• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.32-39
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• 2004

Biological neurons generally have chaotic characteristics for permanent or transient period. The effects of chaotic response of biological neuron have not yet been verified by using analytical methods. Even though the transient chaos of neuron could be beneficial to overcoming the local minimum problem, the permanent chaotic response gives adverse effect on optimization problems in general. To solve optimization problems, which are needed in almost all neural network applications such as pattern recognition, identification or prediction, and control, the neuron should have one stable fixed point. In this paper, the dynamic characteristics of the chaotic dynamic neuron and the condition that produces the chaotic response are analyzed, and the convergence conditions are presented.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.372-381
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• 2006

The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.04a
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• pp.397-400
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• 2011

MDO Optimization for a low pressure axial compressor rotor has been carried out to improve aerodynamic performance and structural stability. Global optimized solution was obtained from an artificial neural network model with genetic algorithm. Optimized rotor model has a high blade loading near hub and near zero incidence flow angle near tip region to reduce the incidence loss and flow separation at trailing edge region. Also the rotor shape is converged to a trapezoid shape to reduce the maximum stress occurred at the root of the blade. Numerical simulation results show that rotor has 87.6% rotor efficiency and safety factor over than 3.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.299-304
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• 2023

Among artificial intelligence techniques, deep learning is a model that has been used in many places and has proven its effectiveness. However, deep learning models are not used effectively in everywhere. In this paper, we will show the limitations of deep learning models through comparison of regression analysis and deep learning models, and present a guide for effective use of deep learning models. In addition, among various techniques used for optimization of deep learning models, data normalization and data shuffling techniques, which are widely used, are compared and evaluated based on actual data to provide guidelines for increasing the accuracy and value of deep learning models.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.107-113
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• 2024

Scheduling of semiconductor test facilities has been crucial since effective scheduling contributes to the profits of semiconductor enterprises and enhances the quality of semiconductor products. This study aims to solve the scheduling problems for the wafer burn-in test facilities of the semiconductor back-end process by utilizing simulation and deep reinforcement learning-based methods. To solve the scheduling problem considered in this study. we propose novel state, action, and reward designs based on the Markov decision process. Furthermore, a neural network is trained by employing the recent RL-based method, named proximal policy optimization. Experimental results showed that the proposed method outperformed traditional heuristic-based scheduling techniques, achieving a higher due date compliance rate of jobs in terms of total job completion time.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.347-357
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• 2017

The applications of active control is being more popular nowadays. Several control algorithms have been developed to determine optimum control force. In this paper, a Chaotic Particle Swarm Optimization (CPSO) technique, based on Logistic map, is used to compute the optimum control force of active tendon system. A chaotic exploration is used to search the solution space for optimum control force. The response control of Multi-Degree of Freedom (MDOF) shear buildings, equipped with active tendons, is introduced as an optimization problem, based on Instantaneous Optimal Active Control algorithm. Three MDOFs are simulated in this paper. Two examples out of three, which have been previously controlled using Lattice type Probabilistic Neural Network (LPNN) and Block Pulse Functions (BPFs), are taken from prior works in order to compare the efficiency of the current method. In the present study, a maximum allowable value of control force is added to the original problem. Later, a twenty-story shear building, as the third and more realistic example, is considered and controlled. Besides, the required Central Processing Unit (CPU) time of CPSO control algorithm is investigated. Although the CPU time of LPNN and BPFs methods of prior works is not available, the results show that a full state measurement is necessary, especially when there are more than three control devices. The results show that CPSO algorithm has a good performance, especially in the presence of the cut-off limit of tendon force; therefore, can widely be used in the field of optimum active control of actual buildings.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.129-134
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• 2008

In this paper, we carry out the simulation for an optimal solution of one-dimensional nonlinear photonic crystal structure using Genetic algorithm, and show the proposed method to apply for photonic transistors. Unlike a conventional steepest descent method for an optimization, the proposed method based on Genetic Algorithm has advantages for finding out excellent solutions without any analytic forms, which can easily apply to other applications. Also, as several solutions around global minimum solution can be obtained, it is very good optimization tool to give us the patterns about the optimal structure of a photonic crystal transistor. To design an all-optical filter transistor, Neural network algorithm is firstly performed for an initial design and then Genetic Algorithm is finally used to get the optimal solution. From the simulation of one-dimensional photonic crystal transistor, 27dB of the switching On/Off ratio is obtained.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.1
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• pp.1-9
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• 2019

In electric agricultural machine the gearbox is used to increase torque and lower the output speed of the motor shaft. The gearbox consists of several shafts, helical gears and spur gears works in series. Optimization plays an important role in gear design as reducing the weight or volume of a gear set will increase its service life and improve the bearing capacity. In this paper the basic design parameters for gear like shaft diameter and face width are considered as the input variables. The bending stress and material volume is considered as the objective function. ANSYS was used to investigate the bending stress when the variable was changed. Artificial Neural Network (ANN) was used to obtain the mathematical model of the system based on the bending stress behaviour. The ANN was used since the output system is nonlinear. The Genetic Algorithm (GA)　technique of optimization is used to obtain the optimized values of shaft diameter and face width on the pinion based on the ANN mathematical model and the results are compared as that obtained using the traditional method. The ANN and GA were performed using MATLAB. The simulation results were shown that the proposed algorithm was successfully calculated the value of shaft diameter and face width to obtain the minimal bending stress and material volume of the gearbox.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.3
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• pp.177-186
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• 2019

Wind power tower has been used to augment the performance of VAWT (Vertical Axis Wind Turbine). However, inappropriately designed wind power tower could reduce the performance of VAWT. Hence, an optimization study was conducted on a wind power tower. Six design variables were selected, such as the outer radius and the inner radius of the guide wall, the adoption of the splitter, the inner radius of the splitter, the number of the guide wall and the circumferential angle. For the objective function, the periodic averaged torque obtained at the VAWT was selected. In the optimization, Design of Experiment (DOE), Genetic Algorithm (GA), and Artificial Neural Network (ANN) have been applied in order to avoid a localized optimized result. The ANN has been continuously improved after finishing the optimization process at each generation. The performance of the VAWT was improved more than twice when it operated within the optimized wind power tower compared to that obtained at a standalone.