• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4C
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• pp.364-370
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• 2010

In this paper, we propose a cooperative channel sensing scheme in the presence of feedback errors. Accurate local sensing results may not directly be applied to cooperative sensing due to feedback errors. We consider the cooperative channel sensing that utilizes local sensing results in good feedback channel condition. Finally, simulation results show that the proposed scheme can maximize the detection probability while guaranteeing desired false alarm probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1042-1062
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• 2010

Cooperative spectrum sensing (CSS) is an effective technology for alleviating the unreliability of local spectrum sensing due to fading/shadowing effects. Unlike most existing solutions, this paper considers the use of CSS technology in decentralized networks where a fusion center is not available. In such a decentralized network, some attackers may sneak into the ranks of cooperative users. On the basis of recent advances in bio-inspired consensus algorithms, an attack-proof, decentralized CSS scheme is proposed in which all secondary users can maintain cooperative sensing by exchanging information locally instead of requiring centralized control or data fusion. Users no longer need any prior knowledge of the network. To counter three potential categories of spectrum sensing data falsification (SSDF) attacks, some anti-attack strategies are applied to the iterative process of information exchange. This enables most authentic users to exclude potentially malicious users from their neighborhood. As represented by simulation results, the proposed scheme can generally ensure that most authentic users reach a consensus within the given number of iterations, and it also demonstrates much better robustness against different SSDF attacks than several existing schemes.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.725-728
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• 1988

Image restoration technique using dual sensor is presented in this paper. Digital Radiography image (1024xlO24) is obtained by conventional resolution sensor. We also obtain local DR image data by high resolution sensor. Two dimensional maximum entropy power spectrum estimation (2-D ME PSE) is applied to low resolution image and high resolution image for the purpose of the power spectrum estimation of each image. A class of linear algebraic restoration filter, parametric projection filter (PPF), is derived from the power spectrums of each image. It is shown that the noise energy may be considerably reduced through the PPF.

• Nuclear Engineering and Technology
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• v.44 no.2
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• pp.207-224
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• 2012

This article discusses selects of some outstanding problems in nuclear reactor analysis, with proposed approaches thereto and numerical test results, as follows: i) multi-group approximation in the transport equation, ii) homogenization based on isolated single-assembly calculation, and iii) critical spectrum in Monte Carlo depletion.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.612-615
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• 2003

The error diffusion is a good method to reconstruct the continuous tones of an image to the bilevel tones However the reconstruction of edge characteristic by the nor diffusion is represented work when power spectrum is analyzed fer display error. In this paper, we present an edge enhanced error diffusion method to preprocess original image to achieve the enhancement for the edge characteristic. The preprocessing algorithm consist of two processes. First the difference value between the current pixel and the local average of the surrounding pixel in original image is obtained. Second, the weighting function is composed by the magnitude and the sign of the local average. To confirm the effect of the proposed method, it is compared with the conventional edge enhanced error diffusion methods by measuring the radially averaged power spectrum densities (RAPSDs) for their display errors. The comparison result demonstrate the superiority of the proposed method over the conventional ones.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.59-64
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• 2018

A cooperative spectrum sensing has been proposed to improve the sensing performance in cognitive radio (CR) network. However, cooperative sensing causes additional overhead for reporting the result of local sensing to the fusion center. In this paper, we propose a technique to reduce the overhead of data transmission of cooperative sensing for applying the quantum data fusion technique in cognitive radio networks by omitting the lowest quantized in the local sensed results. If a CR node senses the lowest quantized level, it will not send its local sensing data in the corresponding sensing period. The fusion center can implcitly know that a spectific CR node sensed lowest level if there is no report from that CR node. The goal of proposed sensing policy is to reduce the overhead of quantized data fusion scheme for cooperative sensing. Also, our scheme can be adapted to all quantized data fusion schemes because it only deal with the form of the quantized data report. The experimental results show that the proposed scheme improves performance in terms of reporting overhead.

• Earthquakes and Structures
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• v.7 no.4
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• pp.587-607
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• 2014

Progressive collapse, which is referred to as the collapse of the entire building under local damages, is a common failure mode happened by earthquakes. The collapse process highly depends on the whole structural system. Since, asymmetry of the building plan leads to the local damage concentration; it may intensify the progressive collapse mechanism of asymmetric buildings. In this research the progressive collapse of regular and irregular 6-story RC ordinary moment resisting frame buildings are studied in the presence of the earthquake loads. Collapse process and collapse propagation are investigated using nonlinear time history analyses (NLTHA) in buildings with 5%, 15% and 25% mass asymmetry with respect to the number of collapsed hinges and story drifts criteria. Results show that increasing the value of mass eccentricity makes the asymmetric buildings become unstable earlier and in the early stages with lower number of the collapsed hinges. So, with increasing the mass eccentricity in building, instability and collapse of the entire building occurs earlier, with lower potential of the progressive collapse. It is also demonstrated that with increasing the mass asymmetry the decreasing trend of the number of collapsed beam and column hinges is approximately similar to the decreasing trend in the average story drifts of the mass centers and stiff edges. So, as an alternative to a much difficult-to-calculate local response parameter of the number of collapsed hinges, the story drift, as a global response parameter, measures the potential of progressive collapse more easily.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.10
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• pp.795-803
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• 2015

The boundary layer developing over the suction surface of a first-stage turbine blade for power generation has been investigated in this study. For three locations selected in the region where local thermal load changes dramatically, mean velocity, turbulence intensity, and one-dimensional energy spectrum are measured with a hot-wire anemometer. The results show that the suction-surface boundary layer suffers a transition from a laminar flow to a turbulent one. This transition is confirmed to be a "separated-flow transition", which usually occurs in the shear layer over a separation bubble. The local minimum thermal load on the suction surface is found at the initiation point of the transition, whereas the local maximum thermal load is observed at the location of very high near-wall turbulence intensity after the transition process. Frequency characteristics of turbulent kinetic energy before and after the transition are understood clearly from the energy spectrum data.

• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.1-10
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• 2011

The spectrum sensing is one of the fundamental functions to realize the cognitive radios. One of problems in the spectrum sensing is that the performance of spectrum sensing can be degraded due to fading and shadowing. In order to overcome the problem, cooperative spectrum sensing method is proposed, which uses a distributed detection model and can increase sensing performance. However, the performance of cooperative spectrum sensing can be still affected by the interference factors such as obstacle and malicious user. Especially, most of cooperative spectrum sensing methods only considered the stationary primary user. In the ubiquitous environment, however the mobile primary users should be considered. In order to overcome the aforementioned problem, in this paper we propose a robust spectrum detection method based on localization where we estimate the location of the mobile primary user, and then based on the location and transmission range of primary user we detect interference users if there are, and then the local sensing reporting from detected interference users are excluded in the decision fusion process. Through simulation, it is shown that the sensing performance of the proposed scheme is more accurate than that of conventional other schemes

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.402-410
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• 2017

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.