• International journal of advanced smart convergence
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• v.9 no.3
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• pp.232-238
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• 2020

In this paper, we study on aerial objects detection and position estimation algorithm for the safety of UAV that flight in BVLOS. We use the vision sensor and LiDAR to detect objects. We use YOLOv2 architecture based on CNN to detect objects on a 2D image. Additionally we use a clustering method to detect objects on point cloud data acquired from LiDAR. When a single sensor used, detection rate can be degraded in a specific situation depending on the characteristics of sensor. If the result of the detection algorithm using a single sensor is absent or false, we need to complement the detection accuracy. In order to complement the accuracy of detection algorithm based on a single sensor, we use the Kalman filter. And we fused the results of a single sensor to improve detection accuracy. We estimate the 3D position of the object using the pixel position of the object and distance measured to LiDAR. We verified the performance of proposed fusion algorithm by performing the simulation using the Gazebo simulator.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.265-267
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• 2013

Edges include various important informations of the objects. These edges are being applied in numerous areas and there is a detection method using mask in existing edge detection methods. These existing edge detection methods are simple to realize. However, because the fixed mask is used, edge detection characteristics in complicated noise environments are somewhat unsatisfactory. Therefore, to compensate for the weakness in the existing detection methods, edge detection algorithm which uses the standard deviation of local mask and noise elimination was proposed.

• Convergence Security Journal
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• v.13 no.6
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• pp.11-16
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• 2013

Mobile node must move optionally and perform the router and the host functions at the same time. These characteristics of nodes have become a potential threatening element of a variety of attacks. In particular, a black hole which malicious node causes packet loss among them is one of the most important issues. In this paper, we propose distributed detection technique using monitoring tables in all node and cooperative detection technique based cluster for an efficient detection of black hole attack. The proposed technique performs by dividing into local detection and cooperative detection process which is composed of process of step 4 in order to improve the accuracy of the attack detection. Cluster head uses a black hole list to cooperative detection. The performance of the proposed technique was evaluated using ns-2 simulator and its excellent performance could be confirmed in the experiment result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1385-1402
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• 2019

Quickly picking up some valuable information from massive manufacturing event stream usually faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of large volume, high velocity, many variety and small value. Aiming to solve the problem above for the current complex event processing methods because of not sharing detection during the detecting process for massive manufacturing event streams, an efficient complex event processing method based on multipattern sharing is presented in this paper. The achievement of this paper lies that a multipattern sharing technology is successfully used to realize the quick detection of complex event for massive manufacturing event streams. Specially, in our scheme, we firstly use pattern sharing technology to merge all the same prefix, suffix, or subpattern that existed in single pattern complex event detection models into a multiple pattern complex event detection model, then we use the new detection model to realize the quick detection for complex events from massive manufacturing event streams, as a result, our scheme can effectively solve the problems above by reducing lots of redundant building, storing, searching and calculating operations with pattern sharing technology. At the end of this paper, we use some simulation experiments to prove that our proposed multiple pattern processing scheme outperforms some general processing methods in current as a whole.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.8
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• pp.640-647
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• 2018

This study analyzes the tracking accuracy(tracking errors) of fighter radar. Measurement error, detection failure, and radar cross section(RCS) fluctuation in radar measurements degrade the measurement quality and hence affect the tracking accuracy. Therefore, these radar measurement characteristics need to be considered when analyzing the tracking accuracy. In this paper, a method for analyzing the tracking accuracy is proposed; this method considers the detection error, detection probability, and RCS fluctuation. Results from experiments conducted with the proposed method show that the detection probability and RCS fluctuation affect tracking accuracy.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.929-937
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• 2020

In this manuscript, the UNet++ model, which is one of the representative deep learning techniques for semantic segmentation, was used to detect changes in temporal satellite images. To analyze the learning results according to various loss functions, we evaluated the change detection results using trained UNet++ models by binary cross entropy and the Jaccard coefficient. In addition, the learning results of the deep learning model were analyzed compared to existing pixel-based change detection algorithms by using WorldView-3 images. In the experiment, it was confirmed that the performance of the deep learning model could be determined depending on the characteristics of the loss function, but it showed better results compared to the existing techniques.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.22 no.3
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• pp.222-229
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• 2009

A noble infrared sensor was studied for passenger conditional detection in vehicle, This research relates to uncooled infrared sensors for detecting the presence, type and temperature of occupants in vehicle. It sense that the occupants purpose to control the smart airbag for safety in the case of adult or child and to control the automatic air conditioning for convenience. This paper described the design and the fabrication of microbolometers which were composed of 2 by 8 elements using the surface micromachining technology. The characteristics of the array were investigated in the spectral region of $8{\sim}12{\mu}m$. The fabricated detectors exhibited the thermal mass of $7.05{\times}10^{-9}\;J/K$, the thermal conductance of $1.03{\times}10^{-6}\;W/K$, the thermal time constant of 6.8 ms, the responsivity of $2.96{\times}10^4\;V/W$ and the detectivity of $1.01{\times}10^9\;cmHz^{1/2}/W$, at the chopper frequency of 10 Hz and the bias current of $4.4{\mu}A$. We could successfully detect the human body condition in the divided zone. As a results, we concluded that microbolometer optimized in this research could be useful for the application of passenger conditional detection in vehicle.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.112-119
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• 2000

A two-color infrared (IR) seeker employs two detection-bands for efficient target tracking under countermeasures and background noises. This paper describes a study on the detection-band selections for the two-color seeker. To select the detection-bands, we analysis infrared radiation characteristics of a target, a flare, and background noises and then other characteristics closely related with the received power, such as the atmospheric transmission, acquisition range, background noise effect, and signal-to-noises. The analysis shows that the pertinent bands of two-color IR seeker are 3 um~5 um for target detection and 1.5 um~l.7 um for counter-countermeasures. The results of our study will play a significant role in the further study of two-color seekers.

• Particle and aerosol research
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• v.13 no.4
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• pp.159-164
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• 2017

The detection efficiency and characteristics of an optical particle counter (OPC), with various sample nozzle outlet diameters, were experimentally investigated. The OPC system, which was built with original design, was made up of a diode laser, two photodetectors, and a variety of optics such as a beam splitter and a concave mirror. The cone-shaped sampling nozzle was designed to be changeable to alter the outlet diameter, within the range of 1 to 3 mm. For samples, sets of polystyrene latex (PSL) standard particle with various sizes of 1 to $3{\mu}m$, were used. As a result, detection efficiency of the OPC greatly decreased with larger nozzle outlet diameter. Moreover, increased nozzle outlet diameter means broader sample flow, thus caused light interference and multiple scattering which results in abnormal high peaks in scattered light signal. The ratio of abnormal peaks to regular signal of single particle increased with larger nozzle outlet diameter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.204-209
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• 2013

The image processing has been applied to various fields along with development of visual media. The boundary parts in which brightness of image dramatically changes are important factors in order to analysis characteristics of image because edge contains important information and significant features. A number of researches for detecting these edges have been conducted and conventional edge detection methods using relationship between adjacent pixels are that operation speed is superior, but the edge detection characteristics are insufficient because they use fixed mask without considering gray-level variation. In this paper, the novel algorithm using grey-level variation of image in mask is proposed.