• Journal of the Korea Computer Industry Society
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• v.5 no.2
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• pp.325-330
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• 2004

The Big O notation of a sorting algorithm analysis is an asymptotic algorithm analysis which gives information of a rough mathematical function with an infinite increase of a sample size, without any specification of a probabilistic model. Hence. in an application with a limited finite number of data, it is necessary to test efficiencies of sorting algorithms. I estimated probabilistic models which analyze the number of exchanges varying input sizes to sort. The estimated models to explain the relationship of sorting efficiency on the sample size (N of the sample size and S of the number of exchange of elements) are S=0.9305 $N^{1.339}$ for Quick sort algorithm with O(nlogn) time complexity, and S=0.2232 $N^{2.0130}$ for Insertion sort algorithm with O( $n^2$) time complexity. Furthermore, there are strongly supports that more than 99% of the above relationship could be explained by the estimated models (p＜0.001). These findings suggest it is necessary to analyze sorting algorithm efficiency in applications with a finite number of data or a newly developed sorting algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.710-715
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• 2004

The design problem of manufacturing system is addressed, adopting the closed queueing network model with multiple loops and re-entrant flows. The entire design problem is divided into two hierarchical sub-problems of (1) determining the station configuration and (2) optimizing the lot constitution; then they are tackled by neighbor search algorithm (NSA) and greedy mean value analysis (GMVA), respectively. Unlike the conventional MVA concerning multi-class closed queueing networks, the GMVA doesn't stick to a fixed lot proportion; rather it tries to find the optimal balance. The NSA, on the other hand, improves the object function value by altering the station configuration successively with its superior neighbor. The moderate time complexity, presented in big-${o}$ notation, enables us to apply the method even to the large-size practical cases, and the CPU time of an enlarged problem can be approximated by the same equation. The validity of our analytic approach is backed up by simulation studies with a widespread simulation package.

• Journal of IKEEE
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• v.23 no.3
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• pp.978-983
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• 2019

Under the influence of Autonomous Driving and AI, it is important to accurately recognize and judge objects through cameras. In particular, since a method of recognizing an object using a camera can obtain a large amount of information visually compared to other methods, many image signal processing methods have been studied to extract an accurate image. In addition, a lot of research is being carried out to implementation about hardware. In this work, we compare the principles and characteristics of the sorting algorithms that are frequently used in image signal processing and summarize the performance evaluation. Based on this, we define an efficient algorithm when implemented in hardware among efficient sorting algorithms.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.6
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• pp.800-807
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• 2016

We propose a fast algorithm using Extreme Contour Point (ECP) to detect the angle of rotated images, is implemented by rotation feature of one covered frame image that can be applied to correct the rotated images like in image processing for real time applications, while CORDIC is inefficient to calculate various points like high definition image since it is only possible to detect rotated angle between one point and the other point. The two advantages of this algorithm, namely compatibility to images in preprocessing by using Sobel edge process for pattern recognition. While the other one is its simplicity for rotated angle detection with cyclic shift of two $1{\times}n$ matrix set without complexity in calculation compared with CORDIC algorithm. In ECP, the edge features of the sample image of gray scale were determined using the Sobel Edge Process. Then, it was subjected to binary code conversion of 0 or 1 with circular boundary to constitute the rotation in invariant conditions. The results were extracted to extreme points of the binary image. Its components expressed not just only the features of angle ${\theta}$ but also the square of radius $r^2$ from the origin of the image. The detected angle of this algorithm is limited only to an angle below 10 degrees but it is appropriate for real time application because it can process a 200 degree with an assumption 20 frames per second. ECP algorithm has an O ($n^2$) in Big O notation that improves the execution time about 7 times the performance if CORDIC algorithm is used.