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.

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.

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: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.

Water level updating model for flow calculation of river networks

Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.

Influence of Sino-US Agricultural Trade on China’s Total Agricultural Output Value Based on Cointegration Model

According to the data of the total trade of agricultural products between China and the United States from 2009 to 2018 and the general description of agriculture in China,this paper adopts the method of econometric model to make a detailed analysis of the agricultural trade between China and the United States by using cointegration analysis,Granger causality test and error correction model in order to explore the impact of agricultural trade between China and the United States on China’s agricultural development. The results of empirical analysis show that there is a balanced relationship between the trade of agricultural products between China and the United States and the development of agriculture in China. The total trade of agricultural products between China and the United States affects the development of China’s agriculture.In addition,in the short term,if the short-term fluctuation deviates from the long-term equilibrium,then the error correction term will reverse it with strength of 0. 378,so that the non-equilibrium state will gradually return to the equilibrium state.

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。

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。

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。

平原圩区复合下垫面水文水动力耦合模型

平原圩区地势低洼,下垫面类型多样,产汇流过程较为复杂,水文过程模拟和预报十分困难。为解决平原圩区洪水预报难题,建立考虑水田、旱地、林地、城市区与水域等多种下垫面类型的产汇流模型,利用MIKE 11 HD水动力模型模拟河道汇流过程,提出面向平原圩区复合下垫面条件的水文水动力耦合模型;并采用BP神经网络进行河道水位预报误差校正,以提高模型精度。选择广州市南沙蕉门河排涝片为研究区,检验耦合模型的水位预报精度,并以2023年“9·7深圳特大暴雨”为移置场景输入,模拟不同排涝措施对河道水位的影响。结果表明:模型能够较好地模拟研究区场次洪水的河道水位过程,率定期和验证期的平均Nash效率系数分别为0.86和0.91,10场洪水中有8场的最高水位模拟误差小于0.05 m;采用BP神经网络校正后所有场次洪水的Nash效率系数均大于0.9,满足洪水预报的精度要求。研究区面临“9·7深圳特大暴雨”场景时存在内涝风险,需提升圩内蓄洪排涝能力。

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。

The Digital Fingerprinting Method for Static Images Based on Weighted Hamming Metric and on Weighted Container Model

The algorithm of fingerprint constructing for still images based on weighted image structure model is proposed. The error correcting codes that are perfect in weighted Hamming metric are used as a base for fingerprint constructing.

约束条件下测量误差模型的统计推断

考虑具有测量误差的线性模型在参数分量具有精确的线性约束条件下的统计推理,提出了偏差校正的拉格朗日乘子检验统计量,得到了受约束的纠偏最小二乘估计量,并在一定的正则性条件下,证明了所得估计量渐近服从正态分布.最后通过数值模拟研究了所提方法的有限样本性质.

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。

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。