The Experts below are selected from a list of 21777 Experts worldwide ranked by ideXlab platform
Andrea Cavallaro - One of the best experts on this subject based on the ideXlab platform.
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hybrid particle filter and mean shift tracker with adaptive Transition Model
International Conference on Acoustics Speech and Signal Processing, 2005Co-Authors: Emilio Maggio, Andrea CavallaroAbstract:We propose a tracking algorithm based on a combination of particle filter and mean shift, and enhanced with a new adaptive state Transition Model. The particle filter is robust to partial and total occlusions, can deal with multi-modal pdf and can recover lost tracks. However, its complexity dramatically increases with the dimensionality of the sampled pdf. Mean shift has a low complexity, but is unable to deal with multi-modal pdf. To overcome these problems, the proposed tracker first produces a smaller number of samples than the particle filter and then shifts the samples toward a close local maximum using mean shift. The Transition Model predicts the state based on adaptive variances. Experimental results show that the combined tracker outperforms the particle filter and mean shift in terms of accuracy in estimating the target size and position while generating 80% less samples than the particle filter.
Y. Ariki - One of the best experts on this subject based on the ideXlab platform.
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Continuous speech recognition under non-stationary musical environments based on speech state Transition Model
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001Co-Authors: M. Fujimoto, Y. ArikiAbstract:We propose a non-stationary noise reduction method based on the speech state Transition Model. Our proposed method estimates the speech signal under non-stationary noisy environments such as musical background by applying the speech state Transition Model to Kalman filtering estimation. The speech state Transition Model represents the state Transition of the speech component in non-stationary noisy speech and is Modeled by using Taylor expansion. In this Model, the state Transition of the noise component is estimated by using linear predictive estimation. In order to evaluate the proposed method, we carried out large vocabulary continuous speech recognition experiments under 3 types of music and compared the results with the conventional parallel Model combination (PMC) method in word accuracy rate. As a result, the proposed method obtained a word accuracy rate that was superior to PMC.
S. K. Hong - One of the best experts on this subject based on the ideXlab platform.
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DTranNER: biomedical named entity recognition with deep learning-based label-label Transition Model.
BMC Bioinformatics, 2020Co-Authors: S. K. HongAbstract:BACKGROUND: Biomedical named-entity recognition (BioNER) is widely Modeled with conditional random fields (CRF) by regarding it as a sequence labeling problem. The CRF-based methods yield structured outputs of labels by imposing connectivity between the labels. Recent studies for BioNER have reported state-of-the-art performance by combining deep learning-based Models (e.g., bidirectional Long Short-Term Memory) and CRF. The deep learning-based Models in the CRF-based methods are dedicated to estimating individual labels, whereas the relationships between connected labels are described as static numbers; thereby, it is not allowed to timely reflect the context in generating the most plausible label-label Transitions for a given input sentence. Regardless, correctly segmenting entity mentions in biomedical texts is challenging because the biomedical terms are often descriptive and long compared with general terms. Therefore, limiting the label-label Transitions as static numbers is a bottleneck in the performance improvement of BioNER. RESULTS: We introduce DTranNER, a novel CRF-based framework incorporating a deep learning-based label-label Transition Model into BioNER. DTranNER uses two separate deep learning-based networks: Unary-Network and Pairwise-Network. The former is to Model the input for determining individual labels, and the latter is to explore the context of the input for describing the label-label Transitions. We performed experiments on five benchmark BioNER corpora. Compared with current state-of-the-art methods, DTranNER achieves the best F1-score of 84.56% beyond 84.40% on the BioCreative II gene mention (BC2GM) corpus, the best F1-score of 91.99% beyond 91.41% on the BioCreative IV chemical and drug (BC4CHEMD) corpus, the best F1-score of 94.16% beyond 93.44% on the chemical NER, the best F1-score of 87.22% beyond 86.56% on the disease NER of the BioCreative V chemical disease relation (BC5CDR) corpus, and a near-best F1-score of 88.62% on the NCBI-Disease corpus. CONCLUSIONS: Our results indicate that the incorporation of the deep learning-based label-label Transition Model provides distinctive contextual clues to enhance BioNER over the static Transition Model. We demonstrate that the proposed framework enables the dynamic Transition Model to adaptively explore the contextual relations between adjacent labels in a fine-grained way. We expect that our study can be a stepping stone for further prosperity of biomedical literature mining.
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DTranNER: biomedical named entity recognition with deep learning-based label-label Transition Model
BMC Bioinformatics, 2020Co-Authors: S. K. HongAbstract:Background Biomedical named-entity recognition (BioNER) is widely Modeled with conditional random fields (CRF) by regarding it as a sequence labeling problem. The CRF-based methods yield structured outputs of labels by imposing connectivity between the labels. Recent studies for BioNER have reported state-of-the-art performance by combining deep learning-based Models (e.g., bidirectional Long Short-Term Memory) and CRF. The deep learning-based Models in the CRF-based methods are dedicated to estimating individual labels, whereas the relationships between connected labels are described as static numbers; thereby, it is not allowed to timely reflect the context in generating the most plausible label-label Transitions for a given input sentence. Regardless, correctly segmenting entity mentions in biomedical texts is challenging because the biomedical terms are often descriptive and long compared with general terms. Therefore, limiting the label-label Transitions as static numbers is a bottleneck in the performance improvement of BioNER. Results We introduce DTranNER, a novel CRF-based framework incorporating a deep learning-based label-label Transition Model into BioNER. DTranNER uses two separate deep learning-based networks: Unary-Network and Pairwise-Network. The former is to Model the input for determining individual labels, and the latter is to explore the context of the input for describing the label-label Transitions. We performed experiments on five benchmark BioNER corpora. Compared with current state-of-the-art methods, DTranNER achieves the best F1-score of 84.56% beyond 84.40% on the BioCreative II gene mention (BC2GM) corpus, the best F1-score of 91.99% beyond 91.41% on the BioCreative IV chemical and drug (BC4CHEMD) corpus, the best F1-score of 94.16% beyond 93.44% on the chemical NER, the best F1-score of 87.22% beyond 86.56% on the disease NER of the BioCreative V chemical disease relation (BC5CDR) corpus, and a near-best F1-score of 88.62% on the NCBI-Disease corpus. Conclusions Our results indicate that the incorporation of the deep learning-based label-label Transition Model provides distinctive contextual clues to enhance BioNER over the static Transition Model. We demonstrate that the proposed framework enables the dynamic Transition Model to adaptively explore the contextual relations between adjacent labels in a fine-grained way. We expect that our study can be a stepping stone for further prosperity of biomedical literature mining.
Emilio Maggio - One of the best experts on this subject based on the ideXlab platform.
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hybrid particle filter and mean shift tracker with adaptive Transition Model
International Conference on Acoustics Speech and Signal Processing, 2005Co-Authors: Emilio Maggio, Andrea CavallaroAbstract:We propose a tracking algorithm based on a combination of particle filter and mean shift, and enhanced with a new adaptive state Transition Model. The particle filter is robust to partial and total occlusions, can deal with multi-modal pdf and can recover lost tracks. However, its complexity dramatically increases with the dimensionality of the sampled pdf. Mean shift has a low complexity, but is unable to deal with multi-modal pdf. To overcome these problems, the proposed tracker first produces a smaller number of samples than the particle filter and then shifts the samples toward a close local maximum using mean shift. The Transition Model predicts the state based on adaptive variances. Experimental results show that the combined tracker outperforms the particle filter and mean shift in terms of accuracy in estimating the target size and position while generating 80% less samples than the particle filter.
Siva Nadarajah - One of the best experts on this subject based on the ideXlab platform.
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laminar turbulent flow simulation for wind turbine profiles using the γ re θt Transition Model
Wind Energy, 2014Co-Authors: Peyman Khayatzadeh, Siva NadarajahAbstract:The accurate prediction of the laminar-turbulence Transition process is fundamental in predicting the aerodynamic performance of wind turbine profiles. Fully turbulent flow simulations have been shown to over-predict the aerodynamic performance and thereby negatively impacting the design of airfoils in flow regimes where the possible presence of laminar flow could be exploited to improve the performance of wind turbine rotors. Correlation-based Transition Modelling offers a fully computational fluid dynamics compatible approach, where the Model integrates completely with the existing turbulence Model, allows for the prediction of various Transition mechanisms, is applicable to three-dimensional flows and compatible to adjoint-based design optimization frameworks. The present paper addresses several modifications necessary for a robust Transition Model and investigates the accuracy of the Model for a wide range of angles of attack and Reynolds numbers, which are necessary for a thorough validation of the correlation-based Transition Model for wind turbine profiles. The Transition Model was employed to predict the Transition locations; and an assessment of the various Transition mechanisms, Reynolds number effects, sectional characteristics and aerodynamic performance for the NLF(1)-0416 and S809 airfoils is presented with comparisons to experimental data and numerical solutions. Copyright © 2013 John Wiley & Sons, Ltd.
