Novel progress in laser welding of dissimilar materials

As the most widely used thermal power machine, the diesel engine plays a key role in today＇s social and economic development. Its dynamic performance could be effectively upgraded by one of its core components, the turbocharger, whose quality largely depends on welding technology for dissimilar materials.     

基于SVM的实时自动超声钢轨伤损检测分类系统

介绍了一个更有效的基于支持向量机的实时超声波钢轨伤损自动检测分类系统.根据钢轨伤损的特点提取特征量,利用基于支持向量机的分类预测算法实现钢轨伤损的实时检测分类,并基于统计处理的计算伤损尺寸.在嵌入式系统DSP中利用该机器学习算法实现了伤损的实时处理和测试.实现了钢轨伤损实时报警、显示伤损类型、所处轨内位置及程度.     

基于遗传算法的数字曲线多边形逼近的实现

平面曲线的多边形逼近是模式识别和计算机舰觉研究中的常用算法,为克服顶点的检测只依靠局部区域、缺乏全局信息的弱点。提出了一种利用遗传算法的最优多边形逼近算法。     

专利无效对比文件判定方法研究

[目的/意义]对比文件是用以判断专利能否授权或无效的重要文件,针对传统信息检索方法的不足且鲜有利用机器学习方法研究对比文件检索的问题,在引入对比文件信息的基础上,构建专利相关性判定模型.[方法/过程]以专利无效判决书中的目标专利与对比文件为数据集进行实验,提取文本相似度、共现词汇和共词数量特征信息,利用GBDT模型将对...     

数字技术应用对企业创新的影响研究

新一代信息技术革命推动下,数字技术正以前所未有的广度和深度嵌入到产品与服务中。本文基于数字化、网络化、智能化等企业创新生态系统新情境,利用2007—2017年中国制造业上市公司数据系统考察数字技术应用如何影响企业创新。研究表明：智慧城市建设有助于促进数字技术在制造业领域的应用,显著地提高了企业创新;“机器联物换人”融合模式的研发投入显著增加而创新产出没有显著增加,智能制造领域的数字技术应用创新有较长的周期性;“群体信息交互”协同模式的研发投入和创新产出都显著为正,数字技术推动了产品创新与商业模式创新的繁荣。此外,国有企业与大中型企业具有数字资源的优势,数字技术应用创新的表现更为突出。     

基于支持向量机的铁谱磨粒自动识别

为了实现支持向量机对磨损磨粒的自动识别,首先对彩色磨粒图像进行图像处理,运用 K －均值聚类、区域生长法和数学形态学将磨粒成功地从图像中分割出来;其次根据各类磨粒形貌特征确定磨粒的形状尺寸、纹理特征和颜色特征参数,并采用相应的方法提取这三方面的参数;由于支持向量机参数的选取对其分类效果影响很大,所以采用遗传算法对其参数做进一步的优化;最后以提取的特征参数为基础,运用优化的支持向量机成功地识别了严重滑动磨粒、球形磨粒、切削磨粒、疲劳磨粒和红色氧化物五类特征磨粒,其识别准确率达90％。结果表明该方法切实可行。     

数控机床定位精度补偿技术(英文)

通过分析多体系统中典型体的基本变换关系,引入相邻体的位置变换和位移变换误差矩阵,运用多体系统运动学提出数控机床定位误差的通用模型,利用８０３１单片机系统开发了智能误差补偿控制器．在ＸＨ７１４加工中进行的一系列实验表明,机床的定位精度提高５０％以上．     

材料试验仪器设备的安全保护

通过试验教学和科学研究的几点认识,提出试验仪器的安全保护值得引起重视的问题,并阐述对仪器设备完好率的提高和教学科研的顺利开展有很大作用     

From substitution to redefinition: A framework of machine learning-based science assessment

This study develops a framework to conceptualize the use and evolution of machine learning (ML) in science assessment. We systematically reviewed 47 studies that applied ML in science assessment and classified them into five categories: (a) constructed response, (b) essay, (c) simulation, (d) educational game, and (e) inter-discipline. We compared the ML-based and conventional science assessments and extracted 12 critical characteristics to map three variables in a three-dimensional framework: construct, functionality, and automaticity. The 12 characteristics used to construct a profile for ML-based science assessments for each article were further analyzed by a two-step cluster analysis. The clusters identified for each variable were summarized into four levels to illustrate the evolution of each. We further conducted cluster analysis to identify four classes of assessment across the three variables. Based on the analysis, we conclude that ML has transformed—but not yet redefined—conventional science assessment practice in terms of fundamental purpose, the nature of the science assessment, and the relevant assessment challenges. Along with the three-dimensional framework, we propose five anticipated trends for incorporating ML in science assessment practice for future studies: addressing developmental cognition, changing the process of educational decision making, personalized science learning, borrowing 'good' to advance 'good', and integrating knowledge from other disciplines into science assessment.     

Utilizing statistical physics and machine learning to discover collective behavior on temporal social networks

Computational social science has become a branch of social science that uses computationally intensive ways to investigate and model social phenomena. Exploitation on mathematics, physics, and computer sciences, and analytic approaches like Social Network Analysis (SNA), Machine Learning (ML), etc, develops and tests the theories of complex social phenomena. In the emerging environment of social media, the new characteristics of social collective behavior and its extensive phenomena have become the hot spot of common concern across many disciplines. In this paper, we propose a general quantitative framework to discover the social collective behavior in temporal social networks. The general framework incorporates the Time-Correlation Function (T.C.F.) in statistical physics and evolutionary approach in Machine Learning, and provides the quantitative evidence of the existence of social collective behavior. Results show collective behaviors are observed and there exists a tiny fraction of users whose behavior are constantly replicated by public, disregard of the behavior itself. Our method is assumption-independent and has the potential to be applied to various temporal systems.