三维实体重建时投影图数据的预处理

介绍三维实体重建过程中投影图数据的处理算法，绘制同一图层的图形时，视图分割算法添加平行于两坐标轴的辅助直线，移动辅助线使其将三视图分割开来；对基于同一坐标系的投影三视图数据，推出从设备坐标系到各视图坐标系的转换公式并为其设计了转换算法，该算法求出三视图坐标系共有的原点，再根据视图分割的结果将每一图形元素转换到新的坐标系统中；隐含点的求取算法先判断同一视图中图元间的位置关系，再求出它们之间的公共点，这些点将用于线框搜索过程中，以上算法处理的投影图点线数据分别存入点线链表中。     

组态软件技术课程教学设计与实践

在工程教育专业认证持续深入推进的背景下,结合电气工程专业特色,对组态软件技术课程教学进行重新设计与教学实践。引入高阶思维临场认知对教学内容进行重构,应用课堂多源数据建立教学评测一体化的教学新模式。实践表明该课程的教学设计有着良好的沉浸式体验感,学生主动建构、解决问题的高阶思维能力得到提升,新的教学模式促进了课程教学的高质量发展。     

A second‐order decoupled implicit/explicit method of the 3D primitive equations of ocean II: finite element spatial discretization

A fully discrete second‐order decoupled implicit/explicit method is proposed for solving 3D primitive equations of ocean in the case of Dirichlet boundary conditions on the side, where a second‐order decoupled implicit/explicit scheme is used for time discretization, and a finite element method based on the P₁(P₁) ? P₁?P₁(P₁) elements for velocity, pressure and density is used for spatial discretization of these primitive equations. Optimal H¹?L²?H¹ error estimates for numerical solution and an optimal L² error estimate for are established under the convergence condition of 0 < h≤β₁,0 < τ≤β₂, and τ≤β₃h for some positive constants β₁,β₂, and β₃. Furthermore, numerical computations show that the H¹?L²?H¹ convergence rate for numerical solution is of O(h + τ²) and an L² convergence rate for is O(h²+τ²) with the assumed convergence condition, where h is a mesh size and τ is a time step size. More practical calculations are performed as a further validation of the numerical method. Copyright © 2016 John Wiley & Sons, Ltd.     

中国大学生创造性4C认知量表修订

目的对创造性4C认知量表(CC4CS)在中国大学生中进行修订,以便探讨创造性4C理论在中国本土文化中的适用性。方法从在线大学生QQ群中招募全国各地3 360名在校大学生为被试,填写CC4CS中文版初测量表,分析其信效度。结果 (1)各项目的区分度与鉴别性较好(t=14.01~34.77,r=0.31~0.55,P均0.001)。(2)经探索性因子分析,得到与原量表一致的创造性4C结构,累积方差解释率为55.22%。(3)总量表Cronbachα系数为0.85,重测信度为0.88,各分量表的α系数在0.72~0.82之间,重测信度在0.81~0.90之间;与Lawdsei认知创造力量表和Rudowicz-Hui创造力概念调查量表的关联效度分别为0.67、0.77;各分量表与总分以及各分量表之间的相关分别在0.53~0.74、0.10~0.60之间,结构效度良好。结论 CC4CS中文版具有较好的心理测量学指标,适用于在中国文化背景下大学生的创造性4C认知测量。     

汽车牵引力控制系统（TCS）研究

文章介绍了汽车牵引力控制系统的原理和控制方法。对汽车简化模型进行两种控制方法的仿真计算,分析了它对汽车牵引性能的控制作用,说明了牵引力控制系统对汽车牵引性能的改善。     

An Adaptive Role for DNA Double-Strand Breaks in Hippocampus-Dependent Learning and Memory

DNA double-strand breaks (DSBs), classified as the most harmful type of DNA damage based on the complexity of repair, lead to apoptosis or tumorigenesis. In aging, DNA damage increases and DNA repair decreases. This is exacerbated in disease, as post-mortem tissue from patients diagnosed with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) show increased DSBs. A novel role for DSBs in immediate early gene (IEG) expression, learning, and memory has been suggested. Inducing neuronal activity leads to increases in DSBs and upregulation of IEGs, while increasing DSBs and inhibiting DSB repair impairs long-term memory and alters IEG expression. Consistent with this pattern, mice carrying dominant AD mutations have increased baseline DSBs, and impaired DSB repair is observed. These data suggest an adaptive role for DSBs in the central nervous system and dysregulation of DSBs and/or repair might drive age-related cognitive decline (ACD), MCI, and AD. In this review, we discuss the adaptive role of DSBs in hippocampus-dependent learning, memory, and IEG expression. We summarize IEGs, the history of DSBs, and DSBs in synaptic plasticity, aging, and AD. DSBs likely have adaptive functions in the brain, and even subtle alterations in their formation and repair could alter IEGs, learning, and memory.     

基于语言计算方法的语言认知实验综述

人脑对语言的理解过程十分复杂,涉及多个脑网络和加工机制。以往的工作大多采用严格控制的实验设计,针对特定的语言现象展开研究,导致了研究结论趋于碎片化,无法形成关于大脑语言理解的总体结论。另一方面,深度学习的出现引发了语言计算领域的技术变革,语言计算模型在多个任务上达到甚至超越了人类的水平。这为进行全局性、高生态效度的人脑语言理解实验带来可能性,促进了语言认知实验中引入语言计算模型方法的快速发展。那么,利用新兴的语言计算方法可以为大脑语言认知机理的研究带来哪些新的机遇和启发呢？该文归纳总结了利用语言计算方法进行语言认知实验的相关工作,并对未来发展趋势予以展望。     

一种基于本体的测试用例研究与应用

测试用例是软件测试的重要组成,也是软件测试成败的关键性因素之一。测试用例的完备性和复用性研究已经成为当前软件测试领域的两大热点。论文提出一种基于本体论的测试用例分析与刻画方法,它统一了测试用例的内涵,并为测试用例提供了可穷举的外延选择。该方法解决了软件测试用例完备性和复用性问题。     

基于网格优化的隐式曲面自适应多边形化

隐式曲面多边形化是隐式曲面绘制的一种常用算法.基于网格优化的隐式曲面快速自适应多边形化算法,首先用多边形化算法生成一个粗糙的初始网格,再利用网格优化方法从网格顶点位置、规则性和网格法向三个方面对粗糙网格进行调整,最后根据网格的局部曲率用多边形细分策略细分优化后的网格.实验结果表明,该算法在网格生成速度和网格规则性上都胜于Marching Cubes的多边形化算法,恢复的隐式曲面能较好地反映形状特征.     

Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like, fingerprint or face. Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’ identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session. However, divergent issues remain unaddressed. This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called, Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s (GWCK-CC). First, a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise present in the raw input face images. Cauchy Kriging Regression function is employed to reduce the dimensionality. Finally, Continuous Czekanowski’s Classification is utilized for proficient classification between the genuine user and attacker. By this way, the proposed GWCK-CC method achieves accurate authentication with minimum error rate and time. Experimental assessment of the proposed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset. The results confirm that the proposed GWCK-CC method enhances authentication accuracy, by 9%, reduces the authentication time, and error rate by 44%, and 43% as compared to the existing methods.