教学课程资源库 更多>>

关键词： 感官检验   棉花   仪器化检验   公证检验  

摘要： 以公证检验为代表的棉花质量检验步入仪器化检验新时代,在此背景下,需要重新定位感官检验的存在价值和功能价值。笔者(均有5年以上棉检师经验)长期从事棉花检验,对棉花感官检验有较深的理论功底和实践经验,文中从仪器化品质检验、棉花质量检验体系、棉花质量检验人才培养、我国棉花标准发展等几个方面探讨感官检验在棉花质量仪器化检验新时代的作用,并给出棉花质量感官检验发展的思考。

关键词： support vector machine   convolutional neural network   knowledge transfer   bag of visual words  

摘要： The problem of deep learning network image classification when a large number of image samples are obtained in life and with only a small amount of knowledge annotation, is preliminarily solved in this paper. First, a support vector machine expert labeling system is constructed by using a bag-of-words model to extract image features from a small number of labeled samples. The labels of a large number of unlabeled image samples are automatically annotated by using the constructed SVM expert labeling system. Second, a small number of labeled samples and automatically labeled image samples are combined to form an augmented training set. A deep convolutional neural network model is created by using an augmented training set. Knowledge transfer from SVMs trained with a small number of image samples annotated by artificial knowledge to deep neural network classifiers is implemented in this paper. The problem of overfitting in neural network training with small samples is solved. Finally, the public dataset caltech256 is used for experimental verification and mechanism analysis of the performance of the new method.

关键词： CNN   image recognition  

摘要： Over the past few years, we have made great progress in image categorization based on convolutional neural networks (CNNs). These CNNs are always trained based on a large-scale image data set;however, people may only have limited training samples for training CNN in the real-world applications. To solve this problem, one intuition is augmenting training samples. In this article, we propose an algorithm called Lavagan (Latent Variables Augmentation Method based on Generative Adversarial Nets) to improve the performance of CNN with insufficient training samples. The proposed Lavagan method is mainly composed of two tasks. The first task is that we augment a number latent variables (LVs) from a set of adaptive and constrained LVs distributions. In the second task, we take the augmented LVs into the training procedure of the image classifier. By taking these two tasks into account, we propose a uniform objective function to incorporate the two tasks into the learning. We then put forward an alternative two-play minimization game to minimize this uniform loss function such that we can obtain the predictive classifier. Moreover, based on Hoeffding's Inequality and Chernoff Bounding method, we analyze the feasibility and efficiency of the proposed Lavagan method, which manifests that the LV augmentation method is able to improve the performance of Lavagan with insufficient training samples. Finally, the experiment has shown that the proposed Lavagan method is able to deliver more accurate performance than the existing state-of-the-art methods.

关键词： Feature extraction   Task analysis   Visualization   Training   Optical fibers   Computational modeling   Phase change materials   Fine-grained image classification   few-shot setting   second-order relation extraction  

摘要： In this paper, we study the fine-grained categorization problem under the few-shot setting, i.e., each fine-grained class only contains a few labeled examples, termed Fine-Grained Few-Shot classification (FGFS). The core predicament in FGFS is the high intra-class variance yet low inter-class fluctuations in the dataset. In traditional fine-grained classification, the high intra-class variance can be somewhat relieved by conducting the supervised training on the abundant labeled samples. However, with few labeled examples, it is hard for the FGFS model to learn a robust class representation with the significantly higher intra-class variance. Moreover, the inter- and intra-class variance are closely related. The significant intra-class variance in FGFS often aggravates the low inter-class variance issue. To address the above challenges, we propose a Target-Oriented Alignment Network (TOAN) to tackle the FGFS problem from both intra- and inter-class perspective. To reduce the intra-class variance, we propose a target-oriented matching mechanism to reformulate the spatial features of each support image to match the query ones in the embedding space. To enhance the inter-class discrimination, we devise discriminative fine-grained features by integrating local compositional concept representations with the global second-order pooling. We conducted extensive experiments on four public datasets for fine-grained categorization, and the results show the proposed TOAN obtains the state-of-the-art.

关键词： hyperspectral image classification   multidimensional CNN   attention mechanism  

摘要： The convolutional neural network (CNN) method has been widely used in the classification of hyperspectral images (HSIs). However, the efficiency and accuracy of the HSI classification are inevitably degraded when small samples are available. This study proposes a multidimensional CNN model named MDAN, which is constructed with an attention mechanism, to achieve an ideal classification performance of CNN within the framework of few-shot learning. In this model, a three-dimensional (3D) convolutional layer is carried out for obtaining spatial-spectral features from the 3D volumetric data of HSI. Subsequently, the two-dimensional (2D) and one-dimensional (1D) convolutional layers further learn spatial and spectral features efficiently at an abstract level. Based on the most widely used convolutional block attention module (CBAM), this study investigates a convolutional block self-attention module (CBSM) to improve accuracy by changing the connection ways of attention blocks. The CBSM model is used with the 2D convolutional layer for better performance of HSI classification purposes. The MDAN model is applied for classification applications using HSI, and its performance is evaluated by comparing the results with the support vector machine (SVM), 2D CNN, 3D CNN, 3D-2D-1D CNN, and CBAM. The findings of this study indicate that classification results from the MADN model show overall classification accuracies of 97.34%, 96.43%, and 92.23% for Salinas, WHU-Hi-HanChuan, and Pavia University datasets, respectively, when only 1% HSI data were used for training. The training and testing times of the MDAN model are close to those of the 3D-2D-1D CNN, which has the highest efficiency among all comparative CNN models. The attention model CBSM is introduced into MDAN, which achieves an overall accuracy of about 1% higher than that of the CBAM model. The performance of the two proposed methods is superior to the other models in terms of both efficiency and accuracy. The results show

关键词： 巨幼细胞性贫血   缺铁性贫血   血常规   临床诊断价值  

摘要： 目的分析研究血常规检验在贫血患者临床诊断中的应用价值。方法将滨州医学院附属医院在2020年1月—2021年7月收治的35例巨幼细胞性贫血患者作为观察1组,从同时间段于该院接受治疗的缺铁性贫血患者中抽取35例作为观察2组,并选取同时间段在该院接受健康体检的35名健康人作为对照组。3组研究对象均接受血常规检验,主要检测红细胞计数、血红蛋白、红细胞分布宽度、平均红细胞血红蛋白量、红细胞平均体积以及平均红细胞血红蛋白浓度,并将3组的各项血常规检验结果进行组间比较分析。结果观察2组、观察1组和对照组的红细胞计数[(3.08±0.55)×10^(12)、(4.01±0.28)×10^(12)、(4.32±0.81)×10^(12)]、血红蛋白[(101.14±15.28)、(83.62±15.31)、(127.48±15.26)g/L]、红细胞分布宽度[(16.86±2.15)%、(20.33±2.08)%、(14.85±2.62)%]、平均红细胞血红蛋白量[(24.18±8.47)、(40.51±8.33)、(36.62±8.29)pg]、红细胞平均体积[(78.33±8.05)、(98.48±7.49)、(90.85±8.58)fL]以及平均红细胞血红蛋白浓度[(274.48±25.63)%、(342.81±25.14)%、(347.88±25.25)%]均差异有统计学意义(F=42.165、73.034、51.030、35.547、55.868、91.586,P<0.05)。结论通过血常规检验可有效诊断贫血,并鉴别贫血的具体类型,可为临床治疗方案的制订提供更准确、更科学的数据参考,做到及早发现及早治疗,因此,血常规检验在临床上具备临床借鉴意义和推广价值。

关键词： 药品检验报告   药品检验机构   假劣药   定案证据   药品监督  

摘要： 目的明晰药品检验报告用于假劣药认定的定案证据作用。方法梳理药品检验报告作为执法和司法定案证据的法律法规、司法解释及有关政策文件,并结合假劣药认定检验的实际情况,分析药品检验报告作为定案证据的法理依据,及其在执法与司法之间的证据转化、发挥作用的注意事项。结果由药品检验机构出具的假劣药认定检验报告,不仅可依法作为查处假劣药的定案证据,而且在执法实践中还发挥着重要作用;检验报告作为司法定案证据,虽无明确的法律规定,但有充分的法理依据和实践基础提供支持;按行政执法和刑事司法衔接工作的有关要求移送案件时,检验报告可在定案证据与司法证据之间转化;经审查具有证据能力是检验报告作为定案证据的前提,这就要求药品检验机构加强检验受理评审、依法依规进行检验,并出具符合审查要求的检验报告,并在必要时出庭作证。结论药品检验机构出具的药品检验报告,在一定条件下可作为假劣药认定的定案证据,并在执法与司法实践中发挥重要作用。

关键词： 护理质量控制   血液标本   血液检验   检验质量  

摘要： 探讨护理质量控制对于提高临床血液标本检验工作质量的作用。方法:选取从2018年9月-2021年9月我院收治的80例接受采血检验的患者作为研究对象,将患者随机分成采用常规血液标本检验的常规组(n=40),与加入护理质量控制的质控组(n=40),对比两组患者的标本合格率、标本错误率、医疗纠纷率等情况。结果:质控组的血液标本合格率为97.50%,显著高于常规组的87.50%,p<0.05。此外质控组包括标本错误率、医疗纠纷率显著均低于常规组,p均<0.05。结论:在临床血液检验工作中开展护理质量控制工作能够有效提高血液标本的合格率,降低标本错误率,避免医疗纠纷,更令患者所满意,值得推广。

关键词： 理化检验   质量控制   突发公共卫生事件   作用  

摘要： 对突发公共卫生事件中理化检验治疗控制的作用展开分析和探讨。方法:我中心于2019年6月起实施理化检验质量控制,将2018年1月至2019年5月这一时段称为实施前,将2019年6月至2020年11月这一时段称为实施后,分别自两个时段中随机抽取23例模拟突发公共卫生事件纳入研究,观察理化检验质量控制实施前后的各方面差异。结果:实施后,突发公共卫生事件的有效控制时间、报告发出时间较实施前更短,民众突发公共卫生事件控制满意率较实施前更高,理化检验结果偏差率较实施前更低(P<0.05)。结论:实施理化检验质量控制能显著缩短对突发公共卫生事件的有效控制时间、报告发出时间,在提升民众满意率的同时有效减少理化检验结果偏差情况的发生。