• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.803-812
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• 2018

The purpose of this study is to develop Particle Swarm Optimization (PSO) automatic calibration algorithm with multi-objective functions by Python, and to evaluate the applicability by applying the algorithm to the Soil and Water Assessment Tool (SWAT) watershed modeling. The study area is the upstream watershed of Gongdo observation station of Anseongcheon watershed ($364.8km^2$) and the daily observed streamflow data from 2000 to 2015 were used. The PSO automatic algorithm calibrated SWAT streamflow by coefficient of determination ($R^2$), root mean square error (RMSE), Nash-Sutcliffe efficiency ($NSE_Q$), and especially including $NSE_{INQ}$ (Inverse Q) for lateral, base flow calibration. The results between automatic and manual calibration showed $R^2$ of 0.64 and 0.55, RMSE of 0.59 and 0.58, $NSE_Q$ of 0.78 and 0.75, and $NSE_{INQ}$ of 0.45 and 0.09, respectively. The PSO automatic calibration algorithm showed an improvement especially the streamflow recession phase and remedied the limitation of manual calibration by including new parameter (RCHRG_DP) and considering parameters range.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1299-1302
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• 2003

In this paper, we propose new method which can provide user the path to the target place efficiently. It stores the state of roads to target place as the form of Q-table and finds the proper path using Q-table.0-table is updated by the information about real traffic which is reported by users. This method can provides the proper path, using less storage and less computation time than the conventional method which stores entire road traffic information and finds the path by graph search algorithm.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.94-96
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• 2009

In this paper, we present the Q-learning method for adaptive traffic signal control on the basis of multi-agent technology. The structure is composed of sixphase agents and one intersection agent. Wireless communication network provides the possibility of the cooperation of agents. As one kind of reinforcement learning, Q-learning is adopted as the algorithm of the control mechanism, which can acquire optical control strategies from delayed reward; furthermore, we adopt dynamic learning method instead of static method, which is more practical. Simulation result indicates that it is more effective than traditional signal system.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.12a
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• pp.174-178
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• 2003

본 논문은 기존의 정규 기저를 이용한 역원 알고리즘인 IT 알고리즘과 TYT 알고리즘을 개선한 GF(q$^{m}$ )＊(q = 2$^n$)에서의 효율적인 역원 알고리즘을 제안한다. 제안된 알고리즘은 작은 n에 대해 GF(q)＊의 원소에 대한 역원을 선행 계산으로 저장하고, m-1을 몇 개의 인수와 나머지로 분해함으로써 역원 알고리즘에 필요한 곱셈의 수를 줄일 수 있는 방법이다. 즉, 작은 양의 데이터에 대한 메모리 저장 공간을 이용하여, GF(q$^{m}$ )＊에서의 역원을 계산하는 데 필요한 곱셈의 수를 줄일 수 있음을 보여준다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.866-869
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• 2015

In this paper, we propose a device for detect front object using low-price the CW Doppler sensor to prevent safety accident such as a bicycle, an electric wheelchair users. For this propose, we develop a signal process board and the object motion detect algorithm using to analyzing output signal of the CW Doppler sensor. Output signal from CW Doppler sensor is analog I and analog Q. The CW Doppler sensor shows phase I and phase Q of object differently when the object approach, stop, drop by sensor. We develop an algorithm that can detect object by discrimination information of phase using the CW Doppler sensor. The verification use firmware of applied hardware and algorithm. Then, the motion information can be confirming output depending on motion object by experiment normally. As a result, we check that the sensing information output by following motion of object and confirm an algorithm and motion of signal processing board.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.2
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• pp.15-22
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• 2004

For an ElGamal-type signature scheme using a generate g of order q, it has been well-known that the message nonce should be chosen randomly in the interval (0, q-1) for each message to be signed. In (2), H. Kuwakado and H. Tanaka proposed a polynomial time algorithm that gives the private key of the signer if two signatures with message nonces 0＜$k_1$, $k_2$$\leq$Ο(equation omitted) are available. Recently, R. Gallant, R. Lambert, and S. Vanstone suggested a method to improve the efficiency of elliptic curve crytosystem using integer decomposition. In this paper, by applying the integer decomposition method to the algorithm proposed by Kuwakado and Tanaka, we extend the algorithm to work in the case when ｜$k_1$ ｜,｜$k_2$, ｜$\leq$Ο(equation mitted) and improve the efficiency and completeness of the algorithm.

• Korean Journal of Mathematics
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• v.24 no.3
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• pp.469-487
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• 2016

In [6] Schensted constructed the Schensted algorithm, which gives a bijection between permutations and pairs of standard tableaux of the same shape. Stanton and White [8] gave analog of the Schensted algorithm for rim hook tableaux. In this paper we give a generalization of Stanton and White's Schensted algorithm for rim hook tableaux. If k is a fixed positive integer, it shows a one-to-one correspondence between all generalized hook permutations $\mathcal{H}$ of size k and all pairs (P, Q), where P and Q are semistandard k-rim hook tableaux and k-rim hook tableaux of the same shape, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3997-4015
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• 2014

Radio frequency identification (RFID) is an emerging wireless communication technology which allows objects to be identified automatically. The tag anti-collision is a significant issue for fast identifying tags due to the shared wireless channel between tags and the reader during communication. The EPCglobal Class-1 Generation-2 which uses Q algorithm for the anti-collision is widely used in many applications such as consumer electronic device and supply chain. However, the increasing application of EPCglobal Class-1 Generation-2 which requires the dynamic environment makes the efficiency decrease critically. Furthermore, its frame length (size) determination and frame termination lead to the suboptimal efficiency. A new anti-collision algorithm is proposed to deal with the two problems for large-scale RFID systems. The algorithm has higher performance than the Q algorithm in the dynamic environment. Some simulations are given to illustrate the performance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.10
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• pp.1104-1111
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• 2003

The paper presents an algorithm for the compensation of gain and phase imbalances to exist between I-phase and Q-phase signal at direct conversion receiver. We propose a gain and phase imbalances blind equalization compensation algorithm by using variable step-size adaptive loop at direct conversion receiver. The blind equalization schemes have trade-off between convergence speed and jitter effect for the compensation of gain and phase imbalance. We propose the variable step-size adaptive loop method, which varies the loop coefficients according to errors, for recovering these problem. By using variable step-size adaptive loops, we propose to speed up the convergence process and reduce the jitter effect and simulation results show that the algorithm compensates signal loss and speeds up convergence time.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.163-168
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• 2002

The voting algorithm for action selection performs self-improvement by Reinforcement learning algorithm in the dynamic environment. The proposed voting algorithm improves the navigation of the robot by adapting the eligibility of the behaviors and determining the Command Set Generator (CGS). The Navigator that using a proposed voting algorithm corresponds to the CGS for giving the weight values and taking the reward values. It is necessary to decide which Command Set control the mobile robot at given time and to select among the candidate actions. The Command Set was learnt online by means as Q-learning. Action Selector compares Q-values of Navigator with Heterogeneous behaviors. Finally, real-world experimentation was carried out. Results show the good performance for the selection on command set as well as the convergence of Q-value.