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    Wyświetlanie 1-4 z 4
    Tytuł:
    Systematic literature review of IoT metrics
    Autorzy:
    Moulla, Donatien Koulla Moulla
    Mnkandla, Ernest
    Abran, Alain
    Powiązania:
    https://bibliotekanauki.pl/articles/38430664.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    IoT metrics
    performance metrics
    software metrics
    energy consumption
    network metrics
    systematic literature review
    Opis:
    The Internet of Things (IoT) touches almost every aspect of modern society and has changed the way people live, work, travel and, do business. Because of its importance, it is essential to ensure that an IoT system is performing well, as desired and expected, and that this can be assessed and managed with an adequate set of IoT performance metrics. The aim of this study is to systematically inventory and classify recent studies that have investigated IoT metrics. The authors conducted a literature review based on studies published between January 2010 and December 2021 using a set of five research questions (RQs) on the current knowledge bases for IoT metrics. A total of 158 IoT metrics were identified and classified into 12 categories according to the different parts and aspects of an IoT system. To cover the overall performance of an IoT system, the 12 categories were organized into an ontology. The results show that the category of network metrics was most frequently discussed in 43% of the studies and, with the highest number of metrics at 37%. This study can provide guidelines for researchers and practitioners in selecting metrics for IoT systems and valuable insights into areas for improvement and optimization.
    Źródło:
    Applied Computer Science; 2023, 19, 1; 64-81
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modelling values of river macrophyte metrics using artificial neural networks
    Autorzy:
    Gebler, D.
    Kayzer, D.
    Budka, A.
    Szoszkiewicz, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/60958.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Stowarzyszenie Infrastruktura i Ekologia Terenów Wiejskich PAN
    Tematy:
    modelling value
    river
    macrophyte
    river ecology
    metrics
    artificial neural network
    water quality
    Opis:
    The results of field research at 230 river sections located throughout Poland were used to examine the possibility of predicting values of macrophyte metrics of ecological status. Artificial intelligence methods such as artificial neural networks were used in the modelling. The physicochemical parameters of water (alkalinity, conductivity, nitrate and ammonium nitrogen, reactive and total phosphorus, and biochemical oxygen demand) were used as the explanatory (modelling) variables. The explained (modelled) parameters were the Polish MIR (Macrophyte Index for Rivers), the British MTR (Mean Trophic Rank) and the French IBMR (River Macrophytes Biological Index). The quality of the constructed models was assessed using the normalized root mean square error (NRMSE) and the r–Pearson’s linear correlation coefficient between variables modelled by the networks and calculated on the basis of the botanical research. These analyses demonstrated that the network modelling MIR values had the highest accuracy. The lowest prediction accuracy was obtained for MTR and IBMR indices. The differences between particular models are likely to result from better adjustment of the Polish method to local rivers (particularly in terms of indicator species used).
    Źródło:
    Infrastruktura i Ekologia Terenów Wiejskich; 2012, 1/IV
    1732-5587
    Pojawia się w:
    Infrastruktura i Ekologia Terenów Wiejskich
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of Psychoacoustic Metrics Using Combination of Wavelet Packet Transform and an Optimized Artificial Neural Network
    Autorzy:
    Pourseiedrezaei, Mehdi
    Loghmani, Ali
    Keshmiri, Mehdi
    Powiązania:
    https://bibliotekanauki.pl/articles/177762.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    sound quality measurement
    psychoacoustic metrics
    wavelet packet transform
    optimized artificial neural network
    Opis:
    In this paper, a modified sound quality evaluation (SQE) model is developed based on combination of an optimized artificial neural network (ANN) and the wavelet packet transform (WPT). The presented SQE model is a signal processing technique, which can be implemented in current microphones for predicting the sound quality. The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. The model is optimized using the particle swarm optimization (PSO) and the back propagation (BP) algorithms. The obtained results reveal that the proposed model shows the lowest mean square error and the highest correlation with human perception while it has the lowest computational cost compared to those of the other models and software.
    Źródło:
    Archives of Acoustics; 2019, 44, 3; 561-573
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Development of a Sound Quality Evaluation Model Based on an Optimal Analytic Wavelet Transform and an Artificial Neural Network
    Autorzy:
    Pourseiedrezaei, Mehdi
    Loghmani, Ali
    Keshmiri, Mehdi
    Powiązania:
    https://bibliotekanauki.pl/articles/1953511.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    analytic wavelet transform
    AWT
    sound quality evaluation
    SQE
    psychoacoustic metrics
    back propagation neural network
    BPNN
    Opis:
    The purpose of this study was to develop a sound quality model for real time active sound quality control systems. The model is based on an optimal analytic wavelet transform (OAWT) used along with a back propagation neural network (BPNN) in which the initial weights and thresholds are determined by particle swarm optimisation (PSO). In the model the input signal is decomposed into 24 critical bands to extract a feature matrix, based on energy, mean, and standard deviation indices of the sub signal scalogram obtained by OAWT. The feature matrix is fed into the neural network input to determine the psychoacoustic parameters used for sound quality evaluation. The results of the study show that the present model is in good agreement with psychoacoustic models of sound quality metrics and enables evaluation of the quality of sound at a lower computational cost than the existing models.
    Źródło:
    Archives of Acoustics; 2021, 46, 1; 55-65
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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