• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.215-222
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• 2001

Neural network based models of semiconductor manufacturing processes have been shown to offer advantages in both accuracy and generalization over traditional methods. However, model development is often complicated by the fact that back-propagation neural networks contain several adjustable parameters whose optimal values unknown during training. These include learning rate, momentum, training tolerance, and the number of hidden layer neurOnS. This paper presents an investigation of the use of genetic algorithms (GAs) to determine the optimal neural network parameters for the modeling of plasma-enhanced chemical vapor deposition (PECVD) of silicon dioxide films. To find an optimal parameter set for the neural network PECVD models, a performance index was defined and used in the GA objective function. This index was designed to account for network prediction error as well as training error, with a higher emphasis on reducing prediction error. The results of the genetic search were compared with the results of a similar search using the simplex algorithm.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.165-173
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• 2006

Network Mobility Basic Support protocol enables mobile network to change their point of attachment to the Internet, but causes some problems such as suboptimal muting and multiple encapsulations. The proposed scheme, combining Prefix Delegation protocol with HMIPv6 concept can provide more effective route optimization and reduce the amount of packet losses and the burden of location registration for handoff. It also uses hierarchical mobile network prefix (HMNP) assignment and provides tree-based routing mechanism to allocate the location address of mobile network nodes (MNNs) and support micro-mobility. In this scheme, Mobility Management Router (MMR) not only maintains the binding informations for all MNNs in nested mobile networks, but also supports binding procedures to reduce the volume of handoff signals oyer the mobile network. The performance is evaluated using NS-2.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1479-1494
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• 2015

Since the Markowitz's mean-variance framework for portfolio analysis, the topic of portfolio optimization has been an important topic in finance. Traditional approaches focus on maximizing the expected return of the portfolio while minimizing its variance, assuming that risky asset returns are normally distributed. The normality assumption however has widely been criticized as actual stock price distributions exhibit much heavier tails as well as asymmetry. To this extent, in this paper we employ the genetic algorithm to find the optimal portfolio under the Value-at-Risk (VaR) constraint, where the tail of risky assets are modeled with the generalized Pareto distribution (GPD), the standard distribution for exceedances in extreme value theory. An empirical study using Korean stock prices shows that the performance of the proposed method is efficient and better than alternative methods.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.203-210
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• 2011

Ant Colony System is a new meta heuristics algorithms to solve hard combinatorial optimization problems. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem. In this paper, we propose the searching method to consider the overlapping edge of the global best path of the previous and the current. This method is that we first determine the overlapping edge of the global best path of the previous and the current will be configured likely the optimal path. And, to enhance the pheromone for the overlapping edges increases the probability that the optimal path is configured. Finally, the performance of Best and Average-Best of proposed algorithm outperforms ACS-3-opt, ACS-Subpath and ACS-Iter algorithms.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.559-566
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• 2016

Software defined network (SDN) can effectively improve the performance of traffic engineering and will be widely used in backbone networks. Therefore, new energy-saving schemes must take SDN into consideration; this action is extremely important owing to the rapidly increasing energy consumption in telecom and Internet service provider (ISP) networks. Meanwhile, the introduction of SDN in current networks must be incremental in most cases, for technical and economic reasons. During this period, operators must manage hybrid networks in which SDN and traditional protocols coexist. In this study, we investigate the energy-efficient traffic engineering problem in hybrid SDN/Internet protocol (IP) networks. First, we formulate the mathematical optimization model considering the SDN/IP hybrid routing mode. The problem is NP-hard; therefore, we propose a fast heuristic algorithm named hybrid energy-aware traffic engineering (HEATE) as a solution. In our proposed HEATE algorithm, the IP routers perform shortest-path routing by using distributed open shortest path first (OSPF) link weight optimization. The SDNs perform multipath routing with traffic-flow splitting managed by the global SDN controller. The HEATE algorithm determines the optimal setting for the OSPF link weight and the splitting ratio of SDNs. Thus, the traffic flow is aggregated onto partial links, and the underutilized links can be turned off to save energy. Based on computer simulation results, we demonstrate that our algorithm achieves a significant improvement in energy efficiency in hybrid SDN/IP networks.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.16 no.2
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• pp.102-107
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• 2003

This study puts focus on the optimization of growth temperature of CIGS absorber layer which affects severely the performance of solar cells. The CIGS absorber layers were prepared by three-stage co-evaporation of metal elements in the order of In-Ga-Se. The effect of the growth temperature of 1st stage was found not to be so important, and 350$^{\circ}C$ to be the lowest optimum temperature. In the case of growth temperature at 2nd/3rd stage, the optimum temperature was revealed to be 550$^{\circ}C$. The XRD results of CIGS films showed a strong (112) preferred orientation and the Raman spectra of CIGS films showed only the Al mode peak at 173cm$\^$-1/. Scanning electron microscopy results revealed very small grains at 2nd/3rd stage growth temperature of 480$^{\circ}C$. At higher temperatures, the grain size increased together with a reduction in the number of the voids. The optimization of experimental parameters above mentioned, through the repeated fabrication and characterization of unit layers and devices, led to the highest conversion efficiency of 15.4% from CIGS-based thin film solar cell with a structure of Al/ZnO/CdS/CIGS/Mo/glass.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.325-338
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• 2012

In this paper, a radio resource allocation scheme for a multi-hop relay transmission in cognitive radio (CR) system is proposed to support the employment of relay nodes in IEEE 802.22 standard for wireless regional area network (WRAN). An optimization problem is formulated to maximize the number of serving secondary users (SUs) under system constraints such as time-divided frame structure for multiplexing and a single resource-unit to every relay-hop. However, due to mathematical complexity, the optimization problem is solved with a sub-optimal manner instead, which takes three steps in the order of user selection, relay/path selection, and frequency selection. In the numerical analysis, this proposed solution is evaluated in terms of service rate denoting as the ratio of the number of serving SUs to the number of service-requesting SUs. Simulation results show the condition of adopting multi-hop relay and the optimum number of relaying hops by comparing with the performance of 1-hop system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4C
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• pp.321-329
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• 2008

H.264/AVC video coding standard enables a considerably higher improvement in coding efficiency compared with previous standards such as MPEG-2, H.263 and MPEG-4. To achieve this, for each macro-block in H.264/AVC, Rate-Distortion Optimization (RDO) technique is employed to select the best motion vector, reference frame, and macro-block mode. As a result, computational complexity is increased significantly whereas RDO achieve higher improvement. This paper presents fast intra mode selection algorithm based on constraints of frequency characteristics which are derived from intra coding modes of H.264/AVC. First of all, we observe the features of each intra mode through the frequency analysis of image. And then proposed Frequency Error Costs (FECs) are calculated to select the best mode which has minimum cost. Computational complexity is considerably reduced because rate-distortion costs only calculate the candidate modes which are set of best mode and its neighbouring two modes. Experimental results show that proposed algorithm reduces the complexity dramatically maintaining the rate-distortion performance compared with H.264/AVC reference software.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.560-567
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• 2020

Incremental Sheet Forming (ISF) is a unique sheet-forming technique. The process is a die-less sheet metal manufacturing process for rapid prototyping and small batch production. In the forming process, the critical parameters affecting the formability of sheet materials are the tool diameter, step depth, feed rate, spindle speed, etc. This study examined the effects of these parameters on the formability in the forming of the varying wall angle conical frustum model for a pure Al3004 sheet with 1mm in thickness. Using Minitab software based on Back Propagation Neural Network (BPNN) and Genetic Algorithm (GA), a second order mathematical prediction model was established to predict and optimize the wall angle. The results showed that the maximum forming angle was 87.071° and the best combination of these parameters to give the best performance of the experiment is as follows: tool diameter of 6mm, spindle speed of 180rpm, step depth of 0.4mm, and feed rate of 772mm/min.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.