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针对基于三元组损失的单一深度距离度量在多样化数据集环境下适应性差,且容易造成过拟合的问题,提出了一种优化三元组损失的深度距离度量学习方法。首先,对经过神经网络映射的三元组训练样本的相对距离进行阈值化处理,并使用线性分段函数作为相对距离的评价函数;然后,将评价函数作为一个弱分类器加入到Boosting算法中生成一个强分类器;最后,采用交替优化的方法来学习弱分类器和神经网络的参数。通过在图像检索任务中对各种深度距离度量学习方法进行评估,可以看到所提方法在CUB-200-2011、Cars-196和SOP数据集上的Recall@1值比之前最好的成绩分别提高了4.2、3.2和0.6。实验结果表明,所提方法的性能优于对比方法,同时在一定程度上避免了过拟合。 相似文献
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Woong-Kee Loh 《计算机、材料和连续体(英文)》2021,66(2):1251-1267
Many database applications currently deal with objects in a metric space. Examples of such objects include unstructured multimedia objects and points of interest (POIs) in a road network. The M-tree is a dynamic index structure that facilitates an efficient search for objects in a metric space. Studies have been conducted on the bulk loading of large datasets in an M-tree. However, because previous algorithms involve excessive distance computations and disk accesses, they perform poorly in terms of their index construction and search capability. This study proposes two efficient M-tree bulk loading algorithms. Our algorithms minimize the number of distance computations and disk accesses using FastMap and a space-filling curve, thereby significantly improving the index construction and search performance. Our second algorithm is an extension of the first, and it incorporates a partitioning clustering technique and flexible node architecture to further improve the search performance. Through the use of various synthetic and real-world datasets, the experimental results demonstrated that our algorithms improved the index construction performance by up to three orders of magnitude and the search performance by up to 20.3 times over the previous algorithm. 相似文献
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Eunice C. Nnaji Donald Adgidzi Michael O. Dioha Daniel R.E. Ewim Zhongjie Huan 《The Electricity Journal》2019,32(10):106672
Access to electricity is still a challenge in many parts of sub-Saharan Africa. In Nigeria, over 70% of the rural dwellers do not have access to electricity. The purpose of this paper is to examine the potential of a smart microgrid for off-grid rural electrification in Nigeria. A combination of design thinking and model-based design methodology is employed to select a suitable microgrid configuration and to develop a smart microgrid model. A system consisting of a solar photovoltaic array, battery energy storage and a diesel generator is selected, and the model is developed in Simulink. Demand data from 10 rural communities in Nigeria are used to validate the performance of the model and the potential for demand management is considered. The use of energy efficient light bulbs is found to reduce the peak electricity demand of the case study communities by 42 to 76%. Combining the proposed system with the use of LED bulbs makes the system to have 56 to 81% less net present cost than a system with a diesel generator alone and incandescent light bulbs. The proposed smart microgrid is found to be more suitable for off-grid rural electrification in Nigeria than diesel generators which are currently used for off-grid electrification in Nigeria. 相似文献
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In recent years, metabolic engineering has made great progress in both academic research and industrial applications. However, we have not found any articles that specifically analyze the current state of metabolic engineering in China in comparison with other countries. Here, we review the current development and future trends of global metabolic engineering, conduct an in-depth benchmarking analysis of the development situation of China’s metabolic engineering, and identify current problems as well as future trends. We searched publications in the Scopus database from 2015 to September 2020 in the field of metabolic engineering, and analyzed the output in general, including publication trends, research distribution, popular journals, hot topics and vital institutions, but also analyzed the share of citations, field-weighted citation impact, and production in collaboration with strategic countries in science and technology. This study aims to serve as a reference for later studies, offering a comprehensive view of China’s contribution to metabolic engineering, and as a tool for the elaboration of national public policy in science and technology. 相似文献
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Pythagorean fuzzy sets (PFSs) were proposed by Yager in 2013 to treat imprecise and vague information in daily life more rigorously and efficiently with higher precision than intuitionistic fuzzy sets. In this paper, we construct new distance and similarity measures of PFSs based on the Hausdorff metric. We first develop a method to calculate a distance between PFSs based on the Hasudorff metric, along with proving several properties and theorems. We then consider a generalization of other distance measures, such as the Hamming distance, the Euclidean distance, and their normalized versions. On the basis of the proposed distances for PFSs, we give new similarity measures to compute the similarity degree of PFSs. Some examples related to pattern recognition and linguistic variables are used to validate the proposed distance and similarity measures. Finally, we apply the proposed methods to multicriteria decision-making by constructing a Pythagorean fuzzy Technique for Order Preference by Similarity to an Ideal Solution and then present a practical example to address an important issue related to social sector. Numerical results indicate that the proposed methods are reasonable and applicable and also that they are well suited in pattern recognition, linguistic variables, and multicriteria decision-making with PFSs. 相似文献
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针对当前较多图像复制-粘贴篡改检测算法主要依靠图像的灰度信息来检测图像特征,没有考虑图像的色彩特征,使其存在误检与漏检的不足,引入余弦调制高斯滤波(cosine modulated Gaussian,CMG),设计了基于颜色度量因子与局部特征聚类的图像复制-粘贴篡改检测算法。利用CMG来求取图像的尺度响应值,并通过极值计算来提取图像的候选特征点;再利用像素点的光谱反射模型来建立颜色度量因子,从候选特征点中确定图像的鲁棒特征点。构建特征点的邻域圆,并求取该圆域中的四元数指数矩(quaternion exponent moments,QEM),从而得到相应的特征向量;利用特征向量来计算特征点间的欧氏距离,完成图像的特征匹配。最后,利用匹配点对的R、G、B值,形成特征点的局部特征,实现图像特征的聚类,准确定位复制-粘贴伪造内容。仿真结果显示,较当前的复制-粘贴伪造检测方法而言,对于简单的复制-粘贴篡改和复杂组合篡改,所提方法都具备更高的检测精度与鲁棒性。 相似文献
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In this paper, we offer a method aiming to minimize the role of distance metric used in clustering. It is well known that distance metrics used in clustering algorithms heavily influence the end results and also make the algorithms sensitive to imbalanced attribute/feature scales. To solve these problems, a new clustering algorithm using a per-attribute/feature ranking operating mechanism is proposed in this paper. Ranking is a rarely used discrete, nonlinear operator by other clustering algorithms. However, it also has unique advantages over the dominantly used continuous operators. The proposed algorithm is based on the ranks of the data samples in terms of their spatial separation and is able to provide a more objective clustering result compared with the alternative approaches. Numerical examples on benchmark datasets prove the validity and effectiveness of the proposed concept and principles. 相似文献