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检索条件"主题词=Baum-welch algorithm"

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Short-Term Stochastic Load Forecasting Using Autoregressive Integrated Moving Average Models and Hidden Markov Model

Short-Term Stochastic Load Forecasting Using Autoregressive ...

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International Conference on Information and Communication Technologies (ICICT)

作者： Hermias, Jeffrel P. Teknomo, Kardi Monje, Jose Claro N. Ateneo Manila Univ Sch Sci & Engn Elect Commun & Comp Engn Dept Katipunan Ave Quezon City Philippines Ateneo Manila Univ Sch Sci & Engn Informat Syst & Comp Sci Dept Katipunan Ave Quezon City Philippines

ISBN: (纸本)9781538621868

Load forecasting, particularly short-term load forecasting (STLF) plays a vital role in the economy streaming and tracking of power system. Many stochastic and artificial intelligence techniques haven been used in order to come up with an accurate (less error) short-term load forecast. Here, we introduce a new approach to short-term load forecasting (STLF) using the conventional Hidden Markov Model (HMM) then compare it with Autoregressive Integrated Moving Average (ARIMA) models. Three-dimensional continuous multivariate Gaussian emission probabilities are used in this experiment for HMM. Meanwhile for ARIMA models, different parameters are used for different kinds of dataset. Comparison is done afterwards to the actual load value using MAPE and RMSE.

关键词： Hidden Markov Model HMM Short-term load forecasting STLF ARIMA Autoregressive Integrated Moving Average baum-welch algorithm Machine learning Artificial Intelligence

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N-best vector quantization for isolated word speech recognition

N-best vector quantization for isolated word speech recognit...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Nose, Masaya Maki, Shuichi Yartiane, Noburnoto Morikawa, Yoshitaka Okayama Univ Grad Sch Nat Sci & Technol Okayama Japan

ISBN: (纸本)9784907764289

Speech recognition is performed by utilizing acoustic and linguistic model. The contribution of this paper is improvement of acoustic model. Acoustic model is constructed by hidden Markov model (HMM). HMM has two representations, that are discrete HMM and continuous HMM. The former uses vector quantization (VQ), whereas the latter uses functions such as (mixture) Gaussian distribution. In Viterbi algorithm, VQ has advantage that it only operates by addition. However VQ also has a problem of distortion. This paper attempts to improve recognition precision in discrete HMM with modified VQ that gives multiple outputs for an input.

关键词： isolated word speech recognition VQ vector quantization discrete HMM acoustic model improvement baum-welch algorithm speaker-independent speech recognition

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Reinforcement Learning aided Smart-home Decision-making in an Interactive Smart Grid

Reinforcement Learning aided Smart-home Decision-making in a...

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IEEE Green Energy and Systems Conference (IGESC)

作者： Li, Ding Jayaweera, Sudharman K. Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA

ISBN: (纸本)9781479973330

In this paper, a complete hierarchical architecture is presented for the Utility-customer interaction, which tightly connect several important research topics, such as customer load prediction, renewable generation integration, power-load balancing and demand response. The complete interaction cycle consists of two stages: (1) Initial interaction (long-term planning) and (2) Real-time interaction (short-term planning). A hidden mode Markov decision process (HM-MDP) model is developed for customer real-time decision making, which outperforms the conventional Markov decision process (MDP) model in handling the non-stationary environment. To obtain a low-complexity, real-time algorithm, that allows to adaptively incorporate new observations as the environment changes, we resort to Q-learning based approximate dynamic programming (ADP). Without requiring specific starting and ending points of the scheduling period, the Q-learning algorithm offers more flexibility in practice. Performance analysis of both exact and approximate algorithms are presented with simulation results, in comparison with other decision making strategies.

关键词： Smart-home hidden mode Markov decision process (HM-MDP) baum-welch algorithm approximate dynamic programming (ADP) Q-learning

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A Prognostic Method for Radar Transmitter Based on DHMM 8

A Prognostic Method for Radar Transmitter Based on DHMM

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2017 Prognostics and System Health Management Conference (PHM-Harbin)

作者： Yu, Pengfei Zhou, Zhenwei Cheng, Liye Lu, Yudong Minist Ind & Informat Technol Elect Res Inst 5 Sci & Technol Reliabil Phys & Applicat Elect Comp Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538603703

For the randomness and uncertainty of fault for Radar transmitter, a prognostic method based on discrete Hidden Markov Model (DHMM) is proposed. In the paper, three monitoring parameters of transmitter are collected and a discrete Hidden Markov Model is established. In order to have a fast convergence, The baum-welch (B-W) algorithm is used for training of DHMM. Finally, the state probability transition curve and reliability transition curve of the transmitter is acquired, therefore health assessment is achieved. The experimental results show that proposed method is a practical method with accuracy and stability.

关键词： transmitter discrete Hidden Markov Model baum-welch algorithm health assessment

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Automatic Classification of Disordered Voices with Hidden Markov Models

Automatic Classification of Disordered Voices with Hidden Ma...

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International Conference on Signal, Image, Vision and their Applications (SIVA)

作者： Benhammoud, Redouane Kacha, Abdellah Univ Mohamed Seddik Ben Yahia Lab Phys Reyonnement & Applicat Jijel Algeria

ISBN: (纸本)9781538671207

The auditory-based method is commonly used in the assessment of voice disorders. This method is subjective in the sense that the evaluation result depends on the listener and a great deal of expertise is required to obtain reproducible evaluations. Automatic assessment of disordered voices provides a support for clinicians to establish a better assessment of the disorder. The main concern of this article is the clinical evaluation of disordered voices by using an automatic classification method based on Hidden Markov Models (HMMs). The HMM classification technique is based on a learning theory that uses baum-welch algorithm, a special case of the EM algorithm. The descriptors that are used as inputs to the HMM classifier are the cepstral MFCC coefficients. The performances of the classification method that is supervised, in terms of classification accuracy are investigated using a database consisting of a concatenation of two Dutch-speaking sentences produced by 28 normophonic and 223 dysphonic speakers.

关键词： disordered voices classification Hidden Markov Models baum-welch algorithm Viterbi algorithm mel frequency cepstral coefficients

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Towards Efficient and Privacy-Preserving Federated Learning for HMM Training

Towards Efficient and Privacy-Preserving Federated Learning ...

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Zheng, Yandong Zhu, Hui Lu, Rongxing Zhang, Songnian Guan, Yunguo Wang, Fengwei Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada

ISBN: (纸本)9798350310900

The hidden Markov model (HMM) has played a pivotal role in various IoT applications due to its ability to model time-varying sequences. Since the datasets usually live in isolated islands and their privacy naturally demands to be seriously considered, the HMM should be trained in a privacy-preserving manner. A typical HMM training framework is federated learning, in which a federated server and many data owners collaboratively train an HMM without revealing data owners' data to the federated server and the trained model to data owners. Since existing HMM training schemes are computationally intensive, we propose an efficient and privacy-preserving federated learning scheme for HMM training to address the efficiency issue in this paper. First, we transform all HMM training computations into matrices- and vectors-based computations over real domains. Then, we introduce our federated HMM training scheme by applying matrix encryption to protect the HMM training privacy. After that, we show that our scheme is privacy-preserving through a rigorous analysis on the security of our scheme. We illustrate that our scheme is efficient through extensive experimental evaluation on the performance of our scheme.

关键词： Federated learning Hidden Markov model baum-welch algorithm Matrix encryption

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Narrowband PLC Channel Modeling using USRP and PSK Modulations

Narrowband PLC Channel Modeling using USRP and PSK Modulatio...

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18th IEEE International Symposium on Power Line Communications and its Applications (ISPLC)

作者： Familua, A. D. Ogunyanda, K. Swart, T. G. Ferreira, H. C. Van Olst, Rex Cheng, L. Univ Witwatersrand Sch Elect & Informat Engn Private Bag 3 ZA-2050 Johannesburg South Africa Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Auckland Pk South Africa

ISBN: (纸本)9781479949809

The indoor narrowband power line communication (NB-PLC) suffers from noise impairments, which emanate from several end-user electrical devices connected across the PLC channel. These noise impairments result into burst errors, which consequently lead to data corruption. Therefore, in order to implement robust communication techniques that will thrive on the noisy PLC channel, a full knowledge and modeling of the noise that exists on the NB-PLC channel is inevitable. This paper thus reports a First-order Markov modeling of NB-PLC channel noise, based on experimental measurements. For the modeling, BPSK, DBPSK, QPSK and DQPSK modulation schemes were implemented using Universal Software Radio Peripheral (USRP). The resulting channel models are useful for improving the robustness of the above modulation schemes as well as designing forward error correction techniques for mitigating the effect of noise impairments. The results are also useful in optimizing NB-PLC system design, thereby, enhancing the accuracy and improving the overall PLC system performance.

关键词： baum-welch algorithm CENELEC Fritchman model noise measurement power line communications USRP

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Automatic Segmentation of Lymph Vessel Wall using Optimal Surface Graph Cut and Hidden Markov Models 37

Automatic Segmentation of Lymph Vessel Wall using Optimal Su...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Jones, Jonathan-Lee Essa, Ehab Xie, Xianghua Swansea Univ Dept Comp Sci Singleton Pk Swansea W Glam Wales

ISBN: (纸本)9781424492701

We present a novel method to segment the lymph vessel wall in confocal microscopy images using Optimal Surface Segmentation (OSS) and hidden Markov Models (HMM). OSS is used to preform a pre-segmentation on the images, to act as the initial state for the HMM. We utilize a steerable filter to determine edge based filters for both of these segmentations, and use these features to build Gaussian probability distributions for both the vessel walls and the background. From this we infer the emission probability for the HMM, and the transmission probability is learned using a baum-welch algorithm. We transform the segmentation problem into one of cost minimization, with each node in the graph corresponding to one state, and the weight for each node being defined using its emission probability. We define the inter-relations between neighboring nodes using the transmission probability. Having constructed the problem, it is solved using the Viterbi algorithm, allowing the vessel to be reconstructed. The optimal solution can be found in polynomial time. We present qualitative and quantitative analysis to show the performance of the proposed method.

关键词： biomedical optical imaging blood vessels hidden Markov models image segmentation medical image processing baum-welch algorithm Optimal Surface Segmentation automatic segmentation confocal microscopy images hidden Markov Models lymph vessel wall optimal surface graph cut presegmentation transmission probability Biomedical imaging Hidden Markov models Image edge detection Image segmentation Microscopy Three-dimensional displays Yttrium

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Dynamic and Interactive Gesture Recognition algorithm Based on Kinect 29

Dynamic and Interactive Gesture Recognition Algorithm Based ...

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第29届中国控制与决策会议

作者： Guangjun Dai Lei Yu Jun Huang College of Mechanical and Electrical Engineering Soochow University

ISBN: (纸本)9781509046584

With the constant development of smart devices, Gesture recognition is applied on more and more fields. Gesture recognition devices currently on the market are inconvenient and expensive. A gesture recognition method based on Kinect is proposed in this paper. The camera of Kinect is used to get gesture images and then a hidden Markov model is established to recognize dynamic gesture so that the operation is simple. The assessment, decoding algorithms and training issues of hidden Markov model in gesture recognition is focused in this paper. Viterbi algorithm and baum-welch algorithm are used to solve the problems above. After simulation experiments, it is discovered that the gesture recognition algorithm based on Kinect proposed in this paper has a high acc uracy rate for a variety of gestures, simultaneously the naturalness of human-computer interaction has been greatly improved.

关键词： Gesture recognition Kinect Hidden Markov models Viterbi algorithm baum-welch algorithm

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Re-estimation of Hidden Markov Model parameters of Phasor Measurement Unit

Re-estimation of Hidden Markov Model parameters of Phasor Me...

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IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Murthy, Ch Roy, D. S. Mohanta, D. K. Natl Inst Sci & Technol Dept EEE Berhampur Orissa India Natl Inst Sci & Technol Dept CSE Berhampur Orissa India MVGR Coll Engn Dept EEE Vizianagaram 535005 India

ISBN: (纸本)9781479974559

In recent times, the power system has expanded in an unprecedented way owing to technological innovations and geographical dimensions. For ensuring proper operation, monitoring and control, a system called Wide Area Measurement System (WAMS) has evolved. Phasor Measurement Unit (PMU) is one of the vital components of WAMS. Any failure in PMU has catastrophic consequences on WAMS. Thus reliability analysis of PMU is of utmost importance. Hidden Markov model (HMM) is one of the important techniques for evaluating reliability. However, while evaluating the reliability of PMU using HMM, the HMM parameters such as state transition probability matrix (A), observation probability matrix (B) and initial state probability (pi) are either assumed or have been derived from related research papers. In this paper, a baum-welch algorithm has been used to re-estimate these HMM parameters of the seven modules of PMU to best fit the observations.

关键词： hidden Markov model phasor measurement unit reliability baum-welch algorithm

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