• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.11 no.1
• /
• pp.35-42
• /
• 2001

The primitive polynomial on GF(q) is used in the area of the scrambler, the error correcting code and decode, the random generator and the cipher, etc. The algorithm that generates efficiently the primitive polynomial on GF(q) was proposed by A.D. Porto. The algorithm is a method that generates the sequence of the primitive polynomial by repeating to find another primitive polynomial with a known primitive polynomial. In this paper, we propose the algorithm that is improved in the A.D. Porto algorithm. The running rime of the A.D. Porto a1gorithm is O($\textrm{km}^2$), the running time of the improved algorithm is 0(m(m+k)). Here, k is gcd(k, $q^m$-1). When we find the primitive polynomial with m odor, it is efficient that we use the improved algorithm in the condition k, m>>1.

• Journal of the Korean Mathematical Society
• /
• v.57 no.4
• /
• pp.1019-1030
• /
• 2020

Efficient computation of r-th root in 𝔽_q has many applications in computational number theory and many other related areas. We present a new r-th root formula which generalizes Müller's result on square root, and which provides a possible improvement of the Cipolla-Lehmer type algorithms for general case. More precisely, for given r-th power c ∈ 𝔽_q, we show that there exists α ∈ 𝔽_q^r such that $$Tr{\left(\begin{array}{cccc}{{\alpha}^{{\frac{({\sum}_{i=0}^{r-1}\;q^i)-r}{r^2}}}\atop{\text{ }}}\end{array}\right)}^r=c,$$ where $Tr({\alpha})={\alpha}+{\alpha}^q+{\alpha}^{q^2}+{\cdots}+{\alpha}^{q^{r-1}}$ and α is a root of certain irreducible polynomial of degree r over 𝔽_q.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.445-446
• /
• 2010

In Gen-2 Q-algorithm, the initial value of $Q_{fp}$, which is the slot-count parameter, is not defined in the standard. In this case, if we let the initial $Q_{fp}$ be large, the number of empty slot will be large during the initial query round. On the other hand, if the initial $Q_{fp}$ is small, almost all the slots will be collided. As a result, it is anticipated that the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we analyze how the performances of Gen-2 Q-algorithm will be affected by the initial slot-count size.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.5
• /
• pp.117-126
• /
• 1989

In this paper, a new placement algorithm for gate array lay out design is proposed. The proposed algorithm can treat the variable-sized macrocells and by considering the I/Q pad locations, the routing between I/Q pads and the internal region of a chip can be automated effectively. The algorithm is composed of 3 parts. which are initial partitioning, initial placement and placement improvement. In the initial placement phase, a given circuit is partitioned into 5 sub-circuits, by clustering method with considers connectivities of cells not only with I/Q pads but also with related partitioned groups is used repeatedly to assign a unique position to each cell. In the placement improvement phase, the concept of probabilistic wiring density is introduced, and cell moving algorithm is proposed to make the density in a chip even.

• Journal of IKEEE
• /
• v.25 no.4
• /
• pp.644-649
• /
• 2021

Self-Imitation Learning is a simple off-policy actor-critic algorithm that makes an agent find an optimal policy by using past good experiences. In case that Self-Imitation Learning is combined with reinforcement learning algorithms that have actor-critic architecture, it shows performance improvement in various game environments. However, its applications are limited to reinforcement learning algorithms that have actor-critic architecture. In this paper, we propose a method of applying Self-Imitation Learning to Deep Q-Network which is a value-based deep reinforcement learning algorithm and train it in various game environments. We also show that Self-Imitation Learning can be applied to Deep Q-Network to improve the performance of Deep Q-Network by comparing the proposed algorithm and ordinary Deep Q-Network training results.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.2
• /
• pp.203-209
• /
• 2019

We will be establish the new $q(1{\leq}q{\leq}p-1)$ stores of firm $F_B$ to gain pop/(p+q) over rival firm $F_A$ that has already operate with p stores in a city of population pop. Han proposes inclusion-exclusion algorithm(IEA) that searches maximal pop top 5 location and select the maximum location take account of locate variation with increasing of $q=1,2,{\cdots},p-1$. This paper reduced the orignal graph into partial graph initially and search only q=1 node continually reduced in accordance with increasing $q=1,2,{\cdots},p-1$. If the final result is shown in the case of steel customer between q, the q locations farther separate in order to improve of solution. For the eleven experimental data, this algorithm is a relative simplicity and more optimal solution than Han's IEA.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.3
• /
• pp.470-475
• /
• 2012

In Q-algorithm of EPCglobal Class-1 Gen-2 RFID system, the initial value of $Q_{fp}$, which is the slot-count parameter, is not defined in the standard. And the values of weight C, which is the parameter for incrementing or decrementing the slot-count size, are not determined. Therefore, if the number of tags is small and we let the initial $Q_{fp}$ be large, the number of empty slot will be large. On the other hand, if we let the initial $Q_{fp}$ be small in spite of many tags, almost all the slots will be collided. Also, if the reader selects an inappropriate weight, there are a lot of empty or collided slots. As a result, the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we propose a scheme to allocate the optimal initial $Q_{fp}$ through the tag number estimation and select the weight based on the slot-count size of current query round.

• Korean Journal of Artificial Intelligence
• /
• v.5 no.1
• /
• pp.1-9
• /
• 2017

Recently, the Q-Learning algorithm, which is one kind of reinforcement learning, is mainly used to implement artificial intelligence system in combination with deep learning. Many research is going on to improve the performance of Q-Learning. Therefore, purpose of theory try to improve the performance of Q-Learning algorithm. This Theory apply Cross Entropy Error to the loss function of Q-Learning algorithm. Since the mean squared error used in Q-Learning is difficult to measure the exact error rate, the Cross Entropy Error, known to be highly accurate, is applied to the loss function. Experimental results show that the success rate of the Mean Squared Error used in the existing reinforcement learning was about 12% and the Cross Entropy Error used in the deep learning was about 36%. The success rate was shown.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.8
• /
• pp.1649-1655
• /
• 2005

For systems with coexisting cellular and hot-spot cells in broadband orhogonal frequency and code division multiplexing (OFCDM) systems, a suitable cell search algorithm is proposed fur the common pilot channel (CPICH) in the forward link using separated I/Q channel cell specific codes(CSSC), in which the cellular cell specific scrambling code (CCSSC) is assigned to the in-phase (Q) pilot channel of all cellular cells, and the exclusive hot-spot cell specific scrambling code (HSCSSC) group is assigned to the quadrature (Q) pilot channel of all hot-spot cells. Therefore, the proposed algorithm enables a mobile station (MS) to search quickly for the most desirable hot-spot cell due to reducing the effect of CCSSC, when a MS wants to use a mobile internet. The computer simulation results show that the proposed cell search algorithm can achieve faster cell search time performance, compared to conventional cell search methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.05a
• /
• pp.529-531
• /
• 2011

In Gen-2 Q-algorithm, the values of weight C, which is the parameter for incrementing or decrementing the slot-count size, are not defined in the standard. In this case, if the reader selects an inappropriate weight, there are a lot of empty or collided slots. As a result, the performance will be degraded because the frame size does not converge to the optimal point quickly during the query round. In this paper, we analyze how the performances of Gen-2 Q-algorithm will be affected by the weight value.