• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.83.2-83.2
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• 2012

Type I AGNs are classified by the presence of broad emission lines while Type II AGNs show narrow emission lines only. All-sky surveys such as SDSS provide large AGN samples for statistical studies. However, the AGN samples suffer selection bias due to the incomplete selection criteria. To investigate the missing Type I AGNs in optical spectroscopic surveys, we start with a sample of SDSS Type II AGNs at 0.02 < z < 0.05, using the MPA-JHU SDSS DR7 catalog. We search for the hidden broad $H{\alpha}$ component with both visual inspection and the multi-component spectral decomposition method. Out of 1383 Type II AGNs, we find a total of 62 missing Type I AGNs (~4.5%). The sample has mean black hole mass, log $(M_{BH}/M_{SUN))=6.48{\pm}0.53$, and luminosity, log $(L_{H{\alpha}}/ergs^{-1})=40.52{\pm}0.33$, with Eddington ratio, log $(L_{bol}/L_{Edd})=-1.51{\pm}0.41$. We will describe the sample and present the $M_{BH}-{\sigma}_*$, and $M_{BH}-M_*$ relations of the sample in the context of the BH-galaxy coevolution.

• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.453-469
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• 2022

The study of immune cellular composition has been of great scientific interest in immunology because of the generation of multiple large-scale data. From the statistical point of view, such immune cellular data should be treated as compositional. In compositional data, each element is positive, and all the elements sum to a constant, which can be set to one in general. Standard statistical methods are not directly applicable for the analysis of compositional data because they do not appropriately handle correlations between the compositional elements. In this paper, we review statistical methods for compositional data analysis and illustrate them in the context of immunology. Specifically, we focus on regression analyses using log-ratio transformations and the alternative approach using Dirichlet regression analysis, discuss their theoretical foundations, and illustrate their applications with immune cellular fraction data generated from colorectal cancer patients.

• Journal of the Ergonomics Society of Korea
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• v.16 no.1
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• pp.119-126
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• 1997

Most of human factors experiments in nuclear industry domain produe lots of experimental data, thus much time is reauired to analyze the data. DAEXESS was developed to reduce resource demands necessary for the analysis work through systematic data analysis requirements and automated data processing based on computer technology. Physilolgical data, human behavior recording data, system log data and verbal protocl can be collected, synthesized and easily analyzed with with respect to time domain in DAEXESS so that analyser is able to look into inte- grated information on operating context. DAEXESS assists analyser to carry out qualitative and quantitative data analysis easily.

• Journal of Integrative Natural Science
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• v.11 no.1
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• pp.44-50
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• 2018

In this paper, we propose a context-aware power management (CPM) scheme using beacons to reduce the power consumption of personal computers (PCs). In the proposed CPM scheme, the PC, smartphone, control server, and Internet of Things (IoT) device are necessary. PC users first log in the control server using their smartphones and select PCs to turn on. Then, the selected PCs automatically go into three different modes, i.e., sleep, shutdown, and standby power off modes, in order when the PC users leave the PCs without turning off them. Further, we develop a testbed with the proposed CPM scheme using the Arduino with Bluetooth low energy (BLE) and relay modules. Finally, it is shown that the proposed CPM scheme outperforms the conventional scheme in terms of the power consumption.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.460-464
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• 2008

To improve and optimize user interfaces of the system, the accurate understanding of users' behavior is an essential prerequisite. Direct questions depend on user' s ambiguous memory and usability tests depend on the researchers' intention instead of users'. Furthermore, they do not provide with natural context of use. In this paper we described the work which examined users' behavior through log analysis in their own environment. 50 users were recruited by consumer segmentation and they were downloaded logging-software in their mobile phone. After two weeks, logged data were gathered and analyzed. The complementary methods such as a user diary and an interview were conducted. The result of the analysis showed the frequency of menu and key access, used time, data storage and several usage patterns. Also, it was found that users could be segmented into new groups by their usage patterns. The improvement of the mobile phone user interface was proposed based on the result of this study.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.217-220
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• 2007

This study outlines a small-sized dialog style ETRI Korean TTS system which applies a HMM based speech synthesis techniques. In order to build the VoiceFont, dialog-style 500 sentences were used in training HMM. And the context information about phonemes, syllables, words, phrases and sentence were extracted fully automatically to build context-dependent HMM. In training the acoustic model, acoustic features such as Mel-cepstrums, logF0 and its delta, delta-delta were used. The size of the VoiceFont which was built through the training is 0.93Mb. The developed HMM-based TTS system were installed on the ARM720T processor which operates 60MHz clocks/second. To reduce computation time, the MLSA inverse filtering module is implemented with Assembly language. The speed of the fully implemented system is the 1.73 times faster than real time.

• Journal of KIISE:Software and Applications
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• v.37 no.10
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• pp.736-744
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• 2010

Log data collected from mobile devices contain diverse and meaningful personal information. However, it is not easy to implement a context-aware mobile agent using this personal information due to the inherent limitation in mobile platform such as memory capacity, computation power and its difficulty of analysis of the data. We propose a method of selective inference for modular Bayesian Network for context-aware mobile agent with effectiveness and reliability. Each BN module performs inference only when it can change the result by comparing to the history module which contains evidences and posterior probability, and gets results effectively using a method of influence score of the modules. We adopt memory decay theory and virtual linking method for the evaluation of the reliability and conservation of casual relationship between BN modules, respectively. Finally, we confirm the usefulness of the proposed method by several experiments on mobile phones.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.709-713
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• 2009

Recently, a lot of research that applies data acquired from devices such as cameras and RFIDs to context aware services is being performed in the field on Life-Log and the sensor network. A variety of analytical techniques has been proposed to recognize various information from the raw data because video and audio data include a larger volume of information than other sensor data. However, manually watching a huge amount of media data again has been necessary to create supervised data for the update of a class or the addition of a new class because these techniques generally use supervised learning. Therefore, the problem was that applications were able to use only recognition function based on fixed supervised data in most cases. Then, we proposed a method of acquiring supervised data from a video sharing site where users give comments on any video scene because those sites are remarkably popular and, therefore, many comments are generated. In the first step of this method, words with a high utility value are extracted by filtering the comment about the video. Second, the set of feature data in the time series is calculated by applying functions, which extract various feature data, to media data. Finally, our learning system calculates the correlation coefficient by using the above-mentioned two kinds of data, and the correlation coefficient is stored in the DB of the system. Various other applications contain a recognition function that is used to generate collective intelligence based on Web comments, by applying this correlation coefficient to new media data. In addition, flexible recognition that adjusts to a new object becomes possible by regularly acquiring and learning both media data and comments from a video sharing site while reducing work by manual operation. As a result, recognition of not only the name of the seen object but also indirect information, e.g. the impression or the action toward the object, was enabled.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.550-552
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• 2005

유비쿼터스 환경의 발달과 함께 모바일 장비에서 수집되어지는 컨텍스트 로그를 활용한 연구가 활발히 진행되고 있다. 하지만 기존의 컨텍스트 정보를 사용한 연구는 사용자 모델링에 그 초점을 맞추거나 단순하게 수집된 정보를 정리하여 한눈에 알아보기 쉽게 보여주는 정도에 그치고 있다. 본 논문에서는 사용자에게 새로운 서비스를 제공하기 위한 방법으로서 모바일 컨텍스트 로그와 외부 센서를 통해 정보를 수집하여 학습한 베이지안 네트워크를 이용하여 랜드마크를 찾아내는 예측 모델을 제안한다. 베이지안 네트워크 설계는 사전에 수집된 컨텍스트 정보를 요일과 주별로 분류하여 각각에 대한 베이지안 네트워크를 cross validation하여 랜드마크 예측에 대한 정확도를 평가하였다. 그리고 분류에서 가장 많이 사용하고 있는 SVM 방법을 사용하여 제안한 방법과의 성능을 비교평가하였다. 랜드마크 예측에 대한 정확도는 주간별로 설계한 베이지안 네트워크보다 요일별로 설계한 베이지안 네트워크가 랜드마크를 예측하는데 정화도가 높음을 확인하였고, 베이지안 네트워크를 사용한 방법이 SVM을 사용한 방법보다. 예측에 한 정확성이 우수하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.65-67
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• 2011

스마트 홈은 유비쿼터스 환경을 가정 내에 실현하여 생활환경의 지능화, 환경친화적인 주거생활, 삶의 질 혁신을 추구하는 주거공간을 만들고자 하는 목표를 가지고 있다. 이러한 목표에 더 가까이 접근하기 위한 연구 및 개발이 이루어지고 있다. 그러나, 현재의 홈 네트워크 시스템은 서로 다른 사용자의 특성을 고려하지 않고 단일화된 서비스만을 제공하고 있다. 이에 본 논문에서는 스마트폰에 있는 GPS, Accelerometer, System Log 등의 정보를 활용하여 더 지능적인 서비스를 제시하고, 스마트 홈의 상황을 사용자가 좀 더 쉽게 파악하고 관리할 수 있도록 스마트폰 기반의 모니터링 및 제어 서비스를 제공한다.