• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5765-5781
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• 2018

Extreme learning machine (ELM) is emerging as a powerful machine learning method in a variety of application scenarios due to its promising advantages of high accuracy, fast learning speed and easy of implementation. However, how to select the optimal hidden layer of ELM is still an open question in the ELM community. Basically, the number of hidden layer nodes is a sensitive hyperparameter that significantly affects the performance of ELM. To address this challenging problem, we propose to adopt multiple kernel learning (MKL) to design a multi-hidden-layer-kernel ELM (MHLK-ELM). Specifically, we first integrate kernel functions with random feature mapping of ELM to design a hidden-layer-kernel ELM (HLK-ELM), which serves as the base of MHLK-ELM. Then, we utilize the MKL method to propose two versions of MHLK-ELMs, called sparse and non-sparse MHLK-ELMs. Both two types of MHLK-ELMs can effectively find out the optimal linear combination of multiple HLK-ELMs for different classification and regression problems. Experimental results on seven data sets, among which three data sets are relevant to classification and four ones are relevant to regression, demonstrate that the proposed MHLK-ELM achieves superior performance compared with conventional ELM and basic HLK-ELM.

• Journal of Industrial Convergence
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• v.16 no.2
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• pp.33-39
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• 2018

This paper describes the design method of the linear PID controller and proposed the design method in the future. The first PID design method is to ensure phase margin and gain margin. This method guarantees stability by designing in the frequency domain. The second method is an internal model control method. This method is to design the PID controller using the parameters of the internal model after identifying the internal model for the control model. Therefore, this method has a strong disturbance characteristic. Finally, a proposed Cascade and smith-Predictor controller. The combination of the cascade controller and the smith-predator of this method is a controller structure that has two advantages: robust control and optimal control. This method can obtain the performance evaluation index as the optimal controller design method. This PID controller design method becomes the basis of the nonlinear method and is being continuously studied.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Computational Structural Engineering
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• v.10 no.2
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• pp.233-242
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• 1997

The main purpose of this paper is to investigate the dynamic optimal design of continuous beams. The computer-aided optimization technique is used to obtain the near-optimal parameters of continuous beam. The computer program is developed to obtain the natural frequency parameters and the forced vibration responses to a transit point load for the continuous beam with variable support spacing, mass and stiffness. The model test data is in good agreement with the computer calculation, which serves to validate the mathematical analysis. The optimization function to describe the design efficiency is defined as a linear combination of four dimensionless span characteristics; the maximum dynamic stress; the stress difference between span segments; the rms deflection under the transit point load; and the total span mass. Studies of three span beams show that the beam with near-optimal parameters can improve design efficiency when compared to a uniform beam with even spacing of the same total span length.

• Journal of the Optical Society of Korea
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• v.19 no.1
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• pp.55-62
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• 2015

In this study we demonstrate ultrahigh-resolution spectral domain optical coherence tomography (UHR SD-OCT) with a linear-wavenumber (k) spectrometer, to accelerate signal processing and to display two-dimensional (2-D) images in real time. First, we performed a numerical simulation to find the optimal parameters for the linear-k spectrometer to achieve ultrahigh axial resolution, such as the number of grooves in a grating, the material for a dispersive prism, and the rotational angle between the grating and the dispersive prism. We found that a grating with 1200 grooves and an F2 equilateral prism at a rotational angle of $26.07^{\circ}$, in combination with a lens of focal length 85.1 mm, are suitable for UHR SD-OCT with the imaging depth range (limited by spectrometer resolution) set at 2.0 mm. As guided by the simulation results, we constructed the linear-k spectrometer needed to implement a UHR SD-OCT. The actual imaging depth range was measured to be approximately 2.1 mm, and axial resolution of $3.8{\mu}m$ in air was achieved, corresponding to $2.8{\mu}m$ in tissue (n = 1.35). The sensitivity was -91 dB with -10 dB roll-off at 1.5 mm depth. We demonstrated a 128.2 fps acquisition rate for OCT images with 800 lines/frame, by taking advantage of NVIDIA's compute unified device architecture (CUDA) technology, which allowed for real-time signal processing compatible with the speed of the spectrometer's data acquisition.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1309-1314
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• 2012

The SAMED filter is introduced as a wide class of multi-stage filters which encompass linear FIR and nonlinear order statistic filters. The output of SAMED filter is linear combination of sub-median outputs. In this paper, optimal SAMED filter is designed for images corrupted by various noise, and performance is analogized. The experimental result shows that the efficiency of each order of SAMED filters is depends on type of noise. It is shown that low order filters are effective in Gaussian environments but high order filters are effective in impulsive case. This result may be used to follow-up research on successive SAMED filters.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.155-161
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• 2019

We introduces a mobile robot that can navigate on a power transmission line arranged in bundled conductors. The designs of the proposed robot are performed for navigation on bundled conductors, and the navigation method for bundled conductors and obstacle avoidance are presented. The robot consists of 13 degrees of freedom (DOF) with a symmetrical structure for the left and right parts, including the four wheel joints. The navigation method is designed using a combination of three motion primitives such as linear motion of counterbalancing box, linear motion of robot arm, and rotational motion of wheel part. To examine the performance of the proposed robot, navigation simulations are conducted using $ADAMS^{TM}$. The robot navigations were simulated on obstacle environments that consisted of two- and four-conductor bundles. Based on the simulation results, the performance of the proposed robot was reviewed through the analysis of the trajectories of end-effectors. We confirmed that the proposed robot was capable of achieving optimal navigation on bundled conductors that included obstacles.

• The KSFM Journal of Fluid Machinery
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• v.10 no.2 s.41
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• pp.7-15
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• 2007

A numerical procedure for optimizing the shape of three-dimensional channel with V-shaped ribs extruded on both walls has been carried out to enhance the turbulent heat transfer. The response surface based optimization is used as an optimization technique with Reynolds-averaged Wavier-stoked analysis. Shear stress transport (SST) turbulence model is used as a turbulence closure. Computational results for average heat transfer rate show good agreements with experimental data. The objective function is defined as a linear combination of heat transfer and friction loss-related terms with a weighting factor. Three dimensionless variables such as, rib pitch-to-rib height ratio, rib height-to-channel height ratio, and the attack angle of the rib are chosen as design variables. Nineteen training points obtained by D-optimal designs for three design variables construct a reliable response surface. In the sensitivity analysis, it is found that the objective function is most sensitive to the ratio of rib height-to-channel height ratio. And, optimal values of design variables have been obtained in a range of the weighting factor.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.8
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• pp.663-671
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• 2007

In this work, shape optimization of a wire-wrapped fuel assembly in a liquid metal reactor has been carried out by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial basis neural network method, a well known surrogate modeling technique for optimization. Sequential Quadratic Programming is used to search the optimal point from the constructed surrogate. Two geometric design variables are selected for the optimization and design space is sampled using Latin Hypercube Sampling. The optimization problem has been defined as a maximization of the objective function, which is as a linear combination of heat transfer and friction loss related terms with a weighing factor. The objective function value is more sensitive to the ratio of the wire spacer diameter to the fuel rod diameter than to the ratio of the wire wrap pitch to the fuel rod diameter. The optimal values of the design variables are obtained by varying the weighting factor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1425-1440
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• 2017

In wireless communications, antenna selection (AS) is a widely used method for reducing comparable cost of multiple RF chains in MIMO systems. As is well known, most of literatures on combining AS with MIMO techniques concern linear modulations such as phase shift keying (PSK) and quadrature amplitude modulation (QAM). The combination of CPM and MIMO has been considered an optimal choice that can improve its capacity without loss of power and spectrum efficiency. The aim of this paper is to investigate joint transmit and receive antenna selection (JTRAS) in CPM MIMO systems. Specifically, modified incremental and decremental JTRAS algorithms are proposed to adapt to arbitrary number of selected transmit or receive antennas. The computational complexity of several JTRAS algorithms is analyzed from the perspective of channel capacity. As a comparison, the performances of bit error rate (BER) and spectral efficiency are evaluated via simulations. Moreover, computational complexity of the JTRAS algorithms is simulated in the end. It is inferred from discussions that both incremental JTRAS and decremental JTRAS perform close to optimal JTRAS in BER and spectral efficiency. In the sense of practical scenarios, adaptive JTRAS can be employed to well tradeoff performance and computational complexity.