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    Wyświetlanie 1-3 z 3
    Tytuł:
    Experimental study of the strip coal pillar models failure with different roof and floor conditions
    Autorzy:
    Qu, Xiao
    Chen, Shaojie
    Yin, Dawei
    Liu, Shiqi
    Powiązania:
    https://bibliotekanauki.pl/articles/2073871.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    kopalnia
    filar węglowy
    górnictwo odkrywkowe
    “roof-strip coal pillar-floor” combined specimen
    failure characteristics
    acoustic emission
    laboratory experiment
    Opis:
    In order to study the failure mechanism and characteristics for strip coal pillars, a monitoring device for strip coal pillar uniaxial compression testing was developed. Compression tests of simulated strip coal pillars with different roof and floor rock types were conducted. Test results show that, with increasing roof and floor strength, compressive strength and elastic modulus of “roof-strip coal pillar-floor” combined specimens increase gradually. Strip coal pillar sample destruction occurs gradually from edge to the interior. First macroscopic failure occurs at the edge of the middle upper portion of the specimen, and then develops towards the corner. Energy accumulation and release cause discontinuous damage in the heterogeneous coal-mass, and the lateral displacement of strip coal pillar shows step and mutation characters. The brittleness and burst tendency of strip coal pillar under hard surrounding rocks are more obvious, stress growth rate decreases, and the rapid growth acoustic emission (AE) signal period can be regarded as a precursor for instability in the strip coal pillar. The above results have certain theoretical value for understanding the failure law and long-term stability of strip coal pillars.
    Źródło:
    Archives of Mining Sciences; 2021, 66, 3; 475--490
    0860-7001
    Pojawia się w:
    Archives of Mining Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An adaptive k nearest neighbour method for imputation of missing traffic data based on two similarity metrics
    Autorzy:
    Wang, Yang
    Xiao, Yu
    Lai, Jianhui
    Chen, Yanyan
    Powiązania:
    https://bibliotekanauki.pl/articles/949848.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    missing traffic data
    similarity metrics
    K-nearest neighbour method
    stochastic characteristics
    metoda porównywania danych
    metryki podobieństwa
    metoda najbliższego sąsiada
    cechy stochastyczne
    Opis:
    Traffic flow is one of the fundamental parameters for traffic analysis and planning. With the rapid development of intelligent transportation systems, a large number of various detectors have been deployed in urban roads and, consequently, huge amount of data relating to the traffic flow are accumulatively available now. However, the traffic flow data detected through various detectors are often degraded due to the presence of a number of missing data, which can even lead to erroneous analysis and decision if no appropriate process is carried out. To remedy this issue, great research efforts have been made and subsequently various imputation techniques have been successively proposed in recent years, among which the k nearest neighbour algorithm (kNN) has received a great popularity as it is easy to implement and impute the missing data effectively. In the work presented in this paper, we firstly analyse the stochastic effect of traffic flow, to which the suffering of the kNN algorithm can be attributed. This motivates us to make an improvement, while eliminating the requirement to predefine parameters. Such a parameter-free algorithm has been realized by introducing a new similarity metric which is combined with the conventional metric so as to avoid the parameter setting, which is often determined with the requirement of adequate domain knowledge. Unlike the conventional version of the kNN algorithm, the proposed algorithm employs the multivariate linear regression model to estimate the weights for the final output, based on a set of data, which is smoothed by a Wavelet technique. A series of experiments have been performed, based on a set of traffic flow data reported from serval different countries, to examine the adaptive determination of parameters and the smoothing effect. Additional experiments have been conducted to evaluate the competent performance for the proposed algorithm by comparing to a number of widely-used imputing algorithms.
    Źródło:
    Archives of Transport; 2020, 54, 2; 59-73
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel combinational evaluation method of voltage and reactive power in regional power grid containing renewable energy
    Autorzy:
    Ji, Yuqi
    Chen, Xuehan
    Xiao, Han
    Shi, Shaoyu
    Kang, Jing
    Wang, Jialin
    Zhang, Shaofeng
    Powiązania:
    https://bibliotekanauki.pl/articles/1955201.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    adaptive weighting coefficient
    combinational evaluation
    renewable energy
    typical scenarios
    adaptacyjny współczynnik wagowy
    ocena kombinacyjna
    energia odnawialna
    typowy scenariusz
    Opis:
    The output of renewable energy is strongly uncertain and random, and the distribution of voltage and reactive power in regional power grids is changed with the access to large-scale renewable energy. In order to quantitatively evaluate the influence of renewable energy access on voltage and reactive power operation, a novel combinational evaluation method of voltage and reactive power in regional power grids containing renewable energy is proposed. Firstly, the actual operation data of renewable energy and load demand are clustered based on the K-means algorithm, and several typical scenarios are divided. Then, the entropy weight method (EWM) and the analytic hierarchy process (AHP) are combined to evaluate the voltage qualified rate, voltage fluctuation, power factor qualified rate and reactive power reserve in typical scenarios. Besides, the evaluation results are used as the training samples for back-propagation (BP) neural networks. The proposed combinational evaluation method can calculate the weight coefficient of the indexes adaptively with the change of samples, which simplifies the calculation process of the indexes’ weight. At last, the case simulation of an actual regional power grid is provided, and the historical data of one year is taken as the sample for training, evaluating and analyzing. And finally, the effectiveness of the proposed method is verified based on the comparison with the existing method. The evaluated results could provide reference and guidance to the operation analysis and planning of renewable energy.
    Źródło:
    Archives of Electrical Engineering; 2021, 70, 4; 925-942
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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