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排序方式: 共有859条查询结果,搜索用时 15 毫秒
851.
矿床成因类型有两种:高温热液裂隙充填矿床和高温热液交代矿床,工业矿体为热液充填型石英矿脉,具有矿物品位较高的特点。根据矿区地球化学特征表明,Mo、W、Bi异常较好,新发现绿柱石(BeO),具有一定的找矿前景,下一步工作中应增加绿柱石矿的化验分析。 相似文献
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854.
神经网络的知识获取与行为解释 总被引:3,自引:0,他引:3
神经网络的缺点之一在于它无法明显地表达其行为的含义.针对神经网络的不同结构,通过给出节点取值不同的约束条件,描述了如何从神经网络提取知识,并对神经网络的行为进行解释.举例说明了从神经网络提取产生式规划的策略. 相似文献
855.
Hyunkyu Shin Yonghan Ahn Mihwa Song Heungbae Gil Jungsik Choi Sanghyo Lee 《计算机、材料和连续体(英文)》2023,75(3):4753-4766
Recently, convolutional neural network (CNN)-based visual inspection has been developed to detect defects on building surfaces automatically. The CNN model demonstrates remarkable accuracy in image data analysis; however, the predicted results have uncertainty in providing accurate information to users because of the “black box” problem in the deep learning model. Therefore, this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification. The visual representative gradient-weights class activation mapping (Grad-CAM) method is adopted to provide visually explainable information. A visualizing evaluation index is proposed to quantitatively analyze visual representations; this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects. In addition, an ablation study, adopting three-branch combinations with the VGG16, is implemented to identify performance variations by visualizing predicted results. Experiments reveal that the proposed model, combined with hybrid pooling, batch normalization, and multi-attention modules, achieves the best performance with an accuracy of 97.77%, corresponding to an improvement of 2.49% compared with the baseline model. Consequently, this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models. 相似文献
856.
由于数据缺失,数据库用户通常无法获得查询结果中的预期答案.它被称为"Why-not问题",即"为什么预期的元组不会出现在结果中".现有的方法通过列举可能的元组值来解释Why-not问题.枚举所给出解释的数量往往太大,无法由用户探索.完整性约束,如函数依赖,被用来排除不合格的解释.然而,许多属性在简化后解释中仅仅表示为变量,用户可能仍然无法理解.由于数据稀疏性,许多不合理的解释也会被推荐给用户.提出通过研究元组间两两比较关系,从而对Why-not问题的解释进行排序的方法.首先,重新定义为什么Why-not问题解释的形式没有变量,以便于用户理解;其次,对元组中的相等/不相等关系进行表示,提出在{0,1}表示的元组对的基础上学习统计模型,从而解决直接在原始数据上学习所带来的稀疏性问题,许多模型可以被用来推断概率,包括统计分布、分类和回归;最后,根据推断的概率对解释进行评价和排序.实验结果证明:利用统计、分类和回归方法计算两两关系概率分布的方法,可以为用户寻找Why-not问题的解释并返回较为高质量的解释. 相似文献
857.
《Journal of Great Lakes research》2023,49(2):515-530
Improvements in electronic tagging techniques provide new opportunities to gain insights into fish habitat selection and behaviours that have been difficult to capture using traditional assessment methods. However, data from acoustic telemetry studies in large freshwater systems may bias our understanding of fish habitat use and behaviour because of the typically low sampling frequency of transmitters as well as the limited spatial coverage and distribution of receivers in a waterbody. This study combined acoustic transmitters and pop-off data storage tags (pDSTs) on individual walleye in Lake Ontario to gain a better understanding of the feasibility and utility of double tagging a large nearshore freshwater fish. High frequency pDST data (every 2 s) revealed a novel diving behaviour by walleye which made repeated rapid dives beyond their standard daily depth range. Comparison of data from the two tag types showed that in this large freshwater system with low overall receiver coverage (∼4%), mean monthly and daily depth and temperature occupancy of walleye were similar, although many of the extreme values observed in the pDST data were not observed in the acoustic data. The accuracy of daily vertical distance travelled by walleye and summertime diving parameters was dependent on sampling frequency and only the pDST logging on a 2 s interval was able to provide reliable results. The results of this study show that novel insights can be gained, for fish large enough to handle the burden of multiples tags, ranging from spatial ecology to diving behaviours. 相似文献
858.
Machine learning algorithms are widely applied to create powerful prediction models. With increasingly complex models, humans' ability to understand the decision function (that maps from a high-dimensional input space) is quickly exceeded. To explain a model's decisions, black-box methods have been proposed that provide either non-linear maps of the global topology of the decision boundary, or samples that allow approximating it locally. The former loses information about distances in input space, while the latter only provides statements about given samples, but lacks a focus on the underlying model for precise ‘What-If'-reasoning. In this paper, we integrate both approaches and propose an interactive exploration method using local linear maps of the decision space. We create the maps on high-dimensional hyperplanes—2D-slices of the high-dimensional parameter space—based on statistical and personal feature mutability and guided by feature importance. We complement the proposed workflow with established model inspection techniques to provide orientation and guidance. We demonstrate our approach on real-world datasets and illustrate that it allows identification of instance-based decision boundary structures and can answer multi-dimensional ‘What-If'-questions, thereby identifying counterfactual scenarios visually. 相似文献
859.