国際生活機能分類(International Classification of Functioning, Disability and Health: ICF)にみた福祉・保健・医療の専門職協働における連携に関する貢献と課題

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Medical Instrumentation Application And Design

The Disabled State

http://ieeexplore.ieee.org/iel5/5/31288/01455959.pdf

Medical instrumentation

In this literature, a new automated control strategy has been developed to manage the power supply from the wind power generation system to the load. The main objective of this research work is to ...

https://journals.sagepub.com/doi/pdf/10.1177/0020294019827330

Automated power management strategy for wind power generation system using pitch angle controller

Improved fault ride through capability of DFIG-wind turbines using customized dynamic voltage restorer

Intelligent Robotic Chair with Thought Control and Communication Aid Using Higher Order Spectra Band Features

Light gradient boosting machine-based phishing webpage detection model using phisher website features of mimic URLs

Accent recognition is one of the most important topics in automatic speaker and speaker-independent speech recognition (SI-ASR) systems in recent years. The growth of voice-controlled technologies has becoming part of our daily life, nevertheless variability in speech makes these spoken language technologies relatively difficult. One of the profound variability is accent. By classifying accent types, different models could be developed to handle SI-ASR. In this paper, we classified three accents in English language recorded from three main ethnicities in Malaysia namely Malay, Chinese and Indian using artificial neural network model. All experiments were performed in speaker-independent and three most accent-sensitive words-independent modes. Mel-bands spectral energy was extracted from eighteen bands taking the statistical values of each speech sample i.e. mean, standard deviation, kurtosis and the ratio of standard deviation to kurtosis to characterize the spectral energy distribution. The system was evaluated using independent test dataset, partial-independent test dataset and training dataset. The best three-class accuracy rate of 99.01% with independent test dataset was obtained. The overall accuracy rate for several trials was averaged to 96.79% with the average learning time at 49 epochs.

Speaker accent recognition through statistical descriptors of Mel-bands spectral energy and neural network model

China aster [Callistephus chinensis (L.) Nees], belonging to the family Asteraceae, is most popular annual flowering plant grown throughout the world. In India, it is grown traditionally for its loose flowers, cut flowers, in arranging in vase, floral decorations, making garlands and venis. It is extensively grown in Karnataka, Tamil Nadu, West Bengal and Maharashtra by marginal and small farmers.

Character Association and Path Co-efficient Analysis in F2 Segregating Population of Cross “Arka Kamini x PG Violet” in China Aster (Callistephus chinensis [L.] Nees)

Sathees Kumar Nataraj

Papers

Improved fault ride through capability of DFIG-wind turbines using customized dynamic voltage restorer

Intelligent Robotic Chair with Thought Control and Communication Aid Using Higher Order Spectra Band Features

Light gradient boosting machine-based phishing webpage detection model using phisher website features of mimic URLs

Speaker accent recognition through statistical descriptors of Mel-bands spectral energy and neural network model

Character Association and Path Co-efficient Analysis in F2 Segregating Population of Cross “Arka Kamini x PG Violet” in China Aster (Callistephus chinensis [L.] Nees)