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。     

不同制样方法对直流辉光放电质谱法测定氮化硼中27种杂质元素的影响

讨论了3种制样方法对直流辉光放电质谱法(dc-GDMS)检测氮化硼中的Na、Mg、Al、Si等27种杂质元素的影响。3种制样方法分别如下所示:方法1,直接把氮化硼压在铟薄片上;方法2,把氮化硼压在铟薄片上后,再盖上一层铟罩;方法3,把压碎后的氮化硼放在针状钽勺上。在优化的辉光放电参数下对比了3种不同制样方法对基体信号强度的影响。试验表明:在方法1中,当氮化硼尺寸约为3mm×3mm,厚度小于1mm时,基体¹¹B的信号可达1.8×10⁷ cps;在方法2中,选择铟孔大小合适的铟罩,基体¹¹B的信号可达1.0×10⁷ cps;方法3获得基体信号强度比方法1、方法2高一个数量级。大部分元素在中分辨率下可获得较好的结果,而对于在高分辨率下也较难分离的元素,可选择丰度较低的同位素在中分辨率下进行测定,如Ge选择⁷⁰Ge⁺,Se选择⁸²Se⁺,Cd选择¹¹¹Cd⁺,Sn选择¹¹⁹Sn⁺,Ag选择¹⁰⁹Ag⁺,Pt选择¹⁹⁴Pt⁺。氮化硼中的杂质元素含量可通过样品片中待测元素含量减去来自于铟薄片或钽勺中该元素贡献的含量来计算获得。将样品平行测定5次,相对标准偏差均在20%以内。对于Al、Si、Ti等元素的测定,3种制样方法的测定结果基本一致;方法1、方法2中检测到的In含量较大,使得铟中的Ni、Cu对氮化硼的测定值影响较大;方法3由于钽中Fe、Cu的贡献导致氮化硼中Fe、Cu的检测值较大,但方法3获得的基体信号强度大,可降低部分元素的检出限,如Cr、Mn、Ga、Ge等。综上所述,方法3为优选方法。     

A dense and compact laser cladding layer with solid metallurgical bonding significantly improved corrosion resistance of Mg alloy for engineering applications

Although Mg alloy attracts great attention for engineering applications because of high specific strength and low density, low corrosion resistance limits its extensive use. In this study, Mg–Al–Zn–Mn alloy was treated via a laser cladding process to generate a dense and compact laser cladding layer with solid metallurgical bonding on the substrate for improving corrosion resistance, effectively hindering the corrosion pervasion into Mg alloy. The corrosion current density declined from 103 μA/cm² for Mg alloy to 13 μA/cm² for the laser cladding layer in NaCl aqueous solution. Moreover, the laser cladding layer was slightly corroded in comparison with Mg alloy in NaCl aqueous solution. Besides, the microhardness of the cladding layer reached a mean value of 170.5 HV, 3.1 times of Mg alloy (56.8 HV) due to the in situ formation of hardening intermetallic phases. Wear resistance of laser cladding layer was also obviously improved. These results demonstrated that the laser cladding layer obviously enhanced anticorrosion property of Mg alloy for engineering applications.     

Formation of a protective layer against corrosion on Mg alloy via alkali pretreatment followed by vanillic acid treatment

Although Mg alloy possesses high specific strength, low density, and good biocompatibility, poor corrosion resistance hinders its further applications. In the present study, an innovative protective layer against corrosion was prepared on the AZ31 Mg alloy via alkali pretreatment followed by vanillic acid treatment. The alkali pretreatment supplied –OH for the AZ31 Mg alloy surface to react with vanillic acid. The vanillic acid treatment played a crucial role in enhancing the corrosion resistance due to the excellent ability to act as a barrier and retard aqueous solution penetration, which effectively isolated the underlying Mg alloy from the corrosive environment. The corrosion current density of alkali and vanillic acid-treated Mg alloy (AZ31V) almost showed two orders of magnitude lower values in comparison with that of the AZ31 Mg alloy, and the corrosion potential of AZ31V Mg alloy increased from −1.41 to −1.25 V. The immersion tests proved that there was no occurrence of severe corrosion. Hence, the alkali pretreatment and vanillic acid treatment may represent a promising method to improve the corrosion resistance of Mg alloy.     

Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.

     

多共振分量融合CNN的行星齿轮箱故障诊断

针对行星齿轮箱中各部件所激起的振动成分混叠、早期故障特征经常被较强的各级齿轮谐波成分以及环境噪声所湮没的问题，提出一种多共振分量融合卷积神经网络（multi-resonance component fusion based convolutional neural network，简称MRCF-CNN）的行星齿轮箱故障诊断方法。首先，对振动信号进行共振稀疏分解，得到包含齿轮谐波成分的高共振分量和可能包含轴承故障冲击成分的低共振分量；其次，构建多共振分量融合卷积神经网络，将得到的高、低共振分量和原始振动信号进行自适应的特征级融合，通过有监督的方式训练模型并进行行星齿轮箱故障诊断。对行星齿轮箱实验数据的分析结果表明，该方法能够有效分类行星齿轮箱中滚动轴承和齿轮的故障，成功对行星齿轮箱故障进行诊断，同时能够进一步增强卷积神经网络对振动信号所蕴含的故障信息的辨识能力。     

高精度多环片自动装配控制技术

为了实现对微小环片零件的自动化装配,搭建了自动装配系统.通过4根直线导向轴与4个直线轴承来提高系统的导向精度和刚度.采用直线导轨进行各装配作业模块之间的切换,保证了微小环片零件的自动装配与取出.在环片的装配方向上,螺旋升降机和光栅尺实现环片的位置精度控制.在Lab VIEW编程环境中,采用分层软件架构和模块化控制思想,避免了不必要的数据循环检测与丢失,能够达到环片组件的装配精度要求.控制系统分为系统初始化模块、参数设置模块、装配模块和取出模块,自动装配系统通过各个模块间的相互交流配合完成装配任务.采用本文中自动装配系统装配环片的实验结果表明,环片零件装配的最大位置误差为26μm,垂直度误差为17μm,平均装配时间为75 s/片,可满足环片组件所需的精度要求.     

一种大型复杂构件加工新模式及新装备探讨

大型复杂构件是航空航天、能源、船舶等领域装备的核心结构件，此类构件通常具有尺寸大、形状复杂、刚性弱等特点。传统“分体离线加工-在线检测”模式存在工艺不稳定、过程复杂、柔性差、周期长等问题，以龙门式多轴数控机床加工为代表的“包容式”加工模式，难以适应大型复杂构件的高效高质量加工制造需求。提出一种基于移动式和吸附式机器人的多机协同原位加工新模式，通过多机器人系统自主寻位、精确定位加工与加工质量原位检测，实现大型复杂构件多安装面并行铣削、制孔与打磨等作业。多机器人系统包括移动式混联机器人、吸附式并联机器人、移动式串联铣削机器人、移动式双臂加工机器人和移动式打磨机器人。构建多机协同原位加工模式，需要揭示多机器人协同原位加工行为与大型弱刚性结构件质量控制的交互机理，面临着本体、测量、工艺和集成四个方面的挑战，需要设计高灵活、高刚度的移动式和吸附式加工机器人，解决移动机器人自主准确寻位和超大结构件原位高精检测难题，攻克加工变形误差在线补偿和振动抑制技术，通过集成实现多机协同高效高精加工，为大型复杂构件的高效高质量制造提供创新技术及装备，并实现此类构件制造核心技术及装备自主可控。     

萃取塔数学模型及过程强化的若干研究进展

萃取塔因生产能力大、占地面积小、密闭性好等优点，在石油、化工、生物、医药和环境工程等多领域被广泛应用。本文从以下几个方面介绍了萃取塔近些年的研究进展：综述了传统萃取塔（脉冲萃取塔、转盘塔与Kühni塔等）的水力学、轴向扩散与传质模型的发展，分析比较了表面张力、传质方向、放大效应等因素对模型的影响；介绍了计算流体动力学（CFD）在萃取塔中单液滴、单相流模拟、液-液两相流模拟、外加能量模拟、与群体平衡模型（PBM）耦合模拟中的应用进展；介绍了国内外设计开发的新型萃取塔，包括改变传统塔的内构件和引入多种外场能量等方式来强化相间传质。研究表明，将先进实验研究方法、准确经验模型和可靠理论计算相结合，将会是萃取塔研究的重要手段和方向。