• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.62.2-62.2
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• 2016

We present a newly developed algorithm based on a Bayesian method for 2D tilted-ring analysis of disk galaxies which operates on velocity fields. Compared to the conventional ones based on a chi-squared minimisation procedure, this new Bayesian-based algorithm less suffers from local minima of the model parameters even with high multi-modality of their posterior distributions. Moreover, the Bayesian analysis implemented via Markov Chain Monte Carlo (MCMC) sampling only requires broad ranges of posterior distributions of the parameters, which makes the fitting procedure fully automated. This feature is essential for performing kinematic analysis of an unprecedented number of resolved galaxies from the upcoming Square Kilometre Array (SKA) pathfinders' galaxy surveys. A standalone code, the so-called '2D Bayesian Automated Tilted-ring fitter' (2DBAT) that implements the Bayesian fits of 2D tilted-ring models is developed for deriving rotation curves of galaxies that are at least marginally resolved (> 3 beams across the semi-major axis) and moderately inclined (20 < i < 70 degree). The main layout of 2DBAT and its performance test are discussed using sample galaxies from Australia Telescope Compact Array (ATCA) observations as well as artificial data cubes built based on representative rotation curves of intermediate-mass and massive spiral galaxies.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.3 s.96
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• pp.290-296
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• 2005

This paper proposes the modeling scheme of the Miniature Universal Testing Machine (MUTM) composed of 36 thin-beam-type bimorph PZTs and the control algorithm to minimize the residual vibration of the MUTM in the dynamic testing of specimens. In the operation of the MUTM, hysteresis, residual displacement and vibration of it are major issues. From the analysis of the MUTM behaviors, the hysteresis is described by the curving fitting scheme with the function of an input voltage. The dynamic characteristics of the MUTM are identified by the frequency domain modeling technique based on the experimental data. The interested bandwidth is focused on 125-315HZ for effective modeling and control. For the robust vibration control of the MUTM, the sliding mode control and the Kalman filter as observer are proposed. The paper also proposes the best input signal type to operate the MUTM effectively. The feasibility of the proposed modeling scheme and control algorithm are tested and verified experimentally.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2814-2818
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• 2000

This paper describes an image processing algorithm capable of recognizing the road lane using a CDF (Cumulative Distribution Function). which is designed for the model function of the road lane. The CDF has distinctive peak points at the vicinity of the lane direction because of the directional and positional continuities of the lane. We construct a scatter diagram by collecting the edge pixels with the direction corresponding to the peak point of the CDF and carry out the principal axis-based line fitting for the scatter diagram to obtain the lane information. As noises play the role of making a lot of similar features to the lane appear and disappear in the image we introduce a recursive estimator of the function to reduce the noise effect and a scene understanding index (SUI) formulated by statistical parameters of the CDF to prevent a false alarm or miss detection. The proposed algorithm has been implemented in a real time on the video data obtained from a test vehicle driven in a typical highway.

• Journal of KIEE
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• v.11 no.1
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• pp.69-77
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• 2001

This paper describes an image processing algorithm capable of recognizing road lanes by using a CDF(cumulative distribution function). The CDF is designed for the model function of road lanes. Based on the assumptions that there are no abrupt changes in the direction and location of road lanes and that the intensity of lane boundaries differs from that of the background, we formulated the CDF, which accumulates the edge magnitude for edge directions. The CDF has distinctive peak points at the vicinity of lane directions due to the directional and the positional continuities of a lane. To obtain lane-related information a scatter diagram was constructed by collecting edge pixels, of which the direction corresponds to the peak point of the CDF, then the principal axis-based line fitting was performed for the scatter diagram. Noises can cause many similar features to appear and to disappear in an image. Therefore, to reduce the noise effect a recursive estimator of the CDF was introduced, and also to prevent false alarms or miss detection a scene understanding index (DUI) was formulated by the statistical parameters of the CDF. The proposed algorithm has been implemented in real time on video data obtained from a test vehicle driven on a typical highway.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.476-481
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• 2004

This paper proposes the modeling scheme of the Miniature Universal Testing Machine (MUTM) composed of 36 thin-beam-type bimorph PZTs and the control algorithm to minimize the residual vibration of the MUTM in the dynamic testing of specimens. In the operation of the MUTM, hysteresis, residual displacement and vibration of it are major issues. From the analysis of the MUTM behaviors, the hysteresis is described by the curving fitting scheme with the function of an input voltage. The dynamic characteristics of the MUTM are identified by the frequency domain modeling technique base on the experimental data. The interest bandwidth is focused on 125-315HZ for effective modeling and control. For the robust vibration control of the MUTM, the sliding mode control and the Kalman filter as observer are proposed. The paper also proposes the best input signal type to operate the MUTM effectively. The feasibility of the proposed modeling scheme and control algorithm are tested and verified experimentally.

• Computers and Concrete
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• v.27 no.4
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• pp.305-317
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• 2021

The bond between the concrete and bar is a main factor affecting the performance of the reinforced concrete (RC) members, and since the steel corrosion reduces the bond strength, studying the bond behavior of concrete and GFRP bars is quite necessary. In this research, a database including 112 concrete beam test specimens reinforced with spliced GFRP bars in the splitting failure mode has been collected and used to estimate the concrete-GFRP bar bond strength. This paper aims to accurately estimate the bond strength of spliced GFRP bars in concrete beams by applying three soft computing models including multivariate adaptive regression spline (MARS), Kriging, and M5 model tree. Since the selection of regularization parameters greatly affects the fitting of MARS, Kriging, and M5 models, the regularization parameters have been so optimized as to maximize the training data convergence coefficient. Three hybrid model coupling soft computing methods and genetic algorithm is proposed to automatically perform the trial and error process for finding appropriate modeling regularization parameters. Results have shown that proposed models have significantly increased the prediction accuracy compared to previous models. The proposed MARS, Kriging, and M5 models have improved the convergence coefficient by about 65, 63 and 49%, respectively, compared to the best previous model.

• Economic and Environmental Geology
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• v.26 no.2
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• pp.207-215
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• 1993

In modem petroleum reservoir engineering, the characterization of reservoir heterogeneities is very important to accurately understand and predict reservoir production performance. Formation evaluation for the description of reservoir is generally conducted by performing the analysis of well logging, core testing, and well testing. However, the measured data points by well logging or core testing are in general very sparse and hence reservoir properties should be interpolated and extrapolated from measured points to uncharacterized areas. In assigning the data for the unknown points, simple averaging technique is not feasible as optimum estimation method since this method does not account the spatial relationship between the data points. The main goal of this work is to develop PC-version of multi-purpose geostatistical model in which several stages are systematically proceeded. In the development of model, the simulator employs a automatic selection of semivariogram function such as exponential or spherical model with the best values of $R^2$. The simulator also implements a special algorithm for the fitting of semivariogram function to experimental sernivariogram. The special algorithm such as trial and error scheme is devised since this method is much more reliable and stable than Gauss-Newton method. The simulator has been tested under stringent conditions and found to be stable. Finally, the validity and the applicability of the developed model have been studied against some existing actual field data.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.13-19
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• 2023

In this paper, we investigated the implementation of a hairstyle recommendation system based on big data and deepfake technology. The proposed hairstyle recommendation system recognizes the facial shapes based on the user's photo (image). Facial shapes are classified into oval, round, and square shapes, and hairstyles that suit each facial shape are synthesized using deepfake technology and provided as videos. Hairstyles are recommended based on big data by applying the latest trends and styles that suit the facial shape. With the image segmentation map and the Motion Supervised Co-Part Segmentation algorithm, it is possible to synthesize elements between images belonging to the same category (such as hair, face, etc.). Next, the synthesized image with the hairstyle and a pre-defined video are applied to the Motion Representations for Articulated Animation algorithm to generate a video animation. The proposed system is expected to be used in various aspects of the beauty industry, including virtual fitting and other related areas. In future research, we plan to study the development of a smart mirror that recommends hairstyles and incorporates features such as Internet of Things (IoT) functionality.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.109-115
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• 2022

The CNN (Convolution Neural Network) algorithm which combines a deep learning technique, and a computer vision technology, makes image classification feasible with the high-performance computing system. In this thesis, the CNN algorithm is applied to the classification problem, by using a typical deep learning framework of TensorFlow and machine learning techniques. The data set required for supervised learning is generated with the same type of bolts. some of which have undamaged threads, but others have damaged threads. The learning model with less quantity data showed good classification performance on detecting damage in a bolt image. Additionally, the model performance is reviewed by altering the quantity of convolution layers, or applying selectively the over and under fitting alleviation algorithm.

• Genomics & Informatics
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• v.4 no.1
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• pp.33-39
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• 2006

Alternative splicing (AS) is an important mechanism of producing transcriptome diversity and microarray techniques are being used increasingly to monitor the splice variants. There exist three types of microarrays interrogating AS events-junction, exon, and tiling arrays. Junction probes have the advantage of monitoring the splice site directly. Johnson et al., performed a genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays (Science 302:2141-2144, 2003), which monitored splicing at every known exon-exon junctions for more than 10,000 multi-exon human genes in 52 tissues and cell lines. Here, we describe an algorithm to deduce the relative concentration of isoforms from the junction array data. Non-negative Matrix Factorization (NMF) is applied to obtain the transcript structure inferred from the expression data. Then we choose the transcript models consistent with the ECgene model of alternative splicing which is based on mRNA and EST alignment. The probe-transcript matrix is constructed using the NMF-consistent ECgene transcripts, and the isoform abundance is deduced from the non-negative least squares (NNLS) fitting of experimental data. Our method can be easily extended to other types of microarrays with exon or junction probes.