• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.88-95
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• 2011

An embedded mobile device mostly has lower computation power than a general purpose computer because of its relatively lower system specifications. Consequently, conventional face tracking and face detection methods, requiring complex algorithms for higher recognition rates, are unsuitable in a mobile environment aiming for real time detection. On the other hand, by applying a real-time tracking and detecting algorithm, we would be able to provide a two-way interactive multimedia service between an user and a mobile device thus providing a far better quality of service in comparison to a one-way service. Therefore it is necessary to develop a real-time face and eye tracking technique optimized to a mobile environment. For this reason, in this paper, we proposes a method of tracking horizontal face position of a user on a T-DMB device for enhancing the quality of 3D DMB content. The proposed method uses the orientation of edges to estimate the left and right boundary of the face, and by the color edge information, the horizontal position and size of face is determined finally to decide the horizontal face. The sobel gradient vector is projected vertically and candidates of face boundaries are selected, and we proposed a smoothing method and a peak-detection method for the precise decision. Because general face detection algorithms use multi-scale feature vectors, the detection time is too long on a mobile environment. However the proposed algorithm which uses the single-scale detection method can detect the face more faster than conventional face detection methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.821-830
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• 2017

The internet is an important infra resource that it controls the economy and society of our country. Also, it is providing convenience and efficiency of the everyday life. But, a case of various are occurred through an using vulnerability of an internet infra resource. Recently various attacks of unknown to the user are an increasing trend. Also, currently system of security control is focussing on patterns for detecting attacks. However, internet threats are consistently increasing by intelligent and advanced various attacks. In recent, the darknet is received attention to research for detecting unknown attacks. Since the darknet means a set of unused IP addresses, no real systems connected to the darknet. In this paper, we proposed an algorithm for finding black IPs through collected the darknet traffic based on a statistics data of port information. The proposed method prepared 8,192 darknet space and collected the darknet traffic during 3 months. It collected total 827,254,121 during 3 months of 2016. Applied results of the proposed algorithm, black IPs are June 19, July 21, and August 17. In this paper, results by analysis identify to detect frequency of black IPs and find new black IPs of caused potential cyber threats.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.50-58
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• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.635-643
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• 2018

This paper propose a method to identify the motor parameters and improve input voltage error which affect the low speed position error of the back-emf(back electromotive force) based sensorless algorithm and to secure the operation reliability and stability even in the case where the load fluctuation is severe and the start and low speed operation frequently occurs. In the model-based observer used in this paper, stator resistance, inductance, and input voltage are particularly influential factors on low speed performance. Stator resistance can cause resistance value fluctuation which may occur in mass production process, and fluctuation of resistance value due to heat generated during operation. The inductance is influenced by the fluctuation due to the manufacturing dispersion and at a low speed where the change of the current is severe. In order to find stator resistance and inductance which have different initial values and fluctuate during operation and have a large influence on sensorless performance at low speed, they are commonly measured through 2-point calculation method by 2-step align current injection. The effect of voltage error is minimized by offsetting the voltage error. In addition, when the command voltage is used, it is difficult to estimate the back-emf due to the relatively large distortion voltage due to the dead time and the voltage drop of the power device. In this paper, we propose a simple circuit and method to detect the voltage by measuring the PWM(Pulse Width Modulation) pulse width and compensate the voltage drop of the power device with the table, thereby minimizing the position error due to the exact estimation of the back-emf at low speed. The suitability of the proposed algorithm is verified through experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2878-2884
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• 2015

Twitter, one of online social network services, is one of the most popular micro-blogs, which generates a large number of automated programs, known as tweet bots because of the open structure of Twitter. While these tweet bots are categorized to legitimate bots and malicious bots, it is important to detect tweet bots since malicious bots spread spam and malicious contents to human users. In the conventional work, temporal information was utilized for the classficiation of human and bot. In this paper, by utilizing geo-tagged tweets that provide high-precision location information of users, we first identify both Twitter users' exact location. Then, we propose a new tweet bot detection algorithm by using both an entropy based on geographic variable of each user and device information of each user. As a main result, the proposed algorithm shows superior bot detection and false alarm probabilities over the conventional result which only uses temporal information.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.835-846
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• 2020

Recent severe climate changes and extreme weather events have caused the uncommon types of forest ecosystem disturbances such as hails and gypsy moths. This paper describes the analysis of the forest ecosystem disturbances using ISODATA (Iterative Self-organizing Data Analysis Technique Algorithm) with the RapidEye and Sentinel-2 images, regarding the cases of the hail damages in Hwasun in 2017 and the gypsy moth damages in the Chiak Mountain in 2020. In the case of hail damages, the comparison of the June image of this study and the July field survey of the previous study showed that the damage severity increased from June to July as the drought overlapped after the trees were injured by the hails. In the case of gypsy moths, significant leaf damages were found from the image of June, and the damages were mainly distributed at the low-altitude slope near Wonju City. We made sure that satellite remote sensing is a very effective method to detect various and unusual forest ecosystem disturbances caused by climate change. Also, it is expected that the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024 can be actively utilized to monitor such forest ecosystem disturbances.

• Current Research on Agriculture and Life Sciences
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• v.31 no.2
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• pp.131-138
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• 2013

The precision aerial application of small farms, such as paddy, upland and orchard fields using agricultural unmanned helicopters became a new paradigm. The objective of this study was to evaluate the performance of a GPS module and algorithm, controlling drift of agricultural helicopter by the crosswind and maintaining the position for emergency landing. Purpose of the drift control, of which an algorithm works while hovering is related with the emergency sequence that coping with abnormal conditions of rotorcraft system. However, the inertial attitude control cannot detect a drifting motion of fuselage moving at the constant velocity, thus the crosswind takes the helicopter away from the landing position. Performance of the drift control module, based on the GPS that a hovering position did not deviate within 5m in diameter, were tested and evaluated. Initially, the reaction against a disturbing gust wind was sensitive, soon the helicopter maintained its locking position and azimuth within 5m in diameter. It was, however, difficult for the helicopter to recognize the swaying and nodding, the some deviation was expected due to the discrepancy characteristics of the GPS signal. The performance of the drift control proved the effectiveness of the module to maintain the position against an unintended drift during the emergency landing or hovering.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1439-1448
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• 2005

In this paper, after similarity of color information in above toro and geometry position analysis of important characteristic information in face and abstraction object that is inputted detects face area using comparison, describe about method to do user certification using ratio information and hair spring degree. Face abstraction algorithm that use color information has comparative advantages than face abstraction algorithm that use form information because have advantage that is not influenced facial degree or site etc. that tip. Because is based on color information, change of lighting or to keep correct performance because is sensitive about color such as background similar to complexion is difficult. Therefore, can be used more efficiently than method to use color information as that detect characteristic information of eye and lips etc. that is facial importance characteristic element except color information and similarity for each object achieves comparison. This paper proposes system that eye and mouth's similarity that calculate characteristic that is ratio red of each individual after divide face by each individual and is segmentalized giving weight in specification calculation recognize user confirming similarity through search. Could experiment method to propose and know that the awareness rate through analysis with the wave rises.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.4
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• pp.395-403
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• 2016

This paper introduces a multi-cell search method for heterogeneous networks (HetNet), in which user equipments need to search multiple cells in its vicinity simultaneously. Due to the difficulty of acquiring channel informations for multiple cells, a non-coherent approach is preferred. In this paper, a non-coherent single-cell search scheme using a weighted vector is proposed, and the successive interference cancellation based multi-cell search algorithm is devised. In order to improve cell search performance, the weighted vector is designed in a way to exploit the general characteristic of wireless channel. Based on the fact that the performance of the proposed single-cell search scheme deviates slowly from the one using the optimal weighted vector, a universal weighted vector is also proposed, which shows the performance close to the optimal ones for various channel environments and signal-to-noise ratio regimes. Simulation results confirm that the proposed multi-cell search algorithm is capable of identifying cells more accurately with the help of the proposed single-cell search scheme, and can detect the remaining cells more effectively by removing the signals of the identified cells from the received signal.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.143-150
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• 2018

With the rapid development of RF based high speed wireless communication technology, devices that can be applied to IoT networks based on RF bandwidth are rapidly spreading, nevertheless, the development speed of the RF communication is not possible to keep up with the spread of the RF band for wireless communication. In this situation, OWC technology that uses visible light source as a transmitter is attracting attention as a technology that can overcome the band exhaustion problem of RF based wireless communication technology. Although, due to the distortion of the LED illumination shape by camera exposure time and LED blinking period, the LED illumination detection rate is degraded and the RoI setting is inaccurate. In this paper, we propose an adaptive median filter applied LED illumination detection algorithm for low rate CamCom, it is possible to detect a clear RoI and LED illumination. This research will be able to play a role as a complementary material of RF based wireless communication technology efficiently.