• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.128-136
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• 1995

The problem addressed in this paper is that of the on-line computational burden of time-optimal control laws for quick, strongly nonlinear systems like revolute robots. It will be demonstrated that a large amount of off-line computation can be substituted for most of the on-line burden in cases of time optimization with constrained inputs if differential point-to- point specifications can be relaxed to cell-to-cell transitions. These cells result from a coarse discretization of likely swaths of state space into a set of nonuniform, contiguous volumes of relatively simple shapes. The cell boundaries approximate stream surfaces of the phase fluid and surfaces of equal transit times. Once the cells have been designed, the bang- bang schedules for the inputs are determined for all likely starting cells and terminating cells. The scheduling process is completed by treating all cells into which the trajectories might unex- pectedly stray as additional starting cells. Then an efficient-to-compute control law can be based on the resulting table of optimal strategies.

• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.89-100
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• 2011

The MAGLEV (Magnetically Levitated Vehicle) system, which is under commercialization as a new transportation system in Korea, is operated by means of unmanned automatic control system. Therefore the plan of train operation should be carefully established and validated in advance. In general, when making the train operation plan, the statistically predicted traffic data is used. However, traffic wave can occur when real train service is operated, and the demand-driven simulation technology is required to review train operation plans and service qualities considering traffic wave. This paper presents a method and model to simulate the MAGLEV's operation considering continuous demand changes. For this purpose, we employed the discrete event model which is suitable for modeling the behavior of railway passenger transportation, and modeled the system hierarchically using DEVS (Discrete Event System Specification) formalism. In addition, through the implementation and experiment using DEVSim++ simulation environment, we tested the feasibility of the proposed model and it is also verified that our demand-driven simulation technology could be used for the prior review of the train operation plans and strategies.

• Journal of Advanced Navigation Technology
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• v.13 no.1
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• pp.87-96
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• 2009

The purpose of this paper is to analyze the power control problem in wireless communications with game theoretic approach. The major contribution of the present paper is that we formulated the problem as a game with a finite number of strategies while most of the previous game theoretic power control literatures modeled with continuous game in which there are infinite number of strategies. It should be noted that the closed-loop power control would be performed in a discrete manner, power up or down from the present level of power with fixed power control step size. We model the current closed-loop power control scheme with the famous Prisoner's dilemma model and show that the power-up strategy is Nash equilibrium. That is, every mobile tries to increase their power and approach to their maximal power. Thus, the outcome of current power control (Nash equilibrium) is inefficient. In order to attain efficient power control for the environment where ICI(Inter-Cell Interference is severe, we developed a new payoff function in which the penalty mechanism is introduced and derived conditions under which power-down becomes Nash equilibrium strategy for all players. Furthermore we examined the trajectory of equilibrium power when the power control game will be played repeatedly.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2018-2030
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• 2015

A two stage ac drive configuration consisting of a single-phase line commutated rectifier and a three-phase voltage source inverter (VSI) is very common in low and medium power applications. The deterministic pulse width modulation (PWM) methods like sinusoidal PWM (SPWM) could not be considered as an ideal choice for modern drives since they result mechanical vibration and acoustic noise, and limit the application scope. This is due to the incapability of the deterministic PWM strategies in sprawling the harmonic power. The random PWM (RPWM) approaches could solve this issue by creating continuous harmonic profile instead of discrete clusters of dominant harmonics. Insufficient filtering at dc link results in the amplitude distortion of the input dc voltage to the VSI and has the most significant impact on the spectral errors (difference between theoretical and practical spectra). It is obvious that the sprawling effect of RPWM undoubtedly influenced by input fluctuation and the discrete harmonic clusters may reappear. The influence of dc link fluctuation on harmonics and their spreading effect in the VSI remains invalidated. A case study is done with four different filter capacitor values in this paper and results are compared with the constant dc input operation. This paper also proposes an ingenious RPWM, a ripple dosed sinusoidal reference-random carrier PWM (RDSRRCPWM), which has the innate capacity of suppressing the effect of input fluctuation in the output than the other modern PWM methods. MATLAB based simulation study reveals the fundamental component, total harmonic distortion (THD) and harmonic spread factor (HSF) for various modulation indices. The non-ideal dc link is managed well with the developed RDSRRCPWM applied to the VSI and tested in a proto type VSI using the field programmable gate array (FPGA).

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2849-2852
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• 1999

This paper describes the fuzzy PD controller using simplified indirect inference method. First, the fuzzy PD controller is derived from the conventional continuous time linear PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. particularly when the process to be controlled is nonlinear. As the SIIM is applied, the fuzzy Inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the Proposed method has the capability of the high speed inference and extending the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control Performance of the one Proposed by D. Misir et at. Final)y. we simulated the contour control of the X-Y tables with direct control strategies using the proposed fuzzy PD controller.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.112-118
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• 2023

As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.342-351
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• 2017

The neural decoding is a procedure that uses spike trains fired by neurons to estimate features of original stimulus. This is a fundamental step for understanding how neurons talk each other and, ultimately, how brains manage information. In this paper, the strategies of neural decoding are classified into three methodologies: rate decoding, temporal decoding, and population decoding, which are explained. Rate decoding is the firstly used and simplest decoding method in which the stimulus is reconstructed from the numbers of the spike at given time (e. g. spike rates). Since spike number is a discrete number, the spike rate itself is often not continuous and quantized, therefore if the stimulus is not static and simple, rate decoding may not provide good estimation for stimulus. Temporal decoding is the decoding method in which stimulus is reconstructed from the timing information when the spike fires. It can be useful even for rapidly changing stimulus, and our sensory system is believed to have temporal rather than rate decoding strategy. Since the use of large numbers of neurons is one of the operating principles of most nervous systems, population decoding has advantages such as reduction of uncertainty due to neuronal variability and the ability to represent a stimulus attributes simultaneously. Here, in this paper, three different decoding methods are introduced, how the information theory can be used in the neural decoding area is also given, and at the last machinelearning based algorithms for neural decoding are introduced.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1187-1191
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• 2016

Background: The prevalence of prostate cancer (PCa) has been increasing in recent years. Treatment strategies are largely based on the results of bone scan screening. Therefore, our aim was to investigate predictors of positive bone metastasis in newly diagnosed PCa patients. Materials and Methods: After extensive review, 336 consecutive patients newly diagnosed with PCa between April 2010 and November 2013 at our institution were enlisted in the study. Patients were divided into two groups according to bone scan results. Univariate analyses (Chi-square test for discrete variables and independent t-test for continuous variables) were applied to determine the potentially significant risk factors associated with distant bone metastasis. Binary logistic regression analyses were used to further investigate the influence of these factors on bone metastasis. Results: The patient mean age was $71.9{\pm}8.6years$ (range: 48 to 94 years). The mean prostate specific antigen (PSA) level and biopsy Gleason score were $260.2{\pm}1107.8ng/mL$ and $7.4{\pm}1.5$, respectively. The body mass index (BMI) for the series was $24.5{\pm}3.4kg/m^2$. Sixty-four patients (19.0%) had a positive bone scan result. Patients with positive bone scan results had a significantly lower BMI ($23.3{\pm}3.5$ vs. $24.8{\pm}3.3$; p=0.003), a higher Gleason score ($8.5{\pm}1.1$ vs. $7.1{\pm}1.5$; p < 0.001), and a higher PSA level ($1071.3{\pm}2337.1$ vs. $69.4{\pm}235.5$; p < 0.001) than those without bone metastasis. Multivariate logistic regression analysis employing the above independent predictors demonstrated that a Gleason score of ${\geq}7$, clinical stage ${\geq}T3$, $BMI{\leq}22kg/m^2$, and an initial PSA level of ${\geq}20ng/mL$ were all independent predictors of bone metastasis. Conclusions: A bone scan might be necessary in newly diagnosed PCa patients with any of the following criteria: clinical stage T3 or higher, a Gleason score of 7 or higher, BMI equal to or less than 22, and a PSA level of 20 or higher.