• The KIPS Transactions:PartC
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• v.16C no.5
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• pp.621-628
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• 2009

Wireless sensor networks (WSNs) have been considered as a promising method for reliably monitoring both civil and military environments under hazardous or dangerous conditions. Due to the special property and difference from the traditional wireless network, the lifetime of the whole network is the most important aspect. The bottleneck nodes widely exist in WSNs and lead to decrease the lifetime of the whole network. In order to find out the bottleneck nodes, the traditional centralized bottleneck detection method MINCUT has been proposed as a solution for WSNs. However they are impractical for the networks that have a huge number of nodes. This paper first proposes a distributed algorithm called DBND (Distributed Bottleneck Node detection) that can reduce the time for location information collection, lower the algorithm complexity and find out the bottleneck nodes quickly. We also give two simple suggestions of how to solve the bottleneck problem. The simulation results and analysis show that our algorithm achieves much better performance and our solutions can relax the bottleneck problem, resulting in the prolonging of the network lifetime.

• Journal of the Korea Computer Graphics Society
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• v.25 no.5
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• pp.11-20
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• 2019

With the development of deep learning technology, research is being actively carried out on user-friendly interfaces that are suitable for use in virtual reality or augmented reality applications. To support the interface using the user's hands, this paper proposes a deep learning-based fingertip detection method to enable the tracking of fingertip coordinates to select virtual objects, or to write or draw in the air. After cutting the approximate part of the corresponding fingertip object from the input image with the Grad-CAM, and perform the convolution neural network with Atrous Convolution for the cut image to detect fingertip location. This method is simpler and easier to implement than existing object detection algorithms without requiring a pre-processing for annotating objects. To verify this method we implemented an air writing application and showed that the recognition rate of 81% and the speed of 76 ms were able to write smoothly without delay in the air, making it possible to utilize the application in real time.

• Smart Structures and Systems
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• v.11 no.6
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• pp.589-604
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• 2013

Identification of damage has become an evolving area of research over the last few decades with increasing the need of online health monitoring of the large structures. The visual damage detection can be impractical, expensive and ineffective in case of large structures, e.g., offshore platforms, offshore pipelines, multi-storied buildings and bridges. Damage in a system causes a change in the dynamic properties of the system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such good sensitive indication of structural damage. Identification of damaged jacket type offshore platform members, based on wavelet packet transform is presented in this paper. The jacket platform is excited by simple wave load. Response of actual jacket needs to be measured. Dynamic signals are measured by finite element analysis result. It is assumed that this is actual response of the platform measured in the field. The dynamic signals first decomposed into wavelet packet components. Then eliminating some of the component signals (eliminate approximation component of wavelet packet decomposition), component energies of remained signal (detail components) are calculated and used for damage assessment. This method is called Detail Signal Energy Rate Index (DSERI). The results show that reduced wavelet packet component energies are good candidate indices which are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and are applicable for finding damages' location.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.47-57
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• 2010

The object detection is essential for identifying objects, location information, and user context-aware in the image. In this paper, we propose a robust object detection system. The System linearly transforms learning data obtained from the background images to Principal components. It organizes the Eigen-background with the selected Principal components which are able to discriminate between foreground and background. The Fuzzy-C-means (FCM) carries out clustering for images with inputs from the Eigen-background information and classifies them into objects and backgrounds. It used various patterns of backgrounds as learning data in order to implement a system applicable even to the changing environments, Our system was able to effectively detect partial movements of a human body, as well as to discriminate between objects and backgrounds removing noises and shadows without anyone frame image for fixed background.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.672-679
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• 2011

In this paper, detection algorithm of face region based on skin color of in the TV images is proposed. In the first, reference image is set to the sampled skin color, and then the extracted of face region is candidated using the Euclidean distance between the pixels of TV image. The eye image is detected by using the mean value and standard deviation of the component forming color difference between Y and C through the conversion of RGB color into CMY color model. Detecting the lips image is calculated by utilizing Q component through the conversion of RGB color model into YIQ color space. The detection of the face region is extracted using basis of knowledge by doing logical calculation of the eye image and lips image. To testify the proposed method, some experiments are performed using front color image down loaded from TV color image. Experimental results showed that face region can be detected in both case of the irrespective location & size of the human face.

• Journal of Korea Multimedia Society
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• v.9 no.2
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• pp.160-171
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• 2006

In this paper, we propose a face detection method using region segmentation to deal with complex images that have various environmental changes such as mixed background and light changes. To reduce the detection error rate due to background elements of the images, we segment the images with the JSEG method. We choose candidate regions of face based on the ratio of skin pixels from the segmented regions. From the candidate regions we detect face regions by using location and color information of eyes and eyebrows. In the experiment, the proposed method works well with the images that have several faces and different face size as well as mixed background and light changes.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.10
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• pp.1551-1558
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• 2001

Recently, a number of underground pipelines have been drastically increased. The integrity of these buried pipelines, especially gas transmitting pipelines, is of importance due to an explosive characteristic of natural gas. The third party damage is known as one of the most critical factor which causes fatal accidents. For this reason, a number of systems detecting third party damage are under development. The major concern in the development of third party damage detection system is to transmit vibration signals out of accelerometer to signal conditioner and data acquisition system without any interference caused by noise. The objective of this paper is to develope a fiber optic accelerometer applicable to third party damage detection system. A fiber optic accelerometer was developed by use of combining principles of one degree of freedom vibration model and an extrinsic Fabry-Perot interferometer. The developed fiber optic accelerometer was designed to perform with a sensitivity of 0.06mVg, a frequency range of less than 6kHz and an amplitude range of -200g to 200g. The developed, accelerometer was compared with a piezoelectric accelerometer and calibrated. In order to verify the developed accelerometer, the field experiment was performed. From the field experiment, vibration signals and the location of impact were successfully detected. The developed accelerometer is expected to be used for the third party damage detection system which requires long distance transmission of signals.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.145-150
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• 2004

In this study for the prosthesis of the spatial and directional sensation for the blind, an ultrasonic scale system and an electronic compass system were developed. The ultrasonic scale utilizes 40 ㎑ sound for the detection of distance to the barrier and the spatial information is transferred to the blind by various sound interval, which is proportional to the distance. The electronic compass utilizes a magnetoresistor bridge for the detection of the magnetic field strength of earth in horizontal plane. The information for the direction of the earth's north is transferred by tactile stimuli by a vibrating motor band around upper head. Detection distance of the ultrasonic scale is ranged from 0.065 to 3.26 meters, and the detection angle resolution of the electronic compass is about 22.5 degrees. The integrated system of the ultrasonic scale and the electronic compass was developed. Distance information is converted to the location of the tactile stimulation along the clockwise direction by a vibrating motor according to the distance installed around upper head of the blind. The intent of this article is to provide an practical prosthetic tool of spatial and directional sensation for the blind. Daily practice of this system will improve the usefulness of this system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4662-4679
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• 2015

Currently, the leakage of internal information has emerged as one of the most significant security concerns in enterprise computing environments. Especially, damage due to internal information leakage by insiders is more serious than that by outsiders because insiders have considerable knowledge of the system's identification and password (ID&P/W), the security system, and the main location of sensitive data. Therefore, many security companies are developing internal data leakage prevention techniques such as data leakage protection (DLP), digital right management (DRM), and system access control, etc. However, these techniques cannot effectively block the leakage of internal information by insiders who have a legitimate access authorization. The security system does not easily detect cases which a legitimate insider changes, deletes, and leaks data stored on the server. Therefore, we focused on the insider as the detection target to address this security weakness. In other words, we switched the detection target from objects (internal information) to subjects (insiders). We concentrated on biometrics signals change when an insider conducts abnormal behavior. When insiders attempt to leak internal information, they appear to display abnormal emotional conditions due to tension, agitation, and anxiety, etc. These conditions can be detected by the changes of biometrics signals such as pulse, temperature, and skin conductivity, etc. We carried out experiments in two ways in order to verify the effectiveness of the emotional recognition technology based on biometrics signals. We analyzed the possibility of internal information leakage detection using an emotional recognition technology based on biometrics signals through experiments.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.252-260
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• 2016

The performance of human detection system is affected by camera location and view angle. In 2D image acquired from such camera settings, humans are displayed in different sizes. Detecting all the humans with diverse sizes poses a difficulty in realizing a real-time system. However, if the size of a human in an image can be predicted, the processing time of human detection would be greatly reduced. In this paper, we propose a method that estimates human size by constructing an indoor scene in 3D space. Since the human has constant size everywhere in 3D space, it is possible to estimate accurate human size in 2D image by projecting 3D human into the image space. Experimental results validate that a human size can be predicted from the proposed method and that machine-learning based detection methods can yield the reduction of the processing time.