• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.425-428
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• 1997

Effectiveness of corrective machining algorithm is verified experimentally in this paper by performing corrective lapping work to single side and double sides hydrostatic tables. Lapping is applied as machining method. Machining information is calculated from measured motion errors by applying the algorithm, without information on rail profile. It is possible to acquire 0.13pm of linear motion error, 1.40arcsec of angular motion error in the case of single side table, and 0.07pm of linear motion error, 1.42arcsec of angular motion error in the case of double sides table. The experiment is performed by the unskilled person after he experienced a little of preliminary machining. Experimental results show that corrective machining algorithm is very effective, and anyone can improve the accuracy of hydrostatic table by using the algorithm.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.72-78
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• 2000

In this study, we propose a model verification algorithm based on DCT and neural network for ATM security system. We construct database about facial images after capturing thirty persons facial images in the same lumination and distance. To simulate model verification, we capture four learning images and test images per a man. After detecting edge in facial images, we detect a characteristic area of square shape using edge distribution in facial images. Characteristic area contains eye bows, eyes, nose, mouth and cheek. We extract characteristic vectors to calculate diagonally coefficients sum after obtaining DCT coefficients about characteristic area. Characteristic vectors is normalized between +1 and -1, and then used for input vectors of neural networks. Not considering passwords, simulations results showed 100% verification rate when facial images were learned and 92% verification rate when facial images weren't learned. But considering passwords, the proposed algorithm showed 100% verification rate in case of two simulations.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.41-47
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• 2019

Defense simulation is actively used to test various weapon systems and evaluate their effectiveness. The AddSIM environment is a simulation framework designed to support the weapon systems dealt with in defense simulation from an integrated point of view and is designed for reuse and scalability. Models used in AddSIM require base model structure fidelity and verification of user code area. Therefore, this paper describes the dynamic verification method used for completeness of models used in AddSIM. For the dynamic verification of user code, the specification method and the verification algorithm are described. Also, we introduce the prototype of the dynamic verifier implemented based on verification specification method and algorithm. The case study analyzes the verification results based on the simulation example implemented in AddSIM environment.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.19-26
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• 2010

In this paper, we suggest a efficient handwritten character verification method in on-line environments which automatically analyses two input character string and computes their similarity degrees. The proposed algorithm first applies the circular projection method to input handwritten strings and extracts their representative features including shape, directions, etc. It then calculates the similarity between two character strings by using an improved dynamic time warping (DTW) algorithm. We improved the conventional DTW algorithm efficiently through adopting the branch-and-bound policy to the existing DTW algorithm which is well-known to produce good results in the various optimization problems. The experimental results to verify the performance of the proposed system show that the suggested handwritten character verification method operates more efficiently than the existing DTW and DDTW algorithms in terms of the speed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1786-1792
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• 2007

In this paper, we proposed a recognition system for resident registration cards using ART-1 and PCA algorithm. To extract registration numbers and issue date, Sobel mask and median filter are applied first and noise removal follows. From the noise-removed image, horizontal smearing is used to extract the regions, which are binarized with recursive binarization algorithm. After that vortical smearing is applied to restore corrupted lesions, which are mainly due to the horizontal smearing. from the restored image, areas of individual codes are extracted using 4-directional edge following algorithm and face area is extracted by the morphologic characteristics of a registration card. Extracted codes are recognized using ART-1 algorithm and PCA algorithm is used to verify the face. When the proposed method was applied to 25 real registration card images, 323 characters from 325 registration numbers and 166 characters from 167 issue date numbers, were correctly recognized. The verification test with 25 forged images showed that the proposed verification algorithm is robust to detect forgery.

• Journal of the Korean Society of Safety
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• v.30 no.4
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• pp.99-105
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• 2015

In this study, displacement estimation algorithm, which is not requiring an absolute reference point unlike the conventional displacement measurement method, is developed using the geophone. To estimate displacement of the bridge, measured velocity time signal is integrated in the frequency domain. And, the estimated displacement is compared with the measured result using a conventional method. Based on the dynamic field test results, it was found that the estimated displacement by the present algorithm is similar to that of a conventional method. The displacement estimation algorithm proposed in this paper can be effectively applied to measure the displacement of a structure, which is difficult to install a displacement transducer at the fixed point.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.391-398
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• 2006

This paper verifies deployment algorithms in wireless sensor networks using SPIN, a widely used model checking tool. In this paper, two deployment algorithms, DSSA(Distributed Self Spreading Algorithm) and TBDA(Tree Based Deployment Algorithm), are verified to check their stability against oscillation as well as energy consumption that is an important factor in wireless sensor networks.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.859-868
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• 2012

A new fault location algorithm based on stationary wavelet transform and its verification experiment results are described for HVDC submarine cables in this paper. For wavelet based fault location algorithm, firstly, 4th level approximation coefficients decomposed by wavelet transform function are superimposed by correlation, then the distance to the fault point is calculated by time delay between the first incident signal and the second reflected signal. For the verification of this algorithm, the real experiments based on various fault conditions and return types of fault current are performed at HVDC submarine cable test yard located in KEPCO(Korea Electric Power Corporation) Power Testing Center of South Korea. It proves that the fault location method proposed in this paper is very simple but very quick and accurate for HVDC submarine cable fault location.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.3
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• pp.67-72
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• 2010

This paper presents biometrical driver verification system in car experiment through analysis of speech, and face information. We have used Mel-scale Frequency Cesptral Coefficients (MFCCs) for speaker verification using speech information. For face verification, face region is detected by AdaBoost algorithm and dimension-reduced feature vector is extracted by using principal component analysis only from face region. In this paper, we apply the extracted speech- and face feature vectors to an SVM kernel with Gaussian Mixture Models(GMM) supervector. The experimental results of the proposed approach show a clear improvement compared to a simple GMM or SVM approach.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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• pp.1099-1104
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• 2013

This paper presents a learning-based visual inspection method that addresses the need for an improved adaptability of a visual inspection system for parts verification in panorama sunroof assembly lines. It is essential to ensure that the many parts required (bolts and nuts, etc.) are properly installed in the PLC sunroof manufacturing process. Instead of human inspectors, a visual inspection system can automatically perform parts verification tasks to assure that parts are properly installed while rejecting any that are improperly assembled. The proposed visual inspection method is able to adapt to changing inspection tasks and environmental conditions through an efficient learning process. The proposed system consists of two major modules: learning mode and test mode. The SVM (Support Vector Machine) learning algorithm is employed to implement part learning and verification. The proposed method is very robust for changing environmental conditions, and various experimental results show the effectiveness of the proposed method.