PARAMETER IDENTIFICATION OF DYNAMIC MODELS USING A BAYES APPROACH

The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies. This is based on the solution of an inverse generalized evaluate problem. The stochastic nature of test data is considered and a normal distribution is used for the measurement frequencies. An additional feature is that the engineer's confidence in the measurement frequencies is quantified and incorporated into the identification procedure. A numerical example demonstrates the efficiency of the method.

DYNAMIC MODELS OF AQUIFEROUS CONTENT OF LIVING FUELS

Forest fire occurrence is closely relative with fuel water content. There are a lot of research about dead fuels. but forest fuels consist of both dead fuels and living fuels. Each large fire occurrence has a good relationship with living fuels. Especially living fuels can influence the production and development of big forest fire, so, we selected Tahe, in Daxingan Mountains, as observation site. According to actual data,we can establish a set of models of different living fuel water content variation with linear -regression method.

Suiting Dynamic Models of Fixed-Bed Catalytic Reactors for Computer-Based Applications

This work investigated the applicability of heterogeneous and pseudo-homogeneous models to predict the dynamic behavior of a fixed-bed catalytic reactor. Some issues concerning the dynamic behavior of the system were discussed, such as the prediction of the inverse response phenomenon. The proposed models (Het- erogeneous I and II and Pseudo-homogeneous) were able to predict with qualitative similarity the main characteristics of the dynamic behavior of a fixed-bed catalytic reactor, including the inverse response. The computational time demanded for the solution of the heterogeneous models was 10 to 50% longer than in the case of the pseudo-homogeneous model, making the use of the former suitable for applications where computational time is not the major restriction (off-line applications). On the other hand, when on-line applications are required, the simplified model (Pseudo-homogeneous model) showed to be a good alternative because this model was able to predict (qualitatively) the dynamics of the reactor using a faster and easier numerical solution.

Simplified Dynamic Models of Grass Field Ecosystem

Some simplified dynamic models of grass field ecosystem are developed and investigated. The maximum simplified one consists of two variables, living grass biomass and soil wetness. The analyses of such models show that there exists only desert regime without grasses if the precipitation p is less than a critical value pc; the grass biomass continuously depends on p if the interaction between grass biomass and the soil wetness is weak, but the strong interaction results in the bifurcation of grass biomass in the vicinity of pc: the grass biomass is rich as p > pc, but it becomes desertification as p<pc. Periodic solutions also exist in the model, if the seasonal cycle of model's parameters is introduced. An improved model consists of three variables, i.e. the living grass biomass x, the nonliving grass biomass accumulated on the ground surface y and the soil wetness z. The behaviours of such three variables model are more complicated. The initial values of y and z play a very important role.

Dynamic models of technology management capability development based on knowledge diffusion

Technology management is recognized as a key for organizations to achieve competitiveness. How to promote an organization’s technology management capability is of great significance in creating efficiencies and achieving a competitive edge. The knowledge essence of technology management capability is introduced and then the correlation between knowledge diffusion and the development of technology management capability is discussed. Further, the basic and extended dynamic models of the development of technology management capability are constructed, and is applied into an enterprise. The results show that the dynamic models can well explain how the knowledge improves the development of technology management capability, and they can be used as an useful tool by an enterprise to promote technology management capability. Finally, the managerial implications of the models are discussed.

Experiments of Reconstructing Discrete Atmospheric Dynamic Models from Data (I)

In this paper, we give some experimental results of our study in reconstructing discrete atmospheric dynamic models from data. After a great deal of numerical experiments, we found that the logistic map, xn +1= 1-uxn2 could be used in monthly mean temperature prediction when it was approaching the chaotic region, and its predictive results were in reverse states to the practical data. This means that the nonlinear developing behavior of the monthly mean temperature system is bifurcating back into the critical chaotic states from the chaotic ones.

Dynamically triggered seismicity on a tectonic scale:A review

Earthquakes triggered by dynamic disturbances have been confirmed by numerous observations and experiments.In the past several decades,earthquake triggering has attracted increasing attention of scholars in relation to exploring the mechanism of earthquake triggering,earthquake prediction,and the desire to use the mechanism of earthquake triggering to reduce,prevent,or trigger earthquakes.Natural earthquakes and large‐scale explosions are the most common sources of dynamic disturbances that trigger earthquakes.In the past several decades,some models have been developed,including static,dynamic,quasi‐static,and other models.Some reviews have been published,but explosiontriggered seismicity was not included.In recent years,some new results on earthquake triggering have emerged.Therefore,this paper presents a new review to reflect the new results and include the content of explosion‐triggered earthquakes for the reference of scholars in this area.Instead of a complete review of the relevant literature,this paper primarily focuses on the main aspects of dynamic earthquake triggering on a tectonic scale and makes some suggestions on issues that need to be resolved in this area in the future.

COVID‑19 and tourism sector stock price in Spain:medium‑term relationship through dynamic regression models

The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest tourist destinations.Stock market values are responding to the evolution of the pandemic,especially in the case of tourist companies.Therefore,being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector,thereby improving the response to the crisis by policymakers and investors.Accordingly,a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term.It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.

The Effect of Key Nodes on theMalware Dynamics in the Industrial Control Network

As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.

Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.

Time-domain dynamic constitutive model suitable for mucky soil site seismic response

Soil nonlinear behavior displays noticeable effects on the site seismic response.This study proposes a new functional expression of the skeleton curve to replace the hyperbolic skeleton curve.By integrating shear modulus and combining the dynamic skeleton curve and the damping degradation coefficient,the constitutive equation of the logarithmic dynamic skeleton can be obtained,which considers the damping effect in a soil dynamics problem.Based on the finite difference method and the multi-transmitting boundary condition,a 1D site seismic response analysis program called Soilresp1D has been developed herein and used to analyze the time-domain seismic response in three types of sites.At the same time,this study also provides numerical simulation results based on the hyperbolic constitutive model and the equivalent linear method.The results verify the rationality of the new soil dynamic constitutive model.It can analyze the mucky soil site nonlinear seismic response,reflecting the deformation characteristics and damping effect of the silty soil.The hysteresis loop area is more extensive,and the residual strain is evident.

Flow Dynamics during the Hydrocarbon Exploitation for Prevention and Management of Water Venues in Oil Field: A Study Case of Crystal Field in Badila/Chad

The southern part of the Lake Chad basin is under the gas and oil petroleum industry due to its hydrocarbon potential for about twenty years. This project stands out as the main challenges of the hydrocarbon production and the management of fluxes particularly the groundwater venues. A comprehensive study is thus conducted to develop a dynamic and analytic model for diagnosing the production performances with a particular view on the management of groundwater venues. The three main concerned reservoirs subdivided on subunits evidence their proper characteristics. The porous media, their densities, the internal flows and the water injection techniques such as water flooding were thus adopted. The oil viscosity variability within the reservoirs creates different levels of mobility between water and oil, highlighting the challenges of water management. The material balance model and the behavior of the well analysis were taken in consideration within the identified aquifer, emphasizing the importance of keeping the pressure through injection. The control of water productions, the management of the reservoir, the well strategical position and the specific completions lead to the model functioning. In addition, the CO log and the Pulsed Neutron indicate their limitations as a result of the water salinity and the porosity of the aquifer. The management of groundwater venues at Badila requires various approaches throughout the lifetime of the Crystal field such as the data acquisition and remediation actions and prevention, under a permanent monitoring of the dynamic fluxes in the reservoirs.

Identification of Artificial Neural Network Models for Three-Dimensional Simulation of a Vibration-Acoustic Dynamic System

Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.

Seasonal Prediction of Tropical Cyclones and Storms over the Southwestern Indian Ocean Region Using the Generalized Linear Models

Tropical cyclones (TCs) and storms (TSs) are among the devastating events in the world and southwestern Indian Ocean (SWIO) in particular. The seasonal forecasting TCs and TSs for December to March (DJFM) and November to May (NM) over SWIO were conducted. Dynamic parameters including vertical wind shear, mean zonal steering wind and vorticity at 850 mb were derived from NOAA (NCEP-NCAR) reanalysis 1 wind fields. Thermodynamic parameters including monthly and daily mean Sea Surface Temperature (SST), Outgoing Longwave Radiation (OLR) and equatorial Standard Oscillation Index (SOI) were used. Three types of Poison regression models (i.e. dynamic, thermodynamic and combined models) were developed and validated using the Leave One Out Cross Validation (LOOCV). Moreover, 2 × 2 square matrix contingency tables for model verification were used. The results revealed that, the observed and cross validated DJFM and NM TCs and TSs strongly correlated with each other (p ≤ 0.02) for all model types, with correlations (r) ranging from 0.62 - 0.86 for TCs and 0.52 - 0.87 for TSs, indicating great association between these variables. Assessment of the model skill for all model types of DJFM and NM TCs and TSs frequency revealed high skill scores ranging from 38% - 70% for TCs and 26% - 72% for TSs frequency, respectively. Moreover, results indicated that the dynamic and combined models had higher skill scores than the thermodynamic models. The DJFM and NM selected predictors explained the TCs and TSs variability by the range of 0.45 - 0.65 and 0.37 - 0.66, respectively. However, verification analysis revealed that all models were adequate for predicting the seasonal TCs and TSs, with high bias values ranging from 0.85 - 0.94. Conclusively, the study calls for more studies in TCs and TSs frequency and strengths for enhancing the performance of the March to May (MAM) and December to October (OND) seasonal rainfalls in the East African (EA) and Tanzania in particular.

The 3-Hour-Interval Prediction of Ground-Level Temperature in South Korea Using Dynamic Linear Models

The 3-hour-interval prediction of ground-level temperature from +00 h out to +45 h in South Korea (38 stations) is performed using the DLM (dynamic linear model) in order to eliminate the systematic error of numerical model forecasts. Numerical model forecasts and observations are used as input values of the DLM. According to the comparison of the DLM forecasts to the KFM (Kalman filter model) forecasts with RMSE and bias, the DLM is useful to improve the accuracy of prediction.

Investigation of Uncertainties of Establishment Schemes in Dynamic Global Vegetation Models

In Dynamic Global Vegetation Models （DGVMs）, the establishment of woody vegetation refers to flowering, fertiliza- tion, seed production, germination, and the growth of tree seedlings. It determines not only the population densities but also other important ecosystem structural variables. In current DGVMs, establishments of woody plant functional types （PFTs） are assumed to be either the same in the same grid cell, or largely stochastic. We investigated the uncertainties in the competition of establishment among coexisting woody PFTs from three aspects： the dependence of PFT establishments on vegetation states; background establishment; and relative establishment potentials of different PFTs. Sensitivity experi- ments showed that the dependence of establishment rate on the fractional coverage of a PFT favored the dominant PFT by increasing its share in establishment. While a small background establishment rate had little impact on equilibrium states of the ecosystem, it did change the timescale required for the establishment of alien species in pre-existing forest due to their disadvantage in seed competition during the early stage of invasion. Meanwhile, establishment purely fiom background （the scheme commonly used in current DGVMs） led to inconsistent behavior in response to the change in PFT specification （e.g., number of PFTs and their specification）. Furthermore, the results also indicated that trade-off between irtdividual growth and reproduction/colonization has significant influences on the competition of establishment. Hence, further development of es- tablishment parameterization in DGVMs is essential in reducing the uncertainties in simulations of both ecosystem structures and successions.

DYNAMIC CRACK MODELS ON PROBLEM OF BRIDGING FIBER PULL-OUT OF COMPOSITE MATERIALS

An elastic analysis of an internal central crack with bridging fibers parallel to the free surface in an infinite orthotropic anisotropic elastic plane was performed. A dynamic model of bridging fiber pull-out of composite materials was presented. Resultingly the fiber failure is governed by maximum tensile stress, the fiber breaks and hence the crack extension should occur in self-similar fashion. By the methods of complex functions, the problem studied can be transformed into the dynamic model to the Reimann-Hilbert mixed boundary value problem, and a straightforward and easy analytical solution is presented. Analytical study on the crack propagation subjected to a ladder load and an instantaneous pulse loading is obtained respectively for orthotropic anisotropic body. By utilizing the solution, the concrete solutions of this model are attained by ways of superposition.

Studying the Effect of the Pre-Stimulation Paradigm on Steady-State Visual Evoked Potentials with Dynamic Models Based on the Zero-Pole Analytical Method

This study explored methods for improving the performance of Steady-State Visual Evoked Potential(SSVEP)-based Brain-Computer Interfaces(BCI), and introduced a new analytical method to quantitatively analyze and reflect the characteristics of SSVEP. We focused on the effect of the pre-stimulation paradigm on the SSVEP dynamic models and the dynamic response process of SSVEP, and performed a comparative analysis of three pre-stimulus paradigms(black, gray, and white). Four dynamic models with different orders(second-and third-order)and with and without a zero point were used to fit the SSVEP envelope. The zero-pole analytical method was adopted to conduct quantitative analysis on the dynamic models, and the response characteristics of SSVEP were represented by zero-pole distribution characteristics. The results of this study indicated that the pre-stimulation paradigm affects the characteristics of SSVEP, and the dynamic models had good fitting abilities with SSVEPs under various types of pre-stimulation. Furthermore, the zero-pole characteristics of the models effectively characterize the damping coefficient, oscillation period, and other SSVEP characteristics. The comparison of zeros and poles indicated that the gray pre-stimulation condition corresponds to a lower damping coefficient, thus showing its potential to improve the performance of SSVEP-BCIs.

New Developments on Existence and Uniqueness of Solutions to Some Models in Atmospheric Dynamics

This survey is concerned with the new developments on existence and uniqueness of solutions of some basic models in atmospheric dynamics, such as two-and three-dimensional quasi-geostrophic models and three-dimensional balanced model. The main aim of this paper is to introduce some results about the global and local (with respect to time) existence of solutions given by the authors in recent years, but others' important contributions and the literature on this subject are also quoted. We discuss briefly the relationships among the existence and uniqueness, physical instability and computational instability. In the appendixes, some key mathematical techniques in obtaining our results are presented, which are of vital importance to other problems in geophysical fluid dynamics as well.

Stability analysis of CO_(2)gas shielded welding short-circuit transition process based on GMAW dynamic model

Base on the arc phase and short-circuit phase and their relationship, the paper considers the changes of the extension of wire, the arc length, liquid bridge resistance and mass of liquid bridge, combines the improved “mass-spring” model with the loop model of welding power system, puts forward the critical judgment condition of droplet transition, and establishes a more accurate dynamic model for describing the short-circuit transition process. The dynamic changes of short-circuit transfer frequency, welding current and voltage, contact droplet and residual droplet equivalent radius and droplet equivalent radius at different wire feeding speeds were calculated and analyzed, and compared with the experimental results. It shows that the fluctuation of droplet displacement, velocity and wire extension length at the optimal arc starting point is the smallest. The smaller the initial liquid bridge curvature radius is, the better the stability of short-circuit transfer is.