Relationship Between Agricultural Credits and Agricultural Economy Based on Error Correct Model in Heilongjiang Province

Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the center of agriculture economy.However,the low comparative advantage in agriculture and pursuit of the capital interests which aggravate the conflicts of supply and demand of agricultural funds.Lacking of fund is the main factor that constrains the development of agricultural economy.In order to analyze the economic effect of agricultural credits on agricultural economy,an error correction model was set up to research the relationship between them,which based on the least square methods.Through the study of the contribution from agricultural credits to total value of agricultural out-put,the empirical evidence for developing the rural financial vigorously was provided,in order to promote the agricultura leconomic development.

Research on the Relationship between Income and Consumption of the Urban Residents in Hunan Province on the Basis of Error Correction Model

By using error correction model, I conduct co-integration analysis on the research of the relationship between the per capita practical consumption and per capita practical disposable income of urban residents in Hunan Province from 1978 to 2009. The results show that there is a co-integration relationship between the per capita practical consumption and the practical per capita disposable income of urban residents, and based on these, the corresponding error correction model is established. Finally, corresponding countermeasures and suggestions are put forward as follows: broaden the income channel of urban residents; create goods consuming environment; perfect socialist security system.

An Online Model Correction Method Based on an Inverse Problem:Part I—Model Error Estimation by Iteration

Errors inevitably exist in numerical weather prediction （NWP） due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model （model error, denoted as ME）. In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System （GRAPES-GFS） for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature （SST） were both based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.

An Online Model Correction Method Based on an Inverse Problem:PartⅡ——Systematic Model Error Correction

An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given the analyses, the ME in each interval （6 h） between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year （2001-2005） GRAPES- GFS （Global Forecast System of the Global and Regional Assimilation and Prediction System） error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.

Improved algorithm of atmospheric refraction error in Longley-Rice channel model

Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use of the vertical section information, it does not agree with the actual propagation path. The atmospheric refraction error correction method of the Longley-Rice channel model has been improved. The improved method makes use of the vertical section information sufficiently and maps the distance between the receiver and transmitter to the radio wave propagation distance, It can exactly reflect the infection of propagation distance for the radio wave propagation loss. It is predicted to be more close to the experimental results by simulation in comparison with the measured data. The effectiveness of improved methods is proved by simulation.

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.

Local Influence on the Error-Correction Variable in a Cointegrated System

The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correction variable, which represents the short-run discrepancy from the equilibrium state in a cointegrated system, plays an important role in the ECM. It is natural to ask how the error-correction mechanism works, or equivalently, how the short-run discrepancy affects the development of the cointegrated system? This paper examines the effect or local influence on the error-correction variable in an error-correction model. Following the argument of the second-order approach to local influence suggested by reference [5], we develop a diagnostic statistic to examine the local influence on the estimation of the parameter associated with the error-correction variable in an ECM. An empirical example is presented to illustrate the application of the proposed diagnostic. We find that the short-run discre pancy may have strong influence on the estimation of the parameter associated with the error-correction model. It is the error-correction variable that the short-run discrepancies can be incorporated through the error-correction mechanism.

A Robust Conformer-Based Speech Recognition Model for Mandarin Air Traffic Control

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.

基于改进JRD及误差修正的轴承剩余寿命预测方法

目前,风电机组齿轮箱性能发生初始退化时难以识别,现有退化指标易出现剧烈波动、单调性较差,且无法准确预测齿轮箱关键部件如轴承的剩余使用寿命(RUL),针对该问题,提出了一种基于改进杰森-瑞丽散度(JRD)及误差修正的双指数模型轴承RUL预测方法。首先,提取了振动信号样本的多域特征指标,利用高斯混合模型(GMM)与指数型权重JRD,得到了样本的后验概率分布向量,再经归一化处理得到置信值(CV);然后,对轴承从初始健康状态退化至当前检查时刻的CV值进行了相空间重构,提取了CV序列的动力学特征,并将其作为相关向量机(RVM)的训练集,获得了支撑整个退化轨迹的相关向量;最后,利用双指数模型拟合了相关向量,外推趋势至失效门限以计算RUL,并引入了差分整合移动平均自回归模型(ARIMA),对拟合相关向量产生的拟合误差进行了预测,以修正预测的结果。实验结果表明:改进后的退化指标单调性指标提高14.3%;且在不同工况、不同时刻下,经误差修正后的轴承的RUL预测结果较未修正之前有明显提高。研究结果表明:该预测方法可为风电机组齿轮箱重要部件的预测性维护提供参考。

一种光波大气折射率剖面模型构建方法

根据经典光波折射率计算公式和ITU-R建议书中水汽密度经验公式,给出了一种新的光波大气折射率计算公式;借鉴Hopfield折射率静力项剖面模型和ITU-R建议书中水汽密度剖面模型,给出了一种基于历史气象探空数据构建光波大气折射率剖面模型的方法。以青岛地区为例,通过对1986—1995年历史气象探空数据的处理并结合参考标准大气,建立了适合当地的光波大气折射率剖面模型;统计剖面模型预测折射率剖面与实测折射率剖面的均方根误差,结果表明:构建的剖面模型具有较好的预测精度,这对光学外测设备的折光修正数据处理具有很好的参考价值。

基于MPC的光电热联合系统建模与控制优化

为解决我国北方地区冬季采暖产生的能源消耗与环境污染问题,改善光资源丰富、较丰富地区光能利用率不足的现状,充分利用谷电和日照优势,针对太阳能利用与建筑采暖相结合的分布式能源系统性问题,提出一种基于TRNSYS动态建模与数值建模相结合的光电热联合供暖系统。综合考虑小范围内供暖温度的时滞性以及系统各设备的出力情况,联合Matlab搭建模型预测控制器(MPC),提出一种基于MPC的误差实时校正优化控制策略。分析表明:采用MPC的控制优化,在热负荷跟踪方面,最大误差降低4.16%,平均误差降低2.79%;在室内温度控制方面,最大偏差降低1.2℃,平均偏差降低0.2℃;在太阳能利用占比方面,太阳辐射强度趋近于800 W/m^(2)时,太阳能利用占比差距达最大8.9%。分析结果说明该系统可以更快速、更准确地跟踪建筑热负荷波动,并且有效抑制室内温度波动,提高清洁能源的利用率。

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。

我国黄金市场期货价格与现货价格的实证研究

选取近10年的黄金期货价格数据和现货价格数据进行分析。对两组时间序列数据进行单位根检验、协整性检验以此分析期货和现货之间的关系,并建立误差修正模型分析期货价格对现货价格的影响程度,通过格兰杰因果检验分析两者之间的效应。结果表明,近10年黄金的期货和现货价格之间确实存在协整关系,两者之间相互影响,影响效果显著性不强。期货价格对现货价格的指导作用效果不强,表明我国期货市场的价格发现作用没有完全发挥。

基于CMA-TRAMS模式地形高度偏差的地面气温误差订正方法研究

采用一元线性方法建立南海台风模式CMA-TRAMS地形高度偏差和地面气温预报误差的回归关系,分别开展不分级、高度偏差分级和地面气温误差分级的三种订正方法的研究,并进行订正效果评估。结果表明,模式地面气温预报误差与地形高度偏差总体呈负的线性相关关系,地面气温预报绝对误差随地形高度偏差绝对值增大而增大(对模式地形高度偏低站点尤为明显),但不同时刻地面气温预报误差特征表现不同,模式对地形高度偏高(即模式地形高于测站高度)和地形高度偏差小于50m的站点,06时地面气温(世界时,下同)预报总体偏低,对地形高度偏低大于50m的站点(即模式地形低于测站高度),06时地面气温预报总体偏高;而无论站点地形高度偏差如何,模式对18时地面气温预报总体偏高。三种订正方法中地面气温误差分级法能有效地减小地面气温预报误差,该方法订正后的分析场准确率可达96%~99%,12~48小时时效预报场准确率总体可提升至90%以上,该方法具有回归关系稳定、效果显著、适用性广、简单易行等特点。

卫星导航中电离层误差校正技术现状与发展

电离层误差严重影响着GNSS的定位精度,GPS、BDS、Galileo、GLONASS有不同的电离层误差校正方法.全文概述了电离层误差校正方法,综述了单频电离层误差校正、双频电离层误差校正及多频电离层误差校正等技术的原理与发展现状.在单频电离层误差校正技术中总结了增强系统中的电离层误差校正技术、北斗全球电离层延迟修正模型(BeiDou global ionospheric delay correction model,BDGIM)、Klobuchar模型、单频电离层误差校正技术的优化—附加国际参考电离层(international reference ionosphere,IRI)约束模型和NeQuick-G模型;在双频电离层误差校正技术中重点总结了双频消电离层误差、无电离层组合模型及PPP-RTK技术中电离层误差校正方法;在多频电离层误差校正技术中介绍了高阶项改正和地磁场建模对电离层误差校正技术的优化与改进.最后,对电离层误差校正技术及其改进方法进行了分析,总结了其发展趋势与方向.

基于红外传感器的多无人机对高速飞行器协同定位跟踪方法综述

为了实现高精度远距离稳定定位与跟踪,多无人机搭载红外传感器对高速飞行器的定位跟踪方法成为研究热点。就此背景进行了综合评述,以相关技术难点及关键技术为切入点,分析了瞬时定位和动态跟踪问题的研究现状和发展方向。在瞬时协同定位方面,首先对目标瞬时定位的算法进行了分析对比,然后对造成定位误差的影响因素进行分析并探讨了误差补偿方法,最后针对传感器资源优化,讨论了能够使计算结果最优的传感器分布阵型。在动态跟踪方面,首先探讨了时间不同步问题对目标跟踪的影响及补偿方法,然后对目标运动学建模的问题进行了分析,最后对最优估计问题中各种滤波算法的应用进行对比。综述表明,无人机搭载红外传感器对高速飞行器进行定位和跟踪,存在误差来源多、相对运动速度快的技术难点,需要特别关注定位算法、误差来源及最优估计算法的影响,以尽可能实现稳定跟踪。

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.

关于动态测量误差修正方法的对比分析

在产品测试过程中,由于测试设备自身误差和测试人员专业素质等因素,导致测量值与实际值相比,会存在一定的偏差。当前,解决这一问题的普遍做法是将测量误差通过一定的算法手段进行去除。针对当前误差修正方法普遍不适用于动态测量误差修正的缺点,论文研究了基于贝叶斯、灰色模型及卡尔曼滤波的动态误差修正方法,并对这几种方法进行了应用对比,为选择合理的误差修正模型提供思路,解决测量数据的误差问题。