The influence of sonication time on the biogenic amines formation as a critical point in uncured dry-fermented beef manufacturing

In this study, the influence of sonication time on the biogenic amines formation as a critical point in uncured dry-fermented beef manufacturing was studied. Samples of musculus semimembranosus were sonicated at different times (5 and 10 min) using ultrasound cold bath (4 °C) in acid whey (US 40 kHz and acoustic power 480 W). The effect of sonication on biogenic amine (BA) formation was investigated during 93 days of ripening period. Other parameters (pH value, water activity, microbial counts) that might provide further information on the product under study were also determined. The use of ultrasound during beef marinating in acid whey has a positive effect on retarding histamine (HIS), cadaverine (CAD), tyramine (TYR) and putrescine (PUT) formation. Moreover, the sonication treatment did not inhibit the growth of lactic acid bacteria (LAB) in dry-fermented beef during the whole ripening period. The pathogen bacteria (Staphylococcus aureus, Clostridium sp., Listeria monocytogenes) were not detected in all samples neither after 31 nor after 93 days of ripening period.     

基于特征融合的中文简历解析方法研究

针对基于规则和统计的传统中文简历解析方法效率低、成本高、泛化能力差的缺点，提出一种基于特征融合的中文简历解析方法，即级联Word2Vec生成的词向量和用BLSTM（Bidirectional Long Short-Term Memory）建模字序列生成的词向量，然后再结合BLSTM和CRF（Conditional Random Fields）对中文简历进行解析（BLSTM-CRF）。为了提高中文简历解析的效率，级联包含字序列信息的词向量和用Word2Vec生成的词向量，融合成一个新的词向量表示；再由BLSTM强大的学习能力融合词的上下文信息，输出所有可能标签序列的分值给CRF层；再由CRF引入标签之间约束关系求解最优序列。利用梯度下降算法训练神经网络，使用预先训练的词向量和Dropout优化神经网络，最终完成对中文简历的解析工作。实验结果表明，所提的特征融合方法优于传统的简历解析方法。     

BIM技术应用于建筑工程造价的对策研究

随着BIM技术的进步,建筑行业越来越重视BIM技术的应用,并在应用过程中取得一定的成效。本文从工程造价决策、设计、招投标、施工、竣工五个阶段阐述BIM技术在建筑工程造价中的应用对策。     

关于信息化测绘时代工程测量发展的思考

如今我国信息化技术全面发展，尤其对于工程测量工作领域来讲，不管是在工程建设和管理方面都产生不小的支撑引导效用。由此，笔者具体结合如今工程建设信息化测绘核心任何以及设备布置细节，进行结构整体安全管理周期和周边地理空间信息技术发展能效整理解析，试图将工程测量最新发展机遇和技术挑战问题处理完全。希望能够借此为日后一定时期范围内相关工程测量规划主体提供合理指导性建议内容，最终为我国各类工程事业可持续发展前景绽放奠定深刻适应基础。     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.     

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。     

某油田油井采出水腐蚀因素研究

我国油田油井生产中采油井管柱腐蚀现象十分普遍,所以在注CO_2驱进行开采过程中要进行采出水的质量分析。对现阶段的油田生产腐蚀及防腐技术进行论述,提出相对应的防腐措施,进一步为油田生产作业提高产量。     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

面向消费类电子产品的自动化生产线成组技术

针对现代消费类电子产品快速更新换代的现状, 介绍了该类产品的自动化生产线成组技术,阐述了通过度量消费类电子产品相似性和派生性特征,对产线进行成组编码的原理,研究产线工位单元的建模方法,描述了建模内容,并通过有无装载板的产线结构,论述了产线成组编码集成的途径和方法.     

基于密集神经网络的灰度图像着色算法

针对在灰度图像着色领域中，传统算法信息提取率不高、着色效果不理想的问题，提出了基于密集神经网络的灰度图像着色算法，以实现改善着色效果，让人眼更好地观察图片信息的目的。利用密集神经网络的信息提取高效性，构建并训练了一个端到端的深度学习模型，对图像中的各类信息及特征进行提取。训练网络时与原图像进行对比，以逐渐减小网络输出结果的信息、分类等各类型的损失。训练完成后，只需向网络输入一张灰度图片，即可生成一张颜色饱满、鲜明逼真的彩色图片。实验结果表明，引入密集网络后，可有效改善着色过程中的漏色、细节信息损失、对比度低等问题，所提算法着色效果较基于VGG网络及U-Net、双流网络结构、残差网络（ResNet）等性能优异的先进着色算法而言取得了显著的改进。