Current knowledge about the processes of luteolysis and maternal recognition of pregnancy in camelids

Camelids have many unique reproductive features that considerably differ from those of other domestic species. Females are induced ovulators with subsequent development of a corpus luteum (CL) with a short lifespan. Plasma progesterone concentration starts to increase on day 4, peaks on day 8–9 and, in non-pregnant animals, basal concentration is reached around day 10–11 post-induction of ovulation. Luteolytic pulses of prostaglandin F_2α (PGF_2α) are firstly detected on day 7 or 8 (approximately on day 5–6 after ovulation), with maximal luteolytic peaks observed between days 9 and 11 post-mating, in coincidence with a high endometrial expression of cyclooxygenase 2, a limiting enzyme in prostaglandins synthesis. Unlike other species, oxytocin seems not to be involved in the luteolytic process in these species. The CL is the main source of progesterone secretion, and its function is required to support pregnancy. Despite constant research efforts, aspects of reproduction and maternal recognition of pregnancy in camelids remain not fully understood. A transient decrease and subsequent recovery in plasma progesterone concentration are observed after day 9 post-mating in pregnant animals in association with a pulsatile release of PGF_2α and a transitory decrease in CL vascularization. Thus, embryo recognition should occur between days 8 and 12 post-mating. In camels, conceptus tissues exhibit aromatizing activity with the capacity to synthesize large amounts of oestradiol. Similarly, llama blastocysts secrete oestradiol-17β during the preimplantation stage, with a higher production during the elongation period. An increase in the endometrial expression of oestrogen receptor α is also observed on day 12 post-mating. All these evidences suggest that oestrogen could be the signal released by the embryo at the time of its recognition in camelids. Besides, nearly 98% of pregnancies are carried out in the left horn. A decrease in the endometrial expression of mucin 1 and 16 genes has been reported, suggesting that these changes are crucial for successful embryo implantation; however, no differences have been observed between horns. Thus, maternal recognition of pregnancy in camelids is a particularly complex process that must occur in a concise time to allow the rescue of the CL and embryo survival.     

Pd Pattern Recognition Based on Linear Discriminant Analysis in GIS

According to the character of PD in GIS, the authors design four kinds of GIS defection models. The GIS gray intensity images are constructed based on mass specimens gathered by the ultra - high frequency and high speeds systems, Aiming at the PD characteristics and its defections, A PCA-FDA method is put forward based on PD images. The principal component analysis is employed to condense the dimension of PD images, then the optimal sets of statistically uncorrelated discriminant vectors are extracted, and the minimum distance classifier is constructed as classifier. The identified results show that this method can effectively elevated the discrimination of the four kinds of defects in GIS PD.     

Developing a Recognition System of Gun-code

Intelligent recognition system of gun code is designed , which achieves online recognition of gun code. We applied video camera to get image, and some special technique in this deformed character recognition. As well, we applied dynamic template matching to get a better result.     

Detecting Technology of Cabel Surface Defect Based on DSP and Image Recognition

The damage of bridge cable directly hazards the normal use of bridge and the safety of people. The paper discussed the signal acquisition and detection system which takes embedded DSP processor as core technology, combined with CCD, video decode chip, SDRAM, FLASH ROM, and established the fast measuring algorithm of the surface corrosion and damage, which includes deteminating the processing region, improving medium filter, sobel edge detection and dynamic threshold segment, close operation of morphology, computing defect area and saving. The experimental resuits show that this method implemented the real-time, non-contact and intelligence detecting of cable surface defect and gives the specifications of the system.     

一种快速判别梨果梗的方法

果梗完好与否是梨品质检测的指标之一,因而对果梗情况进行准确判别具有重要意义。为此,通过计算机视觉系统摄取梨的图像,利用图像处理技术提出了一种能快速判别梨果梗有无的算法。该算法的识别正确率达90%,识别速度大约在20～30ms,实现了对梨果梗进行高速检测的目标。     

属性识别模型在水利水电规划方案综合优选中的应用

针对目前水利水电规划方案综合优选方法中存在的问题，提出了基于变异系数权重的水利水电规划方案综合优选属性识别模型。该模型采用反映评价指标特征值之间差异性的参数——变异系数作为评价指标的权重，减小了权重确定的主观性，且模型求解方法简单易学。实例分析表明，这一模型和方法是合理、可行的。     

基于双池化与多尺度核特征加权CNN的典型牧草识别

针对自然背景下牧草难识别的问题,提出一种基于双池化与多尺度核特征加权的卷积神经网络牧草识别方法。双池化特征加权结构通过将卷积层输出的特征图分别进行最大值池化和均值池化得到两组特征图,引入特征重标定策略,依照各通道特征图对当前任务的重要程度进行加权,以增强有用特征、抑制无用特征;多尺度核特征加权结构通过在卷积层中同时使用3×3和5×5两种卷积核,并将网络的前几层特征复用后进行加权,以提高重要特征的利用率。对10类牧草图像进行识别实验,结果表明,该方法识别率为94. 1%,比VGG-13网络提高了5. 7个百分点,双池化与多尺度特征加权有效提高了牧草识别精度。     

基于PCA-GA-SVM的甘蔗收获机运行状态识别

由于甘蔗收获机在收获过程中智能化水平较低,依靠人工操作很容易对甘蔗收获机的运行状态产生误判,从而造成物流通道堵塞、能源浪费、收割效率低。针对这些问题,提出一种基于主成分分析(PCA)、遗传算法(GA)和支持向量机(SVM)状态识别模型。首先,通过实地采集甘蔗收获机刀盘轴、行走轴、切段轴和风机轴扭矩和行驶速度特征信息,然后通过PCA进行数据降维,最后利用GA优化参数C、γ,使用每个特性信息来训练SVM,对甘蔗收获机运行状态进行分类。结果表明:PCA-GA-SVM状态识别模型对甘蔗收获机运行状态的识别准确率为93.75%,建模时间为3.688 s,与SVM(81.25%,9.487 s)、PCA-SVM(87.5%,5.817 s)和GA-SVM(90%,8.969 s)进行对比,该模型具有最高准确识别率和最快建模速度,具有较大的应用价值。