重标极差方法下时变Hurst指数的构建和实证研究

在时间序列的重标极差分析方法的基础上,定义了一个时变Hurst指数的概念,推导并证明了时变Hurst指数的2条性质,并引入滑动分块自助法计算Hurst指数的标准差.以此为基础,用Hurst指数的下限及随机过程服从随机游走时的Hurst的上限作为分析工具,对Hurst指数能否成功预测市场的反转进行了研究.实证研究表明:时变Hurst指数能成功地预测市场的反转.     

Parietal cortex contributions to information granules following memory consolidation

Previous studies have focused on changes in cerebral cortex activity accompanying memory formation and consolidation. Although the role of the parietal cortex in memory retrieval is well established, it is not well understood how parietal cortex memory con-solidation for mathematical rules is related to granularity of stored information (i.e., degree of detail or precision). Changes in parietal cortex activity associated with memory consolidation were analyzed using the Ebbinghaus paradigm and functional magnetic resonance imaging (fMRI). Over the course of 1 week, participants learned Boolean arithmetic tasks involving stimulus-response mapping rules containing either low- or high-granularity information. FMRI images were collected on day 1 (i.e., low-granularity condition) and day 7 (i.e., high-granularity condition). The present data suggested that with practice, stored information was converted from a low-granularity to a high-granularity form. By following rule learning, it was hypothesized that the process of consolidation would involve an increased degree of rule representation granularity. Evidence for this process was reflected in parietal cortex activity. This finding was consistent with the hypothesis that mnemonic reconstruction in the parietal cortex is required for memory consolidation, and results suggested that information granules are formed during memory consolidation. The present results could increase the understanding of the relationship between memory consolidation and information granularity.     

校企合作创新动态过程案例研究

通过对哈尔滨工业大学功能材料课题组与成都飞机工业集团有限责任公司合作研制记忆合金管接头项目的整体了解和阶段分析,认为校企合作创新过程分为三个阶段:合作决策、合作实施以及后续创新。以案例为基础,重点讨论了校企双方如何在供需角度上形成诉求上的统一而达成合作共识、校企双方如何在具体的合作中实现角色上的转换,以及如何建立和保持一种长期、稳定的合作伙伴关系。     

空间位置记忆广度研究

 空间位置记忆广度是人对空间方位知觉能力和短时记忆能力的一种度量,对人的短时记忆容量研究有重要意义。本文设计了不同编码的两种实验,对100名大学生进行测试,记录测试结果,进行统计分析。主要研究了以下问题：人的空间位置记忆广度值与记忆的位数和出错的次数间是否具有某种关系;如果改变实验条件,空间位置记忆广度值是否就会有很大区别;不同性别的记忆广度值是否会有显著不同。实验结果表明,记忆广度与记忆位数和出错次数符合线性回归关系;不同编码形式的空间位置记忆广度存在显著差异;不同性别的空间位置记忆广度没有显著差异。     

Exploitation of shape memory alloy actuator using resistance feedback control and its development

The relationship between phase transformation and electric re sistivity of Ti-42.6Ni-7Cu SMA was investigated. In the isothermal tensile tests, stress—strain curve shows large hysteresis and nonlinearity, whereas the resistivity—strain curve can be fitted by linearity. The resistivity of SMAs can be determined from the volume fractions of the martensitic and austenitic phas es. This characteristics leads to that the resistance of the SMAs is used as a parameter of strain of SMAs. A SMA actuator using resistance feedback control system was proposed, which can control and retain positioning at any positions without using sensor devices, and the deviation of position of this system was less than 3 μm.     

基于uC/OS-Ⅱ的编译型PLC运行系统设计

提出了一种基于uC/OS-Ⅱ的编译型PLC运行系统的设计方法,并对运行系统中任务间调度、内存分配和任务间的通信原理进行了分析讨论。     

Wolfe线性搜索下的超记忆梯度法及其收敛性

研究无约束优化问题,给出了一种新的超记忆梯度法,在较弱条件下证明了算法具有全局收敛性和线性收敛速率.数值试验表明新算法是有效的.     

预烧工艺对KNLNT基陶瓷相结构和电性能的影响

采用传统固相法制备了(K0.46Na0.50Li0.04)(Nb0.85Ta0.15)O3(KNLNT)无铅压电陶瓷材料,研究了不同预烧温度和不同预烧保温时间对KNLNT基无铅压电陶瓷的相结构、显微结构和电性能的影响.结果表明:当预烧850℃保温9h时,1135℃烧结2h的陶瓷材料各项性能较佳,其电性能参数分别为d33=248pC/N,εr=1130,tanδ=0.019,Kp=0.54,Qm=70,Pr=28.38μC/cm,Ec=14.31kV/cm.     

无阻尼弱耗散抽象发展方程全局吸引子的存在性

用半群理论和定义泛函的方法,考察无阻尼弱耗散抽象发展方程解的长时间动力学行为.在非线性项满足较弱的耗散型条件下,运用收缩函数理论和能量估计技巧验证了解半群的渐近紧性,得到了全局吸引子在空间V_θ×H×L_μ~2(R+;V_θ)中的存在性.     

基于AM-LSTM模型的超短期风电功率预测

近年来,中国的风力发电产业高速发展。然而风力发电具有不稳定性,风电功率超短期预测结果的准确性直接影响到电网安全有效的运行。为了进一步提高风电功率超短期预测的精确度,提出了长短期记忆网络-注意力模型(AM-LSTM)风电功率预测模型,该模型将长短期记忆网络(long-term and short-term memory,LSTM)和注意力模型(attention model,AM)相结合, LSTM网络能够处理好风速、风向等时间序列变量与风电功率之间的非线性关系,注意力模型能够优化LSTM网络的权重,从而使预测结果更加准确。采用真实的风电场历史数据进行实验,结果表明:提出的AM-LSTM预测模型能够有效利用多变量时间序列数据进行风电场发电功率的超短期预测,比传统的BP神经网络和LSTM网络具有更精确的预测效果。该预测模型为风电场地电力调度提供了科学参考。