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31.
Deep Web中蕴涵了海量的高质量信息.文中从Deep Web数据源的功能属性和非功能属性两个方面对数据源的质量进行度量,建立了一种基于综合模糊评价指标体系的扩展的数据源质量估计模型.实验结果表明该模型得到的数据源质量排序序列和人工排序序列的Kendall’s距离较扩展前有了很大提高,而且质量估计结果也能使数据源的选择得到较高精确度.  相似文献   
32.
The performance of computer vision algorithms can severely degrade in the presence of a variety of distortions. While image enhancement algorithms have evolved to optimize image quality as measured according to human visual perception, their relevance in maximizing the success of computer vision algorithms operating on the enhanced image has been much less investigated. We consider the problem of image enhancement to combat Gaussian noise and low resolution with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising or super resolution, allowing for optimization of the enhancement CNN to maximize retrieval performance. This framework allows us to couple enhancement to the retrieval problem. We also consider the problem of adapting image features for robust retrieval performance in the presence of distortions. We show through experiments on distorted images of the Oxford and Paris buildings datasets that our algorithms yield improved mean average precision when compared to using enhancement methods that are oblivious to the task of image retrieval. 1  相似文献   
33.
针对传统干扰资源分配算法在处理非线性组合优化问题时需要较完备的先验信息,同时决策维度小,无法满足现代通信对抗要求的问题,该文提出一种融合噪声网络的深度强化学习通信干扰资源分配算法(FNNDRL)。借鉴噪声网络的思想,该算法设计了孪生噪声评估网络,在避免Q值高估的基础上,通过提升评估网络的随机性,保证了训练过程的探索性;基于概率熵的物理意义,设计了基于策略分布熵改进的策略网络损失函数,在最大化累计奖励的同时最大化策略分布熵,避免策略优化过程中收敛到局部最优。仿真结果表明,该算法在解决干扰资源分配问题时优于所对比的平均分配和强化学习方法,同时算法稳定性较高,对高维决策空间适应性强。  相似文献   
34.
Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance metrics.  相似文献   
35.
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??Deep shale gas reservoirs buried underground with depth being more than 3 500 m are characterized by high in-situ stress, large horizontal stress difference, complex distribution of bedding and natural cracks, and strong rock plasticity. Thus, during hydraulic fracturing, these reservoirs often reveal difficult fracture extension, low fracture complexity, low stimulated reservoir volume (SRV), low conductivity and fast decline, which hinder greatly the economic and effective development of deep shale gas. In this paper, a specific and feasible technique of volume fracturing of deep shale gas horizontal wells is presented. In addition to planar perforation, multi-scale fracturing, full-scale fracture filling, and control over extension of high-angle natural fractures, some supporting techniques are proposed, including multi-stage alternate injection (of acid fluid, slick water and gel) and the mixed- and small-grained proppant to be injected with variable viscosity and displacement. These techniques help to increase the effective stimulated reservoir volume (ESRV) for deep gas production. Some of the techniques have been successfully used in the fracturing of deep shale gas horizontal wells in Yongchuan, Weiyuan and southern Jiaoshiba blocks in the Sichuan Basin. As a result, Wells YY1HF and WY1HF yielded initially 14.1×104 m3/d and 17.5×104 m3/d after fracturing. The volume fracturing of deep shale gas horizontal well is meaningful in achieving the productivity of 50×108 m3 gas from the interval of 3 500–4 000 m in Phase II development of Fuling and also in commercial production of huge shale gas resources at a vertical depth of less than 6 000 m.  相似文献   
36.
Trapping and recombination of free carriers by deep level T3 has been studied. Occupancy of the level by electrons and dynamics of its filling and emptying as a function of illumination with monoenergetic photons in 0.69–1.55 eV range has been monitored by the thermally stimulated currents method. We have found that level T3 behaves more like a recombination center than like an ordinary electron trap. Besides trapping free electrons from conduction band, this trap can also communicate with valence band, trapping holes. The capture cross section for trapping a hole is estimated to be comparable or even larger than the capture cross section for trapping an electron. However, in many experimental conditions free electrons are generated more abundantly than free holes, and free carrier mobility and thermal velocity are both much higher for electrons than for holes. Therefore, electron trapping often prevails, so that this frequently detected defect, has been up to now most often perceived as a deep electron trap.  相似文献   
37.
张兴  石涌泉 《电子学报》1995,23(11):93-95
本文介绍了适合于薄膜亚微米、深亚微米SOIMOSFET的二维数值模拟软件。该模拟软件同时考虑了两种载流子的产生-复合作用,采用了独特的动态二步法求解泊松方程和电子、空穴的电流连续性方程,提高了计算效率和收敛性。利用此模拟软件较为详细地分析了薄膜SOIMOSFET不同于厚膜SOIMOSFET的工作机理及特性,发现薄膜SOIMOSFET的所有特性几乎都得到了改善。将模拟结果与实验结果进行了对比,两者吻合得较好。  相似文献   
38.
叙述了一种采用MOS结构研究半导体深能级陷阱的深能级瞬态谱(DLTS)测试与分析方法。该方法简便易行,适用范围广。通过对n-Al_(0.2)Ga_(0.8)As中深能级的研究表明,MOS结构和p~+-n结构的DLTS结果相同。  相似文献   
39.
获得了一种研究碲镉汞深能级的方法。通过分析迁移率 载子浓度与温度的关系,可以得到关于深能级的重要依据。  相似文献   
40.
The GaSb layers investigated were grown directly on GaAs substrates by molecular beam epitaxy (MBE) using SnTe source as the n-type dopant. By using admittance spectroscopy, a dominant deep level with the activation energy of 0.23-0.26 eV was observed and its concentration was affected by the Sb4/Ga flux ratio in the MBE growth. A lowest deep-level concentration together with a highest mobility was obtained for GaSb grown at 550°C under a Sb4/Ga beam equivalent pressure (BEP) ratio around 7, which should correspond to the lowest ratio to maintain a Sb-stabilized surface reconstruction. In the Hall measurement, an analysis of the temperature-dependent mobility shows that the ionized impurity concentration increases proportionally with the sample’s donor concentration, suggesting that the ionized impurity was introduced by an SnTe source. In addition, optical properties of an undoped p-, a lightly and heavily SnTe-doped GaSb layers were studied by comparing their photoluminescence spectra at 4.5K.  相似文献   
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