evaluate the imaging quality, which provides a theoretical model to further research the ghost-imaging algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.53-62
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• 2004

In this study, an improved post-processing technique for correcting MRI artifact due to the unknown respiratory motion in the imaging plane is presented. Respiratory motion is modeled by a two-Dimensional linear expending-shrinking movement. Assuming that the body tissues are incompressible fluid like materials, the proton density per unit volume of the imaging object is kept constant. According to the introduced model, respiratory motion imposes phase error, non-uniform sampling and amplitude modulation distortions on the acquired MRI data. When the motion parameters are known or can be estimatead a reconstruction algorithm based on biliner superposition method was used to correct the MRI artifact. In the case of motion parameters are unknown, first, the spectrum shift method is applied to find the respiratory fluctuation function, x directional expansion coefficient and x directional expansion center. Next, y directional expansion coefficient and y directional expansion center are estimated by using the minimum energy method. Finally, the validity of this proposed method is shown to be effective by using the simulated motion images.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.6
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• pp.27-33
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• 1984

This paper is an experimental gaudy on the digital hardware implementation of the R2-MF Receiver for 32 channel configurations used in signalling systems between ESS. There are many methods to detect MF signal by DSP techniques, but the requirement for MF detection needs not sharp frequency response, needs only decision about some specific frequencies exist or not at discrete frequency sampling points. The hardware used to implement this algorithm is Am 2900 series "bit-slice microprocessor" chips based on the microprogramming techniques for real time signal processing. And we used the additional Z-80A processor chips for the system control and the decision about which is the right MF signal from the detected MF spectrums. Hence we could enhance the flexibilities of the hardware and the software, this leads that this system is well suits for signalling systems used in TDM ESS.n TDM ESS.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.361-375
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• 2008

We propose proper methods to impute the item nonresponse in 4-8-4 rotation sample survey. We consider nonignorable nonresponse mechanism that can happen when survey deals with sensitive question (e.g. income, labor force). We utilize modeling imputation method based on Bayesian approach to avoid a boundary solution problem. We also estimate a interview time bias using imputed data and calculate cell expectation and marginal probability on fixed time after removing estimated bias. We compare the mean squared errors and bias between maximum likelihood method and Bayesian methods using simulation studies.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.991-1002
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• 2014

Both Bayesian and maximum likelihood methods are efficient for the estimation of regression coefficients of various Tobit regression models (see. e.g. Chib, 1992; Greene, 1990; Lee and Choi, 2013); however, some researchers recognized that the maximum likelihood method tends to underestimate the disturbance variance, which has implications for the estimation of marginal effects and the asymptotic standard error of estimates. The underestimation of the maximum likelihood estimate in a seemingly unrelated Tobit regression model is examined. A Bayesian method based on an objective noninformative prior is shown to provide proper estimates of the disturbance variance as well as other regression parameters

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.283-294
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• 2006

In this paper, we propose a real-time face detection/tracking methodology with Haar wavelets and skin color. The proposed method boosts face detection and face tracking performance by combining skin color and Haar wavelets in an efficient way. The proposed method resolves the problem such as rotation and occlusion due to the characteristic of the condensation algorithm based on sampling despite it uses same features in both detection and tracking. In particular, it can be applied to a variety of applications such as face recognition and facial expression recognition which need an exact position and size of face since it not only keeps track of the position of a face, but also covers the size variation. Our test results show that our method performs well even in a complex background, a scene with varying face orientation and so on.