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    Wyświetlanie 1-6 z 6
    Tytuł:
    Assessment of wind energy resources using artificial neural networks – case study at Łódź Hills
    Autorzy:
    Korupczyński, R.
    Trajer, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/199792.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    wind speed
    artificial neural network
    wind resources
    measure-correlate-predict
    prędkość wiatru
    sztuczna sieć neuronowa
    zasoby wiatru
    pomiar-korelacja-przewidywanie
    Opis:
    The aim of this paper is to answer the question: Are the Łódź Hills useful for electrical energy production from wind energy or not? Due to access to short-term data related to wind measurements (the period of 2008 and 2009) from a local meteorological station, the measure – correlate – predict approach have been applied. Long-term (1979‒2016) reference data were obtained from ECWMF ERA-40 Reanalysis. Artificial neural networks were used to calculate predicted wind speed. The obtained average wind speed and wind power density was 4.21 ms–1 and 70 Wm–1, respectively, at 10 m above ground level (5.51 ms–1, 170 Wm–1 at 50 m). From the point of view of Polish wind conditions, Łódź Hills may be considered useful for wind power engineering.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 1; 115-124
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Aspect-based sentiment classification model employing whale-optimized adaptive neural network
    Autorzy:
    Balaganesh, Nallathambi
    Muneeswaran, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/2173622.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    aspect-based sentiment analysis
    whale optimization algorithm
    artificial neural network
    opinion mining
    analiza nastrojów oparta na aspektach
    algorytm optymalizacji wielorybów
    sztuczna sieć neuronowa
    eksploracja opinii
    Opis:
    Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137271
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Aspect-based sentiment classification model employing whale-optimized adaptive neural network
    Autorzy:
    Balaganesh, Nallathambi
    Muneeswaran, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/2128172.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    aspect-based sentiment analysis
    whale optimization algorithm
    artificial neural network
    opinion mining
    analiza nastrojów oparta na aspektach
    algorytm optymalizacji wielorybów
    sztuczna sieć neuronowa
    eksploracja opinii
    Opis:
    Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137271, 1--8
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Power quality management in electrical grid using SCANN controller-based UPQC
    Autorzy:
    Varadharajan, Balaji
    Subramanian, Chitra
    Powiązania:
    https://bibliotekanauki.pl/articles/2173547.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    SCANN
    single comprehensive artificial neural network
    UPQC
    unified power quality conditioner
    total harmonic distortion
    particle swarm optimization
    POS
    sztuczna sieć neuronowa pojedyncza kompleksowa
    zunifikowany kondycjoner jakości energii
    współczynnik zawartości harmonicznych
    optymalizacja roju cząstek
    PSO
    Opis:
    The electrical grid integration takes great attention because of the increasing population in the nonlinear load connected to the power distribution system. This manuscript deals with the power quality issues and mitigations associated with the electrical grid. The proposed single comprehensive artificial neural network (SCANN) controller with unified power quality conditioner (UPQC) is modelled in MATLAB Simulink environment. It provides series and shunt compensation that helps mitigate voltage and current distortion at the end of the distribution system. Initially, four proportional integral (PI) controllers are used to control the UPQC. Later the trained SCANN controller replaces four PI Controllers for better control action. PI and SCANN controllers’ simulation results are compared to find the optimal solutions. A prototype model of SCANN controller is constructed and tested. The test results show that the SCANN based UPQC maintains grid voltage and current magnitude within permissible limits under fluctuating conditions.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; e140257, 1--9
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Investigating the FSW parameter’s role on microstructure and mechanical properties of welding AZ31B–AA8110 alloy
    Autorzy:
    Dharmalingam, S.
    Lenin, K.
    Srinivasan, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/2173552.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    AA8011–AZ31B alloy
    FSW
    friction stir welding
    ANN-GA
    artificial neural network based genetic algorithm
    mechanical properties
    stop AA8011–AZ31B
    właściwości mechaniczne
    zgrzewanie tarciowe z mieszaniem materiału zgorzeliny
    algorytm genetyczny
    sztuczna sieć neuronowa
    Opis:
    The influence of friction stir welding (FSW) in automotive applications is significantly high in recent days as it can boast beneficial factors such as less distortion, minimized residual stresses and enhanced mechanical properties. Since there is no emission of harmful gases, it is regarded as a green technology, which has an energy efficient clean environmental solid-state welding process. In this research work, the FSW technique is employed to weld the AA8011–AZ31B alloy. In addition, the L16 orthogonal array is employed to conduct the experiments. The influences of parameters on the factors such as microstructure, hardness and tensile strength are determined. Microstructure images have shown tunnel formation at low rotational speed and vortex occurrence at high rotational speed. To attain high quality welding, the process parameters are optimized by using a hybrid method called an artificial neural network based genetic algorithm (ANN-GA). The confirmation tests are carried out under optimal welding conditions. The results obtained are highly reliable, which exhibits the optimal features of the hybrid method.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; e140098, 1--7
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Robust estimation based nonlinear higher order sliding mode control strategies for PMSG-WECS
    Autorzy:
    Nazir, Awais
    Khan, Safdar Abbas
    Khan, Malak Adnan
    Alam, Zaheer
    Khan, Imran
    Irfan, Muhammad
    Rehman, Saifur
    Nowakowski, Grzegorz
    Powiązania:
    https://bibliotekanauki.pl/articles/27311430.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    wind energy conversion systems
    WECS
    robust control
    maximum power point tracking
    MPPT
    sliding mode control
    SMC
    super-twisting algorithm
    STA
    high gain observer
    artificial neural network
    ANN
    function fitting
    backstepping
    śledzenie maksymalnego punktu mocy
    obserwator o dużym wzmocnieniu
    sztuczna sieć neuronowa
    dopasowanie funkcji
    system konwersji energii wiatrowej
    sterowanie odporne
    sterowanie ślizgowe
    algorytm super skręcania
    Opis:
    The wind energy conversion systems (WECS) suffer from an intermittent nature of source (wind) and the resulting disparity between power generation and electricity demand. Thus, WECS are required to be operated at maximum power point (MPP). This research paper addresses a sophisticated MPP tracking (MPPT) strategy to ensure optimum (maximum) power out of the WECS despite environmental (wind) variations. This study considers a WECS (fixed pitch, 3KW, variable speed) coupled with a permanent magnet synchronous generator (PMSG) and proposes three sliding mode control (SMC) based MPPT schemes, a conventional first order SMC (FOSMC), an integral back-stepping-based SMC (IBSMC) and a super-twisting reachability-based SMC, for maximizing the power output. However, the efficacy of MPPT/control schemes rely on availability of system parameters especially, uncertain/nonlinear dynamics and aerodynamic terms, which are not commonly accessible in practice. As a remedy, an off-line artificial function-fitting neural network (ANN) based on Levenberg-Marquardt algorithm is employed to enhance the performance and robustness of MPPT/control scheme by effectively imitating the uncertain/nonlinear drift terms in the control input pathways. Furthermore, the speed and missing derivative of a generator shaft are determined using a high-gain observer (HGO). Finally, a comparison is made among the stated strategies subjected to stochastic and deterministic wind speed profiles. Extensive MATLAB/Simulink simulations assess the effectiveness of the suggested approaches.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 5; art. no. e147063
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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