基于混沌自适应烟花算法的雷达信号盲源分离

针对传统独立成分分析(independent component analysis, ICA)方法存在收敛速度慢、分离性能不高的问题,将混沌映射策略与自适应爆炸半径相结合,提出一种基于混沌自适应烟花算法(chaotic adaptive fireworks algorithm, CAFWA)的盲源分离(blind source separation, BSS)方法,并应用于雷达辐射源混合信号分选问题。混沌映射策略可以将初始值在解空间内分布更加均匀,爆炸半径能够根据适应度的优劣自适应改变,保证了所提算法局部搜索的精度,满足了全局搜索的多样性。实验结果表明所提算法可以在无噪和有噪情况下均能很好地分选观测信号,而且具有比传统算法更快的收敛速度和更优异的分选性能。     

Parameter estimation for dual-rate sampled Hammerstein systems with dead-zone nonlinearity

The identification of nonlinear systems with multiple sampled rates is a difficult task.The motivation of our paper is to study the parameter estimation problem of Hammerstein systems with dead-zone characteristics by using the dual-rate sampled data.Firstly,the auxiliary model identification principle is used to estimate the unmeasurable variables,and the recursive estimation algorithm is proposed to identify the parameters of the static nonlinear model with the dead-zone function and the parameters of the dynamic linear system model.Then,the convergence of the proposed identification algorithm is analyzed by using the martingale convergence theorem.It is proved theoretically that the estimated parameters can converge to the real values under the condition of continuous excitation.Finally,the validity of the proposed algorithm is proved by the identification of the dual-rate sampled nonlinear systems.     

水泥窑协同处置生活垃圾的燃烧特性分析优化

针对传统方法难以分析掺烧生活垃圾后的水泥窑复杂燃烧特性的问题,引入数据挖掘技术,以国内某水泥厂为对象,采集相关参数数据,使用稳定性选择算法分析各参数对煤耗与NO_x排量的影响系数,通过随机森林算法建立煤耗与NO_x排量的数学模型,结合K-means聚类算法得出关键优化参数及其最优值。结果表明,该方法能够建立精确的煤耗与NO_x排量模型,挖掘出节能减排的关键优化参数及其最优目标值。通过改善关键优化参数至最优值,能够大大降低煤耗与NO_x排量,可指导水泥厂优化窑内燃烧特性。     

基于仿真的无人机空袭沙特油田事件探索性分析

以2019 年9 月也门空袭沙特油田事件为例，提出一种基于仿真的危机事件快速分析方法，将仿真与探索性分析相结合，对危机事件进行全面的分析与处理。根据沙特、也门关于事件细节的公开资料，基于仿真推演复盘此次空袭的作战情况，校验各方在事件声明中细节的合理性和真实性，同时可为我国防御“低、小、慢”无人机的发展和作战研究提供借鉴意义。     

城市疫情传播和复工强度建模及相关性分析

以多源大数据为基础构建模型,分析全国34个典型城市因疫情导致的人口迁徙变化和人力缺口,并通过迁徙基数推算其他各城市的复工强度。仿真结果证明SEIR仓室模型能够较好地模拟此次疫情发展趋势,利用其估计各城市内部新型冠状病毒感染肺炎的基本再生数,结合人力缺口对复工强度进行回顾性的矩阵分析,以总结我国此次抗疫经验。相关性分析阶段对K-means无监督聚类后的城市集群进行回归分析,结果表明对于大部分城市而言,复工强度的大小与其人力缺口、基本再生数以及人均GDP水平有明显的相关关系。     

群体演出活动服装虚拟仿真系统设计与实现

服装在群体演出活动中发挥重要作用,直接影响整体演出效果呈现。为实现群体演出活动服装的虚拟设计和虚拟演练目标,设计实现群体演出活动服装虚拟仿真系统。利用CLO3D建立人体模型,构建服装个体模型,将人体和服装模型导入Unity后,调整服装材质并且添加纹理图,在Unity中复制成群体,开发演出场景虚拟仿真的交互界面,实现群体演出服装整体效果的设计与可视化虚拟仿真。仿真结果表明,虚拟仿真技术能够模拟出群体演出活动的大体效果,节约排练成本,缩短排练的周期。     

多维光纤通信系统性能监测技术

目前光纤通信系统正朝着超高速、大容量、动态化方向快速发展，新的维度和复用方式以及更加复杂的体系架构对光纤传送网安全可靠运行提出了巨大的挑战.多维光纤通信系统中的性能监测技术能对系统所受损伤进行监测与预警，为自适应补偿、传输质量评估和网络资源优化等提供信息来源和管理依据，对提升光纤传送网运行和管理水平具有重要的科学意义和社会意义.该文首先重点介绍色散参量监测、非线性参量监测、调制格式参量监测等多个参量监测的研究现状，随后讨论并分析了多维光纤通信系统性能监测技术的发展趋势.未来性能监测技术将朝着精细化、一体化、智能化、多参量同步监测等方向发展.     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts

By linking measures of forecast accuracy as well as testing procedures with regard to forecast rationality this paper investigates aggregated survey forecasts with forecast horizons of 3, 12, and 24 months for the exchange rates of the Chinese yuan, the Hong Kong dollar, the Japanese yen, and the Singapore dollar vis-à-vis the US dollar and, hence, for four different currency regimes. The rationality of the exchange rate predictions is initially assessed utilizing tests for unbiasedness and efficiency which indicate that the investigated forecasts are irrational in the sense that the predictions are biased. As one major contribution of this paper, it is subsequently shown that these results are not consistent with an alternative, less restrictive, measure of rationality. Investigating the order of integration of the time series as well as cointegrating relationships, this empirical evidence supports the conclusion that the majority of forecasts are in fact rational. Regarding forerunning properties of the predictions, the results are rather mediocre, with shorter term forecasts for the tightly managed USD/CNY FX regime being one exception. As one additional important and novel evaluation result, it can be concluded, that the currency regime matters for the quality of exchange rate forecasts.     

Can online search data improve the forecast accuracy of pork price in China?

Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long-term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid- and long-term forecast horizons.