• Title/Summary/Keyword: HMM(HMM)

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Recognition of Fighting Motion using a 3D-Chain Code and HMM (3차원 체인코드와 은닉마르코프 모델을 이용한 권투모션 인식)

  • Han, Chang-Ho;Oh, Choon-Suk;Choi, Byung-Wook
    • Journal of Institute of Control, Robotics and Systems
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    • v.16 no.8
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    • pp.756-760
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    • 2010
  • In this paper, a new method to recognize various motions of fighting with an aid of HMM is proposed. There are four kinds of fighting motion such as hook, jab, uppercut, and straight as the fighting motion. The motion graph is generalized to define each motion in motion data and the new 3D-chain code is used to convert motion data to motion graphs. The recognition experiment has been performed with HMM algorithm on motion graphs. The motion data is captured by a motion capture system developed in this study and by five actors. Experimental results are given with relatively high recognition rate of at least 85%.

R&D Trend of 4G Radio Transmission System: HMM at ETRI (ETRI 4세대 무선전송 시스템: HMM 개발 동향)

  • Lee, S.K.;Chang, K.H.;Hwang, S.K.
    • Electronics and Telecommunications Trends
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    • v.17 no.5 s.77
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    • pp.15-25
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    • 2002
  • 사용자 요구의 다양화와 그에 따른 트래픽 증가 등 현재의 IMT-2000 기반에서 수용할 수 없는 보다 복잡한 이동 멀티미디어 환경이 도래하고 있다. 이러한 이동 멀티미디어 서비스를 원활히 제공하기 위해 ITU-R을 중심으로 한 전세계 이동통신 업계는 이미 4G 시스템 규격 및 개발에 상당한 진전을 보이고 있다. 이에 따라 ETRI도 지난 2002년 1월부터 4세대 이동통신 초고속 패킷 무선전송 기술 연구 개발에 착수하였다. 이 연구 개발은 크게 전송 시스템과 네트워크 분야로 나뉘어지며, 전송 시스템은 다시 low-tier 서비스를 위한 HMI 시스템과 high-tier 서비스를 위한 HMM 시스템을 두 축으로 하여 진행되고 있다. 본 고에서는 HMM 시스템의 연구 개발 계획, 전개 방향 및 목표 등을 간략하게 소개하고자 한다.

Isolated Word Recognition Using Hidden Markov Models with Bounded State Duration (제한적 상태지속시간을 갖는 HMM을 이용한 고립단어 인식)

  • 이기희;임인칠
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.32B no.5
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    • pp.756-764
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    • 1995
  • In this paper, we proposed MLP(MultiLayer Perceptron) based HMM's(Hidden Markov Models) with bounded state duration for isolated word recognition. The minimum and maximum state duration for each state of a HMM are estimated during the training phase and used as parameters of constraining state transition in a recognition phase. The procedure for estimating these parameters and the recognition algorithm using the proposed HMM's are also described. Speaker independent isolated word recognition experiments using a vocabulary of 10 city names and 11 digits indicate that recognition rate can be improved by adjusting the minimum state durations.

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Determining the Optimal Number of Signal Clusters Using Iterative HMM Classification

  • Ernest, Duker Junior;Kim, Yoon Joong
    • International journal of advanced smart convergence
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    • v.7 no.2
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    • pp.33-37
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    • 2018
  • In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.

A Study on the HMM Structure for Classifying Dog Breeds (개의 품종 분류를 위한 HMM 구조의 연구)

  • Lim, Seong-Min;Kim, Yoon-Joong
    • Proceedings of the Korean Information Science Society Conference
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    • 2012.06b
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    • pp.477-479
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    • 2012
  • 개의 발성은 성도의 물리적인 특징에 따라 고유의 특정 포먼트를 만들어 내며 개의 품종에 따라 다른 물리적 특징을 가지므로 개의 발성을 HMM(Hidden Markov Model)으로 모델링하여 개의 품종을 분류하는 연구를 하였다. 주파수 특징은 MFCC(Mel Frequency Cepstral Coefficients) 12차, 에너지 컴포넌트 1차, 델타 13차, 억셀러레이션(Acceleration) 13차, 총 39차 벡터를 사용하였다. 개의 품종 분류에 적합한 HMM 구조의 설계를 위하여 기본 좌우 모델, 좌우 모델, 좌우 모델2, 전후진 모델, 총 4가지를 제안하고 실험하여 성능을 비교분석하였다. 이 중 전후진 모델이 가장 바람직한 모델로 검증 되었다. 본 모델은 다음과 같은 장점을 갖는다. (1) 기본 좌우 모델과 마찬가지로 1~2회 발성을 갖는 데이터가 입력되어도 처음에서 마지막 상태까지의 이동단계가 최소 3번까지 가능하므로 적은 횟수의 발성 데이터도 처리가 가능하다. (2) 다수 반복된 발성 데이터의 신호도 처리가 가능하다. 즉, 본 모델은 상태의 이동이 후진도 가능하므로 5회이상 반복된 발성 데이터의 신호의 처리도 가능하다.

A Short Report on the Markov Property of DNA Sequences on 200-bp Genomic Units of ENCODE/Broad ChromHMM Annotations: A Computational Perspective

  • Park, Hyun-Seok
    • Genomics & Informatics
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    • v.16 no.3
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    • pp.65-70
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    • 2018
  • The non-coding DNA in eukaryotic genomes encodes a language which programs chromatin accessibility, transcription factor binding, and various other activities. The objective of this short report was to determine the impact of primary DNA sequence on the epigenomic landscape across 200-base pair genomic units by integrating nine publicly available ChromHMM Browser Extensible Data files of the Encyclopedia of DNA Elements (ENCODE) project. The nucleotide frequency profiles of nine chromatin annotations with the units of 200 bp were analyzed and integrative Markov chains were built to detect the Markov properties of the DNA sequences in some of the active chromatin states of different ChromHMM regions. Our aim was to identify the possible relationship between DNA sequences and the newly built chromatin states based on the integrated ChromHMM datasets of different cells and tissue types.

A Study on Character Recognition using HMM and the Mason's Theorem

  • Lee Sang-kyu;Hur Jung-youn
    • Proceedings of the IEEK Conference
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    • summer
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    • pp.259-262
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    • 2004
  • In most of the character recognition systems, the method of template matching or statistical method using hidden Markov model is used to extract and recognize feature shapes. In this paper, we used modified chain-code which has 8-directions but 4-codes, and made the chain-code of hand-written character, after that, converted it into transition chain-code by applying to HMM(Hidden Markov Model). The transition chain code by HMM is analyzed as signal flow graph by Mason's theory which is generally used to calculate forward gain at automatic control system. If the specific forward gain and feedback gain is properly set, the forward gain of transition chain-code using Mason's theory can be distinguished depending on each object for recognition. This data of the gain is reorganized as tree structure, hence making it possible to distinguish different hand-written characters. With this method, $91\%$ recognition rate was acquired.

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Isolated word recognition using the SOFM-HMM and the Inertia (관성과 SOFM-HMM을 이용한 고립단어 인식)

  • 윤석현;정광우;홍광석;박병철
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.6
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    • pp.17-24
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    • 1994
  • This paper is a study on Korean word recognition and suggest the method that stabilizes the state-transition in the HMM by applying the `inertia' to the feature vector sequences. In order to reduce the quantized distortion considering probability distribution of input vectors, we used SOFM, an unsupervised learning method, as a vector quantizer, By applying inertia to the feature vector sequences, the overlapping of probability distributions for the response path of each word on the self organizing feature map can be reduced and the state-transition in the Hmm can be Stabilized. In order to evaluate the performance of the method, we carried out experiments for 50 DDD area names. The results showed that applying inertia to the feature vector sequence improved the recognition rate by 7.4% and can make more HMMs available without reducing the recognition rate for the SOFM having the fixed number of neuron.

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Features Detection in Face eased on The Model (모델 기반 얼굴에서 특징점 추출)

  • 석경휴;김용수;김동국;배철수;나상동
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2002.05a
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    • pp.134-138
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    • 2002
  • The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

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A Dynamic Data Replica Deletion Strategy on HDFS using HMM (HMM을 이용한 HDFS 기반 동적 데이터 복제본 삭제 전략)

  • Seo, Young-Ho;Youn, Hee-Yong
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2014.07a
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    • pp.241-244
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    • 2014
  • 본 논문에서는 HDFS(Hadoop Distributed File System)에서 문제되고 있는 복제정책의 개선을 위해 HMM(Hidden Markov Model)을 이용한 동적 데이터 복제본 삭제 전략을 제안한다. HDFS는 대용량 데이터를 효과적으로 처리할 수 있는 분산 파일 시스템으로 높은 Fault-Tolerance를 제공하며, 데이터의 접근에 높은 처리량을 제공하여 대용량 데이터 집합을 갖는 응용 프로그램에 최적화 되어있는 장점을 가지고 있다. 하지만 HDFS 에서의 복제 메커니즘은 시스템의 안정성과 성능을 향상시키지만, 추가 블록 복제본이 많은 디스크 공간을 차지하여 유지보수 비용 또한 증가하게 된다. 본 논문에서는 HMM과 최상의 상태 순서를 찾는 알고리즘인 Viterbi Algorithm을 이용하여 불필요한 데이터 복제본을 탐색하고, 탐색된 복제본의 삭제를 통하여 HDFS의 디스크 공간과 유지보수 비용을 절약 할 수 있는 전략을 제안한다.

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