• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.73-82
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• 2022

Marine fisheries resources face major anthropogenic threat from unregulated fishing activities; thus require precise detection for protection through marine surveillance. Korea developed an efficient land-based small fishing vessel monitoring system using real-time V-Pass data. However, those data directly do not provide information on fishing activities, thus further efforts are necessary to differentiate their activity status. In Korea, especially in Busan, longlining is practiced by many small fishing vessels to catch several types of fishes that need to be identified for proper monitoring. Therefore, in this study we have improved the existing fishing status classification method by applying Hidden Markov Model (HMM) on V-Pass data in order to further classify their fishing status into three groups, viz. non-fishing, longlining and other types of fishing. Data from 206 fishing vessels at Busan on 05 February, 2021 were used for this purpose. Two tiered HMM was applied that first differentiates non-fishing status from the fishing status, and finally classifies that fishing status into longlining and other types of fishing. Data from 193 and 13 ships were used as training and test datasets, respectively. Using this model 90.45% accuracy in classifying into fishing and non-fishing status and 88.23% overall accuracy in classifying all into three types of fishing statuses were achieved. Thus, this method is recommended for monitoring the activities of small fishing vessels equipped with V-Pass, especially for detecting longlining.

• Molecules and Cells
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• v.20 no.1
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• pp.35-42
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• 2005

A novel combined method for locating box H/ACA small nucleolar RNAs (snoRNAs) is described, together with a software tool. The method adopts both a probabilistic hidden Markov model (HMM) and a minimum free energy (MFE) rule, and filters possible candidate box H/ACA snoRNAs obtained from genomic DNA sequences. With our novel method 12 known box H/ACA snoRNAs, and one strong candidate were identified in 30 nucleolar protein genomic sequences.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.4
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• pp.403-410
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• 2000

In this paper, the development of a stock information retrieval system using speech recognition and its features are described. The system is based on DHMM (discrete hidden Markov model) and PLUs (phonelike units) are used as the basic unit for recognition. End-point detection and echo cancellation are included to facilitate speech input. Continuous speech recognizer is implemented to allow multi-word speech. Data collected over several months are analyzed.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.373-377
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• 2017

This paper presents a voice-activity detection (VAD) method for sound sequences with various SNRs. For real-time VAD applications, it is inadequate to employ a post-processing for the removal of burst clippings from the VAD output decision. To tackle this problem, building on the bi-level hidden Markov model, for which a state layer is inserted into a typical hidden Markov model (HMM), we formulated a robust method for VAD not requiring any additional post-processing. In the method, a forward-inference-ratio test was devised to detect the speech endpoints and Mel-frequency cepstral coefficients (MFCC) were used as the features. Our experiment results show that, regarding different SNRs, the performance of the proposed approach is more outstanding than those of the conventional methods.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.499-503
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• 2008

In this paper, a novel human activity recognition method is proposed which utilizes independent components of activity shape information from image sequences and Hidden Markov Model (HMM) for recognition. Activities are represented by feature vectors from Independent Component Analysis (ICA) on video images, and based on these features; recognition is achieved by trained HMMs of activities. Our recognition performance has been compared to the conventional method where Principle Component Analysis (PCA) is typically used to derive activity shape features. Our results show that superior recognition is achieved with our proposed method especially for activities (e.g., skipping) that cannot be easily recognized by the conventional method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.1-2
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• 2014

본 논문에서는 hidden Markov model (HMM) 기반의 스테레오 신호로부터 서라운드 오디오 신호를 생성하는 기법을 제안한다. 먼저 5.1 채널 오디오 훈련 데이터베이스로부터 MDCT 영역에서 전방/서라운드 채널의 서브밴드 에너지를 프레임 단위로 계산하고, 이를 특징 벡터로 하여 좌측과 우측 채널 두 개의 HMM 이 구성된다. 다음으로, 입력된 스테레오 신호에 대해 HMM decoding 을 통해 서라운드 채널의 MDCT 영역의 서브밴드 에너지가 예측된다. 이 예측된 서브밴드 에너지로부터 역 MDCT 를 통해 서라운드 오디오 신호가 생성된다. 제안된 방법의 성능평가를 위해 MUSHRA 청취 실험을 수행한 결과, 제안된 HMM 기반의 방식으로 생성된 서라운드 오디오 신호가 기존의 패시브 서라운드 디코딩 기반으로 생성된 서라운드 신호에 비해 높은 선호도를 보였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.231-236
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• 2009

본 연구에서는 집이나 사무실과 같은 일상 공간에서 발생할 수 있는 연간의 일상생활행위 (ADL: Activities of Daily Living) 들을 인지하는 분산 모델을 제시한다. 사용자의 환경 정보, 위치 정보 및 행위 정보를 간단한 센서들이 부착된 가정용 기기들과 가구, 식기들을 통해 무선 센서 네트워크를 통해 수집하며 분석한다. 하지만 이와 같은 다양한 기기의 활용과 충분히 분석되어지지 않은 데이터들은 본 논문에서 제시하는 일상 환경에서 고차원의 ADL 모델을 구축하기 어렵게 한다. 그러나 ADL들이 생성하는 센서 데이터들과 센서 데이터들의 순서들은 어떤 행위가, 이루어지고 있는지 인지할 수 있도록 도와준다. 따라서 이 센서 데이터들의 순서를 특정 행위 패턴을 분석하는 데 활용하고, 이를 통해 분산 선형 시간 추론 알고리즘을 제안한다. 이 알고리즘은 센서 네트워크와 같은 소규모 시스템에서 행위를 인지하는 데 적절하다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.178-180
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• 2003

EEG-based brain computer interface(BCI) provides a new communication channel between human brain and computer. EEG data is a multivariate time series so that hidden Markov model (HMM) might be a good choice for classification. However EEG is very noisy data and contains artifacts, so useful features mr expected to improve the performance of HMM. In this paper we addresses the usefulness of principal component features with Hidden Markov model (HHM). We show that some selected principal component features can suppress small noises and artifacts, hence improves classification performance. Experimental study for the classification of EEG data during imagination of a left, right up or down hand movement confirms the validity of our proposed method.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.826-836
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• 2016

Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.22-29
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• 2016

Hidden Markov Model is one of the most successful and popular tools for modeling real world sequential data. Real world signals come in a variety of shapes and variabilities, among which temporal and spectral ones are the prime targets that the HMM aims at. A new problem that is gaining increasing attention is characterizing missing observations in incomplete data sequences. They are incomplete in that there are holes or omitted measurements. The standard HMM algorithms have been developed for complete data with a measurements at each regular point in time. This paper presents a modified algorithm for a discrete HMM that allows substantial amount of omissions in the input sequence. Basically it is a variant of Baum-Welch which explicitly considers the case of isolated or a number of omissions in succession. The algorithm has been tested on online handwriting samples expressed in direction codes. An extensive set of experiments show that the HMM so modeled are highly flexible showing a consistent and robust performance regardless of the amount of omissions.