剪力墙分缝对高层混合结构地震反应的影响

以钢框架-钢筋混凝土实体剪力墙混合结构为对象,通过分别开设一道和两道竖缝,分析它们在不同类型地震波下的地震能量反应,结果表明,剪力墙竖向开缝可以减少结构在地震作用下的滞回耗能量,并使滞回耗能区域过于集中于底部剪力墙的问题得以明显改善,使得耗能性能更为优越的钢构件能够承担相当部分的滞回耗能;分缝越多,改善效果越为明显。     

人工智能应用中基于区间代数的时态推理

时态表示和推理是人工智能领域的重要研究内容之一,它的应用范围分布很广,从逻辑基础研究到知识系统的应用.区间代数是一种独立的与领域无关的时态理论.用区间代数能表示不确定的时态关系,可以很方便地用于时态推理,表达能力强;时态关系的区间表示比较直观,可理解性强;同时区间代数可以进一步扩展到二维空间领域,即将区间代数拓展为矩阵代数,实现二维空间推理.在一维时态推理中,将时态的区间表示和矩阵表示相结合,在提高计算效率的同时,保持了形象直观的时态表示.     

Extraction of Signal Frequency in Laser Ranging of Space Target Based on FPGA

Aiming at the problems in a linear frequency modulation continuous wave(LFMCW) ranging lidar system, such as low signal-to-noise in echo beat frequency and difficult extraction of the signal frequency, a method with high-speed and high accuracy based on field programmable gate array(FPGA) is proposed. Firstly, the laser echo beat signal is sampled by high-speed analog to digital converter(ADC) and then processed in FPGA for data preprocessing. After the signal is processed by the 8 192-point FPGA will obtain the frequency spectrum and then apply the frequency spectrum to data processing and adaptive noise signal peak detection. These two steps realize the real-time and accurate extraction process of the target echo beat signal frequency. Finally, the spectrum data is transmitted to the host computer and processed through the external data acquisition board for real-time spectrum display. Experimental results show that data preprocessing and spectrum data processing can effectively suppress DC bias and system modulation noise. Adaptive noise peak detection can accurately divide the threshold contour according to the dynamic noise of the system and realize the detection of target signal peak. When the sampling clock of the system is 100 MHz, the time needed for each calculation of the spectrum is 81.92 μs, and the refresh rate of the spectrum reaches 12.2 kHz, which meets the real-time requirements of the system.     

图书馆在学习型社会建设中的定位分析

指出学习型社会是和谐社会的重要组成部分,构建学习型社会是图书馆精神的体现。具体介绍了图书馆在构建学习型社会中的4个定位。     

由教育产业化引发的思考

从教育属性、义务教育、城乡二元结构及教育产业化的危害等方面,阐述了教育是一种崇高的公益事业,应向一切能够接受教育的人提供教育,指出推行教育产业化违背了教育宗旨和办学方针,也损害了人民群众的利益。     

高校图书馆员职业道德建设和培养

根据《中国图书馆职业道德准则(施行)》对高校图书馆的职业道德建设和培养的要求,提出几点建议,即职业道德建设和培养离不开管理者自身的建设,并应注重加强对馆员的思想意识和道德修养等方面的建设。     

电刺激小脑顶核对大鼠心肌梗死后心脏神经再生的影响

探讨预先电刺激小脑顶核(FNS)对大鼠心肌梗死(MI)后心脏神经再生的干预作用。将90只Wistar大鼠随机分为:①MI组,结扎左冠状动脉前降支(LAD);②预先电刺激小脑顶核后再予以LAD结扎组(FNS MI组);③毁损小脑顶核后电刺激该部位,再行LAD结扎组(FNL FNS MI组)。各组又分MI后1,7,21d3个时间点。另设假手术组(Sham组)。应用免疫组织化学方法显示心脏组织胆碱乙酰转移酶(CHAT)和酪氨酸羟化酶(TH)。检测各时间点梗死区与非梗死区TH、CHAT阳性神经纤维分布密度,MI组与Sham组相比显著减少(P<0.01);FNS MI组较MI组显著增多(P<0.05);FNL FNS MI组与MI组比较无显著性差异(P>0.05)。因此,电刺激小脑顶核可增加梗死区和非梗死区心肌组织TH、CHAT染色阳性神经纤维密度,促进MI后心脏神经纤维再生。     

技术嵌入与制度吸纳:提高政府技术治理绩效的运作逻辑

目前学界对技术治理研究的框架主要有技术赋权与技术监管、技术生产与技术约束.然而这两种研究视角都将技术治理的治理结构作为主要研究对象,而没有涉及更深层次的从技术治理的制度层面展开研究.基于此,本研究从反思当前我国技术治理绩效提高仍停留于表层的技术手段升级改造,忽略了制度支持在技术治理中的重要作用的事实出发,进而提出一种新...     

Micro-expression recognition algorithm based on the combination of spatial and temporal domains

Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algorithms,an efficient micro-expression recognition algorithm is proposed,which uses convolutional neural networks(CNN) to extract spatial features of micro-expressions,and long short-term memory network(LSTM) to extract time domain features.CNN and LSTM are combined as the basis of micro-expression recognition.In many CNN structures,the visual geometry group(VGG) using a small convolution kernel is finally selected as the pre-network through comparison.Due to the difficulty of deep learning training and over-fitting,the dropout method and batch normalization method are used to solve the problem in the VGG network.Two data sets CASME and CASME II are used for test comparison,in order to solve the problem of insufficient data sets,randomly determine the starting frame,and a fixedlength frame sequence is used as the standard,and repeatedly read all sample frames of the entire data set to achieve trayersal and data amplification.Finallv.a hieh recognition rate of 67.48% is achieved.     

一种局部与全局特征相结合的主题域识别模型

传统的主题域识别技术主要局限于单一领域,缺乏领域间的交互式协同,难以保证识别结果的准确性,因此提出一种局部与全局特征相结合的主题域识别模型.该模型一方面基于实体在领域内的局部特征进行局部识别,另一方面基于领域间协同作用、领域相关度等全局特征对各个局部识别结果进行一致化趋近,从而使识别结果更全面、有效.另外,针对相似矩阵的更新时机、协同作用的量化以及迭代终止条件的设定三个方面对主题域识别算法进行了优化.通过实验验证了本文所提出的关键技术的可行性和有效性.