• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.221-229
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• 2004

Recently, there has been enormous growth in the amount of commercial and scientific data, such as protein sequences, retail transactions, and web-logs. Such datasets consist of sequence data that have an inherent sequential nature. However, only a few of the existing clustering algorithms consider sequentiality. This study presents a method for clustering such sequence datasets. The similarity between sequences must be decided before clustering the sequences. This study proposes a new similarity measure to compute the similarity between two sequences using a sequence element. Two clustering algorithms using the proposed similarity measure are proposed: a hierarchical clustering algorithm and a scalable clustering algorithm that uses sampling and a k-nearest neighbor method. Using a splice dataset and synthetic datasets, we show that the quality of clusters generated by our proposed clustering algorithms is better than that of clusters produced by traditional clustering algorithms.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.274-281
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• 2013

In this study, we consider the challenges in motion planning for automated driving systems. Most of the existing online motion-planning algorithms, which take dynamics into account, find it difficult to operate in an environment with narrow passages. Some of the existing algorithms overcome this by offline preprocessing if environment is known. In this work an online algorithm for motion planning with dynamics in an unknown cluttered environment with narrow passages is presented. It utilizes an idea of hybrid planning with sampling- and discretization-based motion planners, which run simultaneously in a full configuration space and a derived reduced space. The proposed algorithm has been implemented and tested with a real autonomous vehicle. It provides significant improvements in computational time performance over basic planning algorithms and allows the generation of smoother paths than those generated by the recently developed hybrid motion planners.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4108-4125
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• 2015

This paper presents a scalable coding method for depth images by considering the quality of synthesized images in virtual views. First, we design a new edge detection algorithm that is based on calculating the depth difference between two neighboring pixels within the depth map. By choosing different thresholds, this algorithm generates a scalable bit stream that puts larger depth differences in front, followed by smaller depth differences. A scalable scheme is also designed for coding depth pixels through a layered sampling structure. At the receiver side, the full-resolution depth image is reconstructed from the received bits by solving a partial-differential-equation (PDE). Experimental results show that the proposed method improves the rate-distortion performance of synthesized images at virtual views and achieves better visual quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.527-534
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• 2017

The purpose of this study is to develop unmanned equipment that can automatically move to the desired point and measure water quality at the correct depth. For this purpose, we constructed a water sampling lift and water sampling container, an unmanned vessel equipped with a VRS-GPS, an acoustic echo sounder, and a water quality sensor. Also, we developed an automatic navigation algorithm and program, an automatic water sampling program, and a water quality map generation program. As a result of the experiment in the detention pond, the unmanned vessel sailed along the planned route with an accuracy of about 93% within the error range of 3m. In addition, the water quality sensor installed in the lift was able to acquire the water quality of the target area in real time and transmit it to the server via wireless Internet, and it was possible to monitor the water quality of each site in real time. Through field experiments, the water sampling lift was able to control the desired length with an accuracy of about 94%. The stretch length accuracy experiment of the water sampling lift was impossible to measure directly in the water, so it was replaced land-based experiment. We also found some unstable problems due to the weight of the water sampling lift and the weight of the air compressor to operate the water container. Except these two problems, we accomplished purpose of this study. An automated water quality measurement method using an unmanned vessel can be used to measure the quality of water in a difficult to access area and to secure the safety of the worker.

• Proceedings of the Korea Concrete Institute Conference
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• 1993.10a
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• pp.195-200
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• 1993

This paper develops practical and realistic reliability models and methods for the evalusion of system reliability and system reliability-based rating of R.C box-girder bridge superstructures. The precise prediction of reserved carrying capacity of bridge as a system is extremely difficult expecially when the bridges are highly redundant and significantly deteriorated or damaged. This paper proposes a new approach for the evaluation of reserved system carrying capacity of bridges in terms of equivalent system-strength, which may be defined as a bridge system-strength corresponding to the system reliability of the bridge. This can be derived from an inverse process based on the concept of FOSM form of system reliability index. The strength limit state models for R.C box-girder bridges suggested in the paper are based on the basic bending and shear strength. and the system reliability problem of box-girder superstructure is formulated as parallel-series models obtained from the FMA(Failure Mode Approach) based on major failure mechanism or critical failure states of each girder. AFOSM and IST(Importance Sampling Technique) simulation algorithm is used for the reliability analysis of the proposed models.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.413-417
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• 1990

Various sampling methods are used for microprocessor-based measurement and analysis to nonsinusoidal waveforms caused by power electronic converters. The hamonic component generates the indicating errors at the measuring instruments. This can be solved by microprocessor-based measurement and hence the microprocessor-based measuring equipment and its algorithm are developed in this paper. As a result the suggested equipment has very good measuring performances.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.43-59
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• 2014

Cut-off sampling has been widely used for a highly skewed population like a business survey by discarding a part of the population, so called a take-nothing stratum. For a more accurate estimate of the population total, Hwang and Shin (2013) suggested a composite estimator of a take-nothing stratum total that combined the survey results of a take-nothing stratum and a take-some sub-stratum (a part of take-some stratum). In this paper we propose a new cut-off sampling scheme by considering a cost function and a composite estimator based on the proposed sampling scheme. Small simulation studies compared the performances of known composite estimators and the new composite estimator suggested in this study. We also use Briquette Consumption Survey data for real data analysis.

• Journal of Power Electronics
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• v.16 no.1
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• pp.227-237
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• 2016

A novel digital current control strategy for digitally controlled DC-DC switching converters, referred to as Adjacent Cycle Sampling (ACS), is proposed in this paper. For the ACS current control strategy, the available time interval from sampling the current to updating the duty ratio, is approximately one switching cycle. In addition, it is independent of the duty ratio. As a result, the contradiction between the processing speed of the hardware and the transient response speed can be effectively relaxed by using the ACS current control strategy. For digitally controlled buck DC-DC switching converters with trailing-edge modulation, digital current control algorithms with the ACS control strategy are derived for three different control objectives. These objectives are the valley, average, and peak inductor currents. In addition, the sub-harmonic oscillations of the above current control algorithms are analyzed and eliminated by using the digital slope compensation (DSC) method. Experimental results based on a FPGA are given, which verify the theoretical analysis results very well. It can be concluded that the ACS control has a faster transient response speed than the time delay control, and that its requirements for hardware processing speed can be reduced when compared with the deadbeat control. Therefore, it promises to be one of the key technologies for high-frequency DC-DC switching converters.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.121-128
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• 2010

K-means clustering is an iterative algorithm in which items are moved among sets of clusters until the desired set is reached. K-means clustering has been widely used in many applications, such as market research, pattern analysis or recognition, image processing, etc. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using a center of gravity for grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.337-345
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• 2023

In this paper, we propose a 3D LiDAR sensor-based costmap generation and path planning algorithm using it for reliable autonomous flight in complex indoor environments. 3D path planning is essential for reliable operation of UAVs. However, existing grid search-based or random sampling-based path planning algorithms in 3D space require a large amount of computation, and UAVs with weight constraints require reliable path planning results in real time. To solve this problem, we propose a method that divides a 3D space into several 2D spaces and a path planning algorithm that considers the distance to obstacles within each space. Among the paths generated in each space, the final path (Best path) that the UAV will follow is determined through the proposed objective function, and for this purpose, we consider the rotation angle of the 2D space, the path length, and the previous best path information. The proposed methods have been verified through autonomous flight of UAVs in real environments, and shows reliable obstacle avoidance performance in various complex environments.