Initial Conditions Of Parameters Research Articles

Simulation and experimental investigation of high-pressure pneumatic pilot-driven on/off valve with high transient performances for compressed air ejection

A high-pressure pneumatic pilot-driven on/off valve (HPPV) that quickly switches an air inlet line on and off was designed to meet the requirements of a compressed air ejection device with high pressure, high flow rate, and continuous ejection. An HPPV multi-physics field simulation model was established using AMESim simulation software based on the bond graph modeling approach that considered mechanical, gas flow, pipe heat exchange, and pilot valve operation, and experiments were conducted to check the simulation model accuracy. The effects of real gas thermodynamics, pipeline heat exchange, and pilot valve operation on the simulation model accuracy and those of different initial parameter conditions on the dynamic characteristics of the HPPV were investigated in the validated simulation model. The results indicated that the tolerance between simulation and experiment was less than 7.3%, so the simulation model was realistic and reliable. In addition, among the different influencing factors, the pipeline heat transfer significantly affected the simulation model accuracy, and the tolerance between the simulation results without considering the pipeline heat exchange and the experimental results exceeded 10%. The real gas thermal effect slightly affected the simulation model accuracy, and the tolerance between the simulation results under the action of each gas state equation was less than 3%. The pilot valve operation affected only the opening and closing moments of the HPPV and had no effect on the simulation model accuracy. The dynamic characteristics of the HPPV were stable and reliable under different initial parameter conditions.

A numerical simulation of dendritic cells migration and induction of T cell specific proliferation during the initiation of skin inflammatory

Dendritic cells (DCs) are the most potent and specialized antigen-presenting cells (APCs) currently known, which play a crucial role in initiating and amplifying both the innate and adaptive immune responses. During the process of immune function, migration ability of DCs and the number of effector T cells which activated by DCs are closely related to the efficiency of immune function. However, because of the complexity of immune system, in the immune response process caused by the skin chronic inflammatory, much is still unknown about the dynamic changes of cell count with time. Therefore, we created a differential equations model to reflect the initial stages of the immune response process caused by the skin chronic inflammatory via setting the function and initial conditions of parameters. The results showed that the model was able to simulate migration and proliferation of cells in vivo within realistic time scales in accordance with the proliferation and migration efficiency in real terms. In addition, the preliminary model can biologically predict the realistic dynamics of DCs and T cells at different time points. All these results may provide a theoretical reference for studying the immune function of DCs as well as guiding the clinical treatment for immune related diseases further.

A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins: part 1-theoretical model.

A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins was formulated by recapitulating key biological mechanisms, including antigen presentation, activation, proliferation, and differentiation of immune cells, secretion of antidrug antibodies (ADA), as well as in vivo disposition of ADA and therapeutic proteins. This system-level model contains three scales: a subcellular level representing antigen presentation processes by dendritic cells; a cellular level accounting for cell kinetics during humoral immune response; and a whole-body level accounting for therapeutic protein in vivo disposition. The model simulations for in vivo responses against antigenic protein challenge are consistent with many known immunological observations. By simulating immune responses under various initial parameter conditions, the model suggests hypotheses for future experimental investigation and contributes to the mechanistic understanding of immunogenicity. With future experimental validation, this model may potentially provide a platform to generate and test hypotheses about immunogenicity risk assessment and ultimately aid in immunogenicity prediction.

Circuit analysis of the memristive stateful implication gate

A recently published memristive stateful implication (IMP) gate circuit is analysed with confirmation that it cannot achieve the full resistance switching of the output memristor in case both input memristors of the IMP gate are in the OFF resistance state. The initial parameter conditions for the IMP gate are derived, and the minimum resistance of the output memristor achievable is yielded. The HSPICE simulation confirms that a resistance refreshing scheme is indispensable for the IMP gate to function properly.

Application of Differential Evolution in system identification of a servo-hydraulic system with a flexible load

Electro Hydraulic Servo Systems (EHSS) are commonly used in industry. The systems are non-linear in nature and their dynamic equations have several unknown parameters. System identification is a prerequisite to analysis of a dynamic system. One of the most promising novel evolutionary algorithms for solving global optimization problems is the Differential Evolution (DE) algorithm. The DE algorithm is proposed for handling non-linear constraint functions with boundary limits of variables to find the best values for the unknown parameters of a servo-hydraulic system with a flexible load. The DE algorithm guarantees fast speed convergence and accurate solutions regardless of the initial conditions of parameters. Several tests are carried out to validate the accuracy of selected unknown parameters. The results indicate good agreements between the related simulated and measured states.

Contrasting outcomes of spatially implicit and spatially explicit models of vegetation dynamics in a forest-shrubland mosaic

Much contemporary ecology emphasises the importance of taking a spatial perspective in linking ecological patterns and processes. However, collecting and analysing spatial data is expensive. Here we compare spatially implicit and spatially explicit versions of a model of successional dynamics in a forest-shrubland mosaic in a mountain-top reserve in New Caledonia. The models are used to (i) understand the circumstances driving change in abundance of forest and shrubland, and (ii) compare the outcomes of spatially explicit and spatially implicit models of the same system. The spatially explicit model is grid-based and uses a spatially implemented ‘state-and-transition’ approach, with fire spread and seed dispersal the main spatial processes considered. The spatially implicit model is based on a transition matrix approach. Two alternative transition matrices were constructed, one based on field measurements and the other parameterised using output from the spatially explicit model. Although the averaged dynamics of the two models appear similar, the models make very different qualitative predictions about the landscape. Under the same initial parameter conditions two alternative landscape states emerge from the spatially explicit model; this is not the case for the spatially implicit model. Further, the spatial model produces outcomes much closer to those documented historically and inferred from the palæoecological record. The differences between the non-spatial and spatial models arise because, in this system, fine-scale interactions between landscape pattern and process are drivers of coarser scale dynamics, and such interactions are not included in the spatially implicit model. More generally, in order to understand coarse-scale spatial dynamics it may be important to consider local spatial patterns; spatially explicit models are those most likely to incorporate these.

Nonlinear Dynamic Analysis and Nonlinear Structure of Self-Propagating High-Temperature Synthesis Process

The Self-propagating High-temperature Synthesis(SHS) process is a nonlinear one far from thermodynamic equilibrium, many nonlinear structures present in the process. After SHS processes of Ti-C-Fe and Ni-Si systems have been studied by the numbers, the effects of initial parameter conditions on nonlinear structure have been gained. On the base of establishing the model of combustion reaction dynamic characteristics, backward finite-difference approximation equations and central finite-difference approximation equations were applied to numerically calculate and simulate the characteristics of combustion wave and its’ change rules in the SHS process. All the experimental results are consistent with the simulative results.

A new recurrent neurofuzzy network for identification of dynamic systems

In this paper a new structure of a recurrent neurofuzzy network is proposed. The network is based on two interconnected Fuzzy Inference Systems (FISs), one recurrent and another static, that intend to model the behavior of an unknown dynamic system from input–output data. In the proposed structure each rule involves a linear system in a controllable canonical form in order to reduce the online computational load and facilitate the online checking of the stability of the resulted network. The training for the recurrent FIS is made by a gradient-based Real-Time Recurrent Learning Algorithm (RTRLA), while the training for the static FIS is based on a simple gradient method. The initial parameter conditions prior to training are obtained by extracting information from a static FIS trained with delayed input–output signals. To demonstrate the effectiveness of the proposed structure, two nonlinear systems are identified.

Online learning dynamics of multilayer perceptrons with unidentifiable parameters

In the over-realizable learning scenario of multilayer perceptrons, in which the student network has a larger number of hidden units than the true or optimal network, some of the weight parameters are unidentifiable. In this case, the teacher network consists of a union of optimal subspaces included in the parameter space. The optimal subspaces, which lead to singularities, are known to affect the estimation performance of neural networks. Using statistical mechanics, we investigate the online learning dynamics of two-layer neural networks in the over-realizable scenario with unidentifiable parameters. We show that the convergence speed strongly depends on the initial parameter conditions. We also show that there is a quasi-plateau around the optimal subspace, which differs from the well-known plateaus caused by permutation symmetry. In addition, we discuss the property of the final learning state, relating this to the singular structures.

Evaluation of Neural Pattern Classifiers for a Remote Sensing Application

This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing sets.

Nonlinear analysis of a launch vehicle attitude control system

Initial parameter conditions influence on sustained unstable oscillations of attitude control system for large space vehicle, calculating stable region by nonlinear method