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.

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.

Correction of Corporation Value Assessment Model Based on Corporate Social Responsibility

The performance of corporate social responsibility is conducive to the con- tinuous improvement of their profitability, and promotes the upgrading of corporation value. However, it is difficult to confirm, calculate and check the costs and benefits brought by the implementation of corporate social responsibility under the current ac- counting theory system, so it is difficult to estimate whether the fulfillment of corpo- rate social responsibility has any effects on the corporation value assessment. Therefore, based on corporate social responsibility, the correction mode of corpora- tion value assessment is put forward.

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.

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.

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.

水动力学模型的实时组合校正方法研究

受降雨分布不均、地形复杂多变及模型结构等因素限制,水动力学模型的水位模拟精度难以进一步提升。为此,基于一维水动力学模型运行结果,建立了涵盖KNN、SVR、LSTM、GRU等多种智能算法的实时校正模型,并将KNN校正法与WF算法串联耦合,提出了具有降噪功效的KNN-WF校正方法,在此基础上,为充分挖掘各校正算法在不同水位区间的校正优势,提出了一种基于M分位数的水位分级组合校正方法,以均方根误差、决定系数和峰值相对误差综合比较分析各校正方法的校正效果。汉江流域应用结果表明,KNN-WF校正法能有效避免KNN校正结果中存在的噪声和锯齿状峰值,校正后的水位过程与实测水位过程更加吻合,分级组合校正方法能实现不同水位区间的精细化校正从而提升整体校正效果。

基于二次分解、LSTM-ELM和误差修正的空气质量指数预测模型

精准预测空气质量指数(Air Quality Index,AQI)对于制定有效的空气污染治理策略至关重要。为了进一步提升AQI的预测精度,提出了一种新的预测模型,并结合了二次分解(Secondary Decomposition,SD)、优化算法、双尺度预测和误差修正的方法。首先,采用改良的自适应白噪声完全集合经验模态分解(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,ICEEMDAN)和样本熵(Sample Entropy,SE)对原始AQI序列进行分解并重构,获得高频、中频和低频3个频率分量。其次,利用经过北方苍鹰算法(Northern Goshawk Optimization,NGO)优化的变分模态分解(Variational Mode Decomposition,VMD)对高频分量进行二次分解,进一步降低其复杂度。再次,引入向量加权平均算法(Weighed Mean of Vectors Algorithm,INFO)对长短期记忆网络(Long Short-Term Memory,LSTM)和极限学习机(Extreme Learning Machine,ELM)的关键参数进行优化,同时利用INFO-LSTM预测高频分量分解后的子序列,进而利用INFO-ELM分别预测中、低频分量,并将所得预测结果进行线性叠加。最后,利用NGO-VMD和INFO-ELM对误差序列进行分解和预测,并对初次预测结果进行修正,得到最终的AQI预测值。研究选取北京、上海和成都3个典型城市为例进行实证分析,并对比了7个对照试验,发现基于二次分解、LSTM-ELM和误差修正的模型具有最高的预测精度。该模型可为治理空气污染提供理论和技术上的帮助。

外商直接投资对江苏省进出口贸易的影响——基于1998—2023年江苏省对外贸易数据的实证研究

江苏省是我国经济最发达且最具发展潜力的省份之一,改革开放后,出台了一系列持续有效吸引外商直接投资的措施。根据江苏省外商直接投资(FDI)的利用现状和进出口贸易现状,通过平稳性检验和协整检验,并建立回归模型和误差修正模型进行实证分析。实证结果表明,FDI与江苏省进出口贸易存在长期稳定均衡关系。从贸易规模来说,FDI能促进进出口贸易的增长,且对出口贸易的影响大于进口贸易;从贸易结构来说,FDI对改善出口贸易结构产生明显的作用,在进口中,对初级产品的影响大于工业制成品,在出口中,对工业制成品的影响大于对初级产品。可以从营商环境、政策法规、产业集聚、技术溢出、人才与开放合作等方面进行改进,进一步扩大外商直接投资规模。

我国地面伽马总量测量技术发展的回顾与展望

本文回顾了我国地面伽马总量测量技术的发展历程和取得的主要成果,并对其未来的发展趋势进行了展望。文章从方法技术、仪器装备、校准模型、处理解释和技术规范5个方面进行阐述,详细梳理了从20世纪50年代至今,地面伽马总量测量作为最常见的放射性测量基本方法之一,通过仪器测量地表土壤或岩石天然伽马射线的总强度,用于直接寻找放射性铀矿体,并逐渐形成了以地面伽马总量测量为主要手段之一的铀矿勘查工作。这期间,通过地面伽马总量测量发现了大量的铀矿床,现已形成了适用于我国铀矿勘查的地面伽马总量测量体系。

过膨胀循环发动机配气相位偏差对充气模型精度的影响

为提高过膨胀循环发动机充气模型精度,改善发动机性能及提高整车动态工况混合气控制,需精确控制发动机进气凸轮轴的相位。本文分析了进气凸轮轴的开启时刻与关闭时刻对充气模型的影响原理,从理论上分析了相位偏差对充气模型精度的影响,并通过试验验证不同相位偏差对充气模型精度的影响情况,为满足充气模型的偏差不超过±5%的精度需求,进气相位偏差需要控制在±1.5℃A范围内。提出了通过控制装配机械相位精度及增加ECU相位偏差自学习模型的方式,修正进气相位偏差对充气模型的影响,经过试验验证了相位偏差控制的效果。

基于分块交叉信赖域法的航空发动机部件级模型精修方法研究

为了解决传统修模方法易陷入局部最优的问题,提出了一种基于分块交叉信赖域法的航空发动机部件级模型精修方法。该方法首先通过修正函数法进行粗略地缩放,解决原始通用特性图与其真实特性差距较大的问题。在此基础上,采用特性图分块策略,利用试车稳态点数据对特性图进行局部修正,并进一步提出交叉信赖域方法求得其局部区域的修模参数空间,以实现在不同发动机状态下适应性调节优化空间。最后,引入对不同修正区域选取特定的优化空间,避免了单一优化区间带来的局部最优问题。仿真结果表明,相比于传统的修正函数法,本文提出的方法可以使模型各个稳态工况点平均误差从1.13%降低到了0.51%左右,平均精度提升了54%,显著提高了航空发动机数学模型的精度。

基于自适应模型的力修正迭代混合试验方法

传统的力修正迭代混合试验方法采用了固定的模型进行恢复力修正,存在模型不够精准导致迭代轮次增加的问题。针对该问题,提出基于自适应模型的力修正迭代混合试验方法。该方法采用迭代所有轮次的恢复力修正值与每轮迭代物理子结构的真实恢复力搭建自适应模型进行迭代恢复力修正,提高了迭代收敛速度与迭代收敛精度。以单层框架黏滞阻尼器减震结构为例,分析了不同的权重分配系数与初始模型参数对于迭代收敛速度和收敛精度的影响;通过对不同自振周期的结构分别验证,分析结构自振周期对该方法的影响。结果表明:不同的权重分配系数和模型参数对迭代收敛速度与迭代收敛精度有较大影响;在权重分配系数为0.025,初始模型参数为0.80时,迭代收敛速度和收敛精度远高于传统的力修正迭代混合试验方法;基于自适应模型的力修正迭代混合试验方法在不同的单层框架结构迭代收敛速度与迭代收敛精度远优于传统的力修正迭代混合试验方法,对于自振周期小于1.0 s的结构,该方法优势更明显。