This paper surveys expert systems (ES) development using a literature review and classification of articles from 1995 to 2004 with a keyword index and article abstract in order to explore how ES methodologies and applications have developed during this period. Based on the scope of 166 articles from 78 academic journals (retrieved from five online database) of ES applications, this paper surveys and classifies ES methodologies using the following eleven categories: rule-based systems, knowledge-based systems, neural networks, fuzzy ESs, object-oriented methodology, case-based reasoning, system architecture, intelligent agent systems, database methodology, modeling, and ontology together with their applications for different research and problem domains. Discussion is presented, indicating the followings future development directions for ES methodologies and applications: (1) ES methodologies are tending to develop towards expertise orientation and ES applications development is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science, and human behavior could implement ES as another kind of methodology. (3) The ability to continually change and obtain new understanding is the driving power of ES methodologies, and should be the ES application of future works.

http://www.sci.brooklyn.cuny.edu/~kopec/cis718/fall_2005/sdarticle5.pdf

Expert system methodologies and applications—a decade review from 1995 to 2004

We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized da-ta points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approxi-mation for the tangent space at each point, and those tangent spaces are then aligned to give the global coordinates of the data pointswith respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can bequite small in some cases. We illustrate our algorithm using curves and surfaces both in 2D/3D Euclidean spaces and higher dimension-al Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.

Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

Object-oriented analysis

One dilemma with health claims is that too much information can confuse consumers and too little information can mislead them. Instead of using only one side of the package, a laboratory study is used to examine the effectiveness of various front-sided health claims when used in combination with a full health claim on the back panel. The results indicate that the presence of a short health claim on the front label generates more specific attribute-related thoughts, more inferences, and creates a more believable and positive image of the product in the consumers’ mind than does a longer health claim on the front label. This article concludes with a discussion of how labeling information needs to be presented to effectively allow consumers to understand and use claim information.

http://ieeexplore.ieee.org/iel5/5/31255/01454916.pdf

Information processing

In this paper, general requirements of next generation manufacturing systems are discussed, and the strategies to meet these requirements are considered. The production paradigms which apply these strategies are also classified. Particular emphasis is put on the paradigm of Reconfigurable Manufacturing System (RMS). Some key issues of the RMS design are discussed, and a critical review is presented concerning the developments of RMSs. Finally, suggestions of the RMS research are made and future research directions are identified.

/pdf/reconfigurable-manufacturing-systems-the-state-of-the-art-21c90ho9g2.pdf

Reconfigurable manufacturing systems: the state of the art

In this paper, we present new models and algorithms for object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize intrusiveness to viewers when ads are inserted in a video. For human clothing advertising, we design a deep convolutional neural network using face features to recognize human genders in a video stream. Human parts alignment is then implemented to extract human part features that are used for clothing retrieval. Second, we develop a heuristic algorithm to solve the proposed optimization problem. For comparison, we also employ the genetic algorithm to find solutions approaching the global optimum. Our novel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for object-level video advertising.

Object-Level Video Advertising: An Optimization Framework

This paper presents a practical prototype knowledge-based system, called IKMOULD, for mould design in the injection moulding process. It attempts to tackle the problem in a practical and integrative way, unlike the stand-alone and mathematical programs which have been developed in the past to solve only a part of the problem. A total quantitative and structured approach is not feasible in dealing with the complex and multirelated design problems generally involved in mould design. In this system, the computational module, the knowledge-based module and the graphic module for generating mould features are integrated within an interactive CAD-based framework. The knowledge base of the system can be accessed by mould designers through interactive programs so that their own intelligence and experience can also be incorporated with the total mould design. The approach adopted both speeds up the design process and facilitates design standardisation which in turn increases the speed of mould manufacture.

An Interactive Knowledge-Based CAD System For Mould Design in Injection Moulding Processes

The two-dimensional (2-D) system theory iterative learning control (ILC) techniques for linear time-invariant discrete systems are extended to the cases of linear time-variant discrete systems. By exploiting the convergent property of 2-D linear time-variant discrete systems with only one independent variable, a kind of 2-D system theory ILC approach is presented for linear time-variant discrete systems. Sufficient conditions are given for convergence of the proposed ILC rules. Two numerical examples are used to validate the ILC procedures.

Iterative learning control for linear time-variant discrete systems based on 2-D system theory

In this article, two adaptive iterative learning control ILC algorithms are presented for nonlinear continuous systems with non-parametric uncertainties. Unlike general ILC techniques, the proposed adaptive ILC algorithms allow that both the initial error at each iteration and the reference trajectory are iteration-varying in the ILC process, and can achieve non-repetitive trajectory tracking beyond a small initial time interval. Compared to the neural network or fuzzy system-based adaptive ILC schemes and the classical ILC methods, in which the number of iterative variables is generally larger than or equal to the number of control inputs, the first adaptive ILC algorithm proposed in this paper uses just two iterative variables, while the second even uses a single iterative variable provided that some bound information on system dynamics is known. As a result, the memory space in real-time ILC implementations is greatly reduced.

John K. L. Ho

Papers

Object-Level Video Advertising: An Optimization Framework

An Interactive Knowledge-Based CAD System For Mould Design in Injection Moulding Processes

Iterative learning control for linear time-variant discrete systems based on 2-D system theory

Adaptive ILC algorithms of nonlinear continuous systems with non-parametric uncertainties for non-repetitive trajectory tracking

Tribological properties of Tin-based Babbitt bearing alloy with polyurethane coating under dry and starved lubrication conditions