• Journal of the Korean Society for Precision Engineering
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• v.18 no.11
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• pp.155-160
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• 2001

To achieve high precision cutting as well as production capability in the machine tool, it is needed to develop excellent rigidity statically, dynamically and thermally as well. In order to predict the qualitative behavior of a machine tool, simultaneous analysis of mechanics and heat transfer is required. Generally, machine tool designers have solved designing problems based on partial estimation of the specified rigidity. This study clears the inter-relationship between therm, and propose multi-phase optimization of machine tool structure using a genetic algorithm. The multi-phase solution method is consists of a series of mechanical design problem. At this first phase of static design problem, multi-objective optimization for the purpose of minimization of the total weight and static compliance minimization is solved using the Pareto Genetic Algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1189-1196
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• 2018

7 type composites (40, 45, 50, 55, 60, 65, and 70 wt.%)were prepared for the environmentally friendly GIS Spacer. Five kinds of samples were prepared for optimization of the filler content ratio (MS: MA = 1: 9, 3: 7, 5: 5, 7: 3, 9: 1) of epoxy / microsilica and microalumina. As a result of evaluation of the partial discharge resistance characteristic, surface erosion is generally slowed down as the fill amount of micro silica is increased. Also, partial discharge resistance characteristics for the development of insulating materials with optimal mixing ratios of heterologous showed a higher partial resistance of discharge and a decrease in erosion, as the filler content ratio of micro silica was larger. In the future, various researches such as electrical, mechanical, and thermal studies will be needed to develop insulating materials that can commercialize power devices in environmentally friendly insulating gas.

• Steel and Composite Structures
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• v.44 no.2
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• pp.155-169
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• 2022

In the present paper an analytical model was developed to study the non-linear vibrations of Functionally Graded Carbon Nanotube (FG-CNT) reinforced doubly-curved shallow shells using the Multiple Scales Method (MSM). The nonlinear partial differential equations of motion are based on the FGM shallow shell hypothesis, the non-linear geometric Von-Karman relationships, and the Galerkin method to reduce the partial differential equations associated with simply supported boundary conditions. The novelty of the present model is the simultaneous prediction of the natural frequencies and their mode shapes versus different curvatures (cylindrical, spherical, conical, and plate) and the different types of FG-CNTs. In addition to combining the vibration analysis with optimization algorithms based on the genetic algorithm, a design optimization methode was developed to maximize the natural frequencies. By considering the expression of the non-dimensional frequency as an objective optimization function, a genetic algorithm program was developed by valuing the mechanical properties, the geometric properties and the FG-CNT configuration of shallow double curvature shells. The results obtained show that the curvature, the volume fraction and the types of NTC distribution have considerable effects on the variation of the Dimensionless Fundamental Linear Frequency (DFLF). The frequency response of the shallow shells of the FG-CNTRC showed two types of nonlinear hardening and softening which are strongly influenced by the change in the fundamental vibration mode. In GA optimization, the mechanical properties and geometric properties in the transverse direction, the volume fraction, and types of distribution of CNTs have a considerable effect on the fundamental frequencies of shallow double-curvature shells. Where the difference between optimized and not optimized DFLF can reach 13.26%.

• Membrane and Water Treatment
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• v.10 no.4
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• pp.265-275
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• 2019

This study aimed to develop a compact partial nitritation step by forming granules with high Ammonia-Oxidizing Bacteria (AOB) fraction using the Air-lift Granulation Reactor (AGR) and to evaluate the feasibility of treating reject water with high ammonium content by combination with the Anammox process. The partial nitritation using AGR was achieved at high nitrogen loading rate ($2.25{\pm}0.05kg\;N\;m-3\;d^{-1}$). The important factors for successful partial nitritation at high nitrogen loading rate were relatively high pH (7.5~8), resulting in high free ammonia concentration ($1{\sim}10mg\;FA\;L^{-1}$) and highly enriched AOB granules accounting for 25% of the total bacteria population in the reactor. After the establishment of stable partial nitritation, an effluent $NO_2{^-}-N/NH_4{^+}-N$ ratio of $1.2{\pm}0.05$ was achieved, which was then fed into the Anammox reactor. A high nitrogen removal rate of $2.0k\; N\;m^{-3}\;d^{-1}$ was successfully achieved in the Anammox reactor. By controlling the nitrogen loading rate at the partial nitritation using AGR, the influent concentration ratio ($NO_2{^-}-N/NH_4{^+}-N=1.2{\pm}0.05$) required for the Anammox was controlled, thereby minimizing the inhibition effect of residual nitrite.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.12
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• pp.23-30
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• 1998

We present a partial bus-invert coding scheme for system-level power optimization. In the proposed scheme, we select a sub-group of bus lines involved in bus encoding to avoid unnecessary inversion of bus lines not in the sub-group thereby reducing the total number of bus transitions. We propose a heuristic algorithm that selects the sub-group of bus lines for bus encoding. Experiments on benchmark examples indicate that the partial bus-invert coding reduces the total bus transitions by 62.6% on the average, compared to that of the unencoded patterns. We also compare the performance of the proposed heuristic algorithm with that of simulated annealing, which shows that it is highly efficient.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.126-131
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• 2009

This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.

• The KIPS Transactions:PartD
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• v.14D no.2
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• pp.217-224
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• 2007

In order to determine the value and type statically. CTOC uses the SSA Form which separates the variable according to assignment. The SSA Form is widely being used as the intermediate expression of the compiler for data flow analysis as well as code optimization. However, the conventional SSA Form is more associated with variables rather than expressions. Accordingly, the redundant expressions are eliminated to optimize expressions of the SSA From. This paper defines the partial redundant expression to obtain a more optimized code and also implements the technique for eliminating such expressions.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1805-1818
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• 2018

This paper proposes a novel hybrid maximum power point tracking (MPPT) algorithm combining a Whale Optimization Algorithm (WOA) and the conventional Perturb & Observation (P&O) to track/extract the highest amount of power from a solar photovoltaic (SPV) system working under partial shading conditions (PSCs). The proposed hybrid algorithm is based on a WOA which predicts the initial global peak (GP) and is followed by P&O in the final stage to achieve a quicker convergence to a GP. Thus, this hybrid algorithm overcomes the computational burden encountered in a standalone WOA, grey wolf optimization (GWO) and hybrid GWO reported in the literature. The conventional algorithm searches for the maximum power point (MPP) in the predicted region by the WOA. The proposed MPPT technique is modelled and simulated using MATLAB/Simulink for simulating an environment to check its effectiveness in accurately tracking the MPP during the GP region. This hybrid algorithm is compared with a standalone WOA, GWO and hybrid GWO. From the simulating results, it is shown that the proposed algorithm offers high tracking performance and that it increases the output power level of a SPV system under partial shading. The algorithm also verified experimentally on various PSCs.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.2
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• pp.151-157
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• 2013

In this paper, properties of pattern using LBG (Linde-Buzo-Gray) Algorithm was explored including the exactness of K-means algorithm and process time of EM (Expectation Maximization) algorithm in order to develop analysis algorithm of partial discharge pattern in a cable using acoustic data analysis system. Partial discharge was measured by generating inner fault due to lamination of XLPE which is used for cable insulation material. Discharge pattern was analysed by changing the number of swarm article to 2, 4, and 6 in order to interpret swarm structure and properties.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.451-451
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• 2000

This study aims for the optimization of process conditions in a fixed-bed catalytic reactor system with a circulating molten salt bath, in which partial oxidation of propylene to acrolein takes place. Two-dimensional pseudo-homogeneous model is adopted with estimation of suitable parameters and its validity is corroborated by comparing simulation result with experimental data. The temperature of the molten salt and the feed composition are found to exercise significant influence on the yield of acrolein and the magnitude of hot spot. The temperature of the molten salt is usually kept constant. This study, however, suggests that the temperature of the molten salt must be axially adjusted so that the abrupt peak of hot spot should not appear near the reactor entrance. The yield of acrolein is maximized and the position and the magnitude of hot spot are optimized by the method of the iterative dynamic programming (IDP).