• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.796-804
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• 2002

This paper presents a new algorithm of fingerprint image analysis and processing using directional filter bank(DFB). The directional components of ridge is very important in pre-processing steps of fingerprint image processing such as image enhancement by directional filtering followed by estimationg the directional image of ridge patterns. The DFB analyzes input image into directional subband images and synthesizes them to the perfectly reconstructed image. In this paper, a new fingerprint processing algorithm using the DFB is proposed. The algorithm decomposes the fingerprint image into directional subband images and performs directional map generation, foreground segmentation, singular points extraction and image enhancement based on local directional energy estimate.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.67-74
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• 2018

In this paper, we propose an efficient scene based non-uniformity correction algorithm which performs the offset correction using the uniform obtained from input scenes for Infrared camera. In general, pixel outputs of a infrared detector can not be uniform. Therefore, the non-uniformity correction procedure need to be performed to make the image outputs uniform. A typical non-uniformity correction method uses a black body at the laboratory to obtain the output of the infrared detector's pixels for two temperatures, HOT and COLD, and calculates the non-uniformity correction parameters. However, output characteristics of the Infrared detector changes while the Infrared camera is operated, the fixed pattern noise of the Infrared detector and dead pixels are generated. To remove the noise, the offset correction is generally performed. The offset correction procedure usually need the additional device such as a thermo-electric cooler, shutter, or non-uniformity correction lens. Therefore, we introduce a general scene based non-uniformity correction technique without additional equipment, and then we propose an improved non-uniformity correction algorithm based on image to solve the problem of the existing technique.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.10 no.4
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• pp.3-11
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• 2000

In this paper, we design modular multiplication systolic array and exponentiation processor having n bits message black. This processor uses Montgomery algorithm and LR binary square and multiply algorithm. This processor consists of 3 divisions, which are control unit that controls computation sequence, 5 shift registers that save input and output values, and modular exponentiation unit. To verify the designed exponetion processor, we model and simulate it using VHDL and MAX+PLUS II. Consider a message block length of n=512, the time needed for encrypting or decrypting such a block is 59.5ms. This modular exponentiation unit is used to RSA cryptosystem.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.3-13
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• 2021

We propose a moire artifacts removal algorithm for screen-shot images using multiple domain learning. First, we estimate clean preliminary images by exploiting complementary information of the moire artifacts in pixel value and frequency domains. Next, we estimate a clean edge map of the input moire image by developing a clean edge predictor. Then, we refine the pixel and frequency domain outputs to further improve the quality of the results using the estimated edge map as the guide information. Finally, the proposed algorithm obtains the final result by merging the two refined results. Experimental results on a public dataset demonstrate that the proposed algorithm outperforms conventional algorithms in quantitative and qualitative comparison.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.133-141
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• 2001

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However is we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set. eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case, set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.103-111
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• 2023

Consensus, that aims to converge the states of agents to the same states through information exchanges between agents, has been widely studied to control the multi-agent systems. In real systems, the measurement variables of each agent may be different, the loss of information across communication may occur, and the different networks for each state may need to be constructed for safety. Moreover, the input saturation and the disturbances in the system may cause instability. Therefore, this paper studies the PID(Proportional-Integral-Derivative)-based consensus control to achieve the swarm behavior of the multi-agent systems considering the heterogeneous state information, the input saturations, and the disturbances. Specifically, we consider the multiple follower agents and the single leader agent modeled by the second-order systems, and investigate the conditions to achieve the consensus based on the stability of the error system. It is confirmed that the proposed algorithm can achieve the consensus if only the connectivity of the position graph is guaranteed. Moreover, by extending the consensus algorithm, we study the formation control problem for the multi-agent systems. Finally, the validity of the proposed algorithm was verified through the simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.1
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• pp.32-38
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• 1993

In this paper a effective classification method for character recognition is proposed. The existing classification methods select candidates by comparing an unknown input character, with all the standard patterns based on the similarity measur. The proposed method, however, groups similiar characters together and uses their average distance as representative value of the group. We divided the character region into several sub-region and applied ISODATA algorithm to partial vectors of each sub-region to anstruct appropriate number of groups. After computing the distance between partial feature vector and its mapping group, we could collect all the information of input character ultimately. The proposed method showed improvement in the processing speed and certainty in classification than the existing methods.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.920-926
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• 2005

Nearest neighbor algorithms classify an unseen input instance by selecting similar cases and use the discovered membership to make predictions about the unknown features of the input instance. The usefulness of the nearest neighbor algorithms have been demonstrated sufficiently in many real-world domains. In nearest neighbor algorithms, it is an important issue to assign proper weights to the attributes. Therefore, in this paper, we propose a new method which can automatically assigns to each attribute a weight of its importance with respect to the target attribute. The method has been implemented as a computer program and its effectiveness has been tested on a number of machine learning databases publicly available.

• Journal of information and communication convergence engineering
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• v.4 no.4
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• pp.151-154
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• 2006

In this work, a novel optimal multiuser detection (MUD) approach, which not only achieves the optimal maximum-likelihood (ML)-like performance but also has reasonably low computational complexity, for Space-time coded OFDM (ST-OFDM) systems is presented. In the proposed detection scheme, the signal model is firstly re-expressed into linearly equivalent one. Then, with the linearly equivalent signal model, a new jointly MUD algorithm is proposed to detect signals. The ML-like bit-error-rate (BER) performance and reasonably low complexity of the proposed detection are verified by computer simulations.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1243-1246
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• 2003

It is known that structure of Human's brain information system is controlled by cerebral cortex mainly. Cerebral cortex is divided by sensory area, motor area and associated area largely. Sensory area takes part in information from environment and motor area is actuation by decision as associated area determined. It is possible to copy brain information system by input-output pattern. but there is difficulty in modeling of memorizing new information. Such action is performed by Limbic Lobe and Papez circuit which is controlled by intrinsic emotion. So we need of definition of emotion's role in decision. In this paper, we define roles of emotion in intrinsic decision using Dynamic Cognitive Maps(DCMs). The emotion is evaluated by outside information then intrinsic decision performed as how much emotion variated. The dynamic cognitive maps take part in emotion evaluating process.