• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.669-675
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• 2010

For detecting requirement errors in early system development phase, the behaviors of a system should be described in formal methods and be analyzed with analysis techniques such as reachability analysis and cycle detection. However, since they are usually based on explicit exploration of system state space, state explosion problem may be occurred when a system becomes complex. That is, the memory and execution time for exploration exponentially increase due to a huge state space. In this paper, we analyze the fundamental causes of this problem in concurrent systems and explore the state space without composing concurrent state spaces for reducing the memory requirement for exploration. Also our new technique keeps a visited history minimally for reducing execution time. Finally we represent experimental results which show the efficiency of our technique.

• Journal of Applied Reliability
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• v.18 no.3
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• pp.220-228
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• 2018

Purpose: The purpose of this study is to investigate the tail behavior of the life distribution which exhibits an increasing failure rate or other positive aging effects after a certain time point. Methods: We characterize the tail behavior of the life distribution with regard to certain reliability measures such as failure rate, mean residual life and reliability function and derive several stochastic properties regarding such life distributions. Also, utilizing an L-statistic and its asymptotic normality, we propose new nonparametric testing procedures which verify if the life distribution has an increasing tail failure rate. Results: We propose the IFR-Tail (Increasing Failure Rate in Tail), DMRL-Tail (Decreasing Mean Residual Life in Tail) and NBU-Tail (New Better than Used in Tail) classes, all of which represent the tail behavior of the life distribution. And we discuss some stochastic properties of these proposed classes. Also, we develop a new nonparametric test procedure for detecting the IFR-Tail class and discuss its relative efficiency to explore the power of the test. Conclusion: The results of our research could be utilized in the study of wide range of applications including the maintenance and warranty policy of the second-hand system.

• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.701-709
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• 2015

Image stitching method is a technique for obtaining an high-resolution image by combining two or more images. In X-ray image for clinical diagnosis, the size of the imaging region taken by one shot is limited due to the field-of-view of the equipment. Therefore, in order to obtain a high-resolution image including large regions such as a whole body, the synthesis of multiple X-ray images is required. In this paper, we propose a rapid stitching method of digital X-ray images using template-based registration. The proposed algorithm use principal component analysis(PCA) and k-nearest neighborhood(k-NN) to determine the location of input images before performing a template-based matching. After detecting the overlapping position using template-based matching, we synthesize input images by alpha blending. To improve the computational efficiency, reduced images are used for PCA and k-NN analysis. Experimental results showed that our method was more accurate comparing with the previous method with the improvement of the registration speed. Our stitching method could be usefully applied into the stitching of 2D or 3D multiple images.

• The Journal of the Korea Contents Association
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• v.16 no.9
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• pp.247-253
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• 2016

Some previous studies had proven that ninhydrin or its analogues are not effective in developing fingermarks on some types of paper. This study has evaluated the efficiency of four reagents for detecting latent fingermarks on six unusual paper surfaces. Among the six types of paper surfaces, none of the reagents worked on the newspaper. The 1,2-IND/Zn developed fingermarks on most of the surfaces tested.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.9
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• pp.444-449
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• 2001

In this paper, a novel ultraviolet sensor is presented based on a photoluminescent porous silicon. Porous silicon layer was formed by chemical etching of surface of pn junction in a $HF(48%)-HNO_3(60%)-H_20$ solution. Incident ultraviolet(UV) light is converted to visible light by photoluminescent porous silicon layer, and then this visible light generates electron-hole pairs in the pn junction, which produces a photocurrent flow through the device. In order to maximize detection efficiency, the peak sensitivity wavelength of the pn junction diode was matched with the peak wavelength of Photoluminescence from porous silicon layer. The porous silicon ultraviolet sensor showed a large output current as UV intensity increases and but very low sensitivity to visible light. The detection sensitivity of porous silicon sensor was calculated as 2.91mA/mW. These results are expected to open up a possibility that the present porous silicon sensor can be used for detecting UV light in a visible background, compared to silicon UV detectors which have an undesirable response to visible light.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.359-362
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• 2004

In multi-dimensional image, ICA-based feature extraction algorithm, which is proposed in this paper, is for the purpose of detecting target feature about pixel assumed as a linear mixed spectrum sphere, which is consisted of each different type of material object (target feature and background feature) in spectrum sphere of reflectance of each pixel. Landsat ETM+ satellite image is consisted of multi-dimensional data structure and, there is target feature, which is purposed to extract and various background image is mixed. In this paper, in order to eliminate background features (tidal flat, seawater and etc) around target feature (laver farm) effectively, pixel spectrum sphere of target feature is projected onto the orthogonal spectrum sphere of background feature. The rest amount of spectrum sphere of target feature in the pixel can be presumed to remove spectrum sphere of background feature. In order to make sure the excellence of feature extraction method based on ICA, which is proposed in this paper, laver farm feature extraction from Landsat ETM+ satellite image is applied. Also, In the side of feature extraction accuracy and the noise level, which is still remaining not to remove after feature extraction, we have conducted a comparing test with traditionally most popular method, maximum-likelihood. As a consequence, the proposed method from this paper can effectively eliminate background features around mixed spectrum sphere to extract target feature. So, we found that it had excellent detection efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1533-1556
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• 2017

The concept of trust in vehicular ad hoc networks (VANETs) is usually utilized to assess the trustworthiness of the received data as well as that of the sending entities. The quality of safety applications in VANETs largely depends on the trustworthiness of exchanged data. In this paper, we propose a self-organized distributed trust computing framework (DTCF) for VANETs to compute the trustworthiness of each vehicle, in order to filter out malicious nodes and recognize fully trusted nodes. The proposed framework is solely based on the investigation of the direct experience among vehicles without using any recommendation system. A tier-based dissemination technique for data messages is used to filter out non authentic messages and corresponding events before even going farther away from the source of the event. Extensive simulations are conducted using Omnet++/Sumo in order to investigate the efficiency of our framework and the consistency of the computed trust metrics in both urban and highway environments. Despite the high dynamics in such networks, our proposed DTCF is capable of detecting more than 85% of fully trusted vehicles, and filtering out virtually all malicious entities. The resulting average delay to detect malicious vehicles and fraudulent data is showed to be less than 1 second, and the computed trust metrics are shown to be highly consistent throughout the network.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.229-235
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• 2010

In this paper, we propose a method to improve the efficiency of the moving object detection in real-time surveillance camera system. The existing methods, the methods using differential image and background image, are difficult to detect the moving object from outside the video streams. The proposed method keeps the background image if it doesn't be detected moving object using the differential value between a previous frame and a current frame. And the background image is renewed as the moving object is gone in a frame. To decide people and wild pig, the proposed system estimates a bounding box enclosing each moving object in the detecting region. As a result of simulation, the proposed method is better than the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.371-378
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• 2000

In this study, we proposed pathological pattern detection system for X-ray chest image using artificial neural network. In a physical examination, radiologists have checked on the chest image projected the view box by a magnifying glass and found out what the disease is. Here, the detection of X-ray fluoroscopy is tedious and time-consuming for human doing. Lowering of efficiency for chest diagnosis is caused by lots mistakes of radiologist because of detecting the micro pathology from the film of small size. So, we proposed the method for disease detection using artificial neural network and digital image processing on a X-ray chest image. This method composes the function of image sampling, median filter, image equalizer used neural network and pattern recognition used neural network. We confirm this method has improved the problem of a conventional method.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.445-452
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• 2012

With the increasing number of high-rise and large-scale buildings, modern buildings are becoming intelligent, and are incurring high construction costs and requiring careful maintenance. Maintenance works for high-rise buildings significantly depend on human labor, unlike other construction processes that are gradually being automated. The resulting accidents may produce very high social and economic losses. To address this problem, herein, this paper proposes robotic building maintenance system using multi-robot concept, in specific, cleaning a building facade which is directly subjected to minimize human labor; that improves the process efficiency and economic feasibility. The sensor for detecting contamination of building's outer-wall glass is proposed; Kalman filter was used for estimating robots' status with the contamination of the window glass. Task allocation of the sensor based multi-robots for an effective way of task execution is introduced and the feasibility was verified through the simulations.