Rainfall distributional properties control hydrologic model parameter importance.

<p>Process-based ecosystem models are versatile tools providing profound insights into ecosystem processes and interactions between vegetation and environment. The ongoing development of the Biome-BGCMuSo model has delivered multiple improvements in model structure and parameters, and subsequently in simulated ecosystem dynamics. Since the number of parameters has increased during the model development, model parametrisation for biomes or tree species of interest is required to enable reliable model usage in the future.</p><p>Here we explore the issue of site-specific versus multi-site calibration of model parameters for the European beech (Fagus sylvatica L.) along an extended environmental gradient across Central Europe, covering Croatia, Hungary, Slovakia, Poland and the Czech Republic. First, thorough literature search for the plausible ranges of individual model parameters was conducted. This was followed by the sensitivity analysis to identify the most influential model parameters. Finally, model calibration was performed based on the generalised likelihood uncertainty estimation method and the data from long-term research plots located in the five countries. The calibration was conducted at levels of individual sites and the region as a whole to evaluate different aspects of site-specific and multi-site calibration approaches and to develop a generalised parameter set for the European beech in Central Europe.</p>

This paper addresses the development of an advanced parallel two-wheel vehicle that has an underslung vehicle body. The developed parallel two-wheel vehicle has the functions such as the self-standing structure by stable pendulum structure, the seat positioning system for compensating the deviation of the vehicle's gravity center including the passenger, and the active mass system for suppressing swaying of the vehicle body. The vehicle will be used by diverse passengers in the future. However, the sway suppression control of the vehicle body was developed without consideration for differences of the passenger's physical constitution in the conventional study. In this paper, the mathematical model of the swaying of vehicle body with passenger and its model parameters identification are proposed. In the model parameters identification, it is clarified that the model parameters of each passenger are similar except for the passenger's mass. Furthermore, the control parameters designed by backstepping method are independent to interference from the model parameters. Therefore, in design of the sway suppression control to the diverse passengers, the only passenger's mass is given to the control system. Finally, effectiveness of the proposed control system is verified by experiments using the parallel two-wheel vehicle.

Electrorheological fluids (ERFs) offer rapid control of damping using very low power requirements. Different models have been proposed to simulate the hysteresis phenomenon of ERFs. A modular test facility was designed to perform measurements of a specific electrorheological fluid in squeeze and shear modes in Part I. Based on these measurement cycles, material models for squeeze and shear modes are proposed and corresponding model parameters are identified within this parameter space. The fitted models are benchmarked against the measurement data and are capable of resembling the fluid’s dynamic properties at harmonic excitation, including transitions between Bingham-like and visco-elastic material behavior. Once model parameters are identified, the dynamics of an ERF are resembled excellently by phenomenological models. However, clear trends within the parameter space cannot be stated for all model parameters which prevents the derivation of analytical statements for the model parameters. Since the identification of model parameters is not transferable, a new adaptation is necessary for every application. Nevertheless, the presented procedure can be applied directly in these cases and is robust.

Immediately after a stimulus appears in the visual field, there is often a short period of facilitated processing of stimuli at or near this location. This period is followed by one in which processing is impaired, rather than facilitated. This impairment has been termed inhibition of return (IOR). In the present study, the time course of this phenomenon was examined in two ways. (1) A graphical meta-analysis plotted the size of the effect as a function of the stimulus onset asynchrony (SOA) of the two stimuli. This analysis showed that IOR is impressively stable for SOAs of 300-1,600 msec. It also showed that the literature does not provide any clear sense of the duration of IOR. (2) An empirical approach was, therefore, taken to fill this gap in our knowledge of IOR. In three experiments, IOR was tested using SOAs between 600 and 4,200 msec. IOR was robust for approximately 3 sec and appeared to taper off after this point; the observed duration varied somewhat as a function of the testing conditions. In addition, for the first second, the degree of inhibition was inversely related to distance of the target from the original stimulus, but for the next 2 sec this spatial distribution was not observed. Theories of the mechanisms and function of IOR must conform to these spatial and temporal properties.

Contemporary tasks of complex system simulation are often related to the issue of uncertainty management. It comes from the lack of information or knowledge about the simulated system as well as from restrictions of the model set being used. One of the powerful tools for the uncertainty management is ensemble-based simulation, which uses variation in input or output data, model parameters, or available versions of models to improve the simulation performance. Furthermore the system of models for complex system simulation (especially in case of hiring ensemble-based approach) can be considered as a complex system. As a result, the identification of the complex model's structure and parameters provide additional sources of uncertainty to be managed. Within the presented work we are developing a conceptual and technological approach to manage the ensemble-based simulation taking into account changing states of both simulated system and system of models within the ensemble-based approach. The states of these systems are considered as a subject of classification with consequent inference of better strategies for ensemble evolution over the simulation time and ensemble aggregation. Here the ensemble evolution enables implementation of dynamic reactive solutions which can automatically conform to the changing states of both systems. The ensemble aggregation can be considered within a scope of averaging (regression way) or selection (classification way, which complement the classification mentioned earlier) approach. The technological basis for such approach includes ensemble-based simulation techniques using domain-specific software combined within a composite application; data science approaches for analysis of available datasets (simulation data, observations, situation assessment etc.); and machine learning algorithms for classes identification, ensemble management and knowledge acquisition.

Temporal properties of spatially aggregated meteorological time series

This paper deals with the identification of material parameters in constitutive models of cyclic plasticity for sheet metals using the bending moment vs. curvature diagrams obtained by cyclic bending tests. After a brief discussion on the identification for some simple plasticity models, a new identification method based on the iterative multipoint approximation concept is presented for complicated elastic-plastic consititutive models incorporating many numbers of material parameters. As an example, eight material parameters in Chaboche-Rosselier's model were identified by this method. This approach has been verified by comparing the simulated stress-strain curve using the constitutive model incorporating the identified material parameters with the experimental curve determined by uniaxial tension.

Parameter identification of reduced-order electrochemical model simplified by spectral methods and state estimation based on square-root cubature Kalman filter

Smart sensors are the core of condition monitoring of power equipment. However, energy supply for sensors in complex electric power field is difficult and in the spotlight. Solar energy as an easily available energy is a good solution to this problem. Accurate identification of photovoltaic cell model parameters can ensure the subsequent stable energy supply. The purpose of this paper is to realize the accurate identification of photovoltaic cell model parameters, so as to serve the energy supply of monitor devices. Firstly, the basic circuit of photovoltaic panel energy supply is built. Secondly, the U-I characteristic curve of photovoltaic cell under different loads is measured. Thirdly, the equivalent parameter model of photovoltaic cell is constructed. Finally, the model parameters are accurately identified based on Levenberg Marquarelt (LM)-Genetic Algorithm (GA). LM algorithm is used for fast global calculation to determine the approximate range of global optimal solution, and then GA is used to further iterate within this range to obtain high-precision local extremum. The proposed method achieved a better fitting effect and results has higher precision in parameter identification of photovoltaic cell. Moreover, under the rated working condition, the errors between the identified parameters and the numerical values provided by the manufacturer are within ±5%. This paper presents an optimization algorithm combining LM and GA, which can accurately identify the parameters of photovoltaic model under different solar radiation levels, and provide strong technical support for the energy supply of sensors and other monitoring equipment.

A procedure for the experimental identification of the material standard linear solid model parameters by means of dynamic mechanical analysis test instrument measurements is presented. Since the standard linear solid material stress–strain functional D(ω) relationship in the frequency domain formally depends on the standard linear solid material parameters, a procedure able to identify these parameters from test measurement estimates is proposed in this work. Nevertheless, a critical, nonlinear and non-parametric approach is to be followed since the number of the material standard linear solid block components is generally unknown, and the material D(ω) shows a highly nonlinear dependency on the unknown standard linear solid material parameters. For these reasons, measurement and test model noise is expected to strongly influence the accuracy of the identification results. A multi-step procedure is presented, consisting first in the non-parametric identification of a frequency dependent, two degrees of freedom model instrument frame by means of a polynomial rational function, where polynomial order and parameters, such as polynomial coefficients and pole-residue couples, are optimally identified by means of an algebraic numerical technique and of an iterative stabilization procedure. Another procedure able to identify the material D(ω) polynomial rational functional relationship in the frequency domain is also proposed, taking into account the dynamic contribution of the instrument frame, of the inertial contribution of the distributed mass of the beam and of the lumped mass of the instrument force measuring system. An effective procedure, able to identify the standard linear solid material model parameters in the time domain from the identified material physical poles, is finally proposed. Some application examples, concerning the identification of the standard linear solid model of a known material and of an unknown composite material, are shown and discussed as well.

Geologic controls on ice sheet sensitivity to deglacial climate forcing in the Ross Embayment, Antarctica

ABSTRACTIn this paper, a new improved search space boundary resizing method for an optimal model's parameter identification for continuous real time transfer function by standard genetic algorithms (SGAs) is proposed and demonstrated. Premature convergence to local minima, as a result of search space boundary constraints, is a key consideration in the application of SGAs. The new method improves the convergence to global optima by resizing or extending the upper and lower search boundaries. The resizing of the search space boundaries involves two processes, first, an identification of initial value by approximating the dynamic response period and desired settling time. Second, a boundary resizing method derived from the initial search space value. These processes brought the elite groups within feasible boundary regions by consecutive execution and enhanced the SGAs in locating the optimal model's parameters for the identified transfer function. This new method is applied and examined on two processes, a third-order transfer function model with and without random disturbance and raw data of excess oxygen. The simulation results assured the new improved search space resizing method's efficiency and flexibility in assisting SGAs to locate optimal transfer function model parameters in their explorations.

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model’s ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification.

Performance Estimate of Vertical Inflow Diffuser in Temperature-stratified Type Thermal Storage Tank by CFD Analysis - Model Parameter Identification and Theoretical Study of Performance Limit -

Several stochastic models, with various degrees of complexity, have been proposed to model the neuronal activity from different parts of the human brain. In this article, we use a simple Ornstein-Uhlenbeck process (OUP) to model the spike activity recorded from the subthalamic nucleus of patients suffering from Parkinson's disease at the time of implantation of the electrodes for deep brain stimulation. From the recorded data, which contains information about the spike times of a single neuron, we identify and extract the model parameters of the OUP. We then use these parameters to numerically simulate the inter-spike intervals and the voltage across the neuron membrane. We finally assess how well the proposed mathematical model fits to the measured data and compare it with other commonly adopted stochastic models. We show an excellent agreement between the computer-generated data according to the OUP model and the measured one, as well as the superiority of the OUP model when compared to the Poisson process model and the random walk model; thus, establishing the validity of the OUP as a simple yet biologically plausible model of the neuronal activity recorded from the subthalamic nucleus of Parkinson's disease patients.