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关键词： 硬币分类   硬币整理   硬币包装   机械系统设计  

摘要： 为了降低钱币在流通过程中的磨损度,国家在小面值钱币的印刷和发行方面,相对于保存和流通寿命较低的纸币来讲更加倾向于使其硬币化,因此目前硬币的发行和流通量正处于一个不断增长的态势。而硬币分类、整理及包装工作又是一项十分繁杂的工作。基于此现象,本文首先介绍了硬币分类、整理、包装机械系统的设计思路,继而提出了其总体设计方案,最后展开了硬币分类、整理、包装机械系统设计中关键问题的研究。

关键词： 粪便微生物检测   儿童腹泻   病毒   细菌   寄生虫  

摘要： 目的:探讨粪便微生物在儿童腹泻诊断中的应用效果,以及对治疗方式的指导作用研究。方法:选取2015年2月～2018年1月儿科、急诊科、门诊消化科就诊的腹泻患儿200例,均行粪便微生物检测,分析病原类型及防治措施。结果:对所采集的200例腹泻患儿粪便标本进行粪便微生物检测,共检出各类型腹泻病原微生物152例,总检出率为76. 00%。肠道致病菌检出15份,检出率7. 5%。腹泻病毒检出136份,检出率为61. 00%。寄生虫检出1份隐孢子虫,检出率为0. 5%。结论:粪便微生物检测能够确定腹泻患儿感染类型和感染的病原微生物品种,为腹泻患儿的临床敏感性用药提供有效依据,具有较高的应用价值,建议推广使用。

关键词： 政府专利奖励   知识产权能力   中小企业   企业绩效  

摘要： 为检验政府专利奖励对中小企业绩效的政策效应,以江苏省226家中小企业为研究对象,采用多元回归方法对政府专利奖励和中小企业绩效之间的关系进行了实证研究,并从创造、管理、运营、保护4个维度探讨了知识产权能力在其中的调节作用。研究结果表明:(1)政府专利奖励对中小企业绩效存在正向影响;(2)企业知识产权能力有助于放大政府专利奖励对企业绩效的促进作用。研究为中小企业优化知识产权建设,提升绩效、政府建立健全专利奖励政策提供了理论依据。

关键词： 突发食物中毒   理化检验   质量控制  

摘要： 本篇文章主要探讨了突发食物中毒事件中理化检验质量控制的作用。选择的方法是选取本疾控中心在2016年7月至2018年6月的突发食物中毒事件作为分析的对象,将分析对象分成对照组和观察组。其中2016年7月至2017年6月为对照组,2017年7月至2018年6月为试验组,分别对阶段内的突发性食物中毒事件进行理化检验分析。分析结果表明和试验组比较,对照组理化检验质量控制人数明显多于试验组且具有显著性差异。因此在突发食物中毒事件中理化检验质量控制具有重要作用。

关键词： 海测产品设计   质量元素   二级检查一级验收   关键技术难点   高精度  

摘要： 海测产品质量,事关海事交通安全,同时与港航项目工期与造价控制,为业主决策提供依据是否科学等紧密相关。各单位应高度重视产品质量的监管工作,并结合海测服务特点,从产品设计、生产、质检三方面进行严格控制,落实“二级检查一级验收”工作。首先要依据质量管理体系和规范,以产品优良率的考核目标为中心,全面建立质量管理制度,提高质检工作实效;其二要考虑水深数据成果的多样性、基准的差异性,大力探索关键技术难点与处理方法,以确保海测成果的高精度。

关键词： Angiosperms   DNA barcoding   Genetic distances   Herbarium collections   Native and introduced species   Sample age effect   REFERENCE LIBRARY   SPECIMENS   EXAMPLE  

摘要： We have produced DNA barcodes for Finnish plant taxa. In this study, we specifically report the barcoding success for herbarium materials varying widely in age, also paying attention on success rate variation and genetic distances among different plant families. Additionally, we investigated whether the level of intraspecific variation differs between native and introduced species. The specimens had been collected between years 1867 and 2013. Among all studied specimens, the average success rates for any barcode (matK or rbcL), rbcL, matK and both barcodes equaled 81, 79, 55 and 53%, respectively, and among species (at least one specimen per species barcoded successfully) 95, 95, 74 and 73%, respectively. We found significant age effects on the barcoding success, the greatest decline being visible in over 100-year-old samples. Plant families showed differences in overall success rates and sample age effects, as well as in intraspecific and interspecific variation levels, while the average level of intraspecific variation appeared similar among native and introduced species. Besides being valuable for the identification of species, DNA barcoding with sufficient sampling is also a tool to investigate specific evolutionary questions, such as biogeographic patterns or the adaptive capacity of invasive and other alien plant species.

关键词： Supervised image classification   Refining training samples   Outlier detection   Median absolute deviation   SUPPORT VECTOR MACHINES   DECISION TREES   ACCURACY   NOISE  

摘要： Supervised image classification refers to the task of extracting information classes from a multi-band remote sensing image. The selection of training samples is critical and directly influences supervised classification accuracy. However, some impure training samples are possible selected because of human mistakes or limited labeling conditions, which leads to a reduction in the classification accuracy. To solve this issue, median absolute deviation (MAD) is adopted to refine training samples. A comparison of the full and refined training samples is conducted for the same classifier, i.e., maximum likelihood classification (MLC) or support vector machine (SVM), through experimental evaluation with two sets of experiments. The results of experiments show that the overall accuracy and the kappa coefficient of the refined training samples significantly outperform those of the full training samples for the same classifier (MLC or SVM). It shows that refining training samples using the MAD can effectively eliminate the influence of impure training samples so that the more reliable and accurate results can be obtained.

关键词： SVM classification and regression   iron ore   machine vision system   dry and wet sample image   SELECTION   MODEL   DIAGNOSIS   FEATURES   FIELD   SIZE  

摘要： The aim of the present study is to analysing the effect of water absorption on iron ore samples in the performances of SVM-based machine vision system. Two types of SVM-based machine vision system (classification and regression) were designed and developed, and performances were compared with dry and wet ore sample images. The images of the ore samples were captured in both the conditions (wet and dry) to examine the proposed model performance. A total of 280 image features were extracted and optimised using sequential forward floating selection (SFFS) algorithm for model development. The iron ore samples were collected from an Indian iron ore mine (Guamine), and image capturing system was fabricated in the laboratory for executing the proposed study. The results indicated that a different set of optimised features obtained for dry and wet sample images in both the models (classification and regression). Furthermore, the performance of both the models with dry sample images was found to be relatively better than the wet sample images.

关键词： Local accuracy   Land cover change   Non-collocated   Reference sample data   Sampling   Class occurrence pattern indices   Cross-correlation   Pseudo cross-variograms   VALIDATION DATA SET   THEMATIC ACCURACY   UNITED-STATES   CLASSIFICATION ACCURACY   DESIGN   TIME   AREA   MAP   DATABASE   SOIL  

摘要： Accuracy is an integral component in consumption of land cover and change information. Accuracy characterization in change categorization is more complicated than that in single-date classifications, especially when pursued at a local or per-pixel basis. In this paper, reference sample data consisting of reference class labels at sample locations are referred to as (model) training and validation sample data, respectively, when they are used for building and testing predictive models of local accuracy. They are preferably collocated to verify land cover and change types at the same locations across multiple single-date classifications. With collocated training data, methods devised for local accuracy mapping were usually implemented as direct extensions to those designed for accuracy predictions in single-date land cover maps, given that both response and explanatory variables are defined properly based on change maps. However, alternative methods are required when collocated training sample data are unavailable or sparse but non-collocated data collected at sample units not aligned across time exist or can be made available more conveniently and flexibly. This is typically the case for change-classification accuracy analyses, whereby prior sampling design and sample data may not remain informative in time but can be made adaptive by restructuring and augmentation to better represent strata of transitions as well as persistence. Such adaptability leads to great cost-saving in sampling than undertaking collocated sampling anew each time performing or revising accuracy mapping for a specific change analysis over a particular period. With a coherent review of related research, this paper proposes a geostatistical method for predictive mapping of local accuracies in any land-cover change maps created properly through adaptive use of complexly configured training sample data. This method works on a functional relation between accuracies in change-categorization and singl