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.

Real-time model correction using Kalman filter for Raman-controlled cell culture processes

Raman spectroscopy has found extensive use in monitoring and controlling cell culture processes.In this context,the prediction accuracy of Raman-based models is of paramount importance.However,models established with data from manually fed-batch cultures often exhibit poor performance in Raman-controlled cultures.Thus,there is a need for effective methods to rectify these models.The objective of this paper is to investigate the efficacy of Kalman filter(KF)algorithm in correcting Raman-based models during cell culture.Initially,partial least squares(PLS)models for different components were constructed using data from manually fed-batch cultures,and the predictive performance of these models was compared.Subsequently,various correction methods including the PLS-KF-KF method proposed in this study were employed to refine the PLS models.Finally,a case study involving the auto-control of glucose concentration demonstrated the application of optimal model correction method.The results indicated that the original PLS models exhibited differential performance between manually fed-batch cultures and Raman-controlled cultures.For glucose,the root mean square error of prediction(RMSEP)of manually fed-batch culture and Raman-controlled culture was 0.23 and 0.40 g·L^(-1).With the implementation of model correction methods,there was a significant improvement in model performance within Raman-controlled cultures.The RMSEP for glucose from updating-PLS,KF-PLS,and PLS-KF-KF was 0.38,0.36 and 0.17 g·L^(-1),respectively.Notably,the proposed PLS-KF-KF model correction method was found to be more effective and stable,playing a vital role in the automated nutrient feeding of cell cultures.

A Brief Analysis of the Self-correction Model——from the View of Linking Theory

Based on the theory of the post method pedagogy and the teaching practice，this paper proposes a new teaching model，the self-correction model，and explains it from the point of view of linking theory.

Analysis of the Self-correction Model——from the view of cognitive psychology

In view of the learners Chinglish,this paper puts forward a new teaching model-the Self-correction Model and makes an analysis of it from the view of cognitive psychology.

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.

Parameters Sensitivity Analysis and Correction for Concrete Damage Plastic Model

In order to understand the effect of hardening ductility parameters and softening ductility parameters of the concrete damage plastic model in LS-DYNA,a sensitivity and reliability analysis of these parameters through a convenient cube unit test was conducted. The results showed that the peak strength strain was independent of the hardening ductility parameter DH,but affected by AH,BH,and CH. The softening ductility was mainly related to the softening ductility parameter AS,but not affected by the damage ductility exponent BS. In case that the model with default parameters failed to match the AS-controlled damage softening phase,an optimized model with an AS correction was developed. The corrected model with the AS value of 2 matched well with the code model,and exhibited good feasibility in predicting the stress-strain curve of different grades of concrete. Moreover,the practicability of the corrected model was further validated by the conventional triaxial test. The simulated curve exhibited favorable consistence with the trial curve. Therefore,the model with parameter correction could provide a prospective reference for predicting the mechanical properties of concrete.

A MAXIMUM ENTROPY CHUNKING MODEL WITH N-FOLD TEMPLATE CORRECTION

This letter presents a new chunking method based on Maximum Entropy (ME) model with N-fold template correction model.First two types of machine learning models are described.Based on the analysis of the two models,then the chunking model which combines the profits of conditional probability model and rule based model is proposed.The selection of features and rule templates in the chunking model is discussed.Experimental results for the CoNLL-2000 corpus show that this approach achieves impressive accuracy in terms of the F-score:92.93%.Compared with the ME model and ME Markov model,the new chunking model achieves better performance.

Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System:Sequential Implementation

This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The results show considerable improvement in terms of the mean biases of rawinsonde observation-minus-background （OmB） residuals for observed water vapor, wind and temperature variables. The time series spectral analysis shows whitening of bias-corrected OmB residuals, and mean biases for rawinsonde observation-minus-analysis （OmA） are also improved. Some wind and temperature biases in the control experiment near the equatorial tropopause nearly vanish from the bias-corrected experiment. Despite the analysis improvement, the bias correction scheme has only a moderate impact on forecast skill. Significant interaction is also found among quality-control, satellite observation bias correction, and background bias correction, and the latter positively impacts satellite bias correction.

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.

Application of MODTRAN Model in Atmospheric Correction of HJ/CCD Data

MODTRAN model was used for the atmospheric correction of one HJ-1B / CCD2 image,and the effect of atmospheric correction was evaluated from the changes of spectral characteristics of typical ground objects,the comparison with the MODIS surface reflectance product,and the effect on normalized differential vegetation index( NDVI). The results show that atmospheric correction eliminated the increase effect in visible bands and the absorption in near-infrared band. Atmospheric correction results and the MODIS surface reflectance product with high accuracy were highly consistent in the reflectance of vegetation,water and residents,and the average error of vegetation was 12.8%. According to the comparison of changing characteristics of NDVI before and after atmospheric correction,it could be found that atmospheric correction had corrected NDVI of mixed pixels and made it more reasonable. NDVI of each kind of ground objects improved,among which NDVI of vegetation increased most greatly,which can help differentiate vegetation from other ground objects. In a word,MODTRAN model has a good effect on atmospheric correction of HJ /CCD images.

On the Community Correction Model of the State and Domain Jurisprudence

The"state and domain phenomenon"emerged at the modeling period of the crowd system,and with the development of cities,communities are extruded by the multivariate space and the crowd system,forming the social need of a basic guarantee of community correction. After the expansion of communities,the functional integration period begins,which leads to the period of urban transition,when community and the jurisprudential basis begin to coincide with each other,bringing the community correction model. In the industrialized period,the urban population made great contribution to the community correction of cities. Fromthe perspective of globalization,urban integration will experience the anguish of community correction,and crime and punishment will enter communities of the whole society,forming normal community correction model,so the service systemof community correction begins with the formation of the community-based management system,the crowd-based management range,the fundamental organization structure and the coincidence of the organizing function.

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.

The Correctional Model of Population Development Equation

The problem of population development has always been the key problem of restricting the development of our country. In order to increase the prediction accuracy, we analyze the exponential model, logistic model and continuous model. Also, the improved discrete population development model is provided to control the quantity and improve the quality of population.

广东人口规模预测及其对经济增长的影响研究

以1995-2021年广东省常住人口数据为基础,采用多种模型对2025-2040年广东常住人口规模进行了综合预测。利用人口惯性因子、政策调整因子、经济发展因子对预测结果进行修正,广东常住人口规模在2040年将达到1725842万人。计量实证分析表明,广东常住人口对经济增长存在显著的促进作用,其发挥作用的渠道既包括供给侧的劳动就业和人力资本,也包括需求侧的消费与投资。政策上应注重吸引外来人口持续流入,并将现有人口优势转化为经济增长优势,从供给侧与需求端双管齐下,充分释放人口红利。

工程降水对钻孔应变的干扰特征及干扰机制研究

抽水干扰在钻孔应变观测中常见且影响显著,为了能真实捕捉到地震前兆信息,须对其干扰机制进行研究.随着城市化进程,基坑工程及其伴随的工程降水逐步增多,工程降水对钻孔应变的干扰比普通单井抽水干扰更为严重.工程降水对钻孔应变观测有何干扰特征、干扰机制如何、干扰数据能否修复等问题对地震异常的探测具有重要意义.本文基于莱阳体应变受到附近御龙湾项目工程降水干扰的观测案例,研究了工程降水的干扰特征,修复了干扰畸变,通过数值模拟确定了干扰机制.结果表明:(1)工程降水在干扰体应变趋势变化的基础上叠加了方向明确、量级稳定的固体潮畸变,可以通过一阶差分逆运算进行修复.修复固体潮畸变并扣除背景速率后,工程降水干扰量级达-15.65×10^(-9)/d;(2)通过建立基于集中载荷模型的网格算法,模拟出工程降水在体应变异常初期和后期分别产生6.61×10^(9) N和1.73×10^(10) N的集中力载荷,干扰量级明显大于常见的单井抽水干扰,压性干扰集中于体应变仪与抽水井连线的垂直方向;(3)通过固体潮畸变特征及力学机制分析确定了工程降水造成固体潮畸变的干扰机制为"抽水引起压性趋势下降—抽水暂停引起张性台阶上升—抽水引起压性趋势下降"周期行为,固体潮畸变时间间隔(频次)和钻孔水位之间呈显著同步特征,线性系数为0.737.

考虑骨料类型的沥青混合料疲劳预估模型

为了更准确预测不同骨料类型沥青混合料的疲劳性能,本研究基于JTG D50—2017《公路沥青路面设计规范》中的疲劳预估模型(JTG模型)进行了深入探讨。采用均匀试验设计法,对AC-16C型沥青混合料进行了四点弯曲小梁疲劳试验。通过67组玄武岩、石灰岩和花岗岩沥青混合料的疲劳数据,对JTG模型参数进行了修正。研究发现:(1)简化的JTG模型适用于沥青混合料疲劳寿命预估,不同骨料类型对模型参数a的取值有影响;(2)玄武岩沥青混合料疲劳寿命最长,石灰岩最短,花岗岩居中;(3)修正后的JTG模型与实际疲劳试验数据高度吻合。本研究为沥青混合料的疲劳性能提供了更为精确的预估模型。

空间自回归模型下不完整大数据缺失值插补算法

针对不完整大数据因其自身结构具有不规则性,导致在进行缺失值插补时计算量大、插补精度低的问题,提出空间自回归模型下不完整大数据缺失值插补算法。利用迁移学习算法在动态权重下过滤出原始数据中冗余数据,区分异常和正常数据,提取残缺数据,采用最小二乘回归对残缺数据实施修补。将缺失值插补分为3种类型,分别为一阶空间自回归模型插补、空间自回归模型插补和多重插补法。根据实际情况将修补后数据插补到合适的位置,实现不完整大数据缺失值插补。实验结果表明,所提方法具有良好的缺失值插补能力。