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551.
A fast calculation of the reliability is meaningful to the in-line inspection of corroding natural gas pipelines. However, the traditional Monte Carlo simulation(MCS) method is time consuming for the low possibilities of the pipeline failure. The artificial neural network(ANN) is preferable for the complex nonlinear situation. An optimization of artificial neural network modeling methodology for the reliability assessment of corroding natural gas pipelines is proposed in this paper. To reduce the influence of training sets random behaviors on the calculation results, some algorithms are used to optimize the sequence of the training samples and the initial parameters of ANN models. The optimized model is applied to the reliability assessment of a corroded pipe with two successive inline inspections. According to the physical parameters of the pipeline, the trend of corroding pipeline reliability in time is predicted. The comparison between the trained ANN model, the MCS method and non-optimized ANN model shows the advantages the proposed modeling process. The methodology given in this paper is general and it can be applied to evaluate the reliability of other kind of structure safeties in practical systems. 相似文献
552.
553.
针对拉萨市道路交通噪声污染问题,运用人工神经网络理论和方法对拉萨市道路交通噪声的等效连续声级进行预测。经检验,计算值与实测值接近,从而为道路交通噪声的预测提供了一种新的途径。 相似文献
554.
Modeling and spatial prediction of pre-settlement patterns of forest distribution using witness tree data 总被引:1,自引:0,他引:1
At the time of European settlement, land surveys were conducted progressively westward throughout the United States. Outside
of the original 13 colonies, surveys generally followed the Public Land Survey system in which trees, called witness trees,
were regularly recorded at 1 mi by 1 mi grid intersections. This unintentional sampling provides insight into the composition
and structure of pre-European settlement forests, which is used as baseline data to assess forest change following settlement.
In this paper, a model for the Public Land Surveys of east central Alabama is developed. Assuming that the locations of trees
of each species are realized from independent Poisson processes whose respective log intensities are linear functions of environmental
covariates (i.e., elevation, landform, and physiographic province), the species observed at the survey grid intersections
are independently sampled from a generalized logistic regression model. If all 68 species found in the survey were included,
the model would be highly over-parameterized, so only the distribution of the most common taxon, pines, will be considered
at this time. To assess the impact of environmental factors not included in the model, a hidden Gaussian random field shall
be added as a random effect. A Markov Chain Monte Carlo algorithm is developed for Bayesian inference on model parameters,
and for Bayes posterior prediction of the spatial distribution of pines in east central Alabama.
Received: June 2004 / Revised: November 2004 相似文献
555.
We develop the non-parametric maximum likelihood estimator (MLE) of the full Mbh capture-recapture model which utilizes both initial capture and recapture data and permits both heterogeneity (h) between animals and behavioural (b) response to capture. Our MLE procedure utilizes non-parametric maximum likelihood estimation of mixture distributions (Lindsay, 1983; Lindsay and Roeder, 1992) and the EM algorithm (Dempsteret al., 1977). Our MLE estimate provides the first non-parametric estimate of the bivariate capture-recapture distribution.Since non-parametric maximum likelihood estimation exists for submodels Mh (allowing heterogeneity only), Mb (allowing behavioural response only) and M0 (allowing no changes), we develop maximum likelihood-based model selection, specifically the Akaike information criterion (AIC) (Akaike, 1973). The AIC procedure does well in detecting behavioural response but has difficulty in detecting heterogeneity. 相似文献
556.
二维水质模型横向扩散系数的人工神经网络模拟 总被引:4,自引:0,他引:4
将人工神经网络的理论和方法引入河流横向扩散系数的理论预测中,提出了基于BP人工神经网络的横向扩散系统预测模型,应用国内外河流的实测样本对模型进行训练与检验表明,该模型用于横向扩散系数的计算不仅可行而且精度较高,为河流横向扩散系数预测研究开辟了新途径。 相似文献
557.
加速基因算法在海洋环境预报中的应用 总被引:10,自引:2,他引:10
基于自然选择和自然基因机制的基因算法是一种优秀的寻优方法,它利用遗传操作算子来模拟生物界的优生劣汰的规律,是一种多路径全局优化方法。为增强经典基因算法对实际模型参数变化范围的适应性,加快其全局寻优速度和简化算法参数设置技术,本文给邮了一种改进基因算法,并在海渔期预报,冰情预报中得到成功应用。该法可广泛用于各种工程模型优化之中。 相似文献
558.
B-P网络用于水质综合评价方法的研究 总被引:13,自引:2,他引:13
本文应用误差反向传播(B—P)算法的人工神经网络建立了水质综合评价模型。该模型应用于实例的水质评价结果表明B—P网络用于水质综合评价具有客观性和实用性。 相似文献
559.
基于粗集、遗传神经网络的环境质量评价 总被引:1,自引:0,他引:1
基于粗集、遗传神经网络的环境质量评价方法利用粗集对属性的归约功能将数据库中的数据进行归约,并将归约后的数据作为训练数据提供给BP神经网络;再用遗传算法和BP算法相结合的混合算法来训练网络预测模型的结构(在得到最优网络结构的同时也得到网络的最优权值和阈值)。通过实例表明该方法是有效的,为环境质量评价提供了一种新的研究思路和分析方法。 相似文献
560.