Promoting metacognitive skills through peer scaffolding in a CSCL environment

This paper aims to better understand the development of students’ metacognitive learning processes when participating actively in a CSCL system called KnowCat. To this end, a longitudinal case study was designed, in which 18 university students took part in a 12-month (two semesters) learning project. The students followed an instructional process, using specific features of the KnowCat design to support and improve their interaction processes, especially peer-learning processes. Our research involved both supervising the students’ collaborative learning processes throughout the learning project and focusing our analysis on the qualitative evolution of their interaction processes and of their metacognitive learning processes. The results of the current research suggest that the pedagogical use of the KnowCat system may favour and improve the development of the students’ metacognitive learning processes. In addition, the implications of the design of CSCL networks and related pedagogical issues are discussed.     

Video analysis based on volumetric event detection

During the past decade, feature extraction and knowledge acquisition based on video analysis have been extensively researched and tested on many applications such as closed-circuit television(CCTV)data analysis, large-scale public event control, and other daily security monitoring and surveillance operations with various degrees of success. However, since the actual video process is a multi-phased one and encompasses extensive theories and techniques ranging from fundamental image processing, computational geometry and graphics, and machine vision, to advanced artificial intelligence, pattern analysis, and even cognitive science, there are still many important problems to resolve before it can be widely applied. Among them, video event identification and detection are two prominent ones. Comparing with the most popular frame-to-frame processing mode of most of today's approaches and systems, this project reorganizes video data as a 3D volume structure that provides the hybrid spatial and temporal information in a unified space. This paper reports an innovative technique to transform original video frames to 3D volume structures denoted by spatial and temporal features. It then highlights the volume array structure in a so-called "pre-suspicion" mechanism for a later process. The focus of this report is the development of an effective and efficient voxel-based segmentation technique suitable to the volumetric nature of video events and ready for deployment in 3D clustering operations. The paper is concluded with a performance evaluation of the devised technique and discussion on the future work for accelerating the pre-processing of the original video data.     

Robust active suspension design subject to vehicle inertial parameter variations

This paper presents an approach in designing a robust controller for vehicle suspensions considering changes in vehicle inertial properties. A four-degree-of-freedom half-car model with active suspension is studied in this paper, and three main performance requirements are considered. Among these requirements, the ride comfort performance is optimized by minimizing the H∞ norm of the transfer function from the road disturbance to the sprung mass acceleration, while the road holding performance and the suspension deflection limitation are guaranteed by constraining the generalized H2 (GH2) norms of the transfer functions from the road disturbance to the dynamic tyre load and the suspension deflection to be less than their hard limits, respectively. At the same time, the controller saturation problem is considered by constraining its peak response output to be less than a given limit using the GH2 norm as well. By solving the finite number of linear matrix inequalities (LMIs) with the minimization optimization procedure, the controller gains, which are dependent on the time-varying inertial parameters, can be obtained. Numerical simulations on both frequency and bump responses show that the designed parameter-dependent controller can achieve better active suspension performance compared with the passive suspension in spite of the variations of inertial parameters.     

Comparison of two novel MRAS based strategies for identifying parameters in permanent magnet synchronous motors

Two model reference adaptive system (MRAS) estimators are developed for identifying the parameters of permanent magnet synchronous motors (PMSM) based on the Lyapunov stability theorem and the Popov stability criterion, respectively. The proposed estimators only need online measurement of currents, voltages, and rotor speed to effectively estimate stator resistance, inductance, and rotor flux-linkage simultaneously. The performance of the estimators is compared and verified through simulations and experiments, which show that the two estimators are simple, have good robustness against parameter variation, and are accurate in parameter tracking. However, the estimator based on the Popov stability criterion, which can overcome parameter variation in a practical system, is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator, in contrast to only one integral unit in the estimator based on the Lyapunov stability theorem. In addition, the estimator based on the Popov stability criterion does not need the expertise that is required in designing a Lyapunov function.     

Correlation of direct piezoelectric effect on EAPap under ambient factors

Direct piezoelectricity of electro-active papers(EAPap)is analysed in this paper. The test setups for direct effect are designed and determined. Different ambient factors impacting the piezoelectricity of EAPap, such as temperature, humidity, and strain rate, are applied and analyzed. Strong piezoelectricity of EAPap is found on the basis of the test results and in comparison with polyvinylidene fluoride(PVDF)and lead zirconate titanate(PZT)-5H. The maximum piezoelectric constant is achieved to be 504 pC/N. The reason of strong piezoelectricity of EAPap is discussed in this paper. The potential of EAPap as a biomimetic actuator and sensor is also investigated.     

Application of a remote image surveillance system in a robotic weapon

As one of the major steps toward a fully intelligent autonomous robotic weapon, we have made progress in three major areas, (1) design of the surveillance system by an AVR microcontroller, (2) implementation of the design mechanism, and (3) performance of the human- machine interface surveillance system via the LabVIEW graphical programming environment, so that the supervisor can control the vehicle with a keyboard or a specially adapted mouse. In order to accomplish all these achievements, there have been major additions and overhauls in both system software codes and system circuit board developments. All these developments, including a new algorithm and hardware implementation, are described in this article. The experimental results have shown the practicality of the AVR microcontroller, the LabVIEW graphical programming environment, and ZigBee wireless technology applied to a robotic weapon.     

Self-assembly of discrete self-similar fractals

In this paper, we search for theoretical limitations of the Tile Assembly Model (TAM), along with techniques to work around such limitations. Specifically, we investigate the self-assembly of fractal shapes in the TAM. We prove that no self-similar fractal weakly self-assembles at temperature 1 in a locally deterministic tile assembly system, and that certain kinds of discrete self-similar fractals do not strictly self-assemble at any temperature. Additionally, we extend the fiber construction of Lathrop et al. (2009) to show that any discrete self-similar fractal belonging to a particular class of “nice” discrete self-similar fractals has a fibered version that strictly self-assembles in the TAM.     

Representation before computation

My main objective is to point out a fundamental weakness in the conventional conception of computation and suggest a promising way out. This weakness is directly related to a gross underestimation of the role of object representation in a computational model, hence confining such models to an unrealistic (input) environment, which, in turn, leads to “unnatural” computational models. This lack of appreciation of the role of structural object representation has been inherited from logic and partly from mathematics, where, in the latter, the centuries-old tradition is to represent objects as unstructured “points”. I also discuss why the appropriate fundamental reorientation in the conception of computational models will bring the resulting study of computation closer to the “natural” computational constrains. An example of the pertinent, class-oriented, representational formalism developed by our group over many years—Evolving Transformation System (ETS)—is briefly outlined here, and several related general lines of research are suggested.