• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.17-31
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• 2006

In this paper, passport recognition and face verification methods which can automatically recognize passport codes and discriminate forgery passports to improve efficiency and systematic control of immigration management are proposed. Adjusting the slant is very important for recognition of characters and face verification since slanted passport images can bring various unwanted effects to the recognition of individual codes and faces. Therefore, after smearing the passport image, the longest extracted string of characters is selected. The angle adjustment can be conducted by using the slant of the straight and horizontal line that connects the center of thickness between left and right parts of the string. Extracting passport codes is done by Sobel operator, horizontal smearing, and 8-neighborhood contour tracking algorithm. The string of codes can be transformed into binary format by applying repeating binary method to the area of the extracted passport code strings. The string codes are restored by applying CDM mask to the binary string area and individual codes are extracted by 8-neighborhood contour tracking algerian. The proposed RBF network is applied to the middle layer of RBF network by using the fuzzy logic connection operator and proposing the enhanced fuzzy ART algorithm that dynamically controls the vigilance parameter. The face is authenticated by measuring the similarity between the feature vector of the facial image from the passport and feature vector of the facial image from the database that is constructed with PCA algorithm. After several tests using a forged passport and the passport with slanted images, the proposed method was proven to be effective in recognizing passport codes and verifying facial images.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.1
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• pp.65-70
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• 1981

L.G. Khachiyan's algorithm for solving a system of strict (or open) linear inequalities with integral coefficients is described. This algorithm is based on the construction of a sequence of ellipsoids in R$^n$ of decreasing n-dimensional volume and contain-ing feasible region. The running time of the algorithm is polynomial in the number of bits of computer core memory required to store the coefficients. It can be applied to solve linear programming problems in polynomially bounded time by the duality theorem of the linear programming problem. But it is difficult to use in solving practical problems. Because it requires the computation of a square roots, besides other arithmatic operations, it is impossible to do these computations exactly with absolute precision.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.68-79
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• 1998

In this paper, we propose a model-based FDI(fault detetion and isolation) algorithm to detect and isolate fault in a linear system. The proposed algorithm is gased on an HFC(hydrid fault classifier) which consists of an FCART2(fault classifier by ART2 neural network) and an FCFM(fault classifier by fault models) which operate in parallel to isolate faults. The proposed algorithm is functionally composed of three main parts-parameter estimation, fault detection, and isolation. When a change in the system occurs, the estimated parameters go through a transition zone in which errors between the system output and the stimated output and the estimated output cross a predetermined thrseshold, and in this zone the estimated parameters are tranferred to the FCART2 for fault isolation. On the other hand, once a fault in the system is detected, the FCFM statistically isolates the fault by using the error between ach fault model out put and the system output. From the computer simulation resutls, it is verified that the proposed model-based FDI algorithm can be performed successfully to detect and isolate faults in a position control system of a DC motor.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4976-4994
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• 2018

The diffusion-based salient object detection methods have shown excellent detection results and more efficient computation in recent years. However, the current diffusion-based salient object detection methods still have disadvantage of detecting the object appearing at the image boundaries and different scales. To address the above mentioned issues, this paper proposes a multi-scale diffusion-based salient object detection algorithm with background and objectness seeds. In specific, the image is firstly over-segmented at several scales. Secondly, the background and objectness saliency of each superpixel is then calculated and fused in each scale. Thirdly, manifold ranking method is chosen to propagate the Bayessian fusion of background and objectness saliency to the whole image. Finally, the pixel-level saliency map is constructed by weighted summation of saliency values under different scales. We evaluate our salient object detection algorithm with other 24 state-of-the-art methods on four public benchmark datasets, i.e., ASD, SED1, SED2 and SOD. The results show that the proposed method performs favorably against 24 state-of-the-art salient object detection approaches in term of popular measures of PR curve and F-measure. And the visual comparison results also show that our method highlights the salient objects more effectively.

• Korean Journal of Applied Biomechanics
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• v.21 no.1
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• pp.85-95
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• 2011

Three-dimensional(3D) motion analysis is a useful tool for analyzing sports performance. During the last few decades, advances in motion analysis equipment have enabled us to perform more and more complicated biomechanical analyses. Nevertheless, considering the complexity of biomechanical models and the amount of data recorded from the motion analysis system, subsequent processing of these data is required for event-specific motion analysis. The purpose of this study was to develop a basic golf swing analysis algorithm using a state-of-the-art VICON motion analysis system. The algorithm was developed to facilitate golf swing analysis, with special emphasis on 3D motion analysis and high-speed motion capture, which are not easily available from typical video camera systems. Furthermore, the developed algorithm generates golf swing-specific kinematic and kinetic variables that can easily be used by golfers and coaches who do not have advanced biomechanical knowledge. We provide a basic algorithm to convert massive and complicated VICON data to common golf swing-related variables. Future development is necessary for more practical and efficient golf swing analysis.

• Journal of Korea Game Society
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• v.17 no.1
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• pp.7-16
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• 2017

The 3D game graphic technology has become an important factor in the contents field with the game contents development. In particular, game character technology provides a realistic technique and visual pleasure, as well as an intermediate step in the immersion of the game in which the game might create an optical illusion that enables the player to enjoy heroic adventure in the game. The high expression level of characters in 3D games is a key factor in the development process, with details and carefulness of the character setting work [3]. In this paper, we propose a character representative technique applied to mobile games using mathematical model of radiosity energy, spectral radiance model, and ray tracing model method using 3D unity game engine with sensible AI algorithm for game implementation. As a practical application to the game contents, it was found that the projection of the surface in the rendering process and the game simulation might change according to the lighting condition of the game content environment, so that the high quality of game characters was simulated.

• ETRI Journal
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• v.35 no.3
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• pp.542-545
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• 2013

State-of-the-art voice codecs have been developed to extend the input bandwidth to enhance quality while maintaining interoperability with a legacy codec. Most of them employ a modified discrete cosine transform (MDCT) for coding their extended band. We propose a source filter model-based coding algorithm of MDCT spectral coefficients, apply it to the ITU-T G.711.1 super wideband (SWB) extension codec, and subjectively test it to validate the model. A subjective test shows a better quality over the standardized SWB codec.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05a
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• pp.1-5
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• 2004

In this paper, we propose a novel approach for evolving the architecture of a multi-layer neural network. Our method uses combined ART1 algorithm and Max-Min neural network to self-generate nodes in the hidden layer. We have applied the. proposed method to the problem of recognizing ID number in student identity cards. Experimental results with a real database show that the proposed method has better performance than a conventional neural network.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.823-826
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• 2003

FPGA-based logic emulator with lane gate capacity generally comprises a large number of FPGAs connected in mesh or crossbar topology. However, gate utilization of FPGAs and speed of emulation are limited by the number of signal pins among FPGAs and the interconnection architecture of the logic emulator. The time-multiplexing of interconnection wires is required for multi-FPGA system incorporating several state-of-the-art FPGAs. This paper proposes a circuit partitioning algorithm called SCATOMi(SCheduling driven Algorithm for TOMi)for multi-FPGA system incorporating four to eight FPGAs where FPGAs are interconnected through TOMi(Time-multiplexed, Off-chip, Multicasting interconnection). SCATOMi improves the performance of TOMi architecture by limiting the number of inter-FPGA signal transfers on the critical path and considering the scheduling of inter-FPGA signal transfers. The performance of the partitioning result of SCATOMi is 5.5 times faster than traditional partitioning algorithms. Architecture comparison show that the pin count is reduced to 15.2%-81.3% while the critical path delay is reduced to 46.1%-67.6% compared to traditional architectures.