消音小锯片振动特性研究

采用有限元分析软件ANSYS,对轧钢厂金属小锯片的固有特性进行了系统研究。通过对锯片在打孔、开槽情况下固有频率及振型的计算分析发现:打孔对锯片固有频率无太大影响,但对降噪影响较大;开环形槽对五阶以上固有频率影响相对明显。     

基于混沌-支持向量机的边坡位移预测

通过平均互信息法确定延迟时间、虚假邻点法确定嵌入维数,从而实现混沌时间序列相空间的重构,揭示边坡地质体的变形规律;并以这些数据作为支持向量机的训练样本,用支持向量机的回归特性预测边坡的变形,把由于多变量耦合关系导致的边坡体位移时间序列,利用单变量方法进行分析。结果表明Chaotic-SVM模型具有拟合精度高、泛化能力强的特点,在岩土工程非线性预测中具有一定的应用价值。     

基于Elman神经网络的商品销售额建模及预测

为提高预测精度,采用Elman神经网络对商品销售额进行预测.在分析样本数据特性基础上,采用Elman神经网络对商品销售额的预测进行建模,通过实例验证模型的有效性并对比分析BP神经网络的预测效果.研究结果表明,基于Elman神经网络的商品销售额预测能够较好地反映商品销售额的变化趋势且提高了预测精度,验证了该方法在商品销售额预测中的可行性.     

图书馆预测学的理论延伸——图书馆决策学论纲

图书馆预测学理论是2006年提出的。该理论的提出,主要是为了解决图书馆面临的具体问题的,而图书馆面临的一大重要问题就是决策过程中的决断问题。为了使图书馆决策更加科学严谨,必须找到图书馆决策的依据和方法。由于预测是决策的基础,所以从图书馆预测入手,建立图书馆决策学的基础理论体系,为图书馆决策提供理论指导。     

奥运会男子100m跑成绩的灰色GM(1,1)模型预测

首先讨论了基于对数变换的GM(1,1)模型,并结合C语言程序实现了该模型的程序化。然后以奥运会男子100 m跑为例,从专项训练学角度出发,结合自行编写的C语言程序进行数据处理、分析、验证,并预测了2012年第30届奥运会的男子100 m跑成绩在9.63～9.71 s。     

基于时间序列的港口货物吞吐量GRNN预测模型

为准确进行港口货物吞吐量预测,采用逐步递归的方法,建立基于时间序列的港口货物吞吐量广义回归神经网络(Generalized Regression Neural Network,GRNN)预测模型.利用该模型预测某港口货物吞吐量,结果表明:对于小样本的短期预测,该模型具有适应性好、误差小等特点.     

What the fake? Probing misinformation detection standing on the shoulder of novelty and emotion

One of the most time-critical challenges for the Natural Language Processing (NLP) community is to combat the spread of fake news and misinformation. Existing approaches for misinformation detection use neural network models, statistical methods, linguistic traits, fact-checking strategies, etc. However, the menace of fake news seems to grow more vigorous with the advent of humongous and unusually creative language models. Relevant literature reveals that one major characteristic of the virality of fake news is the presence of an element of surprise in the story, which attracts immediate attention and invokes strong emotional stimulus in the reader. In this work, we leverage this idea and propose textual novelty detection and emotion prediction as the two tasks relating to automatic misinformation detection. We re-purpose textual entailment for novelty detection and use the models trained on large-scale datasets of entailment and emotion to classify fake information. Our results correlate with the idea as we achieve state-of-the-art (SOTA) performance (7.92%, 1.54%, 17.31% and 8.13% improvement in terms of accuracy) on four large-scale misinformation datasets. We hope that our current probe will motivate the community to explore further research on misinformation detection along this line. The source code is available at the GitHub.²     

Improved sales time series predictions using deep neural networks with spatiotemporal dynamic pattern acquisition mechanism

The ability to predict product sales is invaluable for improving many of the routine decisions essential for the running of an enterprise. One significant challenge of sales prediction is that it is hard to dynamically capture changing dependent patterns along the sales time line, because sales are often influenced by complicated and changeable market environment. To address this issue, we model sales prediction as a task of multivariate time series (MTS) prediction, and propose a Spatiotemporal Dynamic Pattern Acquisition Mechanism (SDPA), which comprises four components, described below: (1) In the processing of input data: A Spatiotemporal Dynamic Kernel (SDK) component is designed for MTS to effectively capture different dependent correlation patterns during different time periods. (2) In terms of model design: A Simultaneous Regression (SR) component is proposed to dynamically detect stable correlations by using co-integration based dynamic programming over different time periods. (3) A novel Hierarchical Attention (HA) component is designed to incorporate SDK to detect spatiotemporal attention patterns from the captured dynamic correlations. (4) In the design of loss function, A Change Sensitive and Alignment component (DC) is proposed to provide more future information based on future trend correlations for better model training. The four components are incorporated into a unified framework by considering Homovariance Uncertainty (HU). This is referred to as SDPANet and contributes to model training and sales prediction. Extensive experiments were conducted on two real-world datasets: Galanz and Cainiao, and experimental results show that the proposed method achieves statistically significant improvements compared to the most state-of-the-art baselines, with average 41.5% reduction on RMAE, average 39.5% reduction on RRSE and average 46% improvement on CORR. Experiments are also conducted on two new datasets, which are Traffic and Exchange-Rate from other fields, to further verify the effectiveness of the proposed model. Case studies show that the model is capable of capturing dynamic changing patterns and of predicting future sales trends with greater accuracy.     

中国情报学期刊论文热点关键词演变轨迹与预测

[研究目的]寻找国内情报学领域期刊论文研究热点的分布特性,探讨我国情报学领域学术研究热点的基本演变轨迹。[研究方法]选择国内情报学领域21种期刊论文的关键词进行统计分析,统计时间从2000年至2020年,统计数据在万方数据库中进行。首先选择年度排序在前30次以上的关键词作为热点研究的统计对象,由此从中选出60个关键词作为情报期刊的基本热点关键词。其次用选出的60个关键词在万方数据库的全学科(注:这里的全学科指在万方数据库收录的所有学科)关键词中进行检索,检索时间为2000年至2020年,最后对比分析情报期刊的热点关键词在全学科中的个性化特征和全局性分布特征。[研究结论]实验结果表明,该统计分析揭示了我国情报期刊论文近20年来三个阶段的热点关键词演变轨迹。情报学热点关键词与全学科关键词存在超前和滞后现象,反映了情报学科与全学科具有相互学习和相互推进的发展规律,借此规律对我国未来情报学热点进行预测,预测出我国情报期刊论文未来延续出现和可能出现的72个热点关键词。     

辽宁国际旅游市场时空动态分析及发展研究

辽宁是东北经济、文化和交通中心,也是中国北方旅游大省,改革开放以来,旅游业外向度逐步提高,各地市接待的国际游客日渐增多,市场不断开拓,旅游品牌形象逐年提升。但随着我国各地竞相发展旅游态势的持续高涨及旅游产业进入转型升级的关键时期,国际旅游市场竞争日趋激烈,辽宁国际旅游开拓及发展面临严峻挑战。辽宁国际旅游市场发展需要在把握国际旅游客源市场时空动态演变规律的基础上,从多元化的角度促进国际旅游市场开拓及国际旅游产品开发,注重加快国际客流的合理流动与分布,以提高国际旅游市场竞争力,为辽宁国际旅游培育更为广阔的市场。