• Journal of KIISE:Computer Systems and Theory
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• v.27 no.5
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• pp.529-539
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• 2000

This paper proposes a prefetch-based disk buffer management algorithm, which we call W2R (Veighingjwaiting Room). Instead of using elaborate prefetching schemes to decide which blockto prefetch and when, we simply follow the LRU-OBL (One Block Lookahead) approach and prefetchthe logical next block along with the block that is being referenced. The basic difference is that theW2R algorithm logically partitions the buffer into two rooms, namely, the Weighing Room and theWaiting Room. The referenced, hence fetched block is placed in the Weighing Room, while theprefetched logical next block is placed in the Waiting Room. By so doing, we alleviate some inherentdeficiencies of blindly prefetching the logical next block of a referenced block. Specifically, a prefetchedblock that is never used may replace a possibly valuable block and a prefetched block, thoughreferenced in the future, may replace a block that is used earlier than itself. We show through tracedriven simulation that for the workloads and the environments considered the W2R algorithm improvesthe hit rate by a maximum of 23.19 percentage points compared to the 2Q algorithm and a maximumof 10,25 percentage feints compared to the LRU-OBL algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.7
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• pp.693-700
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• 2016

It is important that we can calculate the order of non-supersingular elliptic curves with large prime factors over the finite field GF(q) to guarantee the security of public key cryptosystems based on discrete logarithm problem(DLP). Schoof algorithm, however, which is used to calculate the order of the non-supersingular elliptic curves currently is so complicated that many papers are appeared recently to update the algorithm. To avoid Schoof algorithm, in this paper, we propose an algorithm to calculate orders of elliptic curves over finite composite fields of the forms $GF(2^m)=GF(2^{rs})=GF((2^r)^s)$ using Weil's theorem. Implementing the program based on the proposed algorithm, we find a efficient non-supersingular elliptic curve over the finite composite field $GF(2^5)^{31})$ of the order larger than $10^{40}$ with prime factor larger than $10^{40}$ using the elliptic curve $E(GF(2^5))$ of the order 36.

• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.9-18
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• 2005

This paper presents the method of calculating the quantization level of the intra-frame in MPEG-4 video encoder. The intra-frame is an essential part in that the quality of the whole GOP is affected by the quality of this frame since the intra-frame, which works as a reference frame within GOP, continuously propagates through other frames. This work proposes how to use bits assigned for gaining the quantization level of the intra-frame, complexity of input images, and GOP structures. The result shows that while existing approaches have the decline in efficiency by using fixed values or show different qualifies depending on the characteristics of the images, the current approach shows the steady results in various images. Comparing with Q2 algorithm obtained in MPEG-4 VM, the approach suggested in this paper gains the benefit of maximum 3.49dB with some variations depending on the characteristics of the images.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.785-794
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• 2002

Automatic stock trading systems should be able to solve various kinds of optimization problems such as market trend prediction, stock selection, and trading strategies, in a unified framework. But most of the previous trading systems based on supervised learning have a limit in the ultimate performance, because they are not mainly concerned in the integration of those subproblems. This paper proposes a stock trading system, called R-Trader, based on reinforcement teaming, regarding the process of stock price changes as Markov decision process (MDP). Reinforcement learning is suitable for Joint optimization of predictions and trading strategies. R-Trader adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing other trading parameters respectively. Technical analysis is also adopted to devise the input features of the system and value functions are approximated by feedforward neural networks. Experimental results on the Korea stock market show that the proposed system outperforms the market average and also a simple trading system trained by supervised learning both in profit and risk management.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.185-189
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• 2012

It is almost impossible to directly find the prime factor, p,q of a large semiprime, n=pq. So Most of the integer factorization algorithms uses a indirect method that find the prime factor of the p=GCD(a-b,n),q=GCD(a+b,n) after getting the congruence of squares of the $a^2{\equiv}b^2$(mod n). Many methods of getting the congruence of squares have proposed, but it is not easy to get with RSA number of greater than a 100-digit number. This paper proposes a fast algorithm to get the congruence of squares. The proposed algorithm succeeded in getting the congruence of squares to a 19-digit number.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.7
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• pp.666-673
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• 2008

To construct the accurate radio satellite navigation system, the efficient communication each satellite with the ground station is very important. Throughout the communication, the orbit of each satellite can be corrected, and those information will be used to analyze the satellite satus by the operator. Since there are limited resources of ground station, the schedule of antenna's azimuth and elevation angle should be optimized. On the other hand, the satellite in the medium earth orbit does not pass the same point of the earth surface due to the rotation of the earth. Therefore, the antenna pass schedule must be updated at the proper moment. In this study, Q learning approach which is a form of model-free reinforcement learning and genetic algorithm are considered to find the optimal antenna schedule. To verify the optimality of the solution, numerical simulations are conducted.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.273-287
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• 2016

White-box cryptography presented by Chow et al. is an obfuscation technique for protecting secret keys in software implementations even if an adversary has full access to the implementation of the encryption algorithm and full control over its execution platforms. Despite its practical importance, progress has not been substantial. In fact, it is repeated that as a proposal for a white-box implementation is reported, an attack of lower complexity is soon announced. This is mainly because most cryptanalytic methods target specific implementations, and there is no general attack tool for white-box cryptography. In this paper, we present an analytic toolbox on white-box implementations of the Chow et al.'s style using lookup tables. According to our toolbox, for a substitution-linear transformation cipher on n bits with S-boxes on m bits, the complexity for recovering the $$O\((3n/max(m_Q,m))2^{3max(m_Q,m)}+2min\{(n/m)L^{m+3}2^{2m},\;(n/m)L^32^{3m}+n{\log}L{\cdot}2^{L/2}\}\)$$, where $m_Q$ is the input size of nonlinear encodings,$m_A$ is the minimized block size of linear encodings, and $L=lcm(m_A,m_Q)$. As a result, a white-box implementation in the Chow et al.'s framework has complexity at most $O\(min\{(2^{2m}/m)n^{m+4},\;n{\log}n{\cdot}2^{n/2}\}\)$ which is much less than $2^n$. To overcome this, we introduce an idea that obfuscates two advanced encryption standard (AES)-128 ciphers at once with input/output encoding on 256 bits. To reduce storage, we use a sparse unsplit input encoding. As a result, our white-box AES implementation has up to 110-bit security against our toolbox, close to that of the original cipher. More generally, we may consider a white-box implementation of the t parallel encryption of AES to increase security.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.1
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• pp.41-45
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• 2008

This paper deals with the estimation method on the initial pole position of a z-axis permanent magnet linear synchronous motor(PMLSM) without magnetic pole sensors such as Hall sensors. The proposed method takes account of the gravitational force at z-axis and also the load conditions. The algorithm consists of two steps. The first step is to approximately estimate the initial q-axis by monitoring the movements due to the test current at predefined different test q-axes. The second step is to estimate the real q-axis as accurately as possible by using the outputs corresponding to torques due to the test current at three different test q-axes in order to avoid the effect of load mass variations. Experimental results on the z-axis PMLSM show good estimation characteristics of the proposed method irrespective of load mass conditions.

• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.1-7
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• 2002

The fast extraction of the silhouettes of a model is very useful for many applications in computer graphics and animation. In this paper, we present an efficient algorithm to compute a sequence of perspective silhouettes for a polyhedral model from a moving viewpoint. The viewpoint is assumed to move along a trajectory q(t), which is a space curve of a time parameter t. Then, we can compute the time-intervals for each edge of the model to be contained in the silhouette by two major computations: (i) intersecting q(t) with two planes and (ii) a number of dot products. If q(t) is a curve of degree n, then there are at most of n + 1 time-intervals for an edge to be in a silhouette. For each time point $t_i$ we can extract silhouette edges by searching the intervals containing $t_i$ among the computed intervals. For the efficient search, we propose two kinds of data structures for storing the intervals: an interval tree and an array. Our algorithm can be easily extended to compute the parallel silhouettes with minor modifications.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.21-27
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• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.