• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.47-50
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• 2002

단백질 모티프(motif)란 유사한 기능을 가진 여러 단백질 서열에서 공통적으로 발견되는 패턴으로서 단백질의 기능을 예측하는 단서로 활용된다. 현재 Prosite, Pfam 등의 데이터베이스에서 정규식(regular expression), 가중치 행렬(weighted matrix). 은닉 마코프 모델(hidden Markov model)의 형태로 4천여종 이상의 모티프가 등록되어 있다. 하지만, 이러한 데이터베이스는 모티프와 단백질간의 일대일 관계만을 저장하고 있기 때문에, 모티프 간의 연관성을 파악하기는 어렵다. 본 논문에서는 모티프 간의 연관 관계를 연관 규칙의 형태로 발견하는 데이터 마이닝 기법을 제시한다. 아울러 HITS 데이터베이스로부터 입수한 단백질-모티프 데이터베이스에 본 기법을 적용함으로써 상당히 높은 연관성을 갖는 모티프 집단이 실제로 존재한다는 것을 밝힌다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1330-1332
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• 2015

최근 지능형 감시 시스템에서 비정상 행동들을 자동으로 감지하는 연구가 활발히 진행되고 있다. 그러나 해결하기 힘든 몇 가지 이슈들이 있는데, 주어진 입력 영상에서 군중들이 중첩될 때 각각의 객체를 인식하는데 어려움이 있다는 점과 비정상 행동을 나타내는 훈련 데이터셋이 제한적이라는 점이다. 이러한 문제들을 해결하기 위해 우리는 군중 영상에서 비정상 행동들을 인식하는 새로운 프레임워크를 제안한다. 제안된 방법은 크게 특징추출모듈과 추출된 특징들을 이용한 행동인식모듈로 구성된다. 중첩문제를 해결하기 위해 움직임 에너지와 고정 에너지를 특성으로 정의하였고 위에 언급한 특징추출모듈에서 두 에너지 값을 계산한다. 그리고 정상/비정상 행동들은 HMM과 최적의 임계값을 도출하는 알고리즘을 사용하는 행동인식모듈에 의해 분류된다. 우리가 제안한 방법은 인공 데이터셋과 실제 비디오 영상 데이터셋을 이용한 실험에 의해 증명한다.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1034-1037
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• 2015

In recent years in the field of robotics, various methods have been developed to create an intimate relationship between people and robots. These methods include speech, vision, and biometrics recognition as well as gesture-based interaction. These recognition technologies are used in various wearable devices, smartphones and other electric devices for convenience. Among these technologies, gesture recognition is the most commonly used and appropriate technology for wearable devices. Gesture recognition can be classified as contact or noncontact gesture recognition. This paper proposes contact gesture recognition with IMU and EMG sensors by using the hidden Markov model (HMM) twice. Several simple behaviors make main gestures through the one-stage HMM. It is equal to the Hidden Markov model process, which is well known for pattern recognition. Additionally, the sequence of the main gestures, which comes from the one-stage HMM, creates some higher-order gestures through the two-stage HMM. In this way, more natural and intelligent gestures can be implemented through simple gestures. This advanced process can play a larger role in gesture recognition-based UX for many wearable and smart devices.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.11-20
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• 2011

Pictorial Structures is known as an effective method that recognizes and tracks human poses. In this paper, the upper body pose is also tracked by PS and a particle filter(PF). PF is one of dynamic programming methods. But Markov chain-based dynamic motion model which is used in dynamic programming methods such as PF, couldn't predict effectively the highly articulated upper body motions. Therefore PF often fails to track upper body pose. In this paper we propose the key pose-based proposal distribution for proper particle prediction based on the similarities between key poses and an upper body silhouette. In the experimental results we confirmed our 70.51% improved performance comparing with a conventional method.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.157-165
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• 2009

In this paper, we propose a distributed model that recognize ADLs of human can be occurred in daily living places. We collect and analyze user's environmental, location or activity information by simple sensor attached home devices or utensils. Based on these information, we provide a lifecare services by inferring the user's life pattern and health condition. But in order to provide a lifecare services well-refined activity recognition data are required and without enough inferred information it is very hard to build an ADL activity recognition model for high-level situation awareness. The sequence that generated by sensors are very helpful to infer the activities so we utilize the sequence to analyze an activity pattern and propose a distributed linear time inference algorithm. This algorithm is appropriate to recognize activities in small area like home, office or hospital. For performance evaluation, we test with an open data from MIT Media Lab and the recognition result shows over 75% accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.85-94
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• 2018

The change of land covers in 2026 was prediceted based on the change of urbanization in 1996, 2006 and 2016 in Seoul and surrounding areas in this study. Landsat images were used as the basic data, and MLP (Multilayer Perceptron) and MCA (Markov Chain Analysis) were integrated for future prediction for the study area. The land cover transition potentials were calculated by setting up sub-models in MLP and the driving factors of land cover transition from 1996 to 2006 and transition probabilities were calculated using MCA to generate the land cover map of 2016. This was compared to the land cover map of 2016 from Landsat. MLP and MCA were verified and the future land covers of 2026 were predicted using the land cover map from Landsat in 2006 and 2016. As a result, it was predicted that the major land cover changes from 1996 to 2006 were from Barren Land and Grass Land to Builtup Area, and the same trend of transition will be remained for 2026. This study is meaningful in that it is applied for the first time to predict the future coating change in Seoul and surrounding areas by the MLP-MCA method.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.3
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• pp.170-175
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• 2009

In this paper, we propose real time automatic speaker verification (ASV) system to protect personal information on personal digital assistant (PDA) device. Recently, the capacity of PDA has extended and been popular, especially for mobile environment such as mobile commerce (M-commerce). However, there still exist lots of difficulties for practical application of ASV utility to PDA device because it requires too much computational complexity. To solve this problem, we apply the method to relieve the computational burden by performing the preprocessing such as spectral subtraction and speech detection during the speech utterance. Also by applying the hidden Markov model (HMM) optimal state alignment and the sequential probability ratio test (SPRT), we can get much faster processing results. The whole system implementation is simple and compact enough to fit well with PDA device's limited memory and low CPU speed.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.597-602
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• 2014

We propose hybrid-sensing system for human tracking. This system uses laser scanners and image sensors and is applicable to wide and crowded area such as hallway of university. Concretely, human tracking using laser scanners is at base and image sensors are used for human identification when laser scanners lose persons by occlusion, entering room or going up stairs. We developed the method of human identification for this system. Our method is following: 1. Best-shot images (human images which show human feature clearly) are obtained by the help of human position and direction data obtained by laser scanners. 2. Human identification is conducted by calculating the correlation between the color histograms of best-shot images. It becomes possible to conduct human identification even in crowded scenes by estimating best-shot images. In the experiment in the station, some effectiveness of this method became clear.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.552-558
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• 2002

Recently, based on the human speech recognition (HSR) model of Fletcher, the multi-band speech recognition has been intensively studied by many researchers. As a new automatic speech recognition (ASR) technique, the multi-band speech recognition splits the frequency domain into several sub-bands and recognizes each sub-band independently. The likelihood scores of sub-bands are weighted according to reliabilities of sub-bands and re-combined to make a final decision. This approach is known to be robust under noisy environments. When the noise is stationary a sub-band SNR can be estimated using the noise information in non-speech interval. However, if the noise is non-stationary it is not feasible to obtain the sub-band SNR. This paper proposes the inverse sub-band distance (ISD) weighting, where a distance of each sub-band is calculated by a stochastic matching of input feature vectors and hidden Markov models. The inverse distance is used as a sub-band weight. Experiments on 1500∼1800㎐ band-limited white noise and classical guitar sound revealed that the proposed method could represent the sub-band reliability effectively and improve the performance under both stationary and non-stationary band-limited noise environments.