• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.109-114
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• 2005

Twofish cryptographic algorithm is concise algorithm itself than Rijndael cryptographic algorithm as AES, and easy of implementation is good, but the processing speed has slow shortcoming. Therefore this paper designed improved MDS block to improve Twofish cryptographic algorithm's speed. Problem of speed decline by a bottle-neck Phenomenon of the Processing speed existed as block that existing MDS block occupies Twofish cryptosystem's critical path. To reduce multiplication that is used by operator in MDS block this Paper removed a bottle-neck phenomenon and low-speed about MDS itself using LUT operation and modulo-2 operation. Twofish cryptosystem including modified MDS block designed by these result confirmed that bring elevation of the processing speed about 10$\%$ than existing Twofish cryptosystem.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.477-484
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• 2001

This paper proposes a hybrid learning algorithm combined momentum and kernel-adatron for improving the performance of regression support vector machine. The momentum is utilized for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution, and the kernel-adatron algorithm is also utilized for the capability by working in nonlinear feature spaces and the simple implementation. The proposed algorithm has been applied to the 1-dimension and 2-dimension nonlinear function regression problems. The simulation results show that the proposed algorithm has better the learning speed and performance of the regression, in comparison with those quadratic programming and kernel-adatron algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1293-1302
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• 1999

In this paper, an on-line process scheme is presented for implementation of a intelligent on-line modeling of nonlinear complex system. The proposed on-line process scheme is composed of FNN-based model algorithm and PLC-based simulator, Here, an adaptive fuzzy-neural networks and HCM(Hard C-Means) clustering method are used as an intelligent identification algorithm for on-line modeling. The adaptive fuzzy-neural networks consists of two distinct modifiable sturctures such as the premise and the consequence part. The parameters of two structures are adapted by a combined hybrid learning algorithm of gradient decent method and least square method. Also we design an interface S/W between PLC(Proguammable Logic Controller) and main PC computer, and construct a monitoring and control simulator for real process system. Accordingly the on-line identification algorithm and interface S/W are used to obtain the on-line FNN model structure and to accomplish the on-line modeling. And using some I/O data gathered partly in the field(plant), computer simulation is carried out to evaluate the performance of FNN model structure generated by the on-line identification algorithm. This simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• Smart Structures and Systems
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• v.4 no.4
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• pp.449-464
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• 2008

An array based, outward monitoring, ultrasonic guided wave based SHM technique using a single transmitter and multiple receivers (STMR), with a small footprint is discussed here. The previous implementation of such SHM arrays used a phase-reconstruction algorithm (that is similar to the beam-steering algorithm) for the imaging of reflectors. These algorithms were found to have a limitation during the imaging of defects/reflectors that are present in the "near-field" of the array. Here, the "near-field" is defined to be approximately 3-4 times the diameter of the compact array. This limitation is caused by approximations in the beam-steering reconstruction algorithm. In this paper, a migration-based reconstruction algorithm, with dispersion correction in the frequency domain, is discussed. Simulation and experimental studies are used to demonstrate that this algorithm improves the reconstruction in the "near-field" without decreasing the ability to reconstruct defects in the "far-field" in both isotropic and anisotropic plates.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.902-908
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• 2013

This paper suggests to compress data selectively for improvement of data compression efficiency, not to perform unconditional compression on data. Whether to compress or not is determined by selective data distinction. By doing so, we can avoid unnecessary compression in the case of low compression efficiency. Cutting down the unnecessary compression, we can improve the performance of the pre-compression algorithm. Especially, the data algorithm which was already compressed could not be compressed efficiently in many cases, even if apply compression algorithm again. Even in these cases, we don't have to compress data unnecessarily. We implemented the proposed function actually and performed experiments with implementation. The experimental results showed normal operation.

• Journal of Broadcast Engineering
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• v.18 no.5
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• pp.730-737
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• 2013

In this paper, Constrained One-Bit Transform (C1BT) based motion estimation using extension of matching error criterion is proposed. C1BT based motion estimation algorithm exploiting Number of Non-Matching Points (NNMP) instead of Sum of Absolute Differences (SAD) that used in the Full Search Algorithm (FSA) facilitates hardware implementation and significantly reduces computational complexity. However, the accuracy of motion estimation is decreased. To improve inaccurate motion estimation, this algorithm based motion estimation extending matching error criterion of C1BT is proposed in this paper. Experimental results show that proposed algorithm has better performance compared with the conventional algorithm in terms of Peak-Signal-to-Noise-Ratio (PSNR).

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.5
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• pp.269-277
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• 1999

In this paper, a novel dead time minimization algorithm is proposed for improving the output waveform of an inverter. The adverse effects of the dead time are mainly described by the voltage drop and the distortion factor of waveforms. The principle of the proposed algorithm is organized with forbidding unnecessary firings fo the inverter switches which are not conducted even though the gate signal is impressed. The proposed methods are explained with the conduction mode of output currents. The H/W and S/W implementation method of the proposed algorithm are also presented. The validity of the proposed algorithm is verified by comparing the simulation and experimental results with conventional methods. It can be concluded from the results that the proposed algorithm has the advantage which is able to reduce the harmonics in the output voltages and which the output voltage can nearly be equal to the reference value. Another advantage of the proposed method is the reduction of total numbers of switching so that the switching losses of inverter drives can be minimized.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.270-279
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• 2000

This paper describes the on-chip learning algorithm of neural networks using the stochastic pulse arithmetic. Stochastic pulse arithmetic is the computation using the numbers represented by the probability of 1' and 0's occurrences in a random pulse stream. This stochastic arithmetic has the merits when applied to neural network ; reduction of the area of the implemented hardware and getting a global solution escaping from local minima by virtue of the stochastic characteristics. And in this study, the on-chip learning algorithm is derived from the backpropagation algorithm for effective hardware implementation. We simulate the nonlinear separation problem of the some character patterns to verify the proposed learning algorithm. We also had good results after applying this algorithm to recognize printed and handwritten numbers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.587-591
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• 2001

This paper deals with inspect algorithm using visual system. One of the major problems that arise during polymer production is the estimation of the noise of the color product.(bad pallets) An erroneous output can cause a lot of losses (production and financial losses). Therefore new methods for real-time inspection of the noise are demanded. For this reason, we have presented a development of vision system algorithm for the defect inspection of PE color pallets. First of all, in order to detect the edge of object, the differential filter is used. And we apply to the labelling algorithm for feature extraction. This algorithm is designed for the defect inspection of pallets. The labelling algorithm permits to separate all of the connected components appearing on the pallets. Labelling the connected regions of a image is a fundamental computation in image analysis and machine vision, with a large number of application. Also, we suggested vision processing program in window environment. Simulations and experimental results demonstrate the performance of the proposal algorithm.