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关键词： 自动包装分类   上下层结构   不同外形   不同尺寸   自动切膜  

摘要： 电子商务的高速发展,随之而来堆积如山的包裹及其分类是电子商业从业人员面临的一个大难题。设计的全自动包装分类一体机主要是针对不同外形、尺寸的商品进行包装、分类。装置包括上层的薄膜包装装置以及设置在下层的分类装置。上层装置包括商品宽度检测装置、摆正装置、利用商品在传送过程中与切割机接触面积的大小判断完成的自动包覆膜以及切膜装置与热收缩膜装置;下层包括高度筛选板,高度筛选板将落料板分为不同的落料路径,每条落料路径上分别设有宽度筛选机构。自动包装分类一体机兼具包装和分类功能,节能环保、占用空间小、一体化程度高,能够大大减轻从业人员的分拣工作。

关键词： 肝功能   血清学指标   脂肪肝   诊断意义  

摘要： 目的探究肝功能与血清学指标水平检验对脂肪肝的诊断作用。方法选择2014年6月至2015年6月来我院就诊的75例脂肪肝患者为研究对象,另取70例健康者为对照组,对比两组受试者的血清学指标和肝功能情况。结果病例组血清学指标和肝功能明显比对照组高,组间数据存在统计学意义,P<0.05。结论肝功能和血清学指标可以作为脂肪肝筛查的有效方式,对于疾病的临床诊断来讲,有着极其重要的现实意义,值得进一步推广。

关键词： 刑事侦查   痕迹检验   作用   技术  

摘要： 随着科技的发展,使得痕迹检验技术在刑侦工作中发挥越来越重要的作用,使得刑侦人员能够抓住痕迹检验的特点,对痕迹进行一系列科学的提取方法,使其得出准确的结论,用以为刑事侦查案件的侦破提供可靠准确的信息支撑,提高刑事侦查人员的办事效率,从而促进社会的长治久安。基于此,本文将着重分析探讨刑事侦查工作中痕迹检验技术的作用及应用,以期能为以后的实际工作起到一定的借鉴作用。

关键词： BRCAl   BRCA2   variants of unknown significance   cDNA   splicing   cancer genetic counseling   SEQUENCE VARIANTS   UNCERTAIN SIGNIFICANCE   CLINICAL-SIGNIFICANCE   SPLICING MUTATIONS   CLASSIFICATION   GUIDELINES   CANCER   RECOMMENDATIONS   PATHOGENICITY   RNA  

摘要： Ascertaining the clinical consequences of BRCA1 and BRCA2 variants of uncertain significance (VUS) is currently indispensable for providing effective genetic counseling and preventive actions for families with hereditary breast and ovarian cancer (HBOC). To this end, we conducted a combination of in silico prediction and cDNA splicing analyses of 13 BRCA1 and 10 BRCA2VUS identified in our cohort as well as a case-control analysis in a population-based sample of 10 recurrent VUS. We observed consistent results between the in silico predictions and sequencing analyses for all analyzed VUS. An abnormal cDNA pattern was observed for variants c.212+1G>A and c.52781G>A in BRCA1 and c.516+2T>A and c.8168A>G in BRCA2 according to in silico splicing prediction. A case-control study of VUS confirmed the polymorphisms of the c.67+62A>G, c.7008-62A>G and c.8851G>A BRCA2 variants previously published. c.4068G>A in the BRCA2 gene can also be considered a polymorphism due to its occurrence at a frequency greater than 1% in our population. Our study shows that employing population-based analysis and a combination of several in silico methods yields highly accurate information, resulting in a reliable tool for selecting variants for cDNA sequencing analysis in routine cancer genetic counseling units.

关键词： Hyperspectral classification   Co-training   Tracking-Learning-Detection   SPECTRAL-SPATIAL CLASSIFICATION   IMAGE SEGMENTATION  

摘要： We present a novel semi-supervised algorithm for classification of hyperspectral data from remote sensors. Our method is inspired by the Tracking-Learning-Detection (TLD) framework, originally applied for tracking objects in a video stream. TLD introduced the co-training approach called P-N learning, making use of two independent 'experts' (or learners) that scored samples in different feature spaces. In a similar fashion, we formulated the hyperspectral classification task as a co-training problem, that can be solved with the P-N learning scheme. Our method uses both spatial and spectral features of data, extending a small set of initial labelled samples during the process of region growing. We show that this approach is stable and achieves very good accuracy even for small training sets. We analyse the algorithm's performance on several publicly available hyperspectral data sets. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Sparse coding   Low-rank   Dictionary learning   Image classification   Structured sparsity   FACE RECOGNITION   K-SVD   DICTIONARY   ALGORITHM  

摘要： In this paper, we propose a novel Structure-Constrained Low-Rank and Partial Sparse Representation algorithm for image classification. First, a Structure-Constrained Low-Rank Dictionary Learning (SCLRDL) algorithm is proposed, which imposes both structure and low-rank restriction on the coefficient matrix. Second, under the assumption that the coefficient of test sample is sparse and correlated with the learned representation of training samples, we propose a Low-Rank and Partial Sparse Representation (LRPSR) algorithm which concatenates training samples and test sample to form a data matrix and finds a low-rank and sparse representation of the data matrix over learned dictionary by low-rank matrix recovery technique. Finally, we design a Sample Selection (SS) procedure to accelerate LRPSR. Experimental results on Caltech 101 and Caltech 256 show that our method outperforms most sparse or low rank based image classification algorithm proposed recently. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Partial least squares   RBF neural network   Genetic algorithm   PLS-GA-RBF algorithm   Small sample classification   RECOGNITION   DESIGN  

摘要： Radial basis function (RBF) neural network can use linear learning algorithm to complete the work formerly handled by nonlinear learning algorithm, and maintain the high precision of the nonlinear algorithm. However, the results of RBF would be slightly unsatisfactory when dealing with small sample which has higher feature dimension and fewer numbers. Higher feature dimension will influence the design of neural network, and fewer numbers of samples will cause network training incomplete or over-fitted, both of which restrict the recognition precision of the neural network. RBF neural network has some drawbacks, for example, it is hard to determine the numbers, center and width of the hidden layer's neurons, which constrain the success of training. To solve the above problems, partial least squares (PLS) and genetic algorithm(GA)are introduced into RBF neural network, and better recognition precision will be obtained, because PLS is good at dealing with the small sample data, it can reduce feature dimension and make low-dimensional data more interpretative. In addition, GA can optimize the network architecture, the weights between hidden layer and output layer of the RBF neural network can ease non-complete network training, the way of hybrid coding and simultaneous evolving is adopted, and then an accurate algorithm is established. By these two consecutive optimizations, the RBF neural network classification algorithm based on PLS and GA (PLS-GA-RBF) is proposed, in order to solve some recognition problems caused by small sample. Four experiments and comparisons with other four algorithms are carried out to verify the superiority of the proposed algorithm, and the results indicate a good picture of the PLS-GA-RBF algorithm, the operating efficiency and recognition accuracy are improved substantially. The new small sample classification algorithm is worthy of further promotion. (C) 2016 Elsevier B.V. All rights reserved.

关键词： FEATURE-EXTRACTION   DIMENSIONALITY REDUCTION  

摘要： Locality preserving projection (LPP) is a popular unsupervised feature extraction (FE) method. In this paper, the spatial-spectral LPP (SSLPP) method is proposed, which uses both the spectral and spatial information of hyperspectral image (HSI) for FE. The proposed method consists of two parts. In the first part, unlabelled samples are selected in a spatially homogeneous neighbourhood from filtered HSI. In the second part, the transformation matrix is calculated by an LPP-based method and by using the spectral and spatial information of the selected unlabelled samples. Experimental results on Indian Pines (IP), Kennedy Space Center (KSC), and Pavia University (PU) datasets show that the performance of SSLPP is superior to spectral unsupervised, supervised, and semi-supervised FE methods in small and large sample size situations. Moreover, the proposed method outperforms other spatial-spectral semi-supervised FE methods for PU dataset, which has high spatial resolution. For IP and KSC datasets, spectral regularized local discriminant embedding (SSRLDE) has the best performance by using spectral and spatial information of labelled and unlabelled samples, and SSLPP is ranked just behind it. Experiments show that SSLPP is an efficient unsupervised FE method, which does not use training samples as preparation of them is so difficult, costly, and sometimes impractical. SSLPP results are much better than LPP. Also, it decreases the storage and calculation costs using less number of unlabelled samples.

关键词： 集体劳动关系   独立工会   罢工权   工人代表制  

摘要： 集体劳动关系是因劳动者自愿联合成独立主体与用人单位或其团体在平等、自主的前提下,就劳动待遇、劳动条件等进行谈判而形成的宏观劳动关系,而集体合同是劳资团体对等谈判达成协议的结果。劳动者联合组成工会的权利之法律依据是《宪法》中关于公民的结社权~①和《劳动法》上劳动者的团结权~②以及《工会法》所规定的职工自愿联合的权利。~③但仅有集体合同制度规定~④而无独立主体之间的利益实质博弈,只是形式上或者纸面上的集体劳动关系。通过建立工会独立性保障机制,落实劳动者的团结权、谈判权、行动权及正确发挥公共机构的平衡作用,引入工人代表制,切实保障实质集体劳动关系的运行,才能有效化解劳资矛盾,使劳、资、政三方各司其职,各负其责,各得其所,进而达到构建和谐劳动关系的终极目标。在修改《劳动合同法》时,应注意与《宪法》《劳动法》相关条款协调一致。

关键词： 油料检验   标准样品   分类  

摘要： 标准样品的多样性使其统一管理较为困难,而将标准样品分别归类可解决这一难题,并为钻研、探究标准样品提供便利,使工作内容更加丰富。标准样品的使用在油料的检验中必不可少,本文介绍标准样品在检验小麦硬度指数时发挥的重要作用,具体阐述标样的分类,并分析总结其表现特点。