• Bulletin of the Korean Mathematical Society
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• v.56 no.2
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• pp.277-283
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• 2019

In this paper, we present a cube root algorithm using a recurrence relation. Additionally, we compare the implementations of the Pocklington and $Padr{\acute{o}}-S{\acute{a}}ez$ algorithm with the Adleman-Manders-Miller algorithm. With the recurrence relations, we improve the Pocklington and $Padr{\acute{o}}-S{\acute{a}}ez$ algorithm by using a smaller base for exponentiation. Our method can reduce the average number of ${\mathbb{F}}_q$ multiplications.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2247-2258
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• 1997

Quadtree, which is hierarchical data structure, is a very important data structure to represent binary images. Since a linear quadtree representation as is a way to store a quadtree is efficient to save space compared with other representations. It is, however, complicated and takes a large amount of time to build the linear quadtree. In this paper, we present O(1) time a linear quadtree building algorithm for a $n{\times}n$ binary image using three-dimensional $n{\times}n{\times}n$ processors on RMESH structure. Our algorithm, by use of O(1) time collapsing algorithm and reported O(1) time sorting algorithm, is simpler and easier to understand than resently presented algorithm on PARBUS structure.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.159-164
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• 2022

The linear assignment problem (LAP) and linear bottleneck assignment problem (LBAP) has been unknown the algorithm to solve the optimal solution within polynomial-time. These problems are classified by NP-hard. Therefore, we can be apply metaheuristic methods or linear programming (LP) software package or Hungarian algorithm (HA) with O(m⁴) computational complexity. This paper suggests polynomial time algorithm with O(mn)=O(m²),m=n time complexity to LAP and LBAP. The select-delete method is simply applied to LAP, and the delete-select method is used to LBAP. For the experimental data without the unique algorithm can be apply to whole data, the proposed algorithm can be obtain the optimal solutions for whole data.

• Journal of Electrical Engineering and information Science
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• v.3 no.3
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• pp.396-401
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• 1998

An optimal algorithm is presented for feasibility test and scheduling of real-time tasks where tasks are preemptable and created dynamically. Each task has an arbitrary creation time, ready time, maximum execution time, and deadline. Feasibility test and scheduling are conducted via the same algorithm. Time complexity of the algorithm is O(n) for each newly created task where n is the number of tasks. This result improves the previous result of O(n log n). It is shown that the algorithm can be used for scheduling tasks with different levels of importance. Time complexity of the algorithm for the problem is O(n\ulcorner) which improves the previous results of O(n\ulcorner log n).

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.3
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• pp.133-141
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• 2002

Most maximum power point tracking(MPPT) control algorithm is based on Perturb and Observe(P&O) and Incremental Conductance(IncCond). In comparison with P&O and IncCond algorithm, the dynamic and tracking characteristic of IncCond algorithm is better than P&O algorithm in condition of rapidly changing solar radiation. But in the case of digital implementation, the InCond algorithm has error en decision of maximum power operation point(MPOP). To solve this problem, this paper proposes a improved IncCond algorithm, which can determine the MPOP correctly by inserting the test signal in control input. This paper proposes a novel MPPT control algorithm for the digitally implemented photovoltaic system in condition of rapidly changing solar radiation. To verify the validity of the proposed control algorithm. the computer simulation and experiment are carried out.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.95-102
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• 2015

Generally, there is no sorting algorithm much faster than O(nlogn). The quicksort has a best performance O(nlogn) in best and average-case, and $O(n^2)$ in worst-case. This paper suggests virtual radix counting bucket sort such that counting the frequency of numbers in each radix digit, and moves the arbitrary number to proper virtual bucket in the array, and divides the array into radix digit numbers virtually. Also, this algorithm moves the data to proper location within an array for using the minimum auxiliary memory. This algorithm performs k-times such that the number of k digits in given data and the time complexity is O(n). Therefore, this algorithm has a O(kn) time complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.922-928
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• 2007

This paper presents the fast adaptive algorithm which applies an approximation scheme into RLS algorithm. The proposed algorithm(D-RLS) derives a QRD RLS algorithm derivation process from RLS algorithm recursively. D-RLS has the similar pattern as the algorithm having the approximation that input signals are separated respectively. Computational complexity of D-RLS is O(N), fewer than $O(N^2)$. To evaluate performance of proposed algorithm, we use the system identification method of FIR and Volterra system. And, finally, we can show D-RLS has an excellent performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.649-655
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• 2009

Mean acquisition time (MAT) is the most important performance measure for code acquisition systems, where a shorter MAT implies a better code acquisition performance. Keshavarzian and Salehi proposed the multiple-shift (MS) algorithm for code acquisition in optical code division multiple access (O-CDMA) systems. Performing two steps acquisition, the MS algorithm has a shorter MAT than that of the conventional serial-search (SS) algorithm. In this paper, we propose a rapid code acquisition algorithm for O-CDMA systems. By using an efficient combination of local signals, correlation value, and the sign of correlation value, the proposed algorithm can provide a shorter MAT compared with that of the MS algorithm. The simulation results show that the proposed algorithm presents a shorter MAT than that of the MS algorithm.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.455-462
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• 2001

In this paper, we propose a deterministic algorithm to solve the resource discovery problem, that is, some subset of machines to learn the existence of each other in a large distributed network. Harchol et al. proposed a randomized algorithm solving this problem within O($log^2\;n$) rounds with high probability, which requires O($nlog^2\;n$) connection communication complexity and O($n^2log^2\;n$) pointer communication complexity, where n is the number of machines in the network. His solution is based on randomization method and it is difficult to determine convergence time. We propose an efficient algorithm which improve performance and the non-deterministic characteristics. Our algorithm requires O(log n) rounds which shows O(mlog n) connection communication complexity and O($n^2log\;n$) pointer communication complexity, where m is the number of links in the network.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.2
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• pp.67-74
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• 2013

Approximate string matching problems have been studied in diverse fields. Recently, fast approximate string matching algorithms are being used to reduce the time and costs for the next generation sequencing. To measure the amounts of errors between two strings, we use a distance function such as the edit distance. Given two strings X(|X| = m) and Y(|Y| = n) over an alphabet ${\Sigma}$, the edit distance between X and Y is the minimum number of edit operations to convert X into Y. The edit distance between X and Y can be computed using the well-known dynamic programming technique in O(mn) time and space. The edit distance also can be computed using the Four-Russians' algorithm whose preprocessing step runs in $O((3{\mid}{\Sigma}{\mid})^{2t}t^2)$ time and $O((3{\mid}{\Sigma}{\mid})^{2t}t)$ space and the computation step runs in O(mn/t) time and O(mn) space where t represents the size of the block. In this paper, we present a parallelized version of the computation step of the Four-Russians' algorithm. Our algorithm computes the edit distance between X and Y in O(m+n) time using m/t threads. Then we implemented both the sequential version and our parallelized version of the Four-Russians' algorithm using CUDA to compare the execution times. When t = 1 and t = 2, our algorithm runs about 10 times and 3 times faster than the sequential algorithm, respectively.