• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.8
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• pp.360-367
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• 2002

In this paper, genetic algorithm based adaptive image enhancement filtering scheme is proposed and Implemented on FPGA board. Conventional filtering methods require a priori noise information for image enhancement. In general, if a priori information of noise is not available, heuristic intuition or time consuming recursive calculations are required for image enhancement. Contrary to the conventional filtering methods, the proposed filter system can find optimal combination of filters as well as their sequent order and parameter values adaptively to unknown noise types using structured genetic algorithms. The proposed image enhancement filter system is mainly composed of two blocks. The first block consists of genetic algorithm part and fitness evaluation part. And the second block consists of four types of filters. The first block (genetic algorithms and fitness evaluation blocks) is implemented on host computer using C code, and the second block is implemented on re-configurabe FPGA board. For gray scale control, smoothing and deblurring, four types of filters(median filter, histogram equalization filter, local enhancement filter, and 2D FIR filter) are implemented on FPGA. For evaluation, three types of noises are used and experimental results show that the Proposed scheme can generate optimal set of filters adaptively without a pioi noise information.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.128-130
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• 1997

In the paper a problem of search for a moving robot by another moving robot is considered. The problem is formalized as minmax and maxmin task also as a game.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.357-357
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• 2000

In this study, we consider a multi-input multi-output styrene polymerization reactor system for which the monomer conversion and the weight average molecular weight are controlled by manipulating the jacket inlet temperature and the feed flow rate. The reactor system is identified by using a linear subspace identification method and then the output feedback model predictive controller is constructed on the basis of the identified model. Here we use the Best Linear Unbiased Estimation (BLUE) filter as a stochastic estimator instead of the Kalman filter. The BLUE filter observes the state successfully without any a priori information of initial states. In contrast to the Kalman filter, the BLUE filter eliminates the offset by observing the state of the augmented system regardless of a priori information of the initial state for an integral white noise augmented system. A BLUE filter has a finite impulse response (FIR) structure which utilizes finite measurements and inputs on the most recent time interval [i-N, i] in order to avoid long processing times.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1998.10a
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• pp.413-420
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• 1998

In this paper, two methods, Stepp's and EQHAZARD, are introduced and applied to a recent earthquake catalog for the entire Korean Peninsula that can estimate the seismicity by incorporating the incompleteness of the earthquake catalog. EQHAZARD method, different from Stepp's method in that it used priori information besides the assumption of stationary Poisson process of the earthquakes, produces the higher seismicity rate for the smaller earthquakes. EQHAZARD method are also used to estimated the incompleteness of the recent earthquake catalog for the southern part of the Korean Peninsula in terms of the Probability of Activity for the specified earthquke magnitude classes and time periods. It is believed that the Probability of Activity thus obtained can be used as a strong priori information in estimating the seismicity for a seismic source within the region where there are not enough earthquakes detected. Finally, it is demonstrated that the arbitrary selection of the methods. of incompleteness analysis brings quite different seismic hazard results, which suggests the need to employ a rigid quantitative method for incompleteness analysis in estimating the seismicity parameters in order to reduce the uncertainty in the Seismic Hazard Results with the EQHAZARD method being one of the competent practical alternatives.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.591-594
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• 2008

This paper explores how to characterise security properties of software components, and how to reason about their suitability for a trustworthy compositional contract. Our framework provides an explicit opportunity for software composers as well as software components to test a priori security properties of software components in a system composition. The proposed framework uses logic programming as a tool to represent security properties of atomic components and reason about their compositional matching with other components.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.689-692
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• 1998

Matching two images is an essential step for many computer vision applications. A new approach to the scale and rotation invariant scene matching is presented. A set of andidate parameters are hypthesized by mapping the angular difference and a new distance measure to the hough space and by detecting maximally consistent points. The proposed method is shown to be much faster than the conventinal one where the relaxation process is repeated until convergence, while providing robust matching performance, without a priori information on the geometrical transformation parameters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.275-280
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• 2016

In this paper, we propose a novel relay selection technique which utilizes a priori decoding information at relays for buffer-aided successive relaying networks. In the conventional relaying schemes, a single relay pair is selected for receiving data from the source and transmitting data to the destination. In the proposed technique, however, all relays except the relay selected for transmitting data to the destination try to decode the received signal from the source, and they store the data if they succeed decoding. The proposed technique selects the relay such that it can succeed its own transmission and it maximizes the number of relays successfully decoding the data from the source at the same time. It is shown that the proposed relaying technique significantly outperforms the conventional buffer-aided relaying schemes in terms of outage probability through extensive computer simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.3
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• pp.4-19
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• 1991

In this paper, we propose a fuzzy neural network pattern classifier utilizing fuzzy information. This system works without any a priori information about the number of clusters or cluster centers. It classifies each input according to the distance between the weights and the normalized input using Bezdek's [1] fuzzy membership value equation. This model returns the correct membership value for each input vector and find several cluster centers. Some experimental studies of comparison with other algorithms will be presented for sample data sets.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.73-75
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• 1987

The paper proposes a class of discrete-time adaptive controller which may be applicable without sufficient a priori information. Against choices of the Information, GPC algorithm may seem to be more robust than any other methods reported, but it is the method based on Indirect approach. It is, therefore, reasonable to propose an algorithm via the zero-gain prediction, in which the control parameters are directly estimated and calculated.

• Journal of Communications and Networks
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• v.11 no.2
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• pp.134-147
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• 2009

Tracking and exploiting instantaneous spectrum opportunities are fundamental challenges in opportunistic spectrum access (OSA) in presence of the bursty traffic of primary users and the limited spectrum sensing capability of secondary users. In order to take advantage of the history of spectrum sensing and access decisions, a sequential decision framework is widely used to design optimal policies. However, many existing schemes, based on a partially observed Markov decision process (POMDP) framework, reveal that optimal policies are non-stationary in nature which renders them difficult to calculate and implement. Therefore, this work pursues stationary OSA policies, which are thereby efficient yet low-complexity, while still incorporating many practical factors, such as spectrum sensing errors and a priori unknown statistical spectrum knowledge. First, with an approximation on channel evolution, OSA is formulated in a multi-armed bandit (MAB) framework. As a result, the optimal policy is specified by the wellknown Gittins index rule, where the channel with the largest Gittins index is always selected. Then, closed-form formulas are derived for the Gittins indices with tunable approximation, and the design of a reinforcement learning algorithm is presented for calculating the Gittins indices, depending on whether the Markovian channel parameters are available a priori or not. Finally, the superiority of the scheme is presented via extensive experiments compared to other existing schemes in terms of the quality of policies and optimality.