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    Wyświetlanie 1-4 z 4
    Tytuł:
    Fast-decision identification algorithm of emission source pattern in database
    Autorzy:
    Dudczyk, J.
    Kawalec, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/199845.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Fast-decision Identification Algorithm
    Specific Emitter Identification
    Emitter Pattern
    database
    superheterodyne ELINT receiver
    baza danych
    superheterodynowy odbiornik ELINT
    algorytm FdIA
    Opis:
    This article presents Fast-decision Identification Algorithm (FdIA) of Source Emission (SE) in DataBase (DB). The aim of this identification process is to define signal vector (V) in the form of distinctive features of this signal which is received in the process of its measurement. Superheterodyne ELectronic INTelligence (ELINT) receiver in the measure procedure was used. The next step in identification process is comparison vector with pattern in DB and calculation of decision function. The aim of decision function is to evaluate similarity degree between vector and pattern. Identification process mentioned above differentiates copies of radar of the same type which is a special test challenge defined as Specific Emitter Identification (SEI). The authors of this method drew up FdIA and three-stage parameterization by the implementation of three different ways of defining the degree of similarity between vector and pattern (called ’Compare procedure’). The algorithm was tested on hundreds of signal vectors coming from over a dozen copies of radars of the same type. Fast-decision Identification Algorithm which was drawn up and implemented makes it possible to create Knowledge Base which is an integral part of Expert DataBase. As a result, the amount of the ambiguity of decisions in the process of Source Emission Identification is minimized.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 2; 385-389
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hybrid genetic algorithm for bi-criteria objectives in scheduling process
    Autorzy:
    Raghavendra, B. V.
    Powiązania:
    https://bibliotekanauki.pl/articles/407393.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    multi-criteria decision
    genetic algorithm
    Pareto method
    Opis:
    Scheduling of multiobjective problems has gained the interest of the researchers. Past many decades, various classical techniques have been developed to address the multiobjective problems, but evolutionary optimizations such as genetic algorithm, particle swarm, tabu search method and many more are being successfully used. Researchers have reported that hybrid of these algorithms has increased the efficiency and effectiveness of the solution. Genetic algorithms in conjunction with Pareto optimization are used to find the best solution for bi-criteria objectives. Numbers of applications involve many objective functions, and application of the Pareto front method may have a large number of potential solutions. Selecting a feasible solution from such a large set is difficult to arrive the right solution for the decision maker. In this paper Pareto front ranking method is proposed to select the best parents for producing offspring’s necessary to generate the new populations sets in genetic algorithms. The bi-criteria objectives minimizing the machine idleness and penalty cost for scheduling process is solved using genetic algorithm based Pareto front ranking method. The algorithm is coded in Matlab, and simulations were carried out for the crossover probability of 0.6, 0.7, 0.8, and 0.9. The results obtained from the simulations are encouraging and consistent for a crossover probability of 0.6.
    Źródło:
    Management and Production Engineering Review; 2020, 11, 2; 107-112
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimizing the Bit-flipping Method for Decoding Low-density Parity-check Codes in Wireless Networks by Using the Artificial Spider Algorithm
    Autorzy:
    Ghaffoori, Ali Jasim
    Abdul-Adheem, Wameedh Riyadh
    Powiązania:
    https://bibliotekanauki.pl/articles/2055251.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    low-density parity-check
    LDPC
    hard-decision Bit-Flipping
    BF
    particle swarm optimization
    PSO
    artificial spider algorithm
    ASA
    Opis:
    In this paper, the performance of Low-Density Parity-Check (LDPC) codes is improved, which leads to reduce the complexity of hard-decision Bit-Flipping (BF) decoding by utilizing the Artificial Spider Algorithm (ASA). The ASA is used to solve the optimization problem of decoding thresholds. Two decoding thresholds are used to flip multiple bits in each round of iteration to reduce the probability of errors and accelerate decoding convergence speed while improving decoding performance. These errors occur every time the bits are flipped. Then, the BF algorithm with a low-complexity optimizer only requires real number operations before iteration and logical operations in each iteration. The ASA is better than the optimized decoding scheme that uses the Particle Swarm Optimization (PSO) algorithm. The proposed scheme can improve the performance of wireless network applications with good proficiency and results. Simulation results show that the ASA-based algorithm for solving highly nonlinear unconstrained problems exhibits fast decoding convergence speed and excellent decoding performance. Thus, it is suitable for applications in broadband wireless networks.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 1; 109--114
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Travel management optimization based on air pollution condition using Markov decision process and genetic algorithm (case study: Shiraz city)
    Autorzy:
    Bagheri, Mohammad
    Ghafourian, Hossein
    Kashefiolasl, Morteza
    Pour, Mohammad Taghi Sadati
    Rabbani, Mohammad
    Powiązania:
    https://bibliotekanauki.pl/articles/223520.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    air pollution
    dynamic optimization
    genetic algorithm
    Markov decision-making process
    zarządzanie transportem
    optymalizacja
    zanieczyszczenie powietrza
    algorytm genetyczny
    proces decyzyjny Markowa
    Opis:
    Currently, air pollution and energy consumption are the main issues in the transportation area in large urban cities. In these cities, most people choose their transportation mode according to corresponding utility including traveller's and trip’s characteristics. Also, there is no effective solution in terms of population growth, urban space, and transportation demands, so it is essential to optimize systematically travel demands in the real network of roads in urban areas, especially in congested areas. Travel Demand Management (TDM) is one of the well-known ways to solve these problems. TDM defined as a strategy that aims to maximize the efficiency of the urban transport system by granting certain privileges for public transportation modes, Enforcement on the private car traffic prohibition in specific places or times, increase in the cost of using certain facilities like parking in congested areas. Network pricing is one of the most effective methods of managing transportation demands for reducing traffic and controlling air pollution especially in the crowded parts of downtown. A little paper may exist that optimize urban transportations in busy parts of cities with combined Markov decision making processes with reward and evolutionary-based algorithms and simultaneously considering customers’ and trip’s characteristics. Therefore, we present a new network traffic management for urban cities that optimizes a multi-objective function that related to the expected value of the Markov decision system’s reward using the Genetic Algorithm. The planned Shiraz city is taken as a benchmark for evaluating the performance of the proposed approach. At first, an analysis is also performed on the impact of the toll levels on the variation of the user and operator cost components, respectively. After choosing suitable values for the network parameters, simulation of the Markov decision process and GA is dynamically performed, then the optimal decision for the Markov decision process in terms of total reward is obtained. The results illustrate that the proposed cordon pricing has significant improvement in performance for all seasons including spring, autumn, and winter.
    Źródło:
    Archives of Transport; 2020, 53, 1; 89-102
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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