• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.114-125
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• 2014

In a conventional sonar system, which uses LFM signal for detecting targets with varying speed, the results of multiple LFM Doppler correlators are aligned and the maximum alined result are selected as a test cell for detecting targets. As the number of the LFM Doppler correlators are increased for accurate target detection, as the required computational complexity and the memory are also increased. This fact makes it difficult to implement the accurate LFM target detector. In this paper, we propose a new fast target detection which is robust for the variation of target speed. Because the proposed method uses the summation of alined results of large numbers of LFM Doppler correlators, the proposed method increase SNR and provide robust SNR for the variation of target speed. And the proposed method can provide very fast target detection by implementing the process, the summation of alined results of large numbers of LFM Doppler correlators, as one summation filter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2866-2879
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• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. Previous studies on game bot detection have proposed many methods to find out discriminable behaviors of bots from humans based on the fact that a bot's playing pattern is different from that of a human. In this paper, we look at the chatting data that reflects gamers' communication patterns and propose a communication pattern analysis framework for online game bot detection. In massive multi-user online role playing games (MMORPGs), game bots use chatting message in a different way from normal users. We derive four features; a network feature, a descriptive feature, a diversity feature and a text feature. To measure the diversity of communication patterns, we propose lightly summarized indices, which are computationally inexpensive and intuitive. For text features, we derive lexical, syntactic and semantic features from chatting contents using text mining techniques. To build the learning model for game bot detection, we test and compare three classification models: the random forest, logistic regression and lazy learning. We apply the proposed framework to AION operated by NCsoft, a leading online game company in Korea. As a result of our experiments, we found that the random forest outperforms the logistic regression and lazy learning. The model that employs the entire feature sets gives the highest performance with a precision value of 0.893 and a recall value of 0.965.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.5
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• pp.29-37
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• 2009

This paper describes a vision-based and real-time system for detecting road signs from within a moving vehicle. The proposed system has the standard architecture with adaboost algorithm to detect road signs in real time. And it uses the value of vortical plane in the process of extracting candidate areas in view of fact that there are vertically most of signs on roads. Although being useful for detecting objects in real time, the conventional adaboost algorithm deteriorates the performance of detection rate in complex circumstance by reason of using only integral images as features. To overcome this problem, this paper proposes the method that improves the reliability of candidates as using the value of vertical plane for extracting candidate area and improves the performance of the detection rate as using integral images to which we add the kind of feature prototype. The experiments of this paper show that the detection rate of the proposed method has higher than that of the conventional adaboost algorithm under the real complex circumstance of roads.

• ETRI Journal
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• v.38 no.2
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• pp.302-313
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• 2016

Use of a passive bistatic radar (PBR) system in the surveillance or monitoring of an area has its advantages. For example, a PBR system is able to utilize any available signal of opportunity (for example, broadcasting, communication, or radio navigation signals) for the purposes of surveillance. With this in mind, there are potentially many research areas to be explored; in particular, the capability of signals from existing and future communication systems, such as 4G and 5G. Long-Term Evolution (LTE) is the world's most current communication system. Given this fact, this paper presents the latest feasibility studies and experimental results from using LTE signals in PBR applications. Details are provided about aspects such as signal characteristics, experimental configurations, and SNR studies. Six experimental scenarios are carried out to investigate the detection performance of our proposed system on ground-moving targets. The ability to detect is demonstrated through use of the cross-ambiguity function. The detection results suggest that LTE signals are suitable as a source signal for PBR.

• Journal of Information Technology Applications and Management
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• v.25 no.1
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• pp.19-32
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• 2018

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection method using Artificial Intelligence techniques over the past years. But, unfortunately, there have been no prior studies proposed an automated fake news detection method for Korean news. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (Topic Modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as multiple discriminant analysis, case based reasoning, artificial neural networks, and support vector machine can be applied. To validate the effectiveness of the proposed method, we collected 200 Korean news from Seoul National University's FactCheck (http://factcheck.snu.ac.kr). which provides with detailed analysis reports from about 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1383-1390
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• 2012

In this paper, we propose a robust pupil detection method using rank order filter and pixel value difference in facial image. We have detected the potential pupil candidates using rank order filter. Many false pupil candidates found at eyebrow are removed using the fact that the pixel difference is much at the boundary between pupil and sclera. The rest pupil candidates are grouped into pairs. Each pair is verified according to geometric constraints such as the angle and the distance between two candidates. A fitness function is obtained for each pair using the pixel values of two pupil regions, we select a pair with the smallest fitness value as a final pupil. The experiments have been performed for 400 images of the BioID face database. The results show that it achieves more than 90% accuracy, and especially the proposed method improves the detection rate and high accuracy for face with spectacle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1892-1897
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• 2017

This paper proposes a finger counting algorithm for a hand with stuck fingers. The proposed algorithm is based on the fact that straight line type shadows are inevitably generated between fingers. It divides the hand region into the thumb region and the four fingers region for effective shadow detection, and generates an edge image in each region. Projection curves are generated by appling a line detection and a projection technique to each edge image, and the peaks of the curves are detected as candidates for finger shadows. And then peaks due to finger shadows are extracted from them and counted. In the finger counting experiment on hand images expressing various shapes with stuck fingers, the counting success rate is from 83.3% to 100% according to the number of fingers, and 93.1% on the whole. It also shows that if hand images are generated under controlled conditions, the failure cases can be sufficiently improved.

• The Journal of the Acoustical Society of Korea
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• v.20 no.1
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• pp.21-29
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• 2001

In order to detect the precise port/starboard direction of arrival of target signal in real noisy ocean environments, Inverse beamforming (IBF) algorithm is surveyed theoretically and the detection performances of IBF are analyzed with simulations. Cardioid Inverse beamforming algorithm was proposed for port/starboard discrimination and the performance was studied with simulations. It is shown that IBF has a 3dB array gain advantage over Conventional beamforming (CBF) under ideal conditions. This 3 dB advantage is proven theoretically and illustrated with simulations. The fact that the IBF beamwidth is narrower than the CBF beamwidth by a factor of 0.68 proves the performance of defection and spatial resolution improvement. Comparing the simulation results of Cardioid Inverse beamforming and Conventional Cardioid beamforming, it is shown that Cardioid Inverse beamformer has enhanced performance in minimum detection level, detection accuracy and resolution. Due to the results of moving target bearing detection test in endfire, it is shown that Cardioid Inverse beamformer has better performance, comparing the Conventional Cardioid beamformer.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.8
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• pp.1787-1792
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• 2005

The join of remote sensing and GIS(Geographic Information System) could be useful in various fields of marine information and land information as well as ITS(Intelligent Transport Systems). This paper is LPF(Low Pass Filtering) for the extraction of island detection in coastal zone Iron SPOT imagery which is 10m resolution photograph. The study area is based on the southern sea in korea. Sobel operator performed the extraction of island detection in coastal zone after the LPF processing by remote sensing. And, GIS was used to generate from raster to vector data. As the result, The best way prove out the 5${\times}$5 convolution mask about the LPF processing of island detection in coastal zone. It is judged the research which it sees with the fact that the presentation of very scientific and reasonable data will be possible from the oceanic dispute will occur from the EEZ(Exclusive Economic Zone).

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.19-39
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• 2018

Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding "artificial intelligence R&D challenge" competition on the title of "searching for fake news." Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields, there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.