全文获取类型
收费全文 | 2347篇 |
免费 | 247篇 |
国内免费 | 64篇 |
专业分类
化学 | 763篇 |
晶体学 | 1篇 |
力学 | 89篇 |
综合类 | 74篇 |
数学 | 808篇 |
物理学 | 923篇 |
出版年
2024年 | 21篇 |
2023年 | 131篇 |
2022年 | 493篇 |
2021年 | 397篇 |
2020年 | 231篇 |
2019年 | 155篇 |
2018年 | 117篇 |
2017年 | 110篇 |
2016年 | 117篇 |
2015年 | 76篇 |
2014年 | 92篇 |
2013年 | 155篇 |
2012年 | 50篇 |
2011年 | 54篇 |
2010年 | 61篇 |
2009年 | 54篇 |
2008年 | 46篇 |
2007年 | 55篇 |
2006年 | 30篇 |
2005年 | 25篇 |
2004年 | 19篇 |
2003年 | 17篇 |
2002年 | 16篇 |
2001年 | 8篇 |
2000年 | 14篇 |
1999年 | 10篇 |
1998年 | 11篇 |
1997年 | 15篇 |
1996年 | 8篇 |
1995年 | 14篇 |
1994年 | 3篇 |
1993年 | 1篇 |
1992年 | 3篇 |
1991年 | 4篇 |
1990年 | 4篇 |
1989年 | 3篇 |
1988年 | 6篇 |
1987年 | 3篇 |
1986年 | 10篇 |
1985年 | 2篇 |
1984年 | 2篇 |
1983年 | 1篇 |
1982年 | 1篇 |
1981年 | 1篇 |
1979年 | 3篇 |
1977年 | 2篇 |
1971年 | 1篇 |
1969年 | 1篇 |
1959年 | 5篇 |
排序方式: 共有2658条查询结果,搜索用时 0 毫秒
61.
构建了基于二阶段异质随机森林的汽油辛烷值预测模型.首先利用样本-位点信息表知识约简模型,筛选出对汽油辛烷值影响大的位点数据作为第一阶段;然后,利用集成学习思想集成支持向量回归和动态时间序列神经网络,构建异质随机森林预测模型作为第二阶段.利用十折交叉法验证模型精度,结果表明该集成学习算法具有有效性和高精度. 相似文献
62.
Randall Claywell Laszlo Nadai Imre Felde Sina Ardabili Amirhosein Mosavi 《Entropy (Basel, Switzerland)》2020,22(11)
The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures. 相似文献
63.
Alberto Guilln Jos Martínez Juan Miguel Carceller Luis Javier Herrera 《Entropy (Basel, Switzerland)》2020,22(11)
The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity. 相似文献
64.
Using molecular simulations, we study the processes of capillary condensation and capillary evaporation in model mesopores. To determine the phase transition pathway, as well as the corresponding free energy profile, we carry out enhanced sampling molecular simulations using entropy as a reaction coordinate to map the onset of order during the condensation process and of disorder during the evaporation process. The structural analysis shows the role played by intermediate states, characterized by the onset of capillary liquid bridges and bubbles. We also analyze the dependence of the free energy barrier on the pore width. Furthermore, we propose a method to build a machine learning model for the prediction of the free energy surfaces underlying capillary phase transition processes in mesopores. 相似文献
65.
The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods. 相似文献
66.
In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These ‘multi-exits’ models can be used to reduce the inference time by performing early exit on the intermediate branches, if the confidence of the prediction is higher than a threshold. They rely on the assumption that not all the samples require the same amount of processing to yield a good prediction. In this paper, we propose a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch with a trained confidence score. Each confidence score is an approximation of the real one produced by the branch, and it is calculated and regularized while training the rest of the model. We evaluate our proposal on a set of image classification benchmarks, using different neural models and early-exit stopping criteria. 相似文献
67.
68.
Javier Esteban-Escao Berta Castn Sergio Castn Marta Chliz-Ezquerro Csar Asensio Antonio R. Laliena Gerardo Sanz-Enguita Gerardo Sanz Luis Mariano Esteban Ricardo Savirn 《Entropy (Basel, Switzerland)》2022,24(1)
Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections. 相似文献
69.
Michele Lo Giudice Giuseppe Varone Cosimo Ieracitano Nadia Mammone Giovanbattista Gaspare Tripodi Edoardo Ferlazzo Sara Gasparini Umberto Aguglia Francesco Carlo Morabito 《Entropy (Basel, Switzerland)》2022,24(1)
The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses. 相似文献
70.
Clara Argerich Martín Ruben Ibáñez Pinillo Anais Barasinski Francisco Chinesta 《Comptes Rendus Mecanique》2019,347(11):754-761
The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm. 相似文献