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.

Sensor placement of long-term health monitoring for large bridges based on the real-time correction of finite element model

The process of optimized placement of long-term health monitoring sensors for large bridges generally begins with finite element models, but there will arise great discrepancies between theoretically-calculated results and actual measurements.Therefore, rectified finite element models need to be rectified by virtue of model rectifying technology. Firstly, the result of construction monitoring and finished state load test is used to real-time modification of finite element model. Subsequently, an accurate finite element model is established. Secondly, the optimizing the layout of sensor with following orthogonality guarantees orthogonal property and linear independence for the measured data. Lastly, the effectiveness and feasibility of method in the paper is tested by real-time modifying finite element model and optimizing the layout of sensor for Nujiang Bridge.

Methodology for local correction of the heights of global geoid models to improve the accuracy of GNSS leveling

At present,one of the methods used to determine the height of points on the Earth’s surface is Global Navigation Satellite System(GNSS)leveling.It is possible to determine the orthometric or normal height by this method only if there is a geoid or quasi-geoid height model available.This paper proposes the methodology for local correction of the heights of high-order global geoid models such as EGM08,EIGEN-6C4,GECO,and XGM2019e_2159.This methodology was tested in different areas of the research field,covering various relief forms.The dependence of the change in corrected height accuracy on the input data was analyzed,and the correction was also conducted for model heights in three tidal systems:"tide free","mean tide",and"zero tide".The results show that the heights of EIGEN-6C4 model can be corrected with an accuracy of up to 1 cm for flat and foothill terrains with the dimensionality of 1°×1°,2°×2°,and 3°×3°.The EGM08 model presents an almost identical result.The EIGEN-6C4 model is best suited for mountainous relief and provides an accuracy of 1.5 cm on the 1°×1°area.The height correction accuracy of GECO and XGM2019e_2159 models is slightly poor,which has fuzziness in terms of numerical fluctuation.

A STUDY ON THE ENSEMBLE FORECAST REAL-TIME CORRECTION METHOD

Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determined that the short-time forecast effect was better than the original objective mode. By selecting four types of integration schemes after multiple mode path integration for those four objective modes, the forecast effect of the multi-mode path integration is better, on average, than any single model. Moreover, multi-mode ensemble forecasting has obvious advantages during the initial 36 h.

Restoring force correction based on online discrete tangent stiffness estimation method for real-time hybrid simulation

In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation（online DTSE） to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that：（1） the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and（2） the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.

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.

Pore-pressure and stress-coupled creep behavior in deep coal:Insights from real-time NMR analysis

Understanding the variations in microscopic pore-fracture structures(MPFS) during coal creep under pore pressure and stress coupling is crucial for coal mining and effective gas treatment. In this manuscript, a triaxial creep test on deep coal at various pore pressures using a test system that combines in-situ mechanical loading with real-time nuclear magnetic resonance(NMR) detection was conducted.Full-scale quantitative characterization, online real-time detection, and visualization of MPFS during coal creep influenced by pore pressure and stress coupling were performed using NMR and NMR imaging(NMRI) techniques. The results revealed that seepage pores and microfractures(SPM) undergo the most significant changes during coal creep, with creep failure gradually expanding from dense primary pore fractures. Pore pressure presence promotes MPFS development primarily by inhibiting SPM compression and encouraging adsorption pores(AP) to evolve into SPM. Coal enters the accelerated creep stage earlier at lower stress levels, resulting in more pronounced creep deformation. The connection between the micro and macro values was established, demonstrating that increased porosity at different pore pressures leads to a negative exponential decay of the viscosity coefficient. The Newton dashpot in the ideal viscoplastic body and the Burgers model was improved using NMR experimental results, and a creep model that considers pore pressure and stress coupling using variable-order fractional operators was developed. The model’s reasonableness was confirmed using creep experimental data. The damagestate adjustment factors ω and β were identified through a parameter sensitivity analysis to characterize the effect of pore pressure and stress coupling on the creep damage characteristics(size and degree of difficulty) of coal.

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.

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.

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.

Transformer real-time reliability model based on operating conditions

Operational reliability evaluation theory reflects real-time reliability level of power system. The component failure rate varies with operating conditions. The impact of real-time operating conditions such as ambient temperature and transformer MVA (megavolt-ampere) loading on transformer insulation life is studied in this paper. The formula of transformer failure rate based on the winding hottest-spot temperature (HST) is given. Thus the real-time reliability model of transformer based on oper- ating conditions is presented. The work is illustrated using the 1979 IEEE Reliability Test System. The changes of operating conditions are simulated by using hourly load curve and temperature curve, so the curves of real-time reliability indices are ob- tained by using operational reliability evaluation.

FEM model for real-time guide wire simulation in vasculature

A model suitable for describing the mechanical response of thin elastic objects is proposed to simulate the deformation of guide wires in minimally invasive interventions. The main objective of this simulation is to provide doctors an opportunity to rehearse the surgery and select an optimal operation plan before the real surgery. In this model the guide wire is discretized with the multi-body representation and its elastic energy derivate from elastic theory is a polynomial function of the nodal displacements. The vascular structure is represented by a tetrahedron mesh extended from the triangular mesh of the artery, which can be extracted from the patient＇s CT image data. The model applies the energy decline process of the conjugate gradient method to the deformation simulation of the guide wire. Experimental results show that the polynomial relationship between elastic energy and nodal displacements tremendously simplifies the evaluation of the conjugate gradient method and significantly improves the model＇s efficiency. Compared with models depending on an explicit scheme for evaluation, the new model is not only non-conditionally stable but also more efficient. The model can be applied to the real-time simulation of guide wire in a vascular structure.

On modeling approach for embedded real-time software simulation testing

Modeling technology has been introduced into software testing field. However, how to carry through the testing modeling effectively is still a difficulty. Based on combination of simulation modeling technology and embedded real-time software testing method, the process of simulation testing modeling is studied first. And then, the supporting environment of simulation testing modeling is put forward. Furthermore, an approach of embedded real-time software simulation testing modeling including modeling of cross-linked equipments of system under testing （SUT）, test case, testing scheduling, and testing system service is brought forward. Finally, the formalized description and execution system of testing models are given, with which we can realize real-time, closed loop, mad automated system testing for embedded real-time software.

Experimental Model and Analytic Solution for Real-time Observation of Vehicle's Additional Steer Angle

The current research of real-time observation for vehicle roll steer angle and compliance steer angle（both of them comprehensively referred as the additional steer angle in this paper） mainly employs the linear vehicle dynamic model, in which only the lateral acceleration of vehicle body is considered. The observation accuracy resorting to this method cannot meet the requirements of vehicle real-time stability control, especially under extreme driving conditions. The paper explores the solution resorting to experimental method. Firstly, a multi-body dynamic model of a passenger car is built based on the ADAMS/Car software, whose dynamic accuracy is verified by the same vehicle＇s roadway test data of steady static circular test. Based on this simulation platform, several influencing factors of additional steer angle under different driving conditions are quantitatively analyzed. Then ε-SVR algorithm is employed to build the additional steer angle prediction model, whose input vectors mainly include the sensor information of standard electronic stability control system（ESC）. The method of typical slalom tests and FMVSS 126 tests are adopted to make simulation, train model and test model＇s generalization performance. The test result shows that the influence of lateral acceleration on additional steer angle is maximal （the magnitude up to 1°）, followed by the longitudinal acceleration-deceleration and the road wave amplitude （the magnitude up to 0.3°）. Moreover, both the prediction accuracy and the calculation real-time of the model can meet the control requirements of ESC This research expands the accurate observation methods of the additional steer angle under extreme driving conditions.

Construction of multi-factor identification model for real-time monitoring and early warning of mine water inrush

As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.

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.

Empirical correction of kinetic model for polymer thermal reaction process based on first order reaction kinetics

Based on the theory of first-order reaction kinetics,a thermal reaction kinetic model in integral form has been derive.To make the model more applicable,the effects of time and the conversion degree on the reaction rate parameters were considered.Two types of undetermined functions were used to compensate for the intrinsic variation of the reaction rate,and two types of correction methods are provided.The model was explained and verified using published experimental data of different polymer thermal reaction systems,and its effectiveness and wide adaptability were confirmed.For the given kinetic model,only one parameter needs to be determined.The proposed empirical model is expected to be used in the numerical simulation of polymer thermal reaction process.

Performance analysis model for real-time Ethernet-based computer numerical control system

In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.

Improved Real-time Implementation of Adaptive Gassian Mixture Model-based Object Detection Algorithm for Fixed-point DSP Processors

Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance, HCI, object-based video compression, etc. One of the most successful moving object detection algorithms is based on Adaptive Gaussian Mixture Model （AGMM）. Although ACMM-hased object detection shows very good performance with respect to object detection accuracy, AGMM is very complex model requiring lots of floatingpoint arithmetic so that it should pay for expensive computational cost. Thus, direct implementation of the AGMM-based object detection for embedded DSPs without floating-point arithmetic HW support cannot satisfy the real-time processing requirement. This paper presents a novel rcal-time implementation of adaptive Gaussian mixture model-based moving object detection algorithm for fixed-point DSPs. In the proposed implementation, in addition to changes of data types into fixed-point ones, magnification of the Gaussian distribution technique is introduced so that the integer and fixed-point arithmetic can be easily and consistently utilized instead of real nmnher and floatingpoint arithmetic in processing of AGMM algorithm. Experimental results shows that the proposed implementation have a high potential in real-time applications.

Real-Time Monitoring Method for Cow Rumination Behavior Based on Edge Computing and Improved MobileNet v3

[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.