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Journal articles on the topic 'Hiden Markov model'

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Author: Grafiati

Published: 1 July 2022

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1

Kim, Sae-Joong, Young-Han Jung, and Chong-Kwan Heo. "Analysis sports using the Hidden Markov Model." Korean Journal of Sports Science 26, no. 3 (June 30, 2017): 1301–9. http://dx.doi.org/10.35159/kjss.2017.06.26.3.1301.

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2

Lay, Khin Khin, and Aung Cho. "Myanmar Named Entity Recognition with Hidden Markov Model." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 1144–47. http://dx.doi.org/10.31142/ijtsrd24012.

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3

Buckby, Jodie, Ting Wang, Jiancang Zhuang, and Kazushige Obara. "Model Checking for Hidden Markov Models." Journal of Computational and Graphical Statistics 29, no. 4 (May 14, 2020): 859–74. http://dx.doi.org/10.1080/10618600.2020.1743295.

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4

Ghods, Vahid, and Mohammad Karim Sohrabi. "Online Farsi Handwritten Character Recognition Using Hidden Markov Model." Journal of Computers 11, no. 2 (March 2016): 169–75. http://dx.doi.org/10.17706/jcp.11.2.169-175.

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5

Bhatia, Varsha. "Applications of Hidden Markov Model in Wireless Sensor Network." International Journal of Psychosocial Rehabilitation 24, no. 4 (April 30, 2020): 6549–57. http://dx.doi.org/10.37200/ijpr/v24i4/pr2020465.

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6

Ye, Fei, and Yifei Wang. "A Novel Method for Decoding Any High-Order Hidden Markov Model." Discrete Dynamics in Nature and Society 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/231704.

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This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.

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7

Grewal, Jasleen K., Martin Krzywinski, and Naomi Altman. "Markov models — hidden Markov models." Nature Methods 16, no. 9 (August 30, 2019): 795–96. http://dx.doi.org/10.1038/s41592-019-0532-6.

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8

Tumilaar, Kezia, Yohanes Langi, and Altien Rindengan. "Hidden Markov Model." d'CARTESIAN 4, no. 1 (February 10, 2015): 86. http://dx.doi.org/10.35799/dc.4.1.2015.8104.

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Hidden Markov Models (HMM) is a stochastic model and is essentially an extension of Markov Chain. In Hidden Markov Model (HMM) there are two types states: the observable states and the hidden states. The purpose of this research are to understand how hidden Markov model (HMM) and to understand how the solution of three basic problems on Hidden Markov Model (HMM) which consist of evaluation problem, decoding problem and learning problem. The result of the research is hidden Markov model can be defined as . The evaluation problem or to compute probability of the observation sequence given the model P(O|) can solved by Forward-Backward algorithm, the decoding problem or to choose hidden state sequence which is optimal can solved by Viterbi algorithm and learning problem or to estimate hidden Markov model parameter to maximize P(O|) can solved by Baum – Welch algorithm. From description above Hidden Markov Model with state 3 can describe behavior from the case studies. Key words: Decoding Problem, Evaluation Problem, Hidden Markov Model, Learning Problem

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9

Lee, Kyung-Ah, Dae-Jong Lee, Jang-Hwan Park, and Myung-Geun Chun. "Face Recognition Using Wavelet Coefficients and Hidden Markov Model." Journal of Korean Institute of Intelligent Systems 13, no. 6 (December 1, 2003): 673–78. http://dx.doi.org/10.5391/jkiis.2003.13.6.673.

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10

Johansson, Mathias, and Tomas Olofsson. "Bayesian Model Selection for Markov, Hidden Markov, and Multinomial Models." IEEE Signal Processing Letters 14, no. 2 (February 2007): 129–32. http://dx.doi.org/10.1109/lsp.2006.882094.

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11

Nguyen, Nguyet, Dung Nguyen, and Thomas P. Wakefield. "Using the Hidden Markov Model to Improve the Hull-White Model for Short Rate." International Journal of Trade, Economics and Finance 9, no. 2 (April 2018): 54–59. http://dx.doi.org/10.18178/ijtef.2018.9.2.588.

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12

Koide, Satoshi, Hiroshi Ohno, Ryuta Terashima, Thanomsak Ajjanapanya, and Itti Rittaporn. "Hidden Markov Flow Network Model: A Generative Model for Dynamic Flow on a Network." International Journal of Machine Learning and Computing 4, no. 4 (2014): 319–27. http://dx.doi.org/10.7763/ijmlc.2014.v4.431.

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13

Abdolhossein Harisi, Rashin, and Hamid Reza Kobravi. "A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles." International Clinical Neuroscience Journal 9, no. 1 (January 17, 2022): e3-e3. http://dx.doi.org/10.34172/icnj.2022.03.

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Background: In this study, three detecting approaches have been proposed and evaluated for online detection of balance situations during quiet standing. The applied methods were based on electromyography of the gastrocnemius muscles adopting the hidden Markov models. Methods: The levels of postural stability during quiet standing were regarded as the hidden states of the Markov models while the zones in which the center of pressure lies within determines the level of stability. The Markov models were trained by using the well-known Baum-Welch algorithm. The performance of a single hidden Markov model, the multiple hidden Markov model, and the multiple hidden Markov model alongside an adaptive neuro-fuzzy inference system (ANFIS), were compared as three different detecting methods. Results: The obtained results show the better and more promising performance of the method designed based on a combination of the hidden Markov models and optimized neuro-fuzzy system. Conclusion: According to the results, using the combined detecting method yielded promising results.

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14

Tseng, Din-Chang, and Ruei-Lung Chen. "Mutiscale Texture Segmentation Using Contextual Hidden Markov Tree Models." International Journal of Machine Learning and Computing 5, no. 3 (June 2015): 198–205. http://dx.doi.org/10.7763/ijmlc.2015.v5.507.

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15

Eddy, Sean R. "Hidden Markov models." Current Opinion in Structural Biology 6, no. 3 (June 1996): 361–65. http://dx.doi.org/10.1016/s0959-440x(96)80056-x.

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16

Kwon, Hyun-Han. "Probabilistic Assessment of Drought Characteristics based on Homogeneous Hidden Markov Model." Journal of the Korean Society of Civil Engineers 34, no. 1 (2014): 145. http://dx.doi.org/10.12652/ksce.2014.34.1.0145.

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17

Sin, Bongkee, and Jin H. Kim. "Nonstationary hidden Markov model." Signal Processing 46, no. 1 (September 1995): 31–46. http://dx.doi.org/10.1016/0165-1684(95)00070-t.

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18

Visser, Ingmar, Maartje E. J. Raijmakers, and Peter C. M. Molenaar. "Fitting Hidden Markov Models to Psychological Data." Scientific Programming 10, no. 3 (2002): 185–99. http://dx.doi.org/10.1155/2002/874560.

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Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.

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19

Shi, Fang Fang, Xian Yi Cheng, and Xiang Chen. "The Summarize of Improved HMM Model." Advanced Materials Research 756-759 (September 2013): 3384–88. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3384.

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The hidden markov model is a kind of important probability model of series data processing and statistical learning and it has been successfully applied in many engineering tasks. This paper introduces the basic principle of hidden markov model firstly, and then discusses the limitations of hidden markov model, as well as the improved hidden markov model which is put forward to solve these problems.

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20

Mitrophanov, Alexander Yu, Alexandre Lomsadze, and Mark Borodovsky. "Sensitivity of hidden Markov models." Journal of Applied Probability 42, no. 03 (September 2005): 632–42. http://dx.doi.org/10.1017/s002190020000067x.

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We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the initial distribution of the underlying Markov chain. Our approach can also be used to assess the sensitivity of other stochastic models, such as mixture processes and semi-Markov processes.

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21

Mitrophanov, Alexander Yu, Alexandre Lomsadze, and Mark Borodovsky. "Sensitivity of hidden Markov models." Journal of Applied Probability 42, no. 3 (September 2005): 632–42. http://dx.doi.org/10.1239/jap/1127322017.

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We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the initial distribution of the underlying Markov chain. Our approach can also be used to assess the sensitivity of other stochastic models, such as mixture processes and semi-Markov processes.

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22

Adams, Stephen, Peter A. Beling, and Randy Cogill. "Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models." IEEE Access 4 (2016): 1642–57. http://dx.doi.org/10.1109/access.2016.2552478.

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23

Liu, Tao, Jin Chen, and Guangming Dong. "Identification of bearing faults using linear discriminate analysis and continuous hidden Markov model." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 10 (April 23, 2015): 1658–72. http://dx.doi.org/10.1177/0954406215582015.

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It is important to diagnose the bearing fault to prevent the serious accident of equipment. This paper introduces a bearing fault identification scheme based on envelope power spectrum analysis, linear discriminate analysis and continuous hidden Markov model. First the envelope power spectrum features are extracted from amplitude demodulated vibration signals from fault bearings. Then, linear discriminate analysis is employed to reduce the feature dimensions, which are helpful for improving the computing speed and diagnosing accuracy. At last, the new linear discriminate analysis features are input into continuous hidden Markov model to train the models under different conditions, respectively. In bearing fault identification, test data are input into the pretrained continuous hidden Markov models, and the bearing state can be detected by the output of continuous hidden Markov model. To validate the effectiveness of the proposed method, experimental samples of four bearing conditions at different fault sizes and loads are utilized to test the continuous hidden Markov model and back-propagation neural network. The result shows that continuous hidden Markov model and linear discriminate analysis-based method have higher accuracy and efficiency than back-propagation neural network.

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24

Rose, Michael Del, Christian Wagner, and Philip Frederick. "Evidence Feed Forward Hidden Markov Model: A New Type Of Hidden Markov Model." International Journal of Artificial Intelligence & Applications 2, no. 1 (January 31, 2011): 1–19. http://dx.doi.org/10.5121/ijaia.2011.2101.

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25

MAZENAN, Mohd Nizam, Tian Swee TAN, Sarah Samson SOH, Azran Azhim Noor AZMI, Hirofumi NAGASHINO, Masatake AKUTAGAWA, Raja IZAMSHAH, Mohd SHAHIR KASIM, and Teruaki ITO. "1306 Malay articulation disorder diagnostic tool design by using Hidden Markov Model." Proceedings of Design & Systems Conference 2015.25 (2015): _1306–1_—_1306–11_. http://dx.doi.org/10.1299/jsmedsd.2015.25._1306-1_.

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26

Zhang, Yanxue, Dongmei Zhao, and Jinxing Liu. "The Application of Baum-Welch Algorithm in Multistep Attack." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/374260.

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The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

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27

GHAHRAMANI, ZOUBIN. "AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 01 (February 2001): 9–42. http://dx.doi.org/10.1142/s0218001401000836.

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We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective makes it possible to consider novel generalizations of hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.

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28

HUANG, BUFU, MENG CHEN, KA KEUNG LEE, and YANGSHENG XU. "HUMAN IDENTIFICATION BASED ON GAIT MODELING." International Journal of Information Acquisition 04, no. 01 (March 2007): 27–38. http://dx.doi.org/10.1142/s0219878907001137.

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Human gait is a dynamic biometrical feature which is complex and difficult to imitate. It is unique and more secure than static features such as passwords, fingerprints and facial features. In this paper, we present intelligent shoes for human identification based on human gait modeling and similarity evaluation with hidden Markov models (HMMs). Firstly we describe the intelligent shoe system for collecting human dynamic gait performance. Using the proposed machine learning method hidden Markov models, an individual wearer's gait model is derived and we then demonstrate the procedure for recognizing different wearers by analyzing the corresponding models. Next, we define a hidden-Markov-model-based similarity measure which allows us to evaluate resultant learning models. With the most likely performance criterion, it will help us to derive the similarity of individual behavior and its corresponding model. By utilizing human gait modeling and similarity evaluation based on hidden Markov models, the proposed method has produced satisfactory results for human identification during testing.

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29

Kersting, K., L. De Raedt, and T. Raiko. "Logical Hidden Markov Models." Journal of Artificial Intelligence Research 25 (April 19, 2006): 425–56. http://dx.doi.org/10.1613/jair.1675.

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Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters. This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evaluation, most likely hidden state sequence and parameter estimation. The resulting representation and algorithms are experimentally evaluated on problems from the domain of bioinformatics.

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30

Farcomeni, Alessio. "Hidden Markov partition models." Statistics & Probability Letters 81, no. 12 (December 2011): 1766–70. http://dx.doi.org/10.1016/j.spl.2011.07.012.

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31

Forchhammer, S., and J. Rissanen. "Partially hidden Markov models." IEEE Transactions on Information Theory 42, no. 4 (July 1996): 1253–56. http://dx.doi.org/10.1109/18.508852.

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32

Altman, Rachel MacKay. "Mixed Hidden Markov Models." Journal of the American Statistical Association 102, no. 477 (March 2007): 201–10. http://dx.doi.org/10.1198/016214506000001086.

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33

Dannemann, Jörn. "Semiparametric Hidden Markov Models." Journal of Computational and Graphical Statistics 21, no. 3 (July 2012): 677–92. http://dx.doi.org/10.1080/10618600.2012.681264.

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34

Barrett, Christian, Richard Hughey, and Kevin Karplus. "Scoring hidden Markov models." Bioinformatics 13, no. 2 (1997): 191–99. http://dx.doi.org/10.1093/bioinformatics/13.2.191.

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35

Eddy, S. R. "Profile hidden Markov models." Bioinformatics 14, no. 9 (October 1, 1998): 755–63. http://dx.doi.org/10.1093/bioinformatics/14.9.755.

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36

Yu, Shun-Zheng. "Hidden semi-Markov models." Artificial Intelligence 174, no. 2 (February 2010): 215–43. http://dx.doi.org/10.1016/j.artint.2009.11.011.

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37

Bueno, Marcos L. P., Arjen Hommersom, Peter J. F. Lucas, and Alexis Linard. "Asymmetric hidden Markov models." International Journal of Approximate Reasoning 88 (September 2017): 169–91. http://dx.doi.org/10.1016/j.ijar.2017.05.011.

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38

Jandera, Ales, and Tomas Skovranek. "Customer Behaviour Hidden Markov Model." Mathematics 10, no. 8 (April 8, 2022): 1230. http://dx.doi.org/10.3390/math10081230.

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In this work, the Customer behaviour hidden Markov model (CBHMM) is proposed to predict the behaviour of customers in e-commerce with the goal to forecast the store income. The model consists of three sub-models: Vendor, Psychology and Loyalty, returning probabilities used in the transition matrix of the hidden Markov model, deciding upon three decision-states: “Order completed”, “Order uncompleted” or “No order”. The model outputs are read by the Viterbi algorithm to estimate if the order has been completed successfully, followed by the evaluation of the forecasted store income. The proposed CBHMM was compared to the baseline prediction represented by the Google Analytics tracking system mechanism (GA model). The forecasted income computed using CBHMM as well as the GA model followed the trend of real income data obtained from the store for the year 2021. Based on the comparison criteria the proposed CBHMM outperforms the GA model in terms of the R-squared criterion, giving a 5% better fit, and with the PG value more than 3 dB higher.

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39

SETIAWATY, B., and L. KRISTINA. "PENDUGAAN PARAMETER MODEL HIDDEN MARKOV *." Journal of Mathematics and Its Applications 4, no. 1 (July 1, 2005): 23. http://dx.doi.org/10.29244/jmap.4.1.23-40.

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Pendugaan parameter untuk model Hidden Markov Elliott et. al. (1995) dilakukan mengunakan Metode Maximum Likelihood dan pendugaan ulang menggunakan metode Expectation Maximization yang melibatkan perubahan ukuran. Dari metode tersebut diperoleh algoritma untuk menduga parameter model.

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40

Benyacoub, Badreddine, Souad ElBernoussi, Abdelhak Zoglat, and EL Moudden Ismail. "Classification with hidden Markov model." Applied Mathematical Sciences 8 (2014): 2483–96. http://dx.doi.org/10.12988/ams.2014.42129.

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41

van der Hoek, John, and Robert J. Elliott. "A modified hidden Markov model." Automatica 49, no. 12 (December 2013): 3509–19. http://dx.doi.org/10.1016/j.automatica.2013.09.012.

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42

Rai, Prerna, and Arvind Lal. "Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model." International Journal of Computer Applications 138, no. 9 (March 17, 2016): 9–13. http://dx.doi.org/10.5120/ijca2016908942.

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43

YANG, XIAOYUAN, XUDONG ZHANG, and ZHIPIN ZHU. "FRAME-BASED IMAGE DENOISING USING HIDDEN MARKOV MODEL." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 03 (May 2008): 419–32. http://dx.doi.org/10.1142/s0219691308002446.

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Wavelet-domain hidden Markov models (HMMs), and in particular, hidden Markov tree (HMT), have been recently proposed and applied to image denoising. In this paper, we present the hidden Markov model and corresponding algorithm based on frame-domain. This model can effectively capture the correlation of wavelet frame coefficients, and we apply this model for image denoising. Furthermore, two new algorithms are developed for denoising. We demonstrate the performance of our new method on some text images with very encouraging results.

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44

Yuan, Shenfang, Jinjin Zhang, Jian Chen, Lei Qiu, and Weibo Yang. "A uniform initialization Gaussian mixture model–based guided wave–hidden Markov model with stable damage evaluation performance." Structural Health Monitoring 18, no. 3 (June 29, 2018): 853–68. http://dx.doi.org/10.1177/1475921718783652.

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During practical applications, the time-varying service conditions usually lead to difficulties in properly interpreting structural health monitoring signals. The guided wave–hidden Markov model–based damage evaluation method is a promising approach to address the uncertainties caused by the time-varying service condition. However, researches that have been performed to date are not comprehensive. Most of these research studies did not introduce serious time-varying factors, such as those that exist in reality, and hidden Markov model was applied directly without deep consideration of the performance improvement of hidden Markov model itself. In this article, the training stability problem when constructing the guided wave–hidden Markov model initialized by usually adopted k-means clustering method and its influence to damage evaluation were researched first by applying it to fatigue crack propagation evaluation of an attachment lug. After illustrating the problem of stability induced by k-means clustering, a novel uniform initialization Gaussian mixture model–based guided wave–hidden Markov model was proposed that provides steady and reliable construction of the guided wave–hidden Markov model. The advantage of the proposed method is demonstrated by lug fatigue crack propagation evaluation experiments.

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45

Härdle, Wolfgang Karl, Ostap Okhrin, and Weining Wang. "HIDDEN MARKOV STRUCTURES FOR DYNAMIC COPULAE." Econometric Theory 31, no. 5 (December 22, 2014): 981–1015. http://dx.doi.org/10.1017/s0266466614000607.

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Understanding the time series dynamics of a multi-dimensional dependency structure is a challenging task. Multivariate covariance driven Gaussian or mixed normal time varying models have only a limited ability to capture important features of the data such as heavy tails, asymmetry, and nonlinear dependencies. The present paper tackles this problem by proposing and analyzing a hidden Markov model (HMM) for hierarchical Archimedean copulae (HAC). The HAC constitute a wide class of models for multi-dimensional dependencies, and HMM is a statistical technique for describing regime switching dynamics. HMM applied to HAC flexibly models multivariate dimensional non-Gaussian time series.We apply the expectation maximization (EM) algorithm for parameter estimation. Consistency results for both parameters and HAC structures are established in an HMM framework. The model is calibrated to exchange rate data with a VaR application. This example is motivated by a local adaptive analysis that yields a time varying HAC model. We compare its forecasting performance with that of other classical dynamic models. In another, second, application, we model a rainfall process. This task is of particular theoretical and practical interest because of the specific structure and required untypical treatment of precipitation data.

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46

Kaur, Navjot, Rajbir Singh Cheema, and Harmandeep Singh Harmandeep Singh. "Multiple Sequence Alignment and Profile Analysis of Protein Family Utsing Hidden Markov Model." International Journal of Scientific Research 2, no. 6 (June 1, 2012): 208–11. http://dx.doi.org/10.15373/22778179/june2013/66.

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47

Park, Soyoung, Jae Hong Lee, and Chan Gook Park. "Hidden Markov Model-Based Walking Direction Improvement in PDR System for Multiple Poses of Smartphone." Journal of Institute of Control, Robotics and Systems 26, no. 9 (September 30, 2020): 754–59. http://dx.doi.org/10.5302/j.icros.2020.20.0073.

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48

H., DR SHAHEEN. "A Pervasive Multi-Distribution Perceptron and Hidden Markov Model for Context Aware Systems." Journal of Research on the Lepidoptera 51, no. 2 (June 25, 2020): 818–33. http://dx.doi.org/10.36872/lepi/v51i2/301136.

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49

SULÍR, Martin, and Jozef JUHÁR. "HIDDEN MARKOV MODEL BASED SPEECH SYNTHESIS SYSTEM IN SLOVAK LANGUAGE WITH SPEAKER INTERPOLATION." Acta Electrotechnica et Informatica 15, no. 4 (December 1, 2015): 8–12. http://dx.doi.org/10.15546/aeei-2015-0029.

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50

Kim, Sae-Joung, Kang-Woong Lee, and Jae-Hyun Do. "Applicability and limitation of Hidden Markov Model and Sports Coding in Tennis." Korean Journal of Sports Science 26, no. 5 (October 31, 2017): 1325–34. http://dx.doi.org/10.35159/kjss.2017.10.26.5.1325.

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