An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers

This study provides evidence supporting the use of the update strategies for the support vector regression (SVR) model. Firstly, the fitting and interpolation method (FIM) is presented to select SVR parameters, and three infill strategies are adopted to search for update points. Secondly, the infill strategy and parameter selection method are illustrated by test functions that illustrate their dependability. The distribution of update points, the sample density and the proportion of update points are discussed. Finally, the adaptive SVR surrogate model is applied to optimize the protective effect of railway wind barriers. The result shows that the parameter selection method has high stability. On the whole, the accuracy of the adaptive SVR model using a suitable infill strategy will be improved with an increasing proportion of update points if the final number of training points is identical. The optimization result shows an optimal porosity of 0.117 when the height of the railway wind barrier is 2.05 m (full scale).

     

Stability, convergence, and performance of an adaptive controlalgorithm applied to a randomly varying system

The stability and performance of a stochastic adaptive control algorithm applied to a randomly varying linear system are investigated. The authors demonstrate that: loss functions on the input-output process converge to their expectation with respect to an invariant probability at a geometric rate, and hence, a form of stochastic exponential asymptotic stability is established; and when the parameter variation and measurement noise are small, it is shown that the performance is nearly optimal, and if an excitation signal is added in the control law, near consistency of the parameter estimates is obtained. Further results include central limit theorems and the law of large numbers of the input-output and parameter processes     

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method.     

An adaptive sliding mode controller design to cope with unmatched uncertainties and disturbance in a MEMS voltage reference source

Tunable micro-electro-mechanical systems (MEMS) capacitors as the fundamental parts are embedded in MEMS AC voltage reference sources (VRS). Being concerned with the accuracy of the output voltage in the reference sources, it gets important to address uncertainties in the physical parameters of the MEMS capacitor. The uncertainties have the great inevitable potentiality of bringing about output voltage perturbation. The output deterioration is more remarkable when the uncertainties are accompanied by disturbance and noise. Manufacturers have been making great attempts to make the MEMS adjustable capacitor with desired rigorous physical characteristics. They have also tried to mitigate physical parameter veracity. However, ambiguity in the values of the parameters inescapably occurs in fabrication procedures since the micro-machining process might itself suffer from uncertainties. Employing a proportional integral (PI) adaptive sliding mode controller (ASMC), both terms of matched and unmatched uncertainties as well as the disturbance, are addressed in this work for the MEMS AC VRS so that a strict voltage is stabilized while the system is simultaneously subjected into uncertainties and exogenous disturbance. Cross-talk, some inertial forces, and electrostatic coercions may appear as matched and unmatched disturbances. Alteration in stiffness and damping coefficients might also take place as matched uncertainties due to variations in the fabrication process or even working environment. The simulation results in the paper are persuasive and the controller design has shown a satisfactory tracking performance.     

An approach to identify design and manufacturing features from a data exchanged part model

Due to the large variety of CAD systems in the market, data exchange between different CAD systems is indispensable. Currently, data exchange standards such as STEP and IGES, etc. provide a unique approach for interfacing among different CAD platforms. Once the feature-based CAD model created in one CAD system is input into another via data exchange standards, many of the original features and the feature-related information may not exist any longer. The identification of the design features and their further decomposition into machining features for the downstream activities from a data exchanged part model is a bottleneck in integrated product and process design and development. In this paper, the feature panorama is succinctly articulated from the viewpoint of product design and manufacturing. To facilitate feature identification and extraction, a multiple-level feature taxonomy and hierarchy is proposed based on the characteristics of part geometry and topology entities. The relationships between the features and their geometric entities are established. A litany of algorithms for the identification of design and machining features are proposed. Besides, how to recognize the intersecting features or compound features based on the featureless chunks of geometry entities is critical and the issue is addressed in the paper. A multi-level compound feature representation and recognition approach are presented. Finally, case studies are used to illustrate the validity of the approach and algorithms proposed for the identification of the features from CAD part models in neutral format.     

A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery

Operational use of remote sensing as a tool for post-fire Mediterranean forest management has been limited by problems of classification accuracy arising from confusion between burned and non-burned land, especially within shaded areas. Object-oriented image analysis has been developed to overcome the limitations and weaknesses of traditional image processing methods for feature extraction from high spatial resolution images. The aim of this work was to evaluate the performance of an object-based classification model developed for burned area mapping, when applied to topographically and non-topographically corrected Landsat Thematic Mapper (TM) imagery for a site on the Greek island of Thasos. The image was atmospherically and geometrically corrected before object-based classification. The results were compared with the forest perimeter map generated by the Forest Service. The accuracy assessment using an error matrix indicated that the removal of topographic effects from the image before applying the object-based classification model resulted in only slightly more accurate mapping of the burned area (1.16% increase in accuracy). It was concluded that topographic correction is not essential prior to object-based classification of a burned Mediterranean landscape using TM data.     

On the theoretical link between design parameters and performance in cross-flow fans: a numerical and experimental study

Cross-flow fan performance strictly depends on the complex configuration of the non-axisymmetrical flow field within the machine. The flow field, in turn, is deeply influenced by the design parameters of both casing and impeller geometry. In this paper, the relationship between the design parameters of the geometrical configuration and fan performance is discussed in a theoretical perspective, analyzing the features of the corresponding flow fields. These are reconstructed by a numerical study on cross-flow fan operation carried out for a representative set of configurations at different throttling conditions. Time-accurate solutions for a two-dimensional viscous and incompressible model of the fan using a sliding mesh technique are calculated with a commercial CFD code. The numerical results are validated with experimental data obtained from tests on performance and from local measurements of the flow field.     

A procedure to design a structural and measurement model of Intellectual Capital: An exploratory study

An experiment was performed to understand the use of Intellectual Capital in a knowledge-based organization. A theoretical model was created; it interconnects the Intellectual Capital components as a way of understanding the intellectual wealth of a learning organization. Hypotheses were formulated from this. Data were then collected at two different time periods. These were then analysed using two scientific tools: concept mapping and structural equations modeling. Both were found to provide valuable information in studying Intellectual Capital in a knowledge-based firm.     

An object-oriented application framework for building water resource information and planning tools applied to the design of a flood analysis system

An object-oriented framework for building computer based modelling tools for water resource planning is presented. The focus is on building a program for flood calculations in river systems with several reservoirs and water transfer structures. The foundation for the flood model is a general application framework for building hydrological modelling tools. The general framework provides the user with tools for describing the structural components of the hydrological system, their relation in the system topology and controlling the behaviour of the system during simulation. Hydrological models are often data intensive, and the framework is equipped with tools to handle both time series and spatially-distributed data efficiently. During the development, effort has been put into supporting future changes and extensions to the model system, as well as creating sound reusable components that will benefit future development and maintenance. A flood modelling application in the Norwegian river Gudbrandsdalslågen is described to illustrate the use of the toolkit.     

A human-centred methodology applied to decision support system design and evaluation in a railway network context

This article describes a methodological process for the design and evaluation of an interactive system in an industrial context. The process is called the U-model, and has been used in many projects since the early 1990s. The article describes a recent case study in which the U-model was adapted and used during a project involving the design of a decision support system intended for a railway investment context.

室外3D散射信道中ULA/UCA天线MIMO性能分析

为更好地解决室外三维(3D)空间域的移动通信问题,提出了一种新型室外3D空间域散射信道统计模型,主要研究MIMO多天线系统的接收性能。在宏蜂窝小区移动通信环境中,模型能够估计多径衰落信道的重要空间信道参数,首先推导了多径衰落信道的到达角(Angle of Arrival,AOA)概率密度分布。此外模型在设置多输入多输出(Multiple Input Multiple Output,MIMO)多天线的均匀线性阵列(Uniform Linear Array,ULA)和圆形阵列(Uniform Circular Array,UCA)的基础上,分析了波达信号不同方位角分布对散射信道中MIMO性能的影响因素,包括MIMO空间相关性、信道容量以及天线阵列配置。数值仿真结果与3D多径衰落信道模型对比,表明本模型的信道参数估计符合理论和实际的通信环境,为评估MIMO天线接收系统与仿真无线通信系统提供了创新性的研究。     

An adaptive low-dimensional control to compensate for actuator redundancy and FES-induced muscle fatigue in a hybrid neuroprosthesis

To restore walking and standing function in persons with paraplegia, a hybrid walking neuroprosthesis that combines a powered exoskeleton and functional electrical stimulation (FES) can be more advantageous than sole FES or powered exoskeleton technologies. However, the hybrid actuation structure introduces certain control challenges: actuator redundancy, cascaded muscle activation dynamics, FES-induced muscle fatigue, and unmeasurable states. In this paper, a human motor control inspired control scheme is combined with a dynamic surface control method to overcome these challenges. The new controller has an adaptive muscle synergy-based feedforward component which requires a fewer number of control signals to actuate multiple effectors in a hybrid neuroprosthesis. In addition, the feedforward component has an inverse fatigue signal to counteract the effects of the muscle fatigue. A dynamic surface control (DSC) method is used to deal with the cascaded actuation dynamics without the need for acceleration signals. The DSC structure was modified with a delay compensation term to deal with the electromechanical delays due to FES. A model based estimator is used to estimate the unmeasurable fatigue and actuator activation signals. The development of the controller and a Lyapunov stability analysis, which yielded semi-global uniformly ultimately boundedness, are presented in the paper. Computer simulations were performed to test the new controller on a 2 degrees of freedom fixed hip model after which preliminary experiments were conducted on one able-bodied male subject in the fixed hip configuration.     

Genetic algorithm with advanced mechanisms applied to the protein structure prediction in a hydrophobic-polar model and cubic lattice

This paper presents a genetic algorithm applied to the protein structure prediction in a hydrophobic-polar model on a cubic lattice. The proposed genetic algorithm is extended with crowding, clustering, repair, local search and opposition-based mechanisms. The crowding is responsible for maintaining the good solutions to the end of the evolutionary process while the clustering is used to divide a whole population into a number of subpopulations that can locate different good solutions. The repair mechanism transforms infeasible solutions to feasible solutions that do not occupy the lattice point for more than one monomer. In order to improve convergence speed the algorithm uses local search. This mechanism improves the quality of conformations with the local movement of one or two consecutive monomers through the entire conformation. The opposition-based mechanism is introduced to transform conformations to the opposite direction. In this way the algorithm easily improves good solutions on both sides of the sequence. The proposed algorithm was tested on a number of well-known hydrophobic-polar sequences. The obtained results show that the mechanisms employed improve the algorithm's performance and that our algorithm is superior to other state-of-the-art evolutionary and swarm algorithms.     

An event based simulation model to evaluate the design of a rail interchange yard,which provides service to high speed and conventional railways

Railway interchanges are key operational points on a railway. However, railways are changing due to rail networks being upgraded to include high speed lines. Therefore, to allow for the integration of high speed lines with current conventional railways there is a prevalent need for railway interchanges to be redesigned. The scope of this paper is to conduct a study to design a railway yard of the future, which will be able to facilitate an interchange between a high speed railway and a conventional railway. The design method implemented in this study is a progressive approach, which is based upon a simulation with subsequent evaluations and revisions. The final designs given in this study have been proven through evaluation to be a promising stream of rail research. The designs have also allowed for a discussion to be carried out on the economic benefits, which can be achieved for the United Kingdom’s High Speed Two (HS2) project by utilising the railway interchanges designed in this study between the new high speed line and the current conventional rail network.     

Team shared mental model as a contributing factor to team performance and students’ course satisfaction in blended courses

The purpose of the study was to examine how team shared mental model (SMM), team performance, and students’ course satisfaction change over time and how Team-SMM affects team performance and students’ course satisfaction. Forty-eight preservice teachers from two undergraduate blended courses participated in this study. The data were obtained via two online questionnaires (shared mental model instrument and Course Satisfaction Scale) administered throughout Spring 2010. Data analysis was conducted using repeated measures ANOVA and multiple linear regression techniques. Results indicated that Team-SMM and students’ course satisfaction changed slightly over time but the change was not statistically significant. Team performance increased significantly over the courses. Additionally, attitudes towards teammates and task were closely linked with effective team performance and students satisfaction. In the early stage of the courses, general team knowledge, resource, and environment explained a significant amount of variance in students’ course satisfaction.     

An algorithm for sampling subsets of H/sub /spl infin// with applications to risk-adjusted performance analysis and model (in)validation

In spite of their potential to reduce computational complexity, the use of probabilistic methods in robust control has been mostly limited to parametric uncertainty, since the problem of sampling causal bounded operators is largely open. In this note, we take steps toward removing this limitation by proposing a computationally efficient algorithm aimed at uniformly sampling suitably chosen subsets of H/sub /spl infin//. As we show in the note, samples taken from these sets can be used to carry out model (in)validation and robust performance analysis in the presence of structured dynamic linear time-invariant uncertainty, problems known to be NP-hard in the number of uncertainty blocks.