• Journal of the Korean Mathematical Society
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• v.44 no.6
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• pp.1479-1503
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• 2007

In this paper, we study a strong k-deformation retract derived from a relative k-homotopy and investigate its properties in relation to both a k-homotopic thinning and the k-fundamental group. Moreover, we show that the k-fundamental group of a wedge product of closed k-curves not k-contractible is a free group by the use of some properties of both a strong k-deformation retract and a digital covering. Finally, we write an algorithm for calculating the k-fundamental group of a dosed k-curve by the use of a k-homotopic thinning.

• International Journal of Advanced Culture Technology
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• v.4 no.4
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• pp.75-80
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• 2016

The process of extracting external boundary of an object is a very important process for recognizing an object in the image. The proposed extraction method consists of two processes: External Boundary Extraction and Thinning. In the first step, external boundary extraction process separates the region representing the object in the input image. Then, only the pixels adjacent to the background are selected among the pixels constituting the object to construct an outline of the object. The second step, thinning process, simplifies the outline of an object by eliminating unnecessary pixels by examining positions and interconnection relations between the pixels constituting the outline of the object obtained in the previous extraction process. As a result, the simplified external boundary of object results in a higher recognition rate in the next step, the object recognition process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.5
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• pp.883-891
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• 1994

In this paper, we introduce new thinning algorithm which is useful for handwritten Korean character by using pixel directivity. At first, the directivity detection is performed before thinning. Each pixel is classified into the straight line of the oblique line based on its directivity. The algorithm using Rutovitz corossing number is applied to the straight line. And the algorithm using Hilditch crossing number is applied to the oblique line. The proposed algorithm is compared with six convention algorithms. Comparison criteria are similarity, noisy branch, and phoneme segmentation rate. Experiments with 570 characters have been conducted. Experimental result shows that the proposed algorithm is superior to six conventional algorithm with respect to similarity and phoneme segmentation rate.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4060-4066
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• 2021

Flow-accelerated corrosion (FAC) of carbon steel piping is a significant problem in nuclear power plants. The basic process of FAC is currently understood relatively well; however, the accuracy of prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a convolutional neural network, which is confined to a straight pipe without geometric changes. Furthermore, a methodology to generate training data is proposed to efficiently train the neural network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.2
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• pp.146-154
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• 2023

In nuclear power plants, ultrasonic test for pipe wall thickness measurement is used during periodic inspections to prevent pipe rupture due to pipe wall thinning. However, when measuring pipe wall thickness using ultrasonic test, a significant amount of measurement error occurs due to the on-site conditions of the nuclear power plant. If the maximum pipe wall thinning rate is decided by the measured pipe wall thickness containing a significant error, the pipe wall thinning rate data have significant uncertainty and systematic overestimation. This study proposes preprocessing of pipe wall thinning measurement data using support vector machine regression algorithm. By using support vector machine, pipe wall thinning measurement data can be smoothened and accordingly uncertainty and systematic overestimation of the estimated pipe wall thinning rate data can be reduced.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.38.3-38
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• 2002

$\textbullet$ The topological map using a thinning needs much simpler computation than that using a Voronoi. $\textbullet$ A thinning can provide much information on the environment (additional nodes). $\textbullet$ Each node created in a local map is considered as temporary and redundant nodes are discarded. $\textbullet$ A global topological map can be built fast and correctly through a thinning algorithm. $\textbullet$ Path planning can be easily achieved with a topological map.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.96-104
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• 1998

A new gray-scale thinning algorithm using local min/max operations is proposed. In this method, erosion and dilation properties of local min/max operations are using for generating new rides and detecting ridges in gray scale image, and gray-scale skeletons are gradually obtained by accumulating the detected ridges. This method can be applicable to the unsegmented image in which object are not specified, and the obtained skeletons correspond to the ridges (high gray values) of an input image.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.310-314
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• 2000

This paper proposes a new parallel architecture for extracting the object from binarized images using recurrent linear threshold neural networks. Binary functions are initially obtained from the existing iterative thinning algorithms, and the linear threshold neural threshold neural networks are then synthesized using the MSP term grouping algorithm. Experimental results show that the proposed architectures can be implemented easier than with other existing methods.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.693-699
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• 2006

An accurate and compact map is essential to an autonomous mobile robot system. For navigation, it is efficient to use an occupancy grid map because the environment is represented by probability distribution. But it is difficult to apply it to the large environment since it needs a large amount of memory proportional to the environment size. As an alternative, a topological map can be used to represent it in terms of the discrete nodes with edges connecting them. It is usually constructed by the Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map using the thinning algorithm. This algorithm can extract only meaningful topological information by using the C-obstacle concept in real-time and is robust to the environment change, because its underlying local grid map is constructed based on the Bayesian update formula. In this paper, the position probability is defined to evaluate the quantitative reliability of the end nodes of this thinning-based topological map (TTM). The global TTM can be constructed by merging each local TTM by matching the reliable end nodes determined by the position probability. It is shown that the proposed TTM can represent the environment accurately in real-time and it is readily extended to the global TTM.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.577-583
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• 2007

Simultaneous Localization and Mapping (SLAM) is the process of building a map of an unknown environment and simultaneously localizing a robot relative to this map. SLAM is very important for the indoor navigation of a mobile robot and much research has been conducted on this subject. Although feature-based SLAM using an Extended Kalman Filter (EKF) is widely used, it has shortcomings in that the computational complexity grows in proportion to the square of the number of features. This prohibits EKF-SLAM from operating in real time and makes it unfeasible in large environments where many features exist. This paper presents an algorithm which reduces the computational complexity of EKF-SLAM by using topological information (TI) extracted through a thinning process. The global map can be divided into local areas using the nodes of a thinning-based topological map. SLAM is then performed in local instead of global areas. Experimental results for various environments show that the performance and efficiency of the proposed EKF-SLAM/TI scheme are excellent.