经验模态分解与均生函数-最优子集耦合模型在年径流预测中的应用

针对径流时间序列的非平稳特性及中长期预测精度低的问题,本文提出一种新的耦合预测方法：基于EMD分解的均生函数-最优子集回归（Mean Generating Function-Optimum Subset Regression,MGF-OSR）模型。首先利用经验模态分解（Empirical Mode Decomposition,EMD）方法对汾河上游上静游、汾河水库、寨上和兰村4座水文站的年径流序列进行平稳化处理,分别得到若干个固有模态函数（Intrinsic Mode Function,IMF）。对各阶固有模态函数分别建立MGF-OSR模型并进行预测,趋势项用直线拟合的方法进行预测,然后通过重构各预测值得到汾河上游4座水文站年径流量的预测结果,并与单独运用MGF-OSR模型的预测结果进行比较。结果表明,运用基于EMD分解的MGF-OSR模型对汾河上游4站年径流进行预测,准确率均为100%,确定性系数在0.975以上;而单一模型的预测准确率均为40%,确定性系数在0.732以下,耦合模型预测精度明显提高。     

Data fusion with factored quantization for stock trend prediction using neural networks

As compared to the continuous temporal distributions, discrete data representations may be desired for simplified and faster data analysis and forecasting. Data compression can introduce one of the efficient ways to reduce continuous historical stock market data and present them in discrete forms; while predicting stock trend, a primary concern is towards up and down directions of the price movement and thus, data discretization for a focused approach can be beneficial. In this article, we propose a quantization-based data fusion approach with a primary motivation to reduce data complexity and hence, enhance the prediction ability of a model. Here, the continuous time-series values are transformed into discrete quantum values prior to applying them to a prediction model. We extend the proposed approach and factorize quantization by integrating different quantization step sizes. Such fused data can reduce the data to mainly concentrate on the stock price movement direction. To empirically evaluate the proposed approach for stock trend prediction, we adopt long short-term memory, deep neural network, and backpropagation neural network models and compare our prediction results with five existing approaches on several datasets using ten performance metrics. We analyze the impact of specific quantization factors and determine the individual best as well as overall best factor sizes; the results indicate a consistent performance enhancement in stock trend prediction accuracy as compared to the considered baseline methods with an improvement up to 7%. To evaluate the impact of quantization-based data fusion, we analyze time required to execute the experiments along with percentage reduction in the number of unique numeric terms. Further, these results are statistically evaluated using Wilcoxon signed-rank test. We discuss the superiority and applicability of factored quantization-based data fusion approach and conclude our work with potential future research directions.     

Predicting the Hate: A GSTM Model based on COVID-19 Hate Speech Datasets

COVID-19 crisis has been accompanied by copious hate speeches widespread on social media. It reinforces the fragmentation of the world, resulting in more significant racial discrimination and distrust between people, leading to crimes, and injuring individuals spiritually or physically. Hate speech is hard to crack for a global recovery in the post-epidemic era. Conducting with Twitter datasets, this paper aims to find the key indicators that influence the trend of hate speech, then builds a Gaussian Spatio-Temporal Mixture (GSTM) model for trends prediction based on the pre-analysis. Findings show that in the early period, the participation of influential users is closely related to the emergence of sentiment peaks, and the interval time is around one week. After hate speech waves up, the indicator of total exposure becomes more critical, suggesting that grass-root release influences at this stage. Compared with three classical time-series predicting models, the GSTM model shows better peak prediction ability and lower residual mean. This work enriches the approaches of predicting unknown but foreseeable hate speeches accompanied by future pandemics.     

国家自然科学基金项目申请量预测的新方法

近年来,国家自然科学基金项目申请量迅猛增长,申请量预测成为宏观调控这一趋势的重要决策依据。通过深入剖析影响申请量的政策因素和资格因素,开发了一个基于潜在申请者的申请量预测方法。实验结果表明,所提方法的平均相对预测误差显著小于多项式拟合、指数平滑、ARMA模型、灰色G(1,1)模型等常见预测方法。而且,新方法的主要创新性在于其能预估申请政策调控对申请量的影响。     

分组零膨胀泊松模型的半参数统计推断

泊松回归模型常常用于计数数据的研究中,然而在实际数据中零值的比例可能远远大于泊松分布中取零值的概率,而且这些零值通常都有其特殊含义.此外计数数据可能是分组数据,即观测到的数据不是确切值而只是已知其落在某一个区间范围之内;或者某些特定的数据,例如工资,要先对它进行人为的分组然后再进行分析.考虑一种零膨胀泊松半参数回归模型来处理上述分组计数数据.该模型中泊松分布的期望与协变量之间采用部分线性连接函数,而零值的概率与协变量之间采用线性连接函数.利用Sieve极大似然估计方法来估计该回归模型中参数和非参数函数,并提出了一种得分检验方法来检验是否存在零膨胀.在一定正则条件下,获得了Sieve极大似然估计的渐近性质,证明了参数部分的估计是强相合,渐近正态及渐近有效的;同时非参数函数的估计达到了最优收敛速度.模拟研究表明,估计和检验方法效果都比较好,最后将此模型和推断方法应用于一组公共卫生领域实际数据研究.     

An improved particle filtering-based approach for health prediction and prognosis of nonlinear systems

Health monitoring of nonlinear systems is broadly concerned with the system health tracking and its prediction to future time horizons. Estimation and prediction schemes constitute as principle components of any health monitoring technique. Particle filter (PF) represents a powerful tool for performing state and parameter estimation as well as prediction of nonlinear dynamical systems. Estimation of the system parameters along with the states can yield an up-to-date and reliable model that can be used for long-term prediction problems through utilization of particle filters. This feature enables one to deal with uncertainty issues in the resulting prediction step as the time horizon is extended. Towards this end, this paper presents an improved method to achieve uncertainty management for long-term prediction of nonlinear systems by using particle filters. In our proposed approach, an observation forecasting scheme is developed to extend the system observation profiles (as time-series) to future time horizon. Particles are then propagated to future time instants according to a resampling algorithm instead of considering constant weights for the particles propagation in the prediction step. The uncertainty in the long-term prediction of the system states and parameters are managed by utilizing dynamic linear models for development of an observation forecasting scheme. This task is addressed through an outer adjustment loop for adaptively changing the sliding observation injection window based on the Mahalanobis distance criterion. Our proposed approach is then applied to predicting the health condition as well as the remaining useful life (RUL) of a gas turbine engine that is affected by degradations in the system health parameters. Extensive simulation and case studies are conducted to demonstrate and illustrate the capabilities and performance characteristics of our proposed and developed schemes.     

全球土壤退化评价

近几十年来，随着世界人口的急剧增长，人为活动引起的土壤退化日趋增强，原先局部的、次要的变化已转化为全球性的重大变化，威胁着人类赖以生存的环境。本文阐述了包括中国在内的全球土壤退化类型、退化程度和引起土壤退化的主要人为因素，以便土壤管理者、决策者和实施机构为预测未来变化，防治土壤退化作出决策提供依据。     

基于局部信息相似性的学科引证知识扩散动态链路预测研究

在知识经济时代,学科知识的传播与扩散促进了学科的协同、交叉、融合、发展与创新。文章利用复杂网络算法对学科引证知识扩散时序演化网络进行动态链路预测分析,以期探索学科知识流动结构变化及演进态势,为学科知识管理及决策制定提供可资借鉴的理论和实践参考。文章以学科引证知识扩散时序演化网络结构信息为基础,采用10项基于局部信息的相似性指标分别对无权和加权知识扩散网络进行动态链路预测分析,并将各指标的预测性能进行了对比。最后,利用无权RA指标和加权AA指标对学科引证知识扩散态势进行了预测。研究表明:不同指标的预测精度在不同的时间段内会动态变化;在学科引证知识扩散网络中,存在一定程度的弱连接效应;不同链路预测指标在无权和加权学科引证知识扩散网络中的适用性存在一定差异。     

基于两步剔除趋势法的中国粮食单产波动特征分析

粮食单产的增加是改革开放以来中国粮食总产上升的主要原因。进行粮食单产波动分解,并分析其多尺度波动特征及主导因素,从而揭示粮食单产变化的规律,对于今后中国制定相关政策,稳定粮食生产,保障粮食安全具有重要的意义。本文基于粮食生产的技术-经济复合系统分析,利用1978-2011年粮食生产统计数据,通过多年滑动平均和曲线拟合两级趋势剔除,进行了单产波动分解,并分析了粮食单产的多尺度波动特征及主导因素。结果表明：①应用两级趋势去除的方法可以将粮食单产分解为长期趋势、中期波动和短期波动3个项,其中短期波动主要受年际气候变化影响,中期波动曲线主要反映了研究期社会经济和粮食政策的变化及其对单产的影响,而长期趋势则主要反映了技术进步和农作制度对于粮食单产的作用;②长期来看,研究期粮食单产以上升趋势为主,总体波动不大,但个别时段波动较大;③中期波动显著大于短期波动,同时,短期波动率在逐渐收窄,而中期波动率没有明显的趋势变化,显示粮食单产波动的主导因素为社会经济条件和宏观政策。研究结果可为今后农业政策的调整提供决策依据。     

基于机器学习算法的研究热点趋势预测模型对比与分析——BP神经网络、支持向量机与LSTM模型

[目的/意义]细粒度分析学科领域热点主题发展脉络并对利用机器学习算法对未来发展趋势进行准确预测研究。[方法/过程]提出一种基于机器学习算法的研究热点趋势预测方法与分析框架，以基因工程领域为例利用主题概率模型识别WOS核心集中论文摘要数据研究热点主题并进行主题演化关联构建，然后选取BP神经网络、支持向量机及LSTM模型等3种典型机器学习算法进行预测分析，最后利用RE指标和精准度指标评价机器学习算法预测效果并对基因工程领域在医药卫生、农业食品等方面研究趋势进行分析。[结果/结论]实验表明基于LSTM模型对热点主题未来发展趋势预测准确度最高，支持向量机预测效果次之，BP神经网络预测效果较差且预测稳定性不足，同时结合专家咨询和文献调研表明本文方法可快速识别基因领域研究主题及发展趋势，可为我国学科领域大势研判和架构调整提供决策支持和参考。     

Excel在图书馆管理文献研究预测中的应用

简要介绍了利用Office软件中的Excel工作表进行文献预测的方法，并以1998—2005年“图书馆管理”及“知识管理”专题文献量的统计数据为例。对建立统计数据的回归方程和相关指数、预测曲线的制作及计算预测数据进行了详细的介绍。确定出了2006—2008年图书馆管理研宄文献量的预测趋势。     

Boundaries and edges rethinking: An end-to-end neural model for overlapping entity relation extraction

Overlapping entity relation extraction has received extensive research attention in recent years. However, existing methods suffer from the limitation of long-distance dependencies between entities, and fail to extract the relations when the overlapping situation is relatively complex. This issue limits the performance of the task. In this paper, we propose an end-to-end neural model for overlapping relation extraction by treating the task as a quintuple prediction problem. The proposed method first constructs the entity graphs by enumerating possible candidate spans, then models the relational graphs between entities via a graph attention model. Experimental results on five benchmark datasets show that the proposed model achieves the current best performance, outperforming previous methods and baseline systems by a large margin. Further analysis shows that our model can effectively capture the long-distance dependencies between entities in a long sentence.     

Distributed dynamic state estimation with parameter identification for large-scale systems

In this paper, we consider a distributed dynamic state estimation problem for time-varying systems. Based on the distributed maximum a posteriori (MAP) estimation algorithm proposed in our previous study, which studies the linear measurement models of each subsystem, and by weakening the constraint condition as that each time-varying subsystem is observable, this paper proves that the error covariances of state estimation and prediction obtained from the improved algorithm are respectively positive definite and have upper bounds, which verifies the feasibility of this algorithm. We also use new weighting functions and time-varying exponential smoothing method to ensure the robustness and improve the forecast accuracy of the distributed state estimation method. At last, an example is used to demonstrate the effectiveness of the proposed algorithm together with the parameter identification.     

Public funds and local biotechnology firm creation

A long stream of academic literature has established that public funding towards research and development matters for economic growth because it relates to increases in innovation, productivity and the like. The impact of public funding on the creation of new firms has received less attention in this literature despite theoretical constructs that support such association. In the present paper we study whether indeed there is a relationship between public research funds and local firm births in the context of the U.S. biotechnology industry. In doing so, we introduce a number of changes that strengthen the robustness of our findings when compared with existing literature. These changes include a direct measure of research expenditures and a considerably lengthier longitudinal dataset which allows us to capture a structural relationship and not a chance event. We empirically demonstrate that increases in the level of research funding from the National Institutes of Health towards biotechnology associate with increases in the number of biotechnology firm births at the Metropolitan Statistical Area level. Further, we reveal that public funds towards established firms associate with local firm births considerably more strongly when compared with funds towards universities and research institutes/hospitals. We conclude the paper with academic and policy implications of the present work that highlight the complexity of factors that underlie the creation of local firms in high technology industries.     

K线理论在地震趋势分析中应用的可行性研究

地震灾害给人类带来了巨大的生命和财产损失,为了尽可能地降低地震灾害损失,人类需要不断地寻找更科学的地震预测预报方法。虽然目前的地震预测预报方法的研究已经取得了很多可喜的进步,但科学进展与实现科学预报地震的目标之间还存在很大的距离。本文将对K线理论在地震趋势分析中应用的可行性进行研究,希望能够提供一种新的地震趋势分析思路。本文根据地震活动趋势分析与投资品价格走势分析的比较,提出了可以用K线理论进行地震活动趋势分析的观点,并定义了地震K线的画法,还通过对徐州周围的历史地震K线图的分析,验证了K线理论在地震趋势分析中应用的可行性。     

学科研究主题发展等级划分与趋势预测方法——以我国智慧图书馆研究为例

【目的/意义】主题发展等级划分是信息组织研究的基础性问题,也是科研人员和科研管理部门进行研究选题和学科服务的重要工作,对学科研究主题进行高效的发展等级划分与趋势预测,能够帮助相关科研人员和机构把握学科领域研究态势,准确做出科研决策。【方法/过程】本文结合主题模型、Sen’s斜率估计法、Mann-Kendall法、指数平滑法,提出一种学科研究主题发展等级划分与趋势预测方法。首先,在主题识别的基础上,形成主题发文度和主题引文度两个指标,并参考波士顿矩阵对学科研究主题发展等级进行划分;然后,融合研究主题发文量、被引量和下载量,形成主题热力度指标,采用指数平滑法对研究主题未来发展态势进行预测。【结果/结论】以我国“智慧图书馆”研究的实验表明,本文所提方法能够对学科领域研究主题进行全方位、细粒度地发展等级划分和趋势预测。【创新/局限】本文所提方法对其他学科研究主题的分析具有普适性,为实现动态情报分析提供了新的视角,局限在于需要提高主题建模的可解读性,并进一步优化趋势预测方法。     

Gray-fuzzy control of a nonlinear two-mass system

This study presents application of a fuzzy controller to a nonlinear two-mass system control. The proposed controller structure is strengthened with a gray estimator. Firstly, a complete state-space mathematical model for a nonlinear two-mass system is developed and numerically simulated. Then, a fuzzy controller is designed to regulate the speed of the system. In order to perform a dynamic and powerful control action, future error values are estimated by gray modeling technique. The gray estimators of the torsional torque and the load machine speed are tested with open-loop and closed-loop control structures to test the robustness of the proposed method for step changes in input parameters. It is observed that the tracking ability of the gray estimators is not influenced for different operation modes. The performances of the control structures, which are supported with gray estimators, are given and no additional feedbacks are required for robust control action. The simulation results are confirmed by experimental results and conclusions are given.     

基于多参数动态时序的技术预测方法与实证研究

技术预测是国家制定科技政策和选择优先发展领域的重要依据,对实施创新驱动战略,发挥科技创新支撑具有十分重要的作用。本文在分析文献与技术发展变化关系的基础上,提出了利用技术主题词频差值来反映一定时间区间内技术发展变化特征的多参数动态时序技术预测方法。以VOCs处理技术领域为例进行实证研究,结果表明,多参数动态时序技术预测方法能够有效识别技术主题的发展变化趋势。     

网络化制造的研究框架与未来主题

采用文献研究方法,总结并提出网络化制造的相关定义及网络化制造系统的基本框架,并对网络化制造的类型和研究现状进行了划分,着重从模式转化、技术创新、社区价值、大数据时代四个方面对网络化制造的未来发展趋势进行了分析和展望,并指出挖掘客户需求将是网络化制造模式升级的关键。     

基于神经网络的电离层F2层临界频率预测方法

在电离层风暴期,现存的电离层F2层临界频率预测方法不能满足实际应用的要求。根据磁层ap系数和太阳黑子月均值作为风暴期训练序列,本文提出了一种基于神经网络的电离层F2层临界频率预测新方法。模拟结果表明,这种新方法比现有的预测方法（STORM模型和Cander提出的神经网络方法）具有更好的预测性能。