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MiE is a facial involuntary reaction that reflects the real emotion and thoughts of a human being. It is very difficult for a normal human to detect a Micro-Expression (MiE), since it is a very fast and local face reaction with low intensity. As a consequence, it is a challenging task for researchers to build an automatic system for MiE recognition. Previous works for MiE recognition have attempted to use the whole face, yet a facial MiE appears in a small region of the face, which makes the extraction of relevant features a hard task. In this paper, we propose a novel deep learning approach that leverages the locality aspect of MiEs by learning spatio-temporal features from local facial regions using a composite architecture of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The proposed solution succeeds to extract relevant local features for MiEs recognition. Experimental results on benchmark datasets demonstrate the highest recognition accuracy of our solution with respect to state-of-the-art methods. 相似文献
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《Advanced Powder Technology》2019,30(12):3088-3098
The purpose of this paper is to give process for preparing monodispersed silver particles with round shape having aspect ratio of 1, because the shape is suitable for preparing silver grids with fine pattern size. We found that the combination of gelatin and hydrazine gave the monodispersed silver particles with the aspect ratio of 1. Presence of the high molecular compounds is crucial probably because they are adsorbed on the surface of growing silver particles and control the uniform crystal growth. In addition, the relationship between these reaction conditions and the particle shape are discussed in detail. 相似文献
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Suzhen Wang Shanshan Geng Zhanfeng Zhang Anshan Ye Keming Chen Zhaosheng Xu Huimin Luo Gangshan Wu Lina Xu Ning Cao 《计算机、材料和连续体(英文)》2019,61(2):739-757
Spark is a distributed data processing framework based on memory. Memory allocation is a focus question of Spark research. A good memory allocation scheme can effectively improve the efficiency of task execution and memory resource utilization of the Spark. Aiming at the memory allocation problem in the Spark2.x version, this paper optimizes the memory allocation strategy by analyzing the Spark memory model, the existing cache replacement algorithms and the memory allocation methods, which is on the basis of minimizing the storage area and allocating the execution area according to the demand. It mainly including two parts: cache replacement optimization and memory allocation optimization. Firstly, in the storage area, the cache replacement algorithm is optimized according to the characteristics of RDD Partition, which is combined with PCA dimension. In this section, the four features of RDD Partition are selected. When the RDD cache is replaced, only two most important features are selected by PCA dimension reduction method each time, thereby ensuring the generalization of the cache replacement strategy. Secondly, the memory allocation strategy of the execution area is optimized according to the memory requirement of Task and the memory space of storage area. In this paper, a series of experiments in Spark on Yarn mode are carried out to verify the effectiveness of the optimization algorithm and improve the cluster performance. 相似文献
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1-read/1-write (1R1W) register file (RF) is a popular memory configuration in modern feature rich SoCs requiring significant amount of embedded memory. A memory compiler is constructed using the 8T RF bitcell spanning a range of instances from 32 b to 72 Kb. An 8T low-leakage bitcell of 0.106 μm2 is used in a 14 nm FinFET technology with a 70 nm contacted gate pitch for high-density (HD) two-port (TP) RF memory compiler which achieves 5.66 Mb/mm2 array density for a 72 Kb array which is the highest reported density in 14 nm FinFET technology. The density improvement is achieved by using techniques such as leaf-cell optimization (eliminating transistors), better architectural planning, top level connectivity through leaf-cell abutment and minimizing the number of unique leaf-cells. These techniques are fully compatible with memory compiler usage over the required span. Leakage power is minimized by using power-switches without degrading the density mentioned above. Self-induced supply voltage collapse technique is applied for write and a four stack static keeper is used for read Vmin improvement. Fabricated test chips using 14 nm process have demonstrated 2.33 GHz performance at 1.1 V/25 °C operation. Overall Vmin of 550 mV is achieved with this design at 25 °C. The inbuilt power-switch improves leakage power by 12x in simulation. Approximately 8% die area of a leading 14 nm SoC in commercialization is occupied by these compiled RF instances. 相似文献
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《能源学会志》2020,93(4):1460-1472
Chemical Looping Combustion (CLC) is one of the emerging technologies for carbon capture, with less energy penalty. The present way of using pulverized coals in a fluidized bed (FB)-CLC have limitations like loss of unconverted char and gaseous combustibles, which could be mitigated by use of coarser fuel particles. Devolatilization time is a critical input for the effective design of FB-CLC systems, primarily when large fuel particles are used. The present study investigates the devolatilization time and the char yield of three coals of two shapes, namely, two high ash Indian coals and a low ash Indonesian coal and a wood (Casuarina equisetifolia) in the size range of +8–25 mm, at different fuel reactor temperatures (800–950 °C) of a hematite based CLC unit. The devolatilization times of single fuel particles during CLC are determined using a visual method called ‘Color Indistinction Method’. Indonesian coal has the longest devolatilization time among the fuels, and biomass has the least. Increasing the bed temperature enhances the rate of volatile release, whereas this effect is less pronounced in larger particles. Devolatilization of Indonesian coal is found to be strongly influenced by the changes in operating conditions. With the decrease in sphericity, a maximum of 56% reduction in devolatilization time is observed for the +20–25 mm slender particles of Indonesian coals when compared to the near-round particles. The maximum average char yields at the end of the devolatilization phase for coal and biomass are about 55–76% and 16% respectively. Char yield in coal particles increases with an increase in particle size, whereas biomass particles show relatively consistent yield across all experimental conditions. Increase in bed temperature reduces the char yields of coal up to 12% and in biomass up to 30%. High volatile Indian coal is the most influenced fuel by the changes in fuels shape. A correlation for determining devolatilization time under CLC environment is presented, and it successfully fits most of the experimental values within ±20% deviation for coals (R2 = 0.95) and within ±15% deviation for biomass (R2 = 0.97). 相似文献
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Ball screws are crucial for improving the reliability and interchangeability of transmission mechanical systems; however, existing contact measurement methods that utilise stylus contact are not efficient, which precludes their use for rapid in-situ geometry evaluation. This paper presents a vision-based two-stage method for rapid measurement of key parameters (raceway arc radii and centre distance) of ball screws. The edge contour information is extracted from the acquired image using the dual-tree complex wavelet transform and non-maximal suppression. In the matching stage, a shape-matching algorithm is used for detecting approximate geometrical centres of raceway arcs. The refinement stage, on the other hand, is implemented for acquiring precise dimensional results. Furthermore, the method of averaging multiple measurements is performed to suppress random noise. A comparative experiment is presented to validate the robustness of the proposed method. Based on experimental results, the calculated mean absolute errors in the measurement of the two raceway arc radii and the centre distance are found to be 0.0082 mm, 0.0079 mm and 0.0055 mm, respectively. This study therefore paves the way for key parameter measurement without removing ball screws. 相似文献
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In this study, a new meta-heuristic algorithm called teaching-learning-based optimization (TLBO) is used for the size and shape optimization of structures. The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. The cross-sectional areas of the bar element and the nodal coordinates of the structural system are the design variables for size and shape optimization, respectively. Displacement, allowable stress and the Euler buckling stress are taken as the constraint for the problem considered. Some truss structures are designed by using this new algorithm to show the efficiency of the TLBO algorithm. The results obtained from this study are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in combined size and shape optimization of the structures. 相似文献
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