Dynamic error research and application of an angular measuring system belonging to a high precision excursion test turntable

Angular measuring system is the most important component of a servo turntable in inertial test apparatus. Its function and precision determine the turntable’s function and precision. It attaches importance to research on inertial test equipment. This paper introduces the principle of the angular measuring system using amplitude discrimination mode. The dynamic errors are analyzed from the aspects of inductosyn, amplitude and function error of double-phase voltage and waveform distortion. Through detailed calculation, theory is provided for practical application; system errors are allocated and the angular measuring system meets the accuracy requirement. As a result, the schedule of the angular measuring system can be used in practice.

Analogue correction method of errors and its application to numerical weather prediction

In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.

上市公司影子银行化对金融稳定的影响与机制研究

影子银行因其规模的日益扩大及其带来的巨大影响而受到政府与学术界的广泛关注。本文基于上市公司财务数据和金融稳定相关数据,运用CRITIC指数构建法和误差修正模型,对上市公司影子银行化对金融稳定的影响与机制进行了理论分析与实证检验。研究发现:上市公司影子银行化提高了经济金融化程度,进而增加金融脆弱性而影响金融稳定。金融稳定更多受到东部、中部地区上市公司影子银行化的影响。相较于规模较小与盈利能力较弱上市公司,规模较大上市公司与盈利能力较强上市公司影子银行化对金融稳定造成的影响更大。研究结果有助于深化影子银行发展对金融稳定影响的理解,对强化上市公司金融化行为监管,维护金融稳定,实现金融高质量发展具有重要的政策意义。

基于向量误差修正模型的电池簇不一致检测方法及智能运维方案

针对储能电站实际运行数据中存在电池数据不完整、数据片段化导致检测不准确的问题,提出基于向量误差修正模型的电池簇不一致检测方法。该方法根据随机电压片段数据构建电池簇与电池单体的向量误差修正模型,计算脉冲响应函数,分析电池单体对电池簇的动态作用机制,判断电池簇不一致程度,再通过方差分解分析确定异常电池单体及后续运维。最后,根据储能电站实际运行数据进行分析,验证了电池簇不一致检测方法及运维方案的可行性和有效性,并在100kW/200kW·h储能平台进行实际工程测试。

机载下投式探空温度传感器设计与实验研究

为降低太阳辐射对机载下投式探空仪中温度传感器的影响,设计了一种阵列式NTC珠状热敏电阻探空温度传感器。该传感器阵列通过探头间的太阳辐射误差比值推算大气环境温度的真实值。首先,通过计算流体动力学(computational fluid dynamics, CFD)方法选出传感器探头的最优引线夹角,再对表面涂覆不同太阳辐射反射率涂层的探头进行太阳辐射误差比值计算;然后利用支持向量机(support vector machine, SVM)拟合仿真,得到辐射误差比值拟合模型;最后,搭建低气压风洞以及太阳模拟器实验平台。实验结果表明,该温度传感器阵列可将测量误差降低至0.065℃,均方根误差降至0.078℃,有望将太阳辐射误差对下投式探空仪温度测量的影响降低至0.1℃以内。

金融深化、技术创新与经济增长的关系研究——以宁夏为例

金融是现代经济的血脉,创新是引领发展的第一动力。通过金融深化与技术创新驱动经济增长,是加快经济发展的科学有效途径。本文运用协整检验和向量误差修正模型,并引入脉冲响应函数,以宁夏为例研究金融深化、技术创新和经济增长之间的长期稳定关系和短期调整关系,以期为各地建设现代化经济体系提供一定的参考。实证结果表明,金融深化对技术创新有正向促进作用,且对技术创新的影响呈上升趋势;金融深化和技术创新是影响宁夏经济增长的重要因素,金融深化的两个指标变量与经济增长存在长期均衡关系,但作用方向不同,金融相关率与经济增长正相关,而货币化率与经济增长负相关。

基于SVM-STL-LSTM的区域短期电力负荷预测研究

针对区域电力负荷的时间序列数据随机性强、预测精度低及单一模型的数据特征提取能力差等问题,提出了一种支持向量机(SVM)、STL时序分解法、长短期记忆神经网络(LSTM)组合的电力负荷预测模型。该模型利用SVM对时间序列的电力负荷数据进行初始预测,并通过STL时序分解法对残差序列进行时序分解,从而提高残差序列的稳定性,减小其随机性,最后用LSTM对SVM的预测误差进行修正。试验结果证明,该方法利用误差修正可有效处理随机性强的数据,有利于预测结果的稳定性,提高预测精度。

China’s Monetary Policy Impacts on Money and Stock Markets

This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.

UV Index Modeling by Autoregressive Distributed Lag (ADL Model)

The objective of this work is to model statistically the ultraviolet radiation index (UV Index) to make forecast (extrapolate) and analyze trends. The task is relevant, due to increased UV flux and high rate of cases non-melanoma skin cancer in northeast of Brazil. The methodology utilized an Autoregressive Distributed Lag model (ADL) or Dynamic Linear Regression model. The monthly data of UV index were measured in east coast of the Brazilian Northeast (City of Natal-Rio Grande do Norte). The Total Ozone is single explanatory variable to model and was obtained from the TOMS and OMI/AURA instruments. The Predictive Mean Matching (PMM) method was used to complete the missing data of UV Index. The results mean squared error (MSE) between the observed UV index and interpolated data by model was of 0.36 and for extrapolation was of 0.30 with correlations of 0.90 and 0.91 respectively. The forecast/extrapolation performed by model for a climatological period (2012-2042) indicated a trend of increased UV (Seasonal Man-Kendall test scored τ = 0.955 and p-value 0.001) if the Total Ozone remain on this tendency to reduce. In those circumstances, the model indicated an increase of almost one unit of UV index to year 2042.

IMPACT OF SUMMER WARMING ON DYNAMICS-STATISTICS-COMBINED METHOD TO PREDICT THE SUMMER TEMPERATURE IN CHINA

Based on NCEP/NCAR daily reanalysis data, climate trend rate and other methods are used to quantitatively analyze the change trend of China's summer observed temperature in 1983—2012. Moreover, a dynamics-statistics-combined seasonal forecast method with optimal multi-factor portfolio is applied to analyze the impact of this trend on summer temperature forecast. The results show that: in the three decades, the summer temperature shows a clear upward trend under the condition of global warming, especially over South China, East China, Northeast China and Xinjiang Region, and the trend rate of national average summer temperature was 0.27℃ per decade. However, it is found that the current business model forecast(Coupled Global Climate Model) of National Climate Centre is unable to forecast summer warming trends in China, so that the post-processing forecast effect of dynamics-statistics-combined method is relatively poor. In this study, observed temperatures are processed first by removing linear fitting trend, and then adding it after forecast to offset the deficiency of model forecast indirectly. After test, ACC average value in the latest decade was 0.44 through dynamics-statistics-combined independent sample return forecast. The temporal correlation(TCC) between forecast and observed temperature was significantly improved compared with direct forecast results in most regions, and effectively improved the skill of the dynamics-statistics-combined forecast method in seasonal temperature forecast.

Dynamic Relation Mechanism between Cotton Future Price and Stock Price of Related Listed Companies

The Dynamic relation mechanism between ZCE cotton futures price and related listed company stock price has been studied based on the metastock historical data in January 1st,2007 to September 1st,2010,Johansen co-integration analysis,Vector error correction model,Granger causality test and variance decomposition method.The results indicated that:long-term equilibrium relationship existed between ZCE cotton futures price and Xinsai share stock price while which changed in the same tendency and speed in the long-term.Cotton futures price is the main reason for the changing of Xinsai share stock price.The lead-lag relationship in changing course had been confirmed that existed between ZCE cotton futures price and the Xinsai share stock price.Meanwhile,the forward pass mechanism of price changing information had been found only from the ZCE cotton futures market to the stock market while showing asymmetry.Conclusions of the study can be used for cotton and related corporate to hedge business risks by the cotton price changes.

“稻谷—大米”价值链价格联系、价格形成及主要影响因素研究

构建“稻谷—大米”价值链,对“稻谷—大米”价值链上价格形成过程进行描述性统计分析,采用Johansen检验、格兰杰因果检验方法及VEC模型方法,对价值链上主要环节价格联系及其主要影响因素进行实证研究。研究表明,稻谷收购市场和大米零售市场存在长期均衡状态,稻谷收购市场和稻谷国内期货市场存在长期均衡状态,但短期价格传导效率较低;“稻谷—大米”价值链上产生的价格增值主要集中在大米零售、大米加工和农户稻谷出售环节;所谓“稻强米弱”造成大米加工企业普遍经营不善的说法不能得到必要证据支持;过于强调国有粮库“顺价销售”出库大米不利于实现以粮食自产为基础的粮食安全目标。最后提出继续实施稻谷最低收购价格政策和以实现粮食自产为基础的粮食安全目标等政策建议。

城镇化背景下能源结构与碳强度的动态关联研究

选用1990—2021年的时间序列数据来估计中国的碳排放强度,并综合使用协整检验、向量误差修正模型和脉冲响应分析等方法,从短期和长期视角研究我国城镇化、能源结构与碳排放强度之间的作用机理。研究表明城镇化对碳排放强度存在较为明显的抑制作用,而能源结构与碳排放强度为正相关关系,且三者之间具有长期协整关系;在中国碳排放强度的短期变动逐渐偏离了平衡后,将会以7~8年的速度从非均衡态调整到均衡态。因此,稳步高效地推进城镇化进程,改善能源结构,可以有效实现碳减排的目标。

基于PECM模型的长江流域经济社会发展与水资源利用动态关系分析

为评估长江流域经济社会综合发展水平和水资源综合利用水平以及两者间的内在关系,采用熵值法、面板协整理论和误差修正模型对2000—2017年间长江流域经济社会发展和水资源利用的面板Granger因果关系进行检验。结果表明:长江流域上中下游3个区域的经济社会发展和水资源利用之间存在长期协整关系;在长期关系中,上游地区经济社会发展与水资源利用之间为双向因果关系,中游地区为经济社会发展到水资源利用的单向因果关系,下游地区为水资源利用到经济社会发展的单向因果关系,上游地区经济社会发展和水资源利用具有更高的关联度;在短期关系中,仅下游地区存在经济社会发展到水资源利用的单向因果关系。基于二者之间存在不同的因果关系,各省市在制定经济发展规划时应因地制宜地制定水资源政策,以期实现经济社会与水资源的协调发展。

基于GRA-ISSA-SVR-EC模型的风电功率组合预测方法

针对风电功率的非线性特征,本文提出一种结合灰色关联度分析(GRA)、改进麻雀算法(ISSA)、支持向量回归(SVR)和误差修正模型(EC)的风电功率组合预测方法。应用GRA筛选出与风电功率关联度较大的影响因素作为模型的输入;引入自适应权重因子和Levy飞行策略提升传统SSA的性能,建立ISSA-SVR模型得到初始预测值;建立误差修正模型进行预测得到误差预测值,最后将初始预测值与误差预测值用加法器合并得到最终结果。仿真结果表明,该模型在对2个风电场的风电功率进行预测时,平均确定系数分别达到0.9996和0.9985,平均绝对误差分别为0.2266 kW和0.0146 MW,均方根误差分别为0.2777 kW和0.0213 MW,相比于其他传统模型预测精度更高。

DYNAMIC CORRECTION OF ROUGHNESS IN THE HYDRODYNAMIC MODEL

Based on the hydrodynamic model and the Xinanjiang model, the fiver stage forecasting model has been proposed. But its performance is not satisfactory as applied to estuary areas. River roughness is a sensitive parameter in the hydrodynamic model, and its value is related to some substantial uncertainties in the tidal fiver. According to roughness tests, a new method of roughness dynamic correction was developed to improve the performance of the stage model. The method was focused on the usage of observed data for the studied section, and its parameters were analyzed. Nested with the dynamic correction of roughness, the stage model was applied to the tidal reach of the Caoe River. The results demonstrate that the roughness dynamic correction can improve the simulation accuracy of the stage model, and especially has the capacity of reducing the errors at peak stages.

压缩感知动态测试信号模型与电能表动态误差测试

文中针对智能电能表动态误差的高效测试问题,分析了长时间范围内复杂动态负荷信号幅度域的快速随机波动特性,建立了压缩感知伪随机动态负荷测试信号的模型,采用Hadamard矩阵和降采样矩阵结合的测量矩阵构建方法,确定了该测试信号的模型参数,提出了压缩感知测量信号的产生方案。采用试验方法,对比给出了压缩感知和m序列动态测试信号条件下电能表动态测试的误差数据。研究结果表明相比较于m序列伪随机测试信号,压缩感知动态测试信号的动态误差测试效率提高了4倍。

基于变分模态分解的中期电力负荷混合预测模型

电力负荷预测直接影响电网规划和运行,但是受到各类因素的影响。为提高预测精度,针对电力负数据时序性和非线性特征,提出一种基于变分模态分解的中期电力负荷混合预测模型(hybrid prediction model of medium-term power load based on variational mode decomposition,HPMMPL-VMD)。在HPMMPL-VMD算法中,首先使用VMD将原始电力负荷序列分解成若干个相对平稳的模态分量,并利用长短时记忆神经网络对各个模态分量进行建模;然后将各个预测分量进行叠加得到电力负荷预测值;最后,使用最小二乘支持向量回归对误差序列进行预测,并将电力负荷预测值与误差预测值相加得到最后预测结果。为验证HPMMPL-VMD的性能,选取其他预测方法与其进行比较,实验结果表明本文所提模型具有较高的预测精度。

江西省农业产业结构与碳排放关系实证研究

基于2005—2020年的江西省11个地级市的面板数据,通过Johansen协整检验、向量误差修正模型(VECM)、格兰杰因果检验,研究了农业产业结构与农业碳排放之间的关系。结果表明:农业产业结构、农业人口、城镇化率、农村电气化水平与农业碳排放之间存在长期的均衡关系,农业人口呈现负的推动作用,农业产业结构、城镇化率、农村电气化水平呈现正的推动作用,农业产业结构与城镇化率在推动农业碳排放增长中发挥了主要作用;在向量误差修正模型中,农业产业结构、城镇化率、农村电气化水平在短期内对农业碳排放具有正向影响,农业人口在短期内对农业碳排放具有负向影响;在格兰杰因果检验中,农业产业结构与农业碳排放存在互为因果的格兰杰关系,农业产业结构会促进农业碳排放的增加,农业碳排放会在一定程度上影响农业产业结构。因此,需要从优化农业产业结构的角度出发,确立农业碳减排目标,减少农业碳排放,推动农业朝着绿色高效方向发展。

Development of the Analogue-Dynamical Method for Error Correction of Numerical Forecasts

Due to the increasing requirement for high-level weather and climate forecasting accuracy, it is necessary to exploit a strategy for model error correction while developing numerical modeling and data assimilation techniques. This study classifies the correction strategies according to the types of forecast errors, and reviews recent studies on these correction strategies. Among others, the analogue-dynamical method has been developed in China, which combines statistical methods with the dynamical model, corrects model errors based on analogue information, and effectively utilizes historical data in dynamical forecasts. In this study, the fundamental principles and technical solutions of the analogue-dynamical method and associated development history for forecasts on different timescales are introduced. It is shown that this method can effectively improve medium- and extended-range forecasts, monthly-average circulation forecast, and short-term climate prediction. As an innovative technique independently developed in China, the analogue- dynamical method plays an important role in both weather forecast and climate prediction, and has potential applications in wider fields.