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    Wyświetlanie 1-4 z 4
    Tytuł:
    Real-time validation of an automatic generation control system considering HPA-ISE with crow search algorithm optimized cascade FOPDN-FOPIDN controller
    Autorzy:
    Babu, Naladi Ram
    Chiranjeevi, Tirumalasetty
    Devarapalli, Ramesh
    Knypiński, Łukasz
    Garcìa Màrquez, Fausto Pedro
    Powiązania:
    https://bibliotekanauki.pl/articles/27312009.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    crow search algorithm
    dish-stirling solar system
    AGC
    RT Lab
    FOPDN-FOPIDN controller
    Opis:
    This article validates the application of RT-Lab for the AGC studies of three-area systems. All the areas are employed with thermal-DSTS systems. A new controller named cascade FOPDN-FOPPIDN is employed. Its parameters are optimized using a CSA, subjecting to a new PI named HPA-ISE. The responses of the FOPDN-FOPIDN controller are related and are superior over PIDN and TIDN controllers. Moreover, the dominance of HPA-ISE is verified with ISE, and it performs better in terms of system dynamics. Further, the system performance reliability is analyzed with the AC-HVDC and is better than the AC system. Besides, sensitivity analysis recommends that the proposed FOPDN-FOPIDN at diverse conditions is robust and more reliability.
    Źródło:
    Archives of Control Sciences; 2023, 33, 2; 371--390
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Single-objective optimal power flow for electric power systems based on crow search algorithm
    Autorzy:
    Fathy, A.
    Abdelaziz, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/140618.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    crow search algorithm
    distribution network
    optimal power flow
    Opis:
    This paper presents the application of a recent meta-heuristic optimization technique named a crow search algorithm (CSA) in solving the problem of an optimal power flow (OPF) for electric power systems. Various constrained objective functions, total fuel cost, active power loss and pollutant emission are proposed. The generators’ output powers, generators’ terminal voltages, transmission lines’ taps and the shunt capacitors’ reactive powers are considered as variables to be designed. The proposed methodology based on the CSA is applied on an IEEE 30-bus system and IEEE 118-bus system. The obtained results via the CSA are compared to others and they ensure the superiority of the CSA in solving the OPF problem in electric power systems.
    Źródło:
    Archives of Electrical Engineering; 2018, 67, 1; 123-138
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Cancer growth treatment using immune linear quadratic regulator based on crow search optimization algorithm
    Autorzy:
    Hussein, Mohammed A.
    Karam, Ekhlas H.
    Habeeb, Rokaia S.
    Powiązania:
    https://bibliotekanauki.pl/articles/1837793.pdf
    Data publikacji:
    2021
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    oncolytic virotherapy
    feedback mechanism
    crow search algorithm
    Immune-LQR
    wiroterapia onkolityczna
    mechanizm sprzężenia zwrotnego
    algorytm wyszukiwania w tłumie
    Opis:
    The rapid and uncontrollable cell division that spreads to surrounding tissues medically termed as malignant neoplasm, cancer is one of the most common diseases worldwide. The need for effective cancer treatment arises due to the increase in the number of cases and the anticipation of higher levels in the coming years. Oncolytic virotherapy is a promising technique that has shown encouraging results in several cases. Mathematical models of virotherapy have been widely developed, and one such model is the interaction between tumor cells and oncolytic virus. In this paper an artificially optimized Immune-Linear Quadratic Regulator (LQR) is introduced to improve the outcome of oncolytic virotherapy. The control strategy has been evaluated in silico on number of subjects. The crow search algorithm is used to tune immune and LQR parameters. The study is conducted on two subjects, S1 and S3, with LQR and Immune-LQR. The experimental results reveal a decrease in the number of tumor cells and remain in the treatment area from day ten onwards, this indicates the robustness of treatment strategies that can achieve tumor reduction regardless of the uncertainty in the biological parameters.
    Źródło:
    Applied Computer Science; 2021, 17, 2; 56-69
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Lung cancer detection using an integration of fuzzy K-Means clustering and deep learning techniques for CT lung images
    Autorzy:
    Prasad, J. Maruthi Nagendra
    Chakravarty, S.
    Krishna, M. Vamsi
    Powiązania:
    https://bibliotekanauki.pl/articles/2173683.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    fuzzy K-means
    artificial neural networks
    SVM
    support vector machine
    crow search optimization algorithm
    algorytm rozmytych k-średnich
    sztuczne sieci neuronowe
    maszyna wektorów wspierających
    algorytm optymalizacji wyszukiwania kruków
    Opis:
    Computer aided detection systems are used for the provision of second opinion during lung cancer diagnosis. For early-stage detection and treatment false positive reduction stage also plays a vital role. The main motive of this research is to propose a method for lung cancer segmentation. In recent years, lung cancer detection and segmentation of tumors is considered one of the most important steps in the surgical planning and medication preparations. It is very difficult for the researchers to detect the tumor area from the CT (computed tomography) images. The proposed system segments lungs and classify the images into normal and abnormal and consists of two phases, The first phase will be made up of various stages like pre-processing, feature extraction, feature selection, classification and finally, segmentation of the tumor. Input CT image is sent through the pre-processing phase where noise removal will be taken care of and then texture features are extracted from the pre-processed image, and in the next stage features will be selected by making use of crow search optimization algorithm, later artificial neural network is used for the classification of the normal lung images from abnormal images. Finally, abnormal images will be processed through the fuzzy K-means algorithm for segmenting the tumors separately. In the second phase, SVM classifier is used for the reduction of false positives. The proposed system delivers accuracy of 96%, 100% specificity and sensitivity of 99% and it reduces false positives. Experimental results shows that the system outperforms many other systems in the literature in terms of sensitivity, specificity, and accuracy. There is a great tradeoff between effectiveness and efficiency and the proposed system also saves computation time. The work shows that the proposed system which is formed by the integration of fuzzy K-means clustering and deep learning technique is simple yet powerful and was effective in reducing false positives and segments tumors and perform classification and delivers better performance when compared to other strategies in the literature, and this system is giving accurate decision when compared to human doctor’s decision.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e139006
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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