• Journal of the Korea Computer Industry Society
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• v.3 no.12
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• pp.1725-1736
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• 2002

We describe a characteristic behavior of edge signal intensity, the strictly monotonic variation of intensity across edges and propose a new algorithm for edge detection based on it. We define an extended directional derivatives, which is nonlocal and beyond scaling in the pixel space, to describe that the algorithm is adaptive to the various widths of edges and relevant as an optimal edge detection algorithm. As an industrial application of the algorithm, we discuss a simple computer vision procedure for an example of visual inspection.

• Structural Engineering and Mechanics
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• v.49 no.3
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• pp.411-425
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• 2014

This paper presents the processing of nonlinear features associated with a damage event by quadratic time-frequency distributions for damage identification in a frame structure. A time-frequency distribution is a function which distributes the total energy of a signal at a particular time and frequency point. As the occurrence of damage often gives rise to non-stationary, nonlinear structural behavior, simultaneous representation of the dynamic response in the time-frequency plane offers valuable insight for damage detection. The applicability of the bilinear time-frequency distributions of the Cohen class is examined for the damage assessment of a frame structure from the simulated acceleration data. It is shown that the changes in instantaneous energy of the dynamic response could be a good damage indicator. Presence and location of damage can be identified using Choi-Williams distribution when damping is ignored. However, in the presence of damping the Page distribution is more effective and offers better readability for structural damage detection.

• ETRI Journal
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• v.31 no.1
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• pp.92-94
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• 2009

We propose a system for mobile-phone fraud detection based on a bidirectional artificial neural network (bi-ANN). The key advantage of such a system is the ability to detect fraud not only by offline processing of call detail records (CDR), but also in real time. The core of the system is a bi-ANN that predicts the behavior of individual mobile-phone users. We determined that the bi-ANN is capable of predicting complex time series (Call_Duration parameter) that are stored in the CDR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4774-4796
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• 2018

Wireless sensor networks are composed of a large number of inexpensive and tiny sensors used in different areas including military, industry, agriculture, space, and environment. Fault tolerance, which is considered a challenging task in these networks, is defined as the ability of the system to offer an appropriate level of functionality in the event of failures. The present study proposed an intelligent throughput descent and distributed energy-efficient mechanism in order to improve fault tolerance of the system against soft and permanent faults. This mechanism includes determining the intelligent neighborhood radius threshold, the intelligent neighborhood nodes number threshold, customizing the base paper algorithm for distributed systems, redefining the base paper scenarios for failure detection procedure to predict network behavior when running into soft and permanent faults, and some cases have been described for handling failure exception procedures. The experimental results from simulation indicate that the proposed mechanism was able to improve network throughput, fault detection accuracy, reliability, and network lifetime with respect to the base paper.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.216-224
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• 2017

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.

• International Journal of Reliability and Applications
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• v.8 no.1
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• pp.53-66
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• 2007

This paper proposes a new non-homogeneous Poisson process software reliability growth model based on the coverage information. The new model incorporates the coverage information in the fault detection process by assuming that only the faults in the covered constructs are detectable. Since the coverage growth behavior depends on the testing strategy, the fault detection process is first modeled for the general testing strategy and then realized for the uniform testing. Finally the model for the uniform testing is empirically evaluated by applying it to real data sets.

• Korean Journal of Agricultural Science
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• v.36 no.1
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• pp.51-56
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• 2009

When the parasitic wasp Microplitis croceipes experiences odors while feeding on sugar water, it learns to associate the odors with sugar and thereafter exhibits typical food searching behavior in response to the odors. Previous studies have shown that this wasp can be used for detection of the small amount of explosives or other volatile chemicals. In the present study, we examined if this wasp can learn and report narcotic odors. Males of M. croceipes were trained to link sugar water with pseudo-narcotic scents that have been used for training narcotic detection dogs, and their behavioral response to the trained odors was observed. The males that had been given either an odor or sugar water did not show any positive response to the odors. However, when the wasps were given a combination of sugar water and either the pseudo-Cocaine, Heroin, LSD or Marihuana, they quickly learned to associate the odors with sugar, and thereafter positively responded to those odors. Our results suggest that this wasp can be used for detection of these narcotics.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.300-307
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• 2002

Damage estimation methods are classified into two groups according to the dependence on the FE model : signal-based and model-based methods. Signal-based damage estimation methods are generally appropriate for detection of damage location, whereas not effective for estimation of damage severities. Model-based damage estimation methods are difficult to apply directly to the structures with a large number of the probable damaged members. It is difficult to obtain the exact model representing the real bridge behavior due to the modeling errors. The modeling errors even may exceed the modal sensitivity on damage. In this study, Model-based damage detection method which can effectively consider the modeling errors is suggested. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness of the presented method.

• Annual Conference of KIPS
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• 2001.10a
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• pp.767-770
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• 2001

Edge detection is one of issues with essential importance in the area of image process. An edge in image is a boundary or contour which a significant change occurs in image intensity. In the paper, we process edge detection algorithms which are based on Potts automata. The dynamical behavior of these automata is completely determined by Lyapunov operators for sequential and parallel update. If Potts Automata convergence to fixed points, then it can be used to image processing. From the generalized Potts automata point of view, we propose a Potts Automata technique for detecting edge. Based on the experimental results we discuss the advantage and efficiency.

• Journal of the Korean Vacuum Society
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• v.9 no.1
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• pp.76-80
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• 2000

CF & $CF_2$ radicals in a $C_4F_8$ inductively coupled plasma were observed with laser induced fluorescence. 251.9nm UV laser was used for the $CF_2$ excitation and 265.3nm UV emitted light for the detection which has the maximum intensity among many induced fluorescence lights. In the case of CF radical detection, 232.9nm UV laser was used for the excitation and 247.6nm for the detection. $CF_2$ radical density increased toward substrate, while CF radical had its maximum at about 10nm away from the substrate. The atomic fluorine density which was studied by the actinometry increased as the position moves away from the substrate. This phenomena was thought to have a close relation with the polymer growth on the wafer. When the bias voltage increased, $CF_2$ , CF radicals decreased while the atomic fluorine increased tio some extent and then decreased, which was thought to be due to the change in the ionization and dissociation.