Identification of variable spacial coefficients for a beam equation from boundary measurements

We consider a system described by the Euler-Bernoulli beam equation, with one end clamped and with torque input at the other end. The output function are the displacement and the angle velocity at the non-clamped end of the beam. We study the identification of the spatially variable coefficients in the beam equation, from input-output data. We show that both the density and the flexural rigidity of the beam (which are assumed to be of class C⁴) can be uniquely determined if the input and output functions are known for all positive times.     

Indistinguishability and identifiability of kinetic models for the MurC reaction in peptidoglycan biosynthesis

An important question in Systems Biology is the design of experiments that enable discrimination between two (or more) competing chemical pathway models or biological mechanisms. In this paper analysis is performed between two different models describing the kinetic mechanism of a three-substrate three-product reaction, namely the MurC reaction in the cytoplasmic phase of peptidoglycan biosynthesis. One model involves ordered substrate binding and ordered release of the three products; the competing model also assumes ordered substrate binding, but with fast release of the three products. The two versions are shown to be distinguishable; however, if standard quasi-steady-state assumptions are made distinguishability cannot be determined. Once model structure uniqueness is ensured the experimenter must determine if it is possible to successfully recover rate constant values given the experiment observations, a process known as structural identifiability. Structural identifiability analysis is carried out for both models to determine which of the unknown reaction parameters can be determined uniquely, or otherwise, from the ideal system outputs. This structural analysis forms an integrated step towards the modelling of the full pathway of the cytoplasmic phase of peptidoglycan biosynthesis.     

Nonlinear ill-posed problem analysis in model-based parameter estimation and experimental design

Discrete ill-posed problems are often encountered in engineering applications. Still, their sound analysis is not yet common practice and difficulties arising in the determination of uncertain parameters are typically not assigned properly. This contribution provides a tutorial review on methods for identifiability analysis, regularization techniques and optimal experimental design. A guideline for the analysis and classification of nonlinear ill-posed problems to detect practical identifiability problems is given. Techniques for the regularization of experimental design problems resulting from ill-posed parameter estimations are discussed. Applications are presented for three different case studies of increasing complexity.     

Strongly consistent coefficient estimate for errors-in-variables models

For the single-input-single-output (SISO) errors-in-variables system it is assumed that the system input is an ARMA process and that the driven noise of the system input and the observation noise are jointly Gaussian. The two-dimensional observation made on system input and output is represented as a two-dimensional (2D) ARMA system of minimum phase driven by a sequence of 2D i.i.d. Gaussian random vectors (innovation representation). It is shown that the resulting ARMA system is identifiable, i.e., its coefficients are uniquely defined under reasonable conditions. Recursive algorithms are proposed for estimating coefficients of the ARMA representation including those contained in the original SISO system. The estimates are proved to be convergent to the true values with probability one and the convergence rate is derived as well. The theoretical results are justified by numerical simulation.     

Joint identification of plant rational models and noise distribution functions using binary-valued observations

System identification of plants with binary-valued output observations is of importance in understanding modeling capability and limitations for systems with limited sensor information, establishing relationships between communication resource limitations and identification complexity, and studying sensor networks. This paper resolves two issues arising in such system identification problems. First, regression structures for identifying a rational model contain non-smooth nonlinearities, leading to a difficult nonlinear filtering problem. By introducing a two-step identification procedure that employs periodic signals, empirical measures, and identifiability features, rational models can be identified without resorting to complicated nonlinear searching algorithms. Second, by formulating a joint identification problem, we are able to accommodate scenarios in which noise distribution functions are unknown. Convergence of parameter estimates is established. Recursive algorithms for joint identification and their key properties are further developed.     

Estimation of soil water distribution: Identifiability and observation design

Identifiability and observation design problems for the Burgers equation, a model for the nonhysteretic infiltration in nonswelling soil, are considered. In estimating initial water distribution from a finite set of measurements, the identifiability under approximation is proved. In predicting the water distributions, an algorithm for observation design problems is provided and analyzed with the maximum error bound. Also, numerical simulations are performed.     

Impact of similarity between avatar and their users on their perceived identifiability: Evidence from virtual teams in Second Life platform

Research on the application of avatars in the virtual teams is growing. In this study, we examined the effect of perceived similarity of an avatar user with his/her avatar on the perceptions of his/her identifiability within a virtual team. The study utilized a sample of 124 users actively involved in Second Life, a virtual world platform. Results of structural equation modeling utilizing the partial least squares method corroborate the hypothesis. An important contribution of this research is to inform practitioners about the critical role that users’ similarity with the avatar plays in enhancing their identifiability. We draw conclusions based on the result and identify some important avenues for future research.     

人性的居住——厦门大学校园风格浅议

厦门大学享有“中国最美丽大学”的美誉,本文对其校园空间的特色进行了分析和说明,总结出几条宝贵的经验,以期对未来校园的规划提供某些帮助。     

Numerical analysis of the global identifiability of reaction-diffusion systems

This article describes a numerical approach which allows for the analysis of the parametric identifiability of non-linear systems. It is shown that the analysis of global identifiability of N parameters of a system can be reduced to finding a global minimum of a specially designed 2N-dimensional function. We discuss the structure of this function and its physical interpretation. As an example we consider identifiability of a diffusion-reaction system and discuss experimental conditions for better identifiability of this system.