• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.12
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• pp.54-61
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• 1997

An accurate extraction technique is developed to determine full euqivalent circuit parameters of Si MOSFETs using 1 set of measured S-parametes without complicated optimization process. This technique is based on the use of anlytic Z-parameters experessions for resistances and inductances and the Y-parameter ones for ntrinsic parameters. This accuracy is proved over the wide range of gate voltage by observing good agreement between measured and fitted Z-parameter equations and frequency-independent response of the extracted intrinsic parameters. Using this technique, gate voltage-dependencies of model parameters are obained in the saturation region and these results show the similar behavior to the short-channel effects expected from the device theory.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.4
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• pp.153-158
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• 2017

When considering the proper function and life cycle length of a product, its surface finish plays an important role. This experimental study was carried out to understand the effect of input factors on surface roughness and how it can be minimized by controlling the input parameters. This experimental work was performed by machining the surface of STD 61 blocks with a surface inclined at $30^{\circ}$ by ball end-milling and optimizing the input parameters using the Taguchi technique. Signal-to-Noise (S/N) ratio and analysis of variance (ANOVA) were applied to find the significance of the input parameters. The optimum level of input parameters to minimize surface roughness was obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2389-2413
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• 2015

High data rates in long-term evolution (LTE) networks can affect the mobility of networks and their performance. The speed and motion of user equipment (UE) can compromise seamless connectivity. However, a proper handover (HO) decision can maintain quality of service (QoS) and increase system throughput. While this may lead to an increase in complexity and operational costs, self-optimization can enhance network performance by improving resource utilization and user experience and by reducing operational and capital expenditure. In this study, we propose the self-optimization of HO parameters based on fuzzy logic control (FLC) and multiple preparation (MP), which we name FuzAMP. Fuzzy logic control can be used to control self-optimized HO parameters, such as the HO margin and time-to-trigger (TTT) based on multiple criteria, viz HO ping pong (HOPP), HO failure (HOF) and UE speeds. A MP approach is adopted to overcome the hard HO (HHO) drawbacks, such as the large delay and unreliable procedures caused by the break-before-make process. The results of this study show that the proposed method significantly reduces HOF, HOPP, and packet loss ratio (PLR) at various UE speeds compared to the HHO and the enhanced weighted performance HO parameter optimization (EWPHPO) algorithms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.27-38
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• 2006

The storage function model (SFM) has been used for the flood forecasting in Korea. The SFM has a simple calculation process and it is known that the model is more reasonable than linear model because it considers non-linearity of flood runoff. However, the determination of parameters is very difficult. In general, the trial and error method which is an manual calibration by the decision of a model manager. This study calibrated the parameters by the trial and error method and optimization technique. The calibrated parameters were compared with the representative parameters which are used in the Flood Control Centers in Korea. Also, the evaluation indexes on objective functions and calibration methods for the comparative analysis of simulation efficiency. As a result, the Genetic Algorithm showed the smallest variation in objective functions and, in this study, it is known that the objective function of SSR (Sum of Squared of Residual) is the best one for the flood forecasting.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.31-31
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• 1999

In the preliminary design stage of an automotive interior, human sensibility is first analyzed and applied to design parameters for satisfying consumers needs using optimization and engineering judgement. Then designers try to design components that meet these needs using empirical and trial-and-error procedures. This process usually yields poor results because it is difficult to find a feasible design that satisfies the targets by trial-and-error (a feasible design is one that satisfies consumers needs and design constraints). To improve this process, we need tools to link the human sensibility with the design parameters that define the geometry of the components of an automotive interior. A methodology is presented for developing a tool for design guidance of an automotive interior. This tool translates the human sensibility into the design parameters that define the geometry of the components of an automotive interior. This tool, called interpreter, rapidly predicts the human sensibility of a given automotive interior and presents design parameters that meet or exceed given human sensibility to satisfy consumers needs and design constraints. The methodology is demonstrated on the interior design of an actual automotive.

• Journal of Power System Engineering
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• v.13 no.5
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• pp.89-94
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• 2009

In this work, the optimal process has been designed by $L_9(3^4)$ orthogonal array and analysis of variance(ANOVA) for thermal-sprayed Ni-based hard coating. Ni-based hard coatings were fabricated by flame spray process on steel substrate. Then, the hardness test and observation of microstructure of the coatings were performed. The results of hardness test were analyzed by ANOVA. The ANOVA results demonstrated that the acetylene gas flow had the greatest effect on hardness of the coatings. The oxygen gas flow was found to have a neglecting effect. From these results, the optimal combination of the flame spray parameters could be predicted. The calculated hardness of the coatings by ANOVA was found to lie close to that of confirmation experimental result. Thus, it was considered that design of experiments design using orthogonal array and ANOVA was useful to determine optimal process of thermal-sprayed Ni-based hard coating.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.10a
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• pp.1-8
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• 1995

This work presents a new method to deal with buckling constraints. The mathematical optimization process of truss structures proposed earlier by the author has been proved to be the most rigorous method. The inclusion of buckling constraints, however, gives rise to a new problem The allowable compression stress of a member changes from one design iteration to another. This changing stress limit creates a good deal of noise in selecting active constraints and makes the solution process unstable. This problem can be overcome by introducing relaxation parameters. This work, however, aims at establishing a more rigorous method by containing the allowable compression stress in the left hand side of the associated constraint.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.5
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• pp.23-29
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• 2013

Recent advances in printing technology have increased the productivity of the roll-to-roll (R2R) printing process for printed circuitry. In the R2R printed electronics, characteristics of printed and coated layers are one of the most important issues that determine the functional quality of final products. The slot-die technology can coat a large area with high uniformity using low-viscosity materials; determining the process parameters is important to obtain excellent coating qualities. In this study, a viscocapillary model was used to predict qualities of coated layers and patterns. On the basis of analysis results, a novel meta model was derived using design of experiment methodology to improve accuracy. Sensitivity analysis was performed to define major parameters in R2R slot-die coating process. The coating speed was found to most significantly affect the coated layer thickness and was easily controlled. The performance of the proposed model is verified through experimental studies. Based on the statistical analysis results, R2R slot die process can be optimized to guarantee a desired thickness.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2709-2716
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• 2020

Compositions of large nuclear cores (e.g. boiling water reactors) are highly heterogeneous in terms of fuel composition, control rod insertions and flow regimes. For this reason, they usually lack high order of symmetry (e.g. 1/4, 1/8) making it difficult to estimate their neutronic parameters for large spaces of possible loading patterns. A detailed hyperparameter optimization technique (a combination of manual and Gaussian process search) is used to train and optimize deep neural networks for the prediction of three neutronic parameters for the Ringhals-1 BWR unit: power peaking factors (PPF), control rod bank level, and cycle length. Simulation data is generated based on half-symmetry using PARCS core simulator by shuffling a total of 196 assemblies. The results demonstrate a promising performance by the deep networks as acceptable mean absolute error values are found for the global maximum PPF (~0.2) and for the radially and axially averaged PPF (~0.05). The mean difference between targets and predictions for the control rod level is about 5% insertion depth. Lastly, cycle length labels are predicted with 82% accuracy. The results also demonstrate that 10,000 samples are adequate to capture about 80% of the high-dimensional space, with minor improvements found for larger number of samples. The promising findings of this work prove the ability of deep neural networks to resolve high dimensionality issues of large cores in the nuclear area.