• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.242-246
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• 2017

In this study, it was investigated about optical simulation in high brightness and high uniformity general lighting using glass light guide plate. And we adopt edge-light emission type light plate. Edge-light type lighting has been used LCD application, especially note PC or smart phone backlight unit. Because it had the good properties such as slim shape and light weight. We thought this type was suitable for general lighting application such as wall attached type or ceiling mount type. But many of edge-light type lighting had problems. It called slanted output light rays. That was main key parameter how could control the direction of output light rays. We investigated the solution of this problems, using ray tracing method, we recognized the major fact of the solution relied on the geometric structure of diffusing dot shape. We set the conditions of aspect ratio in diffusing dot shape such as 0.5 to 1. And, at first, we designed diffusing dots shape based on the results of optical simulation and made specimen. as above condition, and acquired good result in confirming dots shape such as the value of the output rays's peak angle was around 75 degrees. And good light distribution characteristics were measured by slated spectro-radiometer. It was shown that the effective ways of designing light distribution characteristics using optical simulation such as ray tracing linear method for making general lighting using glass light guide plate.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.257-267
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• 2017

Compressed Sensing(CS) deals with linear inverse problems. The theoretical results of CS have had an impact on inference problems and presented amazing research achievements in the related fields including signal processing and information theory. However, in order for CS to be applied in practical environments, there are two significant challenges to be solved. One is to guarantee in real time recovery of CS signals, and the other is that the signals have to be sparse. To this end, the latest researches using deep learning technology have emerged. In this paper, we consider CS problems based on deep learning and discuss the latest research results. And the approaches for CS signal reconstruction using deep learning show superior results in terms of recovery time and performance. It is expected that the approaches for CS reconstruction using deep learning shown in recent studies can not only raise the possibility of utilization of CS, but also be highly exploited in the fields of signal processing and communication areas.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.574-580
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• 2015

The stability condition of linear discrete interval systems with a time-varying delay time is considered. The considered system has interval system matrices for both non-delayed and delayed states with time-varying delay time within given interval values. The proposed condition is derived by using Lyapunov stability theory and expressed by very simple inequality. Compared to previous results, the stability issue on the interval systems is expanded to time-varying delay. Furthermore, the new condition can imply the existing results on the time-invariant case and show the relation between interval time-varying delay time and stability of the system. The proposed condition can be applied to find the stability bound of the discrete interval system. Some numerical examples are given to show the effectiveness of the new condition and comparisons with the previously reported results are also presented.

• Journal of Navigation and Port Research
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• v.31 no.8
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• pp.697-703
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• 2007

In this paper, we focuses on the development of Takagi-Sugeno (T-S) fuzzy modeling in a nonlinear container crane system. A T-S fuzzy model is characterized by fuzzy "if-then" rules which represent the locally input-output relationship whose consequence part is described by a state space equation as subsystem. The T-S fuzzy model in container cranes first obtains a few number of linear models according to operation conditions and blends these conditions using fuzzy membership functions. Parameters of the membership functions are adjusted by a RCGA to have same dynamic characteristics with nonlinear system of a container crane. Simulations are given to illustrate the performance of T-S fuzzy model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.189-194
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• 2020

This paper analyzes the gradient descent method, which is the one most used for learning neural networks. Learning means updating a parameter so the loss function is at its minimum. The loss function quantifies the difference between actual and predicted values. The gradient descent method uses the slope of the loss function to update the parameter to minimize error, and is currently used in libraries that provide the best deep learning algorithms. However, these algorithms are provided in the form of a black box, making it difficult to identify the advantages and disadvantages of various gradient descent methods. This paper analyzes the characteristics of the stochastic gradient descent method, the momentum method, the AdaGrad method, and the Adadelta method, which are currently used gradient descent methods. The experimental data used a modified National Institute of Standards and Technology (MNIST) data set that is widely used to verify neural networks. The hidden layer consists of two layers: the first with 500 neurons, and the second with 300. The activation function of the output layer is the softmax function, and the rectified linear unit function is used for the remaining input and hidden layers. The loss function uses cross-entropy error.

• Analytical Science and Technology
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• v.7 no.3
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• pp.261-269
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• 1994

The iodide formed stoichiometrically for sulfide by its oxidation with iodate was extracted as an ion-pair with methylene green into 1,2-dichloroethane and the extract was measured spectrophotometrically at 656nm for the determination of sulfide. Hydrogen sulfide separated from the sample matrix was introduced into a solution containing pH 3.5 acetate buffer and iodate, in which the hydrogen sulfide was completely converted into iodide. A linear calibration graph was obtained over the range $3{\times}10^{-7}{\sim}1.2{\times}10^{-5}M$ sulfide($0.0096{\sim}0.384{\mu}g$ of $S^{2-}/ml$) and the detection limit was $0.0032{\mu}g/ml$. The apparent molar absorptivity and a correlation coefficient(r) were $6.7{\times}10^4L\;mole^{-1}\;cm^{-1}$ and 0.999, respectively. When applied to the stream water samples, the proposed method gave a relative standard deviation of 1.59% at $5{\times}10^{-6}M$ sulfide level.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5446-5451
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• 2014

Because there are some mistakes by hand in processing electronic maps using a navigation terminal, this paper proposes an automatic offline recognition for traffic signs, which are considered ingredient navigation information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which have been used widely in the field of 2D face recognition as computer vision and pattern recognition applications, was used to recognize traffic signs. First, using PCA, a high-dimensional 2D image data was projected to a low-dimensional feature vector. The LDA maximized the between scatter matrix and minimized the within scatter matrix using the low-dimensional feature vector obtained from PCA. The extracted traffic signs under a real-world road environment were recognized successfully with a 92.3% recognition rate using the 40 feature vectors created by the proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2298-2304
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• 2017

Online signature verification is one of the simple and efficient method of identity verification and has less resistance than other biometric technologies. In training to build a verification model, negative samples are required to build the model, but in most practical applications it is not easy to get negative samples - forgery signatures. In this paper, proposed is a method using someone else's signatures as negative samples. In verification, shape-based features extracted from the time-sequenced signature data are extracted and a support vector machine is used to verify. SVM tries to map a feature vector to a high dimensional space and to draw a linear boundary in the high dimensional space. SVM is one of the best classifiers and has been applied to various applications. Using general handwriting data, i.e., someone else's signatures which have little in common with positive samples improved the verification rate experimentally, which means that signature verification without negative samples is possible.

• Journal of the Korea Society of Computer and Information
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• v.19 no.7
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• pp.1-8
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• 2014

Clustering is one of the most popular unsupervised learning methods, which is widely used to form clusters with homogeneous data. Clustering was used to extract contexts corresponding to clusters and a classification method was applied to each context or cluster individually. However, it is difficult to say that the unsupervised clustering is the best context forming method from the view of classification. In this paper, a new clustering method considering classification was proposed. The proposed method tries to minimize classification error in each cluster when a classification method is applied to each context locally. For this purpose, the proposed method adds constraints forcing two data points belong to the same class to have small distances, and two data points belong to different classes to have large distances in each cluster like in linear discriminant analysis. The usefulness of the proposed method is confirmed by experimental results.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.5
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• pp.77-85
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• 2001

The purpose of this study is to suggest Vergence Information Extracting Algorithm(VIEA) which enables quick and accurate vergence information achievement for automation of vergence and focus control of horizontal moving stereoscopic camera. Firstly, for this purpose, the geometric structure of horizontal moving stereoscopic camera device was analyzed and linear relation between the vergence and the focus control. Then stereoscopic camera was designed and produced with the application of vergence and focus relation formula. Finally, VIEA that uses Cepstrum filter was employed to implement Automatic Vergence and Focus Controlling Stereoscopic Camera System(AVFCSCS). VIEA showed lower vergence achievement time and error ratio in comparison with existing algorithms. The suggested system in this study substantially reduced the controlling time and error-ratio as to make it possible to achieve natural and clear images. It also simplified the handling of stereoscopic camera for the convenience of end-users.