• Smart Structures and Systems
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• v.19 no.1
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• pp.91-103
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• 2017

Generalized fractal dimension is used to detect the presence of partial delamination in a composite laminated beam. The effect of boundary conditions and location of delamination on the fractal dimension curve is studied. Appropriability of higher mode shape data for detection of delamination in the beam is evaluated. It is shown that fractal dimension measure can be used to detect the presence of partial delamination in composite beams. It is found that the torsional mode shape is well suited for delamination detection in beams. First natural frequency of delaminated beam is found to be higher than the healthy beam for certain small and partial width delaminations and some boundary conditions. An explanation towards this counter intuitive phenomenon is provided.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.718-723
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• 2013

In general, the boundary portion of the background and objects are the rapidly changing point and an important elements to analyze characteristics of image. Using these boundary parts, information about the position or shape of an object in the image are detected, and many studies have been continued in order to detect it. Existing methods are that implementation of algorithm is comparatively simple and its processing speed is fast, but edge detection characteristics is insufficient because weighted values are applied to all the pixels equally. Therefore, in this paper, we proposed an algorithm using region segmentation of the mask in order to adaptive edge detection according to image, and the results processed by proposed algorithm indicated superior edge detection characteristics in edge area.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.38-43
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• 2001

This Paper suggests a new feature for shot detection, using the proposed robust feature from the DC image constructed by DCT DC coefficients in the MPEG video stream, and proposes the characterizing value that reflects the characteristic of kind of video (movie, drama, news, music video etc.). The key frames are pulled out from many frames by using the local minima and maxima of differential of the value. After original frame(not do image) are reconstructed for key frame, indexing process is performed through computing parameters. Key frames that are similar to user's query image are retrieved through computing parameters. It is proved that the proposed methods are better than conventional method from experiments. The retrieval accuracy rate is so high in experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.773-778
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• 1991

In this paper we describe a road following method for an autonomous vehicle. From a road image in gray level, a road boundary is detected using a gradient operator, and then the road boundary is converted to orthogonal view of the road showing the vehicle position and heading direction. In this research an efficient road boundary search technique is developed to support real time vehicle control. Also, an obstacle detection method, using images taken from two different positions, has been developed.

• Progress in Medical Physics
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• v.7 no.2
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• pp.79-90
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• 1996

In this paper, we present an automatic threshold decision method to detect the contour of the a 2-D echocarodiogram by using the Bayes estimator for the boundary-like region. The boundary-like region is constructed from the conduction coefficient of the heat anisotro-pic diffusion method which enforces the blurred image during the preprocessing step. For the boundary-like region, we used the Bayes estimator to select an optimal threshold level. From this threshold value, the contour of the echocardigrams can be detected automatically Finally by overlapping the estimated contour to the original echocardiogram, we can obtain the contour enforced ultrasound echocardiogram.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.944-945
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• 2017

Color image edge detection is an important operation in many image processing areas. This paper presents a new method for edge detection based on the Bhattacharyya distance that can handle arbitrary boundaries by exploring several edge patterns. Experiments show promising results compared to some existing methods.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1171-1179
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• 2004

We describe word boundary detection that extracts the boundary between speech and non-speech. The proposed method uses two features. One is the normalized root mean square of speech signal, which is insensitive to white noises and represents temporal information. The other is the normalized met-frequency band energy of voice signal, which is frequency information of the signal. Our method detects word boundaries using a recurrent fuzzy associative memory(RFAM) that extends FAM by adding recurrent nodes. Hebbian learning method is employed to establish the degree of association between an input and output. An error back-propagation algorithm is used for teaming the weights between the consequent layer and the recurrent layer. To confirm the effectiveness, we applied the suggested system to voice data obtained from KAIST.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.367-372
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• 2018

This paper proposed an algorithm that detects boundary nodes effectively in wireless sensor network. A boundary node is a sensor that lies on the border of network holes or the outer boundary of wireless sensor network. Proposed algorithm detects boundary nodes using only the position information of sensors. In addition, to improve detect performance, sensor computes the overlap area of nearest sensor first. Simulation is performed to validate the process of the proposed algorithm. In Simulation, several obstacles are placed and varying number of sensors in the range of 500~1500 are deployed in the area in order to reflect real world. The simulation results shows that proposed algorithm detects boundary nodes effectively that are located on the border of holes and the outer boundary of wireless sensor network.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.3
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• pp.427-435
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• 1997

Conditional sampling techniques are utilized to investigate the relation between the wall skin-friction and stream wise velocity fluctuations in a turbulent boundary layer. Conditionally averaged results using a peak detection and the VITA (variable-interval time-averaging) technique show that a high skin friction is associated with high frequency components of the wall skin-friction fluctuations. The conditionally averaged wall skin-friction fluctuations obtained by using the VITA technique have a positively-skewed characteristics compared with the conditionally averaged stream wise velocity fluctuations. It is confirmed that there exists a phase shift between the wall skin-friction and stream wise velocity fluctuations, which was also found from the long-time averaged space-time correlations. The amount of phase shift between the wall skin-friction and stream wise velocity fluctuations is the same as that from the long-time averaged space-time correlations and does not change despite the variation of the detection threshold.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.271-280
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• 2009

In this paper, we propose a smoke detection method using region growing method in outdoor video sequences. Our proposed method is composed of three steps; the initial change area detection step, the boundary finding and expanding step, and the smoke classification step. In the first step, we use a background subtraction to detect changed areas in the current input frame against the background image. In difference images of the background subtraction, we calculate a binary image using a threshold value and apply morphology operations to the binary image to remove noises. In the second step, we find boundaries of the changed areas using labeling algorithm and expand the boundaries to their neighbors using the region growing algorithm. In the final step, ellipses of the boundaries are estimated using moments. We classify whether the boundary is smoke by using the temporal information.