• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2396-2402
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• 2015

Air compressor, as an essential equipment used in the factory and plant operations, accounts for around 30% of the total electricity consumption in U.S.A, thereby being proposed advanced technologies to reduce electricity consumption. When the fluctuation of the compressed airflow rate is small, the system stability is increased followed by the reduction of the electricity consumption which results in the efficient design of the energy system. In the statistical analysis, the normal distribution, log normal distribution, gamma distribution or the like are generally used to identify system characteristics. However a single distribution may not fit well the data with long tail, representing sudden air flow rate especially in extremes. In this paper, authors decouple the compressed airflow rate into two parts to present a mixture of distribution function and suggest a method to reduce the electricity consumption. This reduction stems from the fact that a general pareto distribution estimates more accurate quantile value than a gaussian distribution when an airflow rate exceeds over a large number.

• Journal of the Korean Vacuum Society
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• v.9 no.4
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• pp.328-334
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• 2000

DLC (Diamond-Like Carbon) films were deposited by ECR-PECVD (electron cyclotron resonance plasma-enhanced chemical vapor deposition) method with the variation of substrate bias voltage under the others are constant except it. We have investigated the ion bombardment effect induced by the substrate bias voltage on films during the deposition of film. The characteristics of the film were analyzed using the Dektak surface profiler, SEM, FTIR spectroscopy, Raman spectroscopy and Nano Indentation tester. FTIR spectroscopy analysis shows that the amount of dehydrogenation in films was increased with the increase of substrate bias voltage and films thickness was decreased. Raman scattering analysis shows that integrated intensity ratio $(I_D /I_G)$ of the D and G peak was increased as the substrate bias voltage increased, and films hardness was increased. From these results, it can be concluded that films deposited at this experimental have the enhanced characteristics of DLC because of the ion bombardment effect on films during the deposition of film.

• Smart Structures and Systems
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• v.25 no.1
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• pp.65-80
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• 2020

In order to protect a structure over its full life cycle, a novel tuned mass damper (TMD), the so-called semi-active eddy current pendulum tuned mass damper (SAEC-PTMD), which can retune its frequency and damping ratio in real-time, is proposed in this study. The structural instantaneous frequency is identified through a Hilbert-Huang transformation (HHT), and the SAEC-PTMD pendulum is adjusted through an HHT-based control algorithm. The eddy current damping parameters are discussed, and the relationship between effective damping coefficients and air gaps is fitted through a polynomial function. The semi-active eddy current damping can be adjusted in real-time by adjusting the air gap based on the linear-quadratic-Gaussian (LQG)-based control algorithm. To verify the vibration control effect of the SAEC-PTMD, an idealized linear primary structure equipped with an SAEC-PTMD excited by harmonic excitations and near-fault pulse-like earthquake excitations is proposed as one of the two case studies. Under strong earthquakes, structures may go into the nonlinear state, while the Bouc-Wen model has a wild application in simulating the hysteretic characteristic. Therefore, in the other case study, a nonlinear primary structure based on the Bouc-Wen model is proposed. An optimal passive TMD is used for comparison and the detuning effect, which results from the cumulative damage to primary structures, is considered. The maximum and root-mean-square (RMS) values of structural acceleration and displacement time history response, structural acceleration, and displacement response spectra are used as evaluation indices. Power analyses for one earthquake excitation are presented as an example to further study the energy dissipation effect of an SAECPTMD. The results indicate that an SAEC-PTMD performs better than an optimized passive TMD, both before and after damage occurs to the primary structure.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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• pp.135-144
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• 2004

In this paper, we propose competitive fuzzy polynomial neurons-based advanced Self-Organizing Neural Networks(SONN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. The proposed SONN dwells on the ideas of fuzzy rule-based computing and neural networks. And it consists of layers with activation nodes based on fuzzy inference rules and regression polynomial. Each activation node is presented as Fuzzy Polynomial Neuron(FPN) which includes either the simplified or regression polynomial fuzzy inference rules. As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership (unction are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SONN architectures, that is, the basic and modified one with both the generic and the advanced type. Here the basic and modified architecture depend on the number of input variables and the order of polynomial in each layer. The number of the layers and the nodes in each layer of the SONN are not predetermined, unlike in the case of the popular multi-layer perceptron structure, but these are generated in a dynamic way. The superiority and effectiveness of the Proposed SONN architecture is demonstrated through two representative numerical examples.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.2
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• pp.70-76
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• 2012

In this paper, an automatic extraction method of glomerulus region from human renal tissue image is presented. The important information reflecting the state of kidneys richly included in the glomeruli, so it should be the first step to extract the glomerulus region from the renal tissue image for the further quantitative analysis of the renal condition. Especially, there is no clear difference between the glomerulus and other tissues, so the glomerulus region can not be easily extracted from its background by the existing segmentation methods. The outer edge of a glomerulus region is regarded as a common property for the regions of this kind ; a two- dimensional Gaussian distribution is used to convolve with an original image first and then the image is thresholded at this blurred image ; a closed curve corresponding to the outer edge can be obtained by usual pattern processing skills like thinning, branch-cutting, hole-filling etc., Finally, the glomerulus region can be obtained by extracting the area in the original image surrounded by the closed curve. The glomerulus regions are correctly extracted by 85 percentages and experimental results show the proposed method is effective.

• Korean Journal of Optics and Photonics
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• v.14 no.6
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• pp.613-622
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• 2003

The multi-configurative microscopic system for inspecting the wire-bonding of reed frame is designed. Rays refracted by objective lens group which is composed of common lens group of x2 and x6 are splitted by beam-splitter, and Rays through the central region and the boundary region of the object imaged at x2 and x6 through imaging lens groups, respectively. The depth of wire structure on the reed frame has about $\pm$3 mm, in order to observe by uniform magnification without the dependency on the variation of objective distance generated by the depth of wire structure on the reed frame, imaging lens groups should be moved on nonlinear locus like mechanically compensated zoom lenses. The nonlinear equations for zoom locus are derived by using the Gaussian bracket. Refraction powers and positions of each groups are numerically determined by solving the equations, and initial design data for each groups is obtained by using Seidel third order aberration theory. The optimization technique is finally utilized to obtain this microscopic system.

• Smart Structures and Systems
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• v.23 no.1
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• pp.1-14
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• 2019

Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift ($J_1$) compared to the LQG controller; 30 and 39% reductions in $J_1$ compared to the COC controller and 3 and 16% reductions in $J_1$ compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.33-41
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• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.

• Journal of The Korean Astronomical Society
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• v.31 no.1
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• pp.1-17
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• 1998

To understand the basic physics underlying large spatial fluctuations of intensity and Doppler shift, we have investigated the dynamical charctersitics of the transition region of the quiet sun by analyzing a raster scan of high resolution UV spectral band containing H Lyman lines and a S VI line. The spectra were taken from a quiet area of $100'\times100'$ located near the disk center by SUMER on board SOHO. The spectral band ranges from 906 A to 950 A with spatial and spectral resolution of 1v and $0.044 {\AA}$, respectively. The parameters of individual spectral lines were determined from a single Gaussian fit to each spectral line. Then, spatial correlation analyses have been made among the line parameters. Important findings emerged from the present analysis are as follows. (1) The integrated intensity maps of the observed area of H I 931 line $(1\times10^4 K)$ and S VI 933 line $(2\times10^5 K)$ look very smilar to each other with the same characterstic size of 5". An important difference, however, is that the intensity ratio of brighter network regions to darker cell regions is much larger in S VI 933 line than that in H I 931 line. (2) Dynamical features represented by Doppler shifts and line widths are smaller than those features seen in intensity maps. The features are found to be changing rapidly with time within a time scale shorter than the integration time, 110 seconds, while the intensity structure remains nearly unchanged during the same time interval. (3) The line intensity of S VI is quite strongly correlated with that of H I lines, but the Doppler shift correlation between the two lines is not as strong as the intensity correlation. The correlation length of the intensity structure is found to be about 5.7' (4100 km), which is at least 3 times larger than that of the velocity structure. These findings support the notion that the basic unit of the transition region of the quiet sun is a loop-like structure with a size of a few $10^3 km$, within which a number of unresolved smaller velocity structures are present.

• Journal of the Korean Institute of Telematics and Electronics
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• v.12 no.2
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• pp.1-10
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• 1975

This paper presents an adaptive data compression algorithm for video data. The coling complexity due to the high correlation in the given data sequence is alleviated by coding the difference data, sequence rather than the data sequence itself. The adaptation to the nonstationary statistics of the data is confined within a code set, which consists of two constant length cades and six modified Shannon.Fano codes. lt is assumed that the probability distributions of tile difference data sequence and of the data entropy are Laplacian and Gaussion, respectively. The adaptive coding performance is compared for two code selection criteria: entropy and $P_r$[difference value=0]=$P_0$. It is shown that data compression ratio 2 : 1 is achievable with the adaptive coding. The gain by the adaptive coding over the fixed coding is shown to be about 10% in compression ratio and 15% in code efficiency. In addition, $P_0$ is found to he not only a convenient criterion for code selection, but also such efficient a parameter as to perform almost like entropy.