浅谈基于粗集理论的文本归类系统

文本归类是处理大量文本数据自动分类的重要技术。基于粗集理论建立的林业文本信息归类系统,是在已知类别的训练集的基础上,通过分析训练数据样本,建立决策表产生区分矩阵构造出区分函数,并化简它,得到最小属性约简,最后应用Apriori算法产生最终分类的规则表,利用产生的规则表,可将林业文本信息数据进行自动归类。     

基于形状记忆合金的TMD半主动控制

针对建筑结构在随机荷载或风荷载作用下可能发生的频率不确定问题,特别是结构自振频率会随振幅的大小而发生漂移的非线性振动问题,提出性能可靠、构造简单的形状记忆合金SMA-TMD自适应半主动控制系统.从理论上探讨形状记忆合金在变刚度TMD控制装置中的应用,并进行相应的参数设计.同时,在研究随机荷载作用下TMD系统动力特性的基础上,结合短时傅里叶变换(STFT)设计出该系统的自适应半主动控制算法.算例表明,SMA-TMD自适应控制系统能较好地识别结构系统的时频特性,并进行有效的控制,具有性能可靠和自适应能力强等特点.该自适应控制系统较传统的TMD被动控制减震效果更好,鲁棒性更强.     

Support vector machine forecasting method improved by chaotic particle swarm optimization and its application

By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects. Foundation item: Project(70572090) supported by the National Natural Science Foundation of China     

浅谈文本类型理论框架下的翻译策略

长期以来,人们对于翻译策略的选择都有着自己的看法。德国功能翻译理论创始人凯瑟林娜.赖斯(Katharina Reiss)提出了以功能主义为基础,以目的为导向的文本类型理论,不仅提供了可行的实际操作方法,同时也为翻译研究及翻译批评提供了理论依据。以赖斯的文本类型理论为框架,将翻译和文本功能结合起来,就能对不同功能的文本采取不同的翻译策略,从而检验文本类型理论在翻译实践中的可行性。     

同一强度等级不同统计参数混凝土制成短柱的可靠性分析

同一强度等级的混凝土可能有不同的概率统计参数,对于都服从正态分布、fcu,k相同、σcu不同的两组同一强度等级的混凝土制成的短柱,采用均值一次二阶矩法进行了可靠性计算分析,得出σcu小的可靠度大,σcu大的可靠度小.     

不平衡数据集上的文本分类特征选择新方法*

为了有效消除声发射信号中的噪声,将广义S变换滤波方法应用于声发射信号去噪,分别采用广义S变换中的充零法、基于带通滤波器设计滤波算子法以及时频滤波法进行滤波比较,针对信号的不同时频特性设计了相应的时频滤波算子。结果表明,基于S变换的三种时频滤波法对声发射信号的去噪均有较好的效果,克服了传统滤波方法滤波因子不能随时间、频率变化而变化的缺陷。其中时频滤波法在高信噪比和低信噪比情况下都能更好地去除噪声,可以满足信号处理的要求。     

MS-DOC文件文本提取研究

关键词搜索广泛应用于情报分析、搜索引擎和计算机取证,对MS DOC文件进行关键词搜索可能漏判,明明存在的关键词却找不到。微软复合文档结构由一系列流组成,流以扇区为单位存储,通过目录结构和扇区分配表对流及其存储空间进行管理。MS DOC文件中的文本存储在WordDocument流中,文本存储不一定连续,通过Table流记录分块情况。关键词可能跨越不相邻扇区,即使在相邻扇区,一个关键词可能一部分是压缩存储,另一部分是非压缩存储,这些都是关键词搜索漏判的原因。根据Table流中的分块信息提取WordDocument流中的文本,并统一编码格式,进而进行关键词搜索,就可以避免漏判。     

Short beam three point bend tests in syntactic foams. Part I: Microscopic characterization of the failure zones

Scanning Electron Microscopic (SEM) studies were carried out on the failure surface of syntactic foam material tested in a short beam three point bend test (SBT) by employing 21 × 15 × 3 mm³ dimension bearing specimens. The syntactic foams were fabricated using glass microballoons in epoxy binder. The failure of the tensile, compression, and shear dominated regions were studied by SEM at different magnifications. The tensile region had characteristic features, such as partial debonding of the microballoons from the matrix and cracking of glass microballoons, apart from matrix cracking and some river pattern features. The compression side was characterized by crushing and collapsing of microballoons, resulting in accumulation of debris with no apparent river pattern for matrix‐rich regions. The midway positions of the SBT failed surface comprised of deformation bands in the matrix and occasional debonding of microballoons. The morphology recorded in the tensile and compression regions corroborated well with the results obtained on these foam samples in those specimens that were subjected to pure uniaxial tension and compression, respectively. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 98: 673–679, 2005     

短纤维/弹性体复合材料的研究 Ⅲ涤纶短纤维与再生胶/聚乙烯复合材料研究

本工作探索了再生胶/高密度聚乙烯硫化共混体(RTPV)与涤纶短纤维(SF)复合材料(SFRTPV)的制备与性能,讨论了制取该复合材料的密炼法动态硫化工艺(D)和开炼法静态硫化工艺(S),SF与RTPV的增粘体系和效果,SF组份作用及SF—RTPV的挤出加工性能。     

A small samples training framework for deep Learning-based automatic information extraction: Case study of construction accident news reports analysis

Knowledge management is crucial for construction safety management. Widely collected and well-organized safety-related documents are recognized to be significant in raising the workers' security awareness and then to prevent hazards and accidents. To improve document processing efficiency, automatic information extraction plays an important role. However, currently, automatic information extraction modeling requires large scale training datasets. It is a big challenge for the engineering industry, especially for the fields which heavily rely on the experts’ knowledge. Limited data sources, and high time and labor costs make it not practical to establish a large-scale dataset. This work proposed a natural language data augmentation-based small samples training framework for automatic information extraction modeling. With the designed cross combination-based text data augmentation algorithm, the deep neural network can be employed to build up automatic information extraction models without large-scale raw data and manual annotations. Characters semantic coding is employed to avoid word segmentation and make sure that the framework can be utilized in different writing language systems. The BiLSTM-CRF model is adopted as the detection core to conduct character classification. Through a case study of two independent accident news report datasets analysis, the proposed framework has been validated. A reliable and robust automatic information extraction model can be established, even though with small samples training.