Multivariable Repetitive-predictive Controllers using Frequency Decomposition

Repetitive control is a methodology for the tracking of a periodic reference signal. This paper develops a new approach to repetitive control systems design using receding horizon control with frequency decomposition of the reference signal. Moreover, design and implementation issues for this form of repetitive predictive control are investigated from the perspectives of controller complexity and the effects of measurement noise. The analysis is supported by a simulation study on a multi-input multi-output robot arm where the model has been constructed from measured frequency response data, and experimental results from application to an industrial AC motor

A transport model of the turbulent scalar-velocity

Performance tests of the third-order turbulence closure for predictions of separating and recirculating flows in backward-facing steps were studied. Computations of the momentum and temperature fields in the flow domain being considered entail the solution of time-averaged transport equations containing the second-order turbulent fluctuating products. The triple products, which are responsible for the diffusive transport of the second-order products, attain greater significance in separating and reattaching flows. The computations are compared with several algebraic models and with the experimental data. The prediction was improved considerably, particularly in the separated shear layer. Computations are further made for the temperature-velocity double products and triple products. Finally, several advantages were observed in the usage of the transport equations for the evaluation of the turbulence triple products; one of the most important features is that the transport model can always take the effects of convection and diffusion into account in strong convective shear flows such as reattaching separated layers while conventional algebraic models cannot account for these effects in the evaluation of turbulence variables

Higher order turbulence closure models

Theoretical models are developed and numerical studies conducted on various types of flows including both elliptic and parabolic. The purpose of this study is to find better higher order closure models for the computations of complex flows. This report summarizes three new achievements: (1) completion of the Reynolds-stress closure by developing a new pressure-strain correlation; (2) development of a parabolic code to compute jets and wakes; and, (3) application to a flow through a 180 deg turnaround duct by adopting a boundary fitted coordinate system. In the above mentioned models near-wall models are developed for pressure-strain correlation and third-moment, and incorporated into the transport equations. This addition improved the results considerably and is recommended for future computations. A new parabolic code to solve shear flows without coordinate tranformations is developed and incorporated in this study. This code uses the structure of the finite volume method to solve the governing equations implicitly. The code was validated with the experimental results available in the literature

Modeling gap seeking behaviors for agent-based crowd simulation

Research on agent-based crowd simulation has gained tremendous momentum in recent years due to the increase of computing power. One key issue in this research area is to develop various behavioral models to capture the microscopic behaviors of individuals (i.e., agents) in a crowd. In this paper, we propose a novel behavior model for modeling the gap seeking behavior which can be frequently observed in real world scenarios where an individual in a crowd proactively seek for gaps in the crowd flow so as to minimize potential collision with other people. We propose a two-level modeling framework and introduce a gap seeking behavior model as a proactive conflict minimization maneuver at global navigation level. The model is integrated with the reactive collision avoidance model at local steering level. We evaluate our model by simulating a real world scenario. The results show that our model can generate more realistic crowd behaviors compared to the classical social-force model in the given scenario

Making a Difference to the Environment: Understanding Undergraduates Environmental Behaviour

Understanding undergraduates’ environmental behaviour is important as they will be the leaders of the country in the near future. They play an important role in protecting and conserving the environment. This paper investigates the undergraduates’ behaviours towards the environment after completing the Environment Economics course. Theory of Planned Behaviour (TPB) together with some other factors such as academic performance, government regulations and perceived importance of nature are employed. Multiple regression analysis shows undergraduates’ behaviours is positively affected by attitude and perceived importance of nature, and negatively influenced by government regulations. Although academic performance is very important to provide an understanding of basic principles of environmental sustainability, it however does not significantly influence their environmental behaviour. The finding hopes to assist the policy makers to plan future strategies so that the undergraduates behaviours can make a difference to nature and the environment

Improvement of the Reynolds-stress model by a new pressure-strain correlation

A study is made to improve the predictions of Reynolds stresses in backward facing step flows, through modifications of the pressure-strain correlation. The mean-strain term of the pressure-strain correlation is formulated only in terms of nonisotropic turbulence in order to take the severe nonisotropic effect caused by a separating flow. This model is compared with other models and results are verified with experimental results

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships