• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.88-93
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• 1998

This paper presents a new solution approach to moving obstacle avoidance problem for a mobile robot. A new concept avoidability measure(AVM) is defined to describe the state of a pair of a robot and an obstacle regarding the collision between them. As an AVM, virtual distance function(VDF) is derived as a function of the distance from the obstacle to the robot and outward speed of the obstacle relative to the robot. By keeping the virtual distance above some positive limit value, the robot avoids the obstacle. In terns of the VDF, an artificial potential field is constructed to repel the robot away from the obstacle and to attract the robot toward a goal location. At every sampling time, the artificial potential field is updated and the force driving the robot is derived form the gradient of the artificial potential field. The suggested algorithm drives the robot to avoid moving obstacles in real time. Since the algorithm considers the mobility of the obstacle as well as the distance, it is effective for moving obstacle avoidance. Some simulation studies show the effectiveness of the proposed approach.

• International Journal of CAD/CAM
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• v.8 no.1
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• pp.45-53
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• 2009

As the point sampling technology evolves rapidly, there has been increasing need in generating tool path from dense point set without creating intermediate models such as triangular meshes or surfaces. In this paper, we present a new tool path generation method from point set using Euclidean distance fields based on Algebraic Point Set Surfaces (APSS). Once an Euclidean distance field from the target shape is obtained, it is fairly easy to generate tool paths. In order to compute the distance from a point in the 3D space to the point set, we locally fit an algebraic sphere using moving least square method (MLS) for accurate and simple calculation. This process is repeated until it converges. The main advantages of our approach are : (1) tool paths are computed directly from point set without making triangular mesh or surfaces and their offsets, and (2) we do not have to worry about no local interference at concave region compared to the other methods using triangular mesh or surface model. Experimental results show that our approach can generate accurate enough tool paths from a point set in a robust manner and efficiently.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.74-80
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• 2015

In cognitive radios, spectrum sensing plays an important role in accurately detecting the presence or absence of a licensed user. However, the intervention of malicious users (MUs) degrades the performance of spectrum sensing. Such users manipulate the local results and send falsified data to the data fusion center; this process is called spectrum sensing data falsification (SSDF). Thus, MUs degrade the spectrum sensing performance and increase uncertainty issues. In this paper, we propose a method based on the Hausdorff distance and a similarity measure matrix to measure the difference between the normal user evidence and the malicious user evidence. In addition, we use the Dempster-Shafer theory to combine the sets of evidence from each normal user evidence. We compare the proposed method with the k-means and Jaccard distance methods for malicious user detection. Simulation results show that the proposed method is effective against an SSDF attack.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.231-236
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• 2021

The purpose and hypothesis of the article was the need to solve the following research tasks: Analysis of psychological and pedagogical literature on the research problem. To identify and experimentally test the effectiveness of organizational and pedagogical conditions affecting the creative self-development of a distance learning student. Research methods: analysis of philosophical and psychological-pedagogical literature on the problem under study; pedagogical experiment; modeling, questioning, testing, analysis of the products of students' creative activity (essays, creative works, creative projects) and the implementation of educational tasks, conversations, observations.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.2
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• pp.146-159
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• 2023

This paper proposes a hybrid algorithm combining K-means clustering and watershed algorithms for flower segmentation and counting. We use the K-means clustering algorithm to obtain the main colors in a complex background according to the cluster centers and then take a color space transformation to extract pixel values for the hue, saturation, and value of flower color. Next, we apply the threshold segmentation technique to segment flowers precisely and obtain the binary image of flowers. Based on this, we take the Euclidean distance transformation to obtain the distance map and apply it to find the local maxima of the connected components. Afterward, the proposed algorithm adaptively determines a minimum distance between each peak and apply it to label connected components using the watershed segmentation with eight-connectivity. On a dataset of 30 images, the test results reveal that the proposed method is more efficient and precise for the counting of overlapped flowers ignoring the degree of overlap, number of overlap, and relatively irregular shape.

• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.170-177
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• 2014

Genetic Algorithms are robust search and optimization techniques but have some problems such as premature convergence and convergence to local extremum. As population diversity converges to low value, the search ability decreases and converges to local extremum but population diversity converges to high value, then the search ability increases and converges to global optimum or genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we propose the genetic operators with the hybrid function of the average Hamming distance and the fitness value to maintain the diversity of the GA's population for escaping from the premature convergence. Results of simulation studies verified the effects of the mutation operator for maintaining diversity and the other operators for improving convergence properties as well as the feasibility of using proposed genetic operators on convergence properties to avoid premature convergence and convergence to local extremum.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.671-672
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• 2008

K-means is a popular one in clustering algorithms, and it minimizes the mutual euclidean distance among the sample points. But K-means has some demerits, such as depending on initial condition, unsupervised learning and local optimum. However mahalanobis distancecan deal this case well. In this paper, the author proposed a new clustering algorithm, named exponential probability clustering, which applied Mahalanobis distance into K-means clustering. This new clustering does possess not only the probability interpretation, but also clustering merits. Finally, the simulation results also demonstrate its good performance compared to K-means algorithm.

• The Journal of the Acoustical Society of Korea
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• v.3 no.1
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• pp.25-35
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• 1984

This paper analysis the Dynamic Time Warping algorithms for time normalization of speech pattern and discusses the Dynamic Programming algorithm for spoken Korean isolated digits recognition. In the DP matching, feature vectors of the reference and test pattern are consisted of first three formant frequencies extracted by power spectrum density estimation algorithm of the ARMA model. The major differences in the various DTW algorithms include the global path constrains, the local continuity constraints on the path, and the distance weighting/normalization used to give the overall minimum distance. The performance criterias to evaluate these DP algorithms are memory requirement, speed of implementation, and recognition accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.3
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• pp.373-379
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• 2012

This paper presents a fault location algorithm based on the adaptively estimated value of the local sequence source impedance for faults on a parallel transmission line. This algorithm uses only the local voltage and current signals of a faulted circuit. The remote current signals and the zero-sequence current of the healthy adjacent circuit are calculated by using the current distribution factors together with the local terminal currents of the faulted circuit. The current distribution factors consist of local equivalent source impedance and the others such as fault distance, line impedance and remote equivalent source impedance. It means that the values of the current distribution factors can change according to the operation condition of a power system. Consequently, the accuracy of the fault location algorithm is affected by the two values of equivalent source impedances, one is local source impedance and the other is remote source impedance. Nevertheless, only the local equivalent impedance can be estimated in this paper. A series of test results using EMTP simulation data show the effectiveness of the proposed algorithm. The proposed algorithm is valid for a double-circuit transmission line system where the equivalent source impedance changes continuously.

• Journal of the Korean association of regional geographers
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• v.7 no.3
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• pp.107-120
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• 2001

This study is concerned with providing a reliable procedure of calibrating a set of places specific distance parameters and with applying it to U.S. inter-State migration flows between 1985 and 1900. It attempts to conform to recent advances in quantitative geography that are characterized by an integration of ESDA(exploratory spatial data analysis) and local statistics. ESDA aims to detect the spatial clustering and heterogeneity by visualizing and exploring spatial patterns. A local statistic is defined as a statistically processed value given to each location as opposed to a global statistic that only captures an average trend across a whole study region. Whereas a global distance parameter estimates an averaged level of the friction of distance, place-specific distance parameters calibrate spatially varying effects of distance. It is presented that a poisson regression with an adequately specified design matrix yields a set of either origin-or destination-specific distance parameters. A case study demonstrates that the proposed model is a reliable device of measuring a spatial dimension of migration, and that place-specific distance parameters are spatially heterogeneous as well as spatially clustered.