• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.146-151
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• 2001

Optical disk drive has excellent advantage of random accessibility of which performance is measured by access time. However, due to the increased rotational velocity of the disk and constraints of mechanical structure, two-stage seek algorithm which executes coarse and fine seeks sequentially has been adopted in most commercial optical disk drives. Although the laser spot is moved to a target track by a single seek operation, the limited operation range of the fine actuator restricts the application of the fine seek algorithm below a few hundreds of tracks. Especially, excessive movement of the objective lens causes a failure in generation of track-cross pulse and results in an unstable seek operation. In this paper, a new control algorithm for extending the fine seek range is proposed with an appropriate control structure. The coarse actuator is utilized to reduce the misalignment between the objective lens and the laser beam axis, and the fine actuator is controlled to follow the reference velocity trajectory. The proposed algorithm is applied to a CD-ROM drive to show its feasibility and some experimental results are presented.

• Journal of Korea Game Society
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• v.17 no.6
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• pp.129-138
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• 2017

In football simulation games, it is very important for the interest of the game to make the stats of the football players close to reality. As the management concept is introduced to the sports simulation game, when the user plays the game for a long time, the existing player character retires. Therefore, the game creates the environment of the game by creating a new player in the game. In this study, we propose a method to create a new player character by using genetic algorithm to have the optimal ability similar to existing players. We compare and evaluate the player character with the existing random generation method, the correction random method and the proposed algorithm, and verify the validity of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.4
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• pp.424-434
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• 2012

This paper presents a path planning algorithm of the unmanned aerial vehicle for the neutralization of the enemy firepower. The long range firepower of the ememy is usually located at the rear side of the mountain which is difficult to bomb. The path planner not only consider the differential constraints of the Unmanned Aerial Vehicle (UAV) but also consider the final approaching angle constraint. This problem is easily solved by incorporating the analytical upper bounded continuous curvature path smoothing algorithm into the Rapidly Exploring Random Tree (RRT) planner. The proposed algorithm can build a feasible path satisfying the kinematic constraints of the UAV on the fly. In addition, the curvatures of the path are continuous over the whole path. Simulation results show that the proposed algorithm can generate a feasible path of the UAV for the bombing mission regardless of the posture of the tunnel.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.995-1000
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• 2016

The maximum cross-correntropy (MCC) algorithm with a set of random symbols keeps its optimum weights undisturbed from impulsive noise unlike MSE-based algorithms and its main factor has been known to be the input magnitude controller (IMC) that adjusts the input intensity according to error power. In this paper, a normalization of the step size of the MCC algorithm by the power of IMC output is proposed. The IMC output power is tracked recursively through a single-pole low-pass filter. In the simulation under impulsive noise with two different multipath channels, the steady state MSE and convergence speed of the proposed algorithm is found to be enhanced by about 1 dB and 500 samples, respectively, compared to the conventional MCC algorithm.

• Economic and Environmental Geology
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• v.56 no.1
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• Genomics & Informatics
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• v.5 no.1
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• pp.1-5
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• 2007

Embryonic stem cells can be differentiated into various types of cells, requiring a tight regulation of transcription. Biomarkers related to each lineage of cells are used to guide the differentiation into neural or any other fates. In previous experiments, we reported the guided differentiation (GD)-specific genes by comparing profiles of random differentiation (RD). Interestingly 68% of differentially expressed genes in GD overlap with that of RD, which makes it difficult for us to separate the lineages by examining several markers. In this paper, we design a prediction model to identify the differentiation into neural fates from any other lineage. From the profiles of 11,376 genes, 203 differentially expressed genes between neural and random differentiation were selected by random variance T-test with 95% confidence and 5% false discovery rate. Based on support vector machine algorithm, we could select 79 marker genes from the 203 informative genes to construct the optimal prediction model. Here we propose a prediction model for the prediction of neural fates from random differentiation which is constructed with a perfect accuracy.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.4
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• pp.364-371
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• 2004

This thesis is proposing novel RPWM(Random PWM) techniques that can locate PWM pulse to do random. RPWM techniques to propose locates SVPWM(Space Vector PWM) pulse by number of each random and principle to locate of pulse applies different random function and locate pulse. For propriety verification of proposed techniques, achieve an simulation and experiment that use MATLAB/SIMULINK about proposed RPWM techniques algorithm and IGBT inverter composition that use DSP(TMS320C31). Specially, analyze harmonic spectra of inverter output current when the induction motor speed is more than 10,000 rpm, confirm that RPWM's effect in high speed degree appears. Proposed RPWM techniques propriety prove from reduction effect of harmonic magnitude that corresponds to an integer times of switching frequency.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.381-389
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• 1996

A simple modification of the standard back-propagation algorithm to eliminate redundant connections(weights and biases) is described. It was motivated by speculations from the distribution of the magnitudes of the weights and the biases, analysis of the classification boundary, and the nonlinearity of the sigmoid function. After initial training, this algorithm eliminates all connections of which magnitude is below a threshold by setting them to zero. The algorithm then conducts retraining in which all weights and biases are adjusted to allow important ones to recover. In studies with Boolean functions, the algorithm reconstructed the theoretical minimum architecture and eliminated the connections which are not necessary to solve the functions. For simulated random signal classification problems, the algorithm produced the result which is consistent with the idea that easier problems require simpler networks and yield lower misclassification rates. Furthermore, in comparison, our algorithm produced better generalization than the standard algorithm by reducing over fitting and pattern memorization problems.

• Journal of Power Electronics
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• v.17 no.2
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• pp.380-388
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• 2017

Since the carrier sequence is not reproducible in a period of the random carrier pulse width modulation (RCPWM) and a higher harmonic spectrum amplitude is likely to affect the quality of the power supply. In addition, electromagnetic interference (EMI) and mechanical vibration will appear. To solve these problems, this paper has proposed an optimal RCPWM based on a genetic algorithm (GA). In the optimal modulation, the range of the random carrier frequency is taken as a constraint and the reciprocal of the maximum harmonic spectrum amplitude is used as a fitness function to decrease the EMI and mechanical vibration caused by the harmonics concentrated at the carrier frequency and its multiples. Since the problems of the hardware make it difficult to use in practical engineering, this paper has presented a hardware system. Simulations and experiments show that the RCPWM is effective. Studies show that the harmonic spectrum is distributed more uniformly in the frequency domain and that there is no obvious peak in the wave spectra. The proposed method is of great value to research on RCPWM and integrated power systems (IPS).

• Journal of Communications and Networks
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• v.7 no.3
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• pp.337-352
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• 2005

In this paper, a fuzzy logic implementation of the random early detection (RED) mechanism [1] is presented. The main objective of the proposed fuzzy controller is to reduce the loss probability of the RED mechanism without any change in channel utilization. Based on previous studies, it is clear that the performance of RED algorithm is extremely related to the traffic load as well as to its parameters setting. Using fuzzy logic capabilities, we try to dynamically tune the loss probability of the RED gateway. To achieve this goal, a two-input-single-output fuzzy controller is used. To achieve a low packet loss probability, the proposed fuzzy controller is responsible to control the $max_{p}$ parameter of the RED gateway. The inputs of the proposed fuzzy controller are 1) the difference between average queue size and a target point, and 2) the difference between the estimated value of incoming data rate and the target link capacity. To evaluate the performance of the proposed fuzzy mechanism, several trials with file transfer protocol (FTP) and burst traffic were performed. In this study, the ns-2 simulator [2] has been used to generate the experimental data. All simulation results indicate that the proposed fuzzy mechanism out performs remarkably both the traditional RED and Adaptive RED (ARED) mechanisms [3]-[5].