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    Wyświetlanie 1-3 z 3
    Tytuł:
    Contactless power interface for plug-in electric vehicles in V2G systems
    Autorzy:
    Miśkiewicz, R.
    Moradewicz, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/200061.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    bi-directional contactless energy transfer (CET)
    contactless power supply
    smart grids
    series resonant converter
    FPGA based control
    Opis:
    In the paper a bi-directional power electronic interface based on an inductive coupled contactless energy transfer system for plug-in vehicles with Vehicle-to-Grid (V2G) capability is presented. To minimize the total losses of the system, a series resonant compensation circuit is applied assuring Near to Zero-Current Switching (N2ZCS) condition for insulated-gate bipolar transistors. The analytical expression of the dc voltage and current gains as well as energy transfer efficiency is given and discussed. The system uses modified FPGA based integral control method adjusting resonant frequency and guarantees very fast and stable operation. Simulation and experimental results illustrating properties of the developed 40-60kHz switching frequency operated 15kW laboratory prototype are presented.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2011, 59, 4; 561-568
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hierarchical Bi-LSTM based emotion analysis of textual data
    Autorzy:
    Mahto, Dashrath
    Yadav, Subhash Chandra
    Powiązania:
    https://bibliotekanauki.pl/articles/2173676.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    emotion analysis
    machine learning
    emotion detection
    deep learning
    hierarchical Bi-LSTM
    analiza emocji
    uczenie maszynowe
    wykrywanie emocji
    głęboka nauka
    hierarchiczna dwukierunkowa pamięć krótkotrwała
    Opis:
    Nowadays, Twitter is one of the most popular microblogging sites that is generating a massive amount of textual data. Such textual data is intended to incorporate human feelings and opinions with related events like tweets, posts, and status updates. It then becomes difficult to identify and classify the emotions from the tweets due to their restricted word length and data diversity. In contrast, emotion analysis identifies and classifies different emotions based on the text data generated from social media platforms. The underlying work anticipates an efficient category and prediction technique for analyzing different emotions from textual data collected from Twitter. The proposed research work deliberates an enhanced deep neural network (EDNN) based hierarchical Bi-LSTM model for emotion analysis from textual data; that classifies the six emotions mainly sadness, love, joy, surprise, fear, and anger. Furthermore, the emotion analysis result obtained by the proposed hierarchical Bi-LSTM model is being compared and validated with the traditional hybrid CNN-LSTM approach regarding the accuracy, recall, precision, and F1-Score. It can be observed from the results that the proposed hierarchical Bi-LSTM achieves an average accuracy of 89% for emotion analysis, whereas the existing CNN-LSTM model achieved an overall accuracy of 75%. This result shows that the proposed hierarchical Bi-LSTM approach achieves desired performance compared to the CNN-LSTM model.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e141001
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning based Tamil Parts of Speech (POS) tagger
    Autorzy:
    Anbukkarasi, S.
    Varadhaganapathy, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/2086879.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    POS tagging
    part of speech
    deep learning
    natural language processing
    BiLSTM
    Bi-directional long short term memory
    tagowanie POS
    części mowy
    uczenie głębokie
    przetwarzanie języka naturalnego
    Opis:
    This paper addresses the problem of part of speech (POS) tagging for the Tamil language, which is low resourced and agglutinative. POS tagging is the process of assigning syntactic categories for the words in a sentence. This is the preliminary step for many of the Natural Language Processing (NLP) tasks. For this work, various sequential deep learning models such as recurrent neural network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bi-directional Long Short-Term Memory (Bi-LSTM) were used at the word level. For evaluating the model, the performance metrics such as precision, recall, F1-score and accuracy were used. Further, a tag set of 32 tags and 225 000 tagged Tamil words was utilized for training. To find the appropriate hidden state, the hidden states were varied as 4, 16, 32 and 64, and the models were trained. The experiments indicated that the increase in hidden state improves the performance of the model. Among all the combinations, Bi-LSTM with 64 hidden states displayed the best accuracy (94%). For Tamil POS tagging, this is the initial attempt to be carried out using a deep learning model.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 6; e138820, 1--6
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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