Nonlinear kinematic tolerance analysis of planar mechanical systems

This paper presents a nonlinear kinematic tolerance analysis algorithm for planar mechanical systems comprised of higher kinematic pairs. The part profiles consist of line and circle segments. Each part translates along a planar axis or rotates around an orthogonal axis. The part shapes and motion axes are parameterized by a vector of tolerance parameters with range limits. A system is analyzed in two steps. The first step constructs generalized configuration spaces, called contact zones, that bound the worst-case kinematic variation of the pairs over the tolerance parameter range. The zones specify the variation of the pairs at every contact configuration and reveal failure modes, such as jamming, due to changes in kinematic function. The second step bounds the worst-case system variation at selected configurations by composing the zones. Case studies show that the algorithm is effective, fast, and more accurate than a prior algorithm that constructs and composes linear approximations of contact zones.     

Adaptive NN control for a class of strict-feedback discrete-time nonlinear systems

In this paper, both full state and output feedback adaptive neural network (NN) controllers are presented for a class of strict-feedback discrete-time nonlinear systems. Firstly, Lyapunov-based full-state adaptive NN control is presented via backstepping, which avoids the possible controller singularity problem in adaptive nonlinear control and solves the noncausal problem in the discrete-time backstepping design procedure. After the strict-feedback form is transformed into a cascade form, another relatively simple Lyapunov-based direct output feedback control is developed. The closed-loop systems for both control schemes are proven to be semi-globally uniformly ultimately bounded.     

Computing isophotos of surface of revolution and canal surface

Isophote of a surface consists of a loci of surface points whose normal vectors form a constant angle with a given fixed vector. It also serves as a silhouette curve when the constant angle is given as π/2. We present efficient and robust algorithms to compute isophotes of a surface of revolution and a canal surface. For the two kinds of surfaces, each point on the isophote is derived by a closed-form solution. To find each connected component in the isophote, we utilize the feature of surface normals. Both surfaces are decomposed into a set of circles, where the surface normal vectors at points on each circle construct a cone. The vectors which form a constant angle with given fixed vector construct another cone. We compute the parametric range of the connected component of the isophote by computing the parametric values of the surface which derive the tangential intersection of these two cones.     

Self-organizing neuro-fuzzy system for control of unknown plants

A cluster-based self-organizing neuro-fuzzy system (SO-NFS) is proposed for control of unknown plants. The neuro-fuzzy system can learn its knowledge base from input-output training data. A plant model is not required for training, that is, the plant is unknown to the SO-NFS. Using new data types, the vectors and matrices, a construction theory is developed for the organization process and the inference activities of the cluster-based SO-NFS. With the construction theory, a compact equation for describing the relation between the input base variables and inference results is established. This equation not only gives the inference relation between inputs and outputs but also specifies the linguistic meanings in the process. New pseudo-error learning control is proposed for closed-loop control applications. Using a cluster-based algorithm, the neuro-fuzzy system in its genesis can be generated by the stimulation of input/output training data to have its initial control policy (IF-THEN rules) for application. With the well-known random optimization method, the generated neuro-fuzzy system can learn its data base for specific applications. The proposed approach can be applied on control of unknown plants, and can levitate the curse of dimensionality in traditional fuzzy systems. Two examples are demonstrated.     

Adaptive fuzzy sliding mode controller for linear systems with mismatched time-varying uncertainties

A new design approach for an adaptive fuzzy sliding mode controller (AFSMC) for linear systems with mismatched time-varying uncertainties is presented. The coefficient matrix of the sliding function can be designed to satisfy a sliding coefficient matching condition provided time-varying uncertainties are bounded. With the sliding coefficient matching condition satisfied, an AFSMC is proposed to stabilize the uncertain system. The parameters of output fuzzy sets in the fuzzy mechanism are on-line adapted to improve the performance of the fuzzy sliding mode control system. The bounds of uncertainties are not required to be known in advance for the AFSMC. Stability of the fuzzy control system is guaranteed and the system is shown to be invariant on the sliding surface. Moreover, chattering around the sliding surface in sliding mode control can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed AFSMC.     

Interpolation functions of feedforward neural networks

Mathematical essence and structures of the feedforward neural networks are investigated in this paper. The interpolation mechanisms of the feedforward neural networks are explored. For example, the well-known result, namely, that a neural network is an universal approximator, can be concluded naturally from the interpolative representations. Finally, the learning algorithms of the feedforward neural networks are discussed.     

An interactive agent-based system for concept-based web search

Search engines are useful tools in looking for information from the Internet. However, due to the difficulties of specifying appropriate queries and the problems of keyword-based similarity ranking presently encountered by search engines, general users are still not satisfied with the results retrieved. To remedy the above difficulties and problems, in this paper we present a multi-agent framework in which an interactive approach is proposed to iteratively collect a user's feedback from the pages he has identified. By analyzing the pages gathered, the system can then gradually formulate queries to efficiently describe the content a user is looking for. In our framework, the evolution strategies are employed to evolve critical feature words for concept modeling in query formulation. The experimental results show that the framework developed is efficient and useful to enhance the quality of web search, and the concept-based semantic search can thus be achieved.     

Transitive mesh space of a progressive mesh

The paper investigates the set of all selectively refined meshes that can be obtained from a progressive mesh. We call the set the transitive mesh space of a progressive mesh and present a theoretical analysis of the space. We define selective edge collapse and vertex split transformations, which we use to traverse all selectively refined meshes in the transitive mesh space. We propose a complete selective refinement scheme for a progressive mesh based on the transformations and compare the scheme with previous selective refinement schemes in both theoretical and experimental ways. In our comparison, we show that the complete scheme always generates selectively refined meshes with smaller numbers of vertices and faces than previous schemes for a given refinement criterion. The concept of dual pieces of the vertices in the vertex hierarchy plays a central role in the analysis of the transitive mesh space and the design of selective edge collapse and vertex split transformations.     

Web-based collaborative inquiry learning

Abstract This study proposes a web-based collaborative inquiry learning system. This system uses the World-wide web (WWW) as a source of knowledge exploration, and provides exploratory problems to guide students to think and explore. A concept map is used as a tool of anchoring and representing knowledge during inquiry process. In the process of learning, learners are allowed to exchange the evidence they have collected, their personal opinions, and the concept maps that they have built. In order to effectively integrate the inquiry learning, collaborative learning, and concept map in the system, this study proposes a collaborative inquiry learning model and related learning activities. Two studies were constructed based on the collaborative inquiry learning model to investigate students' learning processes in the collaborative inquiry learning on the web.