• ETRI Journal
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• 제44권6호
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• pp.977-990
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• 2022

This study proposes a novel method for identifying the primary conspirators involved in terrorist activities. To map the information related to terrorist activities, we gathered information from different sources of real cases involving terrorist attacks. We extracted useful information from available sources and then mapped them in the form of terrorist networks, and this mapping provided us with insights in these networks. Furthermore, we came up with a novel centrality measure for identifying the primary conspirators of a terrorist attack. Because the leaders of terrorist attacks usually direct conspirators to conduct terrorist activities, we designed a novel algorithm that can identify such leaders. This algorithm can identify terrorist attack leaders even if they have less connectivity in networks. We tested the effectiveness of the proposed algorithms on four real-world datasets and conducted an experimental evaluation, and the proposed algorithms could correctly identify the primary conspirators and leaders of the attacks in the four cases. To summarize, this work may provide information support for security agencies and can be helpful during the trials of the cases related to terrorist attacks.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 하계학술대회
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• pp.714-716
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• 2020

We propose a unique client-driven music genre classification solution, that can identify the music genre using a deep convolutional neural network operating on the time-domain signal. The proposed method uses the client device (Jetson TX2) computational resources to identify the music genre. We use the industry famous GTZAN genre collection dataset to get reliable benchmarking performance. HTTP live streaming (HLS) client and server sides are designed locally to validate the effectiveness of the proposed method. HTTP persistent broadcast connection is adapted to reduce corresponding responses and network bandwidth. The proposed model can identify the genre of music files with 97% accuracy. Due to simplicity and it can support a wide range of client hardware.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2005년도 춘계학술대회
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• pp.485-493
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• 2005

Using 1991 FDIC financial statement data, we develop fuzzy clusters of the data set. We also identify the distinctive characteristics of the fuzzy clustering algorithm and compare the closest hard-partitioning result of the fuzzy clustering algorithm with the outcomes of two self-organizing neural networks. When nine clusters are used, our analysis shows that the fuzzy clustering method distinctly groups failed and extreme performance banks from control (healthy) banks. The experimental results also show that the fuzzy clustering method and the self-organizing neural networks are promising tools in identifying potentially failing banks.

• 복식문화연구
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• 제5권4호
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• pp.80-88
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• 1997

The purpose of this study was to identify the denotating of visual message and linguistic message in the advertisement of Jean-wear, and to identify the image type in the advertisement of Jean-wear. Content analysis was done using the advertisement of Jean-wear from several fashion magazines which were published from January, 1996 to August, 1997. The advertisement of Jean-wear reflects the reality using the photos, and is image appeal type, appeals to the model\`s lifestyle, the western value. And that mostly singles person was appeared in the advertisement means solitude in modern society.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2004년도 총회 및 춘계학술발표회
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• pp.311-314
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• 2004

To identify the effect of geology and land use, the hydrogeochemical and multivariate statitstical analysis were executed for stream water collected in headwater region of Daecheong reservoir. Hydrogeochemical analysis was showed the effect of weathering process such as dissolution of calc-silicate minerals to hydrochemistry of stream water with contrasting geology. Cluster and principal components analysis can also help to identify the source of dissolved components in stream water.

• 대한기계학회논문집
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• 제7권2호
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• pp.138-144
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• 1983

To evaluate the dynamic characteristics of machine tool system, the system is modelled as a closed-loop system composed of cutting process and improved machine tool structures. The proposed machine tool structure model is constructed in consideration of energy transfer through the system. A new methodology to identify the machine tool dynamics by adopting impulse response and impulse cutting techniques is also proposed. It is shown that the methodology is successfully applied to a machine tool system to identify its dynamic characteristics employing the improved model.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2004년도 한국우주과학회보 제13권1호
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• pp.93-93
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• 2004

We have examined the solar wind dynamic pressure enhancements during geomagnetic storm main phase. The Dst index has been used to identify more than 100 geomagnetic storms which occurred in the time interval of 1997 to 2001. We have selected only the events having the minimum Dst value less than -50 nT. In order to identify the pressure impact, we have looked at the low latitude ground H data as well as the solar wind pressure data themselves. (omitted)

• Wind and Structures
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• 제19권3호
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• pp.271-294
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• 2014

The impact of artefacts in archived wind observations on the design wind speed obtained by extreme value analysis is demonstrated using case studies. A signpost protocol for detecting candidate artefacts is described and its performance assessed by comparing results against previously validated data. The protocol targets artefacts by exploiting the serial correlation between observations. Additional "sieve" algorithms are proposed to identify types of correctable artefact from their "signature" in the data. In extreme value analysis, artefacts displace valid observations only when they are larger, hence always increase the design wind speed. Care must be taken not identify large valid values as artefacts, since their removal will tend to underestimate the design wind speed.

• 한국환경독성학회:학술대회논문집
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• 한국환경독성학회 2001년도 춘계심포지움 및 학술발표회
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• pp.126-126
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• 2001

Over the last few years, an increased awareness of endocrine disrupting chemicals (EDCs) and their potential to affect wildlife and humans has produced a demand for practical screening methods to identify endocrine activity in a wild range of environmental and industrial chemicals. It is clear that in vivo methods will be required to identify adverse effects produced by these chemicals. (omitted)

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.823-829
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• 2001

Even though a rapid development of modem medical science, paralysis disease is a highly dangerous and murderous disease. Shin et al. (1978) constructed the diagnosis expert system which identify a type of the paralysis disease from symptoms of a paralysis disease patients by using the canonical discriminant analysis. The decision tree-based analysis, however, has advantages over the method used in Shin et al. (1998), such as it does not need assumptions - linearity and normality, and suggest appropriate diagnosis procedure which is easily explained. In this paper, we applied the decision tree to construct the model which Identify a type of the paralysis disease.