• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.204-213
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• 2008

In this paper, we present the practical application of an Unscented Kalman Filter (UKF) for an Indoor Mobile Localization System using ultrasonic sensors. It is true that many kinds of localization techniques have been researched for several years in order to contribute to the realization of a ubiquitous system; particularly, such a ubiquitous system needs a high degree of accuracy to be practical and efficient. Unfortunately, a number of localization systems for indoor space do not have sufficient accuracy to establish any special task such as precise position control of a moving target even though they require comparatively high developmental cost. Therefore, we developed an Indoor Mobile Localization System having high localization performance; specifically, the Unscented Kalman Filter is applied for improving the localization accuracy. In addition, we also present the additive filter named 'Pre-filtering' to compensate the performance of the estimation algorithm. Pre-filtering has been developed to overcome negative effects from unexpected external noise so that localization through the Unscented Kalman Filter has come to be stable. Moreover, we tried to demonstrate the performance comparison of the Unscented Kalman Filter and another estimation algorithm, such as the Unscented Particle Filter (UPF), through simulation for our system.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.20-28
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• 2015

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1424-1429
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• 2007

This paper presents a simple and reliable one-touch type multi-immunosensing lab-on-a-chip (LOC) detecting antibodies as multi-disease markers using electrochemical method suitable for a portable point-of-care system (POCS). The multi-stacked LOC consists of a PDMS space layer for liquids loading, a PDMS valve layer with 50 im in height for the membrane, a PDMS channel layer for the fluid paths, and a glass layer for multi electrodes. For the disposable immunoassay which needs sequential flow control of sample and buffer liquids according to the designed strategies, reliable and easy-controlled on-chip operation mechanisms without any electric power are necessary. The driving forces of sequential liquids transfer are the capillary attraction force and the pneumatic pressure generated by air bladder push. These passive fluid transport mechanisms are suitable for single-use LOC module. Prior to the application of detection of the antibody as a disease marker, the model experiments were performed with anti-DNP antibody and anti-biotin antibody as target analytes. The flow test results demonstrate that we can control the fluid flow easily by using the capillary stop valve and the PDMS check valves. By the model tests, we confirmed that the proposed LOC is easily applicable to the bioanalytic immunosensors using bioelectrocatalysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5588-5613
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• 2018

Virtualization technology has been widely applied in the area of computer security research that provides a new method for system protection. It has been a hotspot in system security research at present. Virtualization technology brings new risk as well as progress to computer operating system (OS). A multi-level perception security model using virtualization is proposed to deal with the problems of over-simplification of risk models, unreliable assumption of secure virtual machine monitor (VMM) and insufficient integration with virtualization technology in security design. Adopting the enhanced isolation mechanism of address space, the security perception units can be protected from risk environment. Based on parallel perceiving by the secure domain possessing with the same privilege level as VMM, a mechanism is established to ensure the security of VMM. In addition, a special pathway is set up to strengthen the ability of information interaction in the light of making reverse use of the method of covert channel. The evaluation results show that the proposed model is able to obtain the valuable risk information of system while ensuring the integrity of security perception units, and it can effectively identify the abnormal state of target system without significantly increasing the extra overhead.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.697-704
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• 2018

Since the face in image content corresponds to individual information that can distinguish a specific person from other people, it is important to accurately detect faces not hidden in an image. In this paper, we propose a method to accurately detect a face from input images using a deep learning algorithm, which is one of the machine learning methods. In the proposed method, image input via the red-green-blue (RGB) color model is first changed to the luminance-chroma: blue-chroma: red-chroma ($YC_bC_r$) color model; then, other regions are removed using the learned skin color model, and only the skin regions are segmented. A CNN model-based deep learning algorithm is then applied to robustly detect only the face region from the input image. Experimental results show that the proposed method more efficiently segments facial regions from input images. The proposed face area-detection method is expected to be useful in practical applications related to multimedia and shape recognition.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.1 s.307
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• pp.31-38
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• 2006

In this paper, we consider one-step-ahead control of waveform parameters (pulse amplitudes and lengths, and FM sweep rate) as well as detection thresholds for optimal range and range-rate tracking in clutter. The optimal control of the combined parameter set minimizes a tracking performance index under a set of parameter constraints. The performance index includes the probability of track loss and a function of estimation error covariances. The track loss probability and the error covariance are predicted using a hybrid conditional average algorithm The effect of the false alarms and clutter interference is taken into account in the prediction. Tracking performance of the one-step-ahead control is presented for several examples and compared with a control strategy heuristically derived from a finite horizon optimization.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.205-216
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• 2014

Multi-camera systems have been widely used as cost-effective tools for the collection of geospatial data for various applications. In order to fully achieve the potential accuracy of these systems for object space reconstruction, careful system calibration should be carried out prior to data collection. Since the structural integrity of the involved cameras' components and system mounting parameters cannot be guaranteed over time, multi-camera system should be frequently calibrated to confirm the stability of the estimated parameters. Therefore, automated techniques are needed to facilitate and speed up the system calibration procedure. The automation of the multi-camera system calibration approach, which was proposed in the first part of this paper, is contingent on the automated detection, localization, and identification of the object space signalized targets in the images. In this paper, the automation of the proposed camera calibration procedure through automatic target extraction and labelling approaches will be presented. The introduced automated system calibration procedure is then implemented for a newly-developed multi-camera system while considering the optimum configuration for the data collection. Experimental results from the implemented system calibration procedure are finally presented to verify the feasibility the proposed automated procedure. Qualitative and quantitative evaluation of the estimated system calibration parameters from two-calibration sessions is also presented to confirm the stability of the cameras' interior orientation and system mounting parameters.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.9
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• pp.889-895
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• 2011

In recent days, some techniques to prevent from radar detection have been applied on aircraft system. RWR(Radar Warning Receiver) can be used for estimating the source location of the aircraft which emits radar pulse. Current existing method of localizing radar pulse emission source is using AOA(Angle Of Arrival) and most techniques are focused on finding exact AOA to find exact location. In this case, however, the exact AOA does not always result in finding exact source location while target aircraft is moving fast. In this paper, a localization method using the phase delay of the radar pulse's low frequency applies and so a scanning method for the interest area does in order to estimate exact source location by using phase delay.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.1
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• pp.12-22
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• 2016

Lidar-based hazard avoidance landing system for lunar lander calculates hazard cost with respect to the desired local landing area in order to identify hazard and designate safe landing point where the cost is minimum basically using slope and roughness of the landing area. In this case, if the parameters are only considered, chosen landing target can be designated near hazard threatening the lander. In order to solve this problem and select optimal safe landing point, hazard cost based on relative distance to hazard should not be considered as well as cost based on terrain parameters. In this paper, the effect of hazard cost based on relative distance to hazard on safe landing performance was analyzed and it was confirmed that landing site designation with two relative distances to hazard results in the best safe landing performance by an experiment using three-dimensional depth camera.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.171-181
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• 2013

Recently, ground penetrating radar(GPR) has been widely used in detecting metallic and nonmetallic buried landmines and a number of related researches have been reported. A novel preprocessing method is proposed in this paper to flag potential locations of buried mine-like objects from GPR array measurements. GPR operates by measuring the reflection of an electromagnetic pulse from discontinuities in subsurface dielectric properties. As the GPR pulse propagates in the geologic medium, it suffers nonlinear attenuation as the result of absorption and dispersion, besides spherical divergence. In the proposed algorithm, a logarithmic transformed regression model which successfully represents the time-varying signal amplitude of the GPR data is estimated at first. Then, background signals may be densely distributed near the regression model and candidate signals of targets may be far away from the regression model in the time-amplitude space. Based on the observation, GPR signals are decomposed into candidate signals of targets and background signals using residuals computed from the estimated value by regression and the measurement of GPR. Candidate signals which may contain target signals and noise signals need to be refined. Finally, targets are detected through the refinement of candidate signals based on geometric signatures of mine-like objects. Our algorithm is evaluated using real GPR data obtained from indoor controlled environment and the experimental results demonstrate remarkable performance of our mine-like object detection method.