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    Wyświetlanie 1-9 z 9
    Tytuł:
    Właściwości sorpcyjne popiołów ze spalania węgla
    Sorption properties of fly ash from coal burning
    Autorzy:
    Polowczyk, I.
    Bastrzyk, A.
    Sawiński, W.
    Koźlecki, T.
    Rudnicki, P.
    Sadowski, Z.
    Sokołowski, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/2071059.pdf
    Data publikacji:
    2010
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    popiół lotny
    arsen(III)
    adsorpcja
    model PSO
    fly ash
    arsenic(III)
    adsorption
    PSO model
    Opis:
    Celem niniejszej pracy było zbadanie zdolności sorpcyjnych popiołu po spalaniu węgla brunatnego pod kątem adsorpcji arsenu (III). Uzyskane wyniki wskazują na znaczną pojemność sorpcyjną popiołu lotnego względem As(III) (33 mg/g), porównywalną do otrzymywanej na żywicach jonowymiennych. Dane doświadczalne dobrze opisuje izoterma Langmuira, a proces adsorpcji kontrolowany jest reakcją chemiczną pseudo drugiego rzędu (model PSO).
    The aim of work was to examine sorption of As(III) species, using fly ash from lignite burning. The results obtained show that the sorption capacity of fly ash (33 mg/g) is comparable to commercial sorbent. Experimental data can be described by means ofLangmuir isotherm. Adsorption process obeys the pseudo-second order (PSO) kinetics.
    Źródło:
    Inżynieria i Aparatura Chemiczna; 2010, 1; 93-94
    0368-0827
    Pojawia się w:
    Inżynieria i Aparatura Chemiczna
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
    Autorzy:
    He, Ping
    Dong, Jie
    Wu, Xiaopeng
    Yun, Lei
    Yang, Hua
    Powiązania:
    https://bibliotekanauki.pl/articles/27309934.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    BP neural network
    photovoltaic power generation
    PSO–GWO model
    PSO–GWO–BP prediction model
    particle swarm optimization
    gray wolf optimization
    back propagation
    standard grey wolf algorithm
    Opis:
    At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
    Źródło:
    Archives of Electrical Engineering; 2023, 72, 3; 613--628
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Storage allocation in automated container terminals: the upper level
    Autorzy:
    Xia, M.
    Zhao, N.
    Mi, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/258912.pdf
    Data publikacji:
    2016
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    storage allocation
    automated container terminal
    upper level model
    PSO algorithm
    Opis:
    Nowadays automation is a trend of container terminals all over the world. Although not applied in current automated container terminals, storage allocation is indispensable in conventional container terminals, and promising for automated container terminals in future. This paper seeks into the storage allocation problem in automated container terminals and proposed a two level structure for the problem. A mixed integer programming model is built for the upper level, and a modified Particle Swarm Optimization (PSO) algorithm is applied to solve the model. The applicable conditions of the model is investigated by numerical experiments, so as the performance of the algorithm in different problem scales. It is left to future research the lower level of the problem and the potential benefit of storage allocation to automated container terminals.
    Źródło:
    Polish Maritime Research; 2016, S 1; 160-174
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Traffic fatalities prediction based on support vector machine
    Autorzy:
    Li, T.
    Yang, Y.
    Wang, Y.
    Chen, C.
    Yao, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/223743.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    traffic accident
    support vector machine
    SVM
    particle swarm optimization (PSO)
    PSO
    prediction model
    optimal parameters
    wypadek drogowy
    Particle Swarm Optimization
    model prognostyczny
    optymalne parametry
    Opis:
    To effectively predict traffic fatalities and promote the friendly development of transportation, a prediction model of traffic fatalities is established based on support vector machine (SVM). As the prediction accuracy of SVM largely depends on the selection of parameters, Particle Swarm Optimization (PSO) is introduced to find the optimal parameters. In this paper, small sample and nonlinear data are used to predict fatalities of traffic accident. Traffic accident statistics data of China from 1981 to 2012 are chosen as experimental data. The input variables for predicting accident are highway mileage, vehicle number and population size while the output variables are traffic fatality. To verify the validity of the proposed prediction method, the back-propagation neural network (BPNN) prediction model and SVM prediction model are also used to predict the traffic fatalities. The results show that compared with BPNN prediction model and SVM model, the prediction model of traffic fatalities based on PSO-SVM has higher prediction precision and smaller errors. The model can be more effective to forecast the traffic fatalities. And the method using particle swarm optimization algorithm for parameter optimization of SVM is feasible and effective. In addition, this method avoids overcomes the problem of “over learning” in neural network training progress.
    Źródło:
    Archives of Transport; 2016, 39, 3; 21-30
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimation of composite load model parameters using improved particle swarm optimization
    Autorzy:
    Regulski, P.
    Gonzalez-Longatt, F.
    Terzija, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/410557.pdf
    Data publikacji:
    2012
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    load modeling
    parameter estimation
    particle swarm optimization (PSO)
    composite load model
    Opis:
    Power system loads are one of its crucial elements to be modeled in stability studies. However their static and dynamic characteristics are very often unknown and usually changing in time (daily, weekly, monthly and seasonal variations). Taking this into account, a measurement-based approach for determining the load characteristics seems to be the best practice, as it updates the parameters of a load model directly from the system measurements. To achieve this, a Parameter Estimation tool is required, so a common approach is to incorporate the standard Nonlinear Least Squares, or Genetic Algorithms, as a method providing more global capabilities. In this paper a new solution is proposed -an Improved Particle Swarm Optimization method. This method is an Artificial Intelligence type technique similar to Genetic Algorithms, but easier for implementation and also computationally more efficient. The paper provides results of several experiments proving that the proposed method can achieve higher accuracy and show better generalization capabilities than the Nonlinear Least Squares method. The computer simulations were carried out using a one-bus and an IEEE 39-bus test system.
    Źródło:
    Present Problems of Power System Control; 2012, 2; 41-51
    2084-2201
    Pojawia się w:
    Present Problems of Power System Control
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Error mitigation algorithm based on bidirectional fitting method for collision avoidance of Unmanned Surface Vehicle
    Autorzy:
    Song, L.
    Chen, Z.
    Mao, Y.
    Dong, Z.
    Xiang, Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/260298.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    Unmanned Surface Vehicle
    position prediction
    error mitigation
    autoregressive model
    particle swarm optimization (PSO)
    Opis:
    Radars and sensors are essential devices for an Unmanned Surface Vehicle (USV) to detect obstacles. Their precision has improved significantly in recent years with relatively accurate capability to locate obstacles. However, small detection errors in the estimation and prediction of trajectories of obstacles may cause serious problems in accuracy, thereby damaging the judgment of USV and affecting the effectiveness of collision avoidance. In this study, the effect of radar errors on the prediction accuracy of obstacle position is studied on the basis of the autoregressive prediction model. The cause of radar error is also analyzed. Subsequently, a bidirectional adaptive filtering algorithm based on polynomial fitting and particle swarm optimization is proposed to eliminate the observed errors in vertical and abscissa coordinates. Then, simulations of obstacle tracking and prediction are carried out, and the results show the validity of the algorithm. Finally, the method is used to simulate the collision avoidance of USV, and the results show the validity and reliability of the algorithm.
    Źródło:
    Polish Maritime Research; 2018, 4; 13-20
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Genetic algorithm-PID controller for model order reduction pantographcatenary system
    Autorzy:
    Al-Awad, Nasir A.
    Abboud, Izz K.
    Al-Rawi, Muaayed F.
    Powiązania:
    https://bibliotekanauki.pl/articles/1837796.pdf
    Data publikacji:
    2021
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    pantograph system
    model reduction
    PSO
    G.A-PID
    IMC
    LQR
    system pantografowy
    redukcja modelu
    Opis:
    Controlling the contact force between the pantograph and the catenary has come to be a requirement for improving the performances and affectivity of high-speed train systems Indeed, these performances can also significantly be decreased due to the fact of the catenary equal stiffness variation. In addition, the contact force can also additionally differ and ought to end up null, which may additionally purpose the loss of contact. Then, in this paper, we current an active manipulate of the minimize order model of pantograph-catenary system. The proposed manipulate approach implements an optimization technique, like particle swarm (PSO), the usage of a frequent approximation of the catenary equal stiffness. All the synthesis steps of the manipulate law are given and a formal evaluation of the closed loop stability indicates an asymptotic monitoring of a nominal steady contact force. Then, the usage of Genetic Algorithm with Proportional-Integral-derivative (G.A-PID) as proposed controller appeared optimum response where, the contacts force consequences to be virtually equal to its steady reference. Finally it seems the advantageous of suggestion approach in contrast with classical manipulate strategies like, Internal mode control(IMC) method, linear quadratic regulator (LQR). The outcomes via the use of MATLAB simulation, suggests (G.A-PID) offers better transient specifications in contrast with classical manipulate.
    Źródło:
    Applied Computer Science; 2021, 17, 2; 28-39
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
    Autorzy:
    Soltani, M.
    Chaari, A.
    Ben Hmida, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/330134.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    model rozmyty Takagi-Sugeno
    algorytm grupowania
    metoda najmniejszych kwadratów
    optymalizacja rojem cząstek
    Takagi-Sugeno fuzzy models
    noise clustering algorithm
    fuzzy c-regression model
    orthogonal least squares
    particle swarm optimization (PSO)
    Opis:
    This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 617-628
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Computationally efficient nonlinear model predictive controller using parallel particle swarm optimization
    Autorzy:
    Diwan, Supriya P.
    Deshpande, Shraddha S.
    Powiązania:
    https://bibliotekanauki.pl/articles/2173694.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    nonlinear model predictive control
    particle swarm optimization
    PSO
    fast dynamic systems
    rotary inverted pendulum
    divide approach
    conquer approach
    kontrola predykcyjna modelu nieliniowa
    optymalizacja roju cząstek
    system dynamiczny szybki
    wahadło obrotowe odwrócone
    Opis:
    As nonlinear optimization techniques are computationally expensive, their usage in the real-time era is constrained. So this is the main challenge for researchers to develop a fast algorithm that is used in real-time computations. This work proposes a fast nonlinear model predictive control approach based on particle swarm optimization for nonlinear optimization with constraints. The suggested algorithm divide and conquer technique improves computing speed and disturbance rejection capability, demonstrating its suitability for real-time applications. The performance of this approach under constraints is validated using a highly nonlinear fast and dynamic real-time inverted pendulum system. The solution presented through work is computationally feasible for smaller sampling times and it gives promising results compared to the state of art PSO algorithm
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 4; art. no. e140696
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-9 z 9

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