• Proceedings of the KIEE Conference
• /
• 2006.11a
• /
• pp.342-344
• /
• 2006

The modem enormous electric power system has made power system analysis much more complex and difficult. For effective analysis of the power system, model reduction and aggregation is required. In this paper, a new aggregation method is presented to aggregate the coherent generators in the large scale power system while matching the power flow. In order to demonstrate the effects of this aggregation method, it is applied to a small scale power system. A multi-variable control technique of $H_{\infty}$ control is also applied to enhance the dynamic stability of the aggregated power system.

• International journal of advanced smart convergence
• /
• v.6 no.3
• /
• pp.17-21
• /
• 2017

In this paper, the service execution accuracy was compared by ontology based rule inference method and machine learning method, and the amount of data at the point when the service execution accuracy of the machine learning method becomes equal to the service execution accuracy of the rule inference was found. The rule inference, which measures service execution accuracy and service execution accuracy using accumulated data and pattern matching on service results. And then machine learning method measures service execution accuracy using cross validation data. After creating a confusion matrix and measuring the accuracy of each service execution, the inference algorithm can be selected from the results.

• Journal of information and communication convergence engineering
• /
• v.16 no.3
• /
• pp.160-165
• /
• 2018

This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program.

• Proceedings of the KIEE Conference
• /
• 2004.07d
• /
• pp.2191-2193
• /
• 2004

The Sliding Mode Control is more robust and give the better performance than the $H_{\infty}$ control if the matching condition is satisfied. So in this paper, a controller which can have the advantages of $H_{\infty}$ control and the SMC is proposed to add the robustness of the SMC to the $H_{\infty}$ controller. The dynamic of proposed sliding surface is the same dynamic as the system controlled by $H_{\infty}$ controller without the uncertainties which satisfy the matching condition.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.15 no.4 s.97
• /
• pp.382-387
• /
• 2005

For successful guided-wave damage inspection, the appropriate signal processing of measured wave signals is very important. The objective of this paper is to introduce an efficient signal processing technique especially suitable for the guided-waves used for damage detection. The key idea of this technique is to model guided-waves by chirp functions of special form considering the dispersion phenomenon. To determine the parameter of the chirp functions simulating guided-waves, the matching pursuit algorithm is employed. The damage information in waveguides can be extracted by pulse-characterizing parameters. The effectiveness of present method is checked with the guided wave-based damage inspection.

• Journal of Broadcast Engineering
• /
• v.17 no.4
• /
• pp.634-639
• /
• 2012

In this paper, we propose nonlinear optimization method for feature matching from multiple view image. Typical solution of feature matching is by solving linear equation. However this solution has large error due to nonlinearity of image formation model. If typical nonlinear optimization method is used, complexity grows exponentially over the number of features. To make complexity lower, we use sparse Levenberg-Marquardt nonlinear optimization for matching of features over multiple view image.

• The Journal of Engineering Research
• /
• v.2 no.1
• /
• pp.63-71
• /
• 1997

In this paper, we describe an approach that determines the identity of surfaces of an object with planar and curved surfaces from range data of the object in the scene. The proposed matching scheme presents that surface correspondence of an object is achieved by simple comparison of values for representing surfaces of the object with model in order to avoid unnecessary matching procedures. We use uniquely assigned Surface Representing Value(SRV) for representing surfaces of the object, which are sums of all weighted view-point independent features. And, the proposed method is simple, quite effective and insensitive to occlusion and noise in sensor data.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.12
• /
• pp.2094-2102
• /
• 2016

In this paper, we propose an extended Kalman filter(EKF)-based simultaneous localization and mapping(SLAM) method using laser corner pattern matching for mobile robots. SLAM is one of the most important problems of mobile robot. However, existing method has the disadvantage of increasing the computation time, depending on the number of landmarks. To improve computation time, we produce the corner pattern using classified and detected corner points. After producing the corner patterns, it is estimated that mobile robot's global position by matching them. The estimated position is used as measurement model in the EKF. To evaluated proposed method, we preformed the experiments in the indoor environments. Experimental results of proposed method are shown to maintain an accuracy and decrease the computation time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.7
• /
• pp.1633-1641
• /
• 1996

In this paper we propose a new method for anlayzing the properties of surface reflectance and reconstructing the shape of object using estimated reflectance parameters. We have investigated the hybrid reflectance surface which has specularreflection and diffuse reflection, which can be explained by Torrance-Sparrow model. Sample sphere made on one maerial is used to estimate the reflectance properties by using LMS algorithm. We can make the reference image which consists of surface normal and brightness value using estimated reflectance parameters, and thenarbitrary shape object made of the same material as sample can be reconstructed by matching with reference image. Photometric matching method proposed in this paper is robust because it mateches object image with the reference imageconsidering its neighbor brightness distribution. Also, in this paper plate diffuse illumination is used to remove intensity disparity with simple scheme. It is expected that the proposed algorithm can be applied to 3D recognition, vision inspection system and other fields.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.3
• /
• pp.601-614
• /
• 2009

This paper derives noninformative priors for scale parameter of exponential distribution when the data are collected in multiple step stress accelerated life tests. We nd the objective priors for this model and show that the reference prior satisfies first order matching criterion. Also, we show that there exists no second order matching prior. Some simulation results are given and using artificial data, we perform Bayesian analysis for proposed priors.