John K. L. Ho

2-D system theory based iterative learning control for linear continuous systems with time delays

This paper presents two-dimensional (2-D) system theory based iterative learning control (ILC) methods for linear continuous multivariable systems with time delays in state or with time delays in input. Necessary and sufficient conditions are given for convergence of the proposed ILC rules. In this paper, we demonstrate that the 2-D linear continuous-discrete Roessers model can be applied to describe the ILC process of linear continuous time-delay systems. Three numerical examples are used to illustrate the effectiveness of the proposed ILC methods.

Object-Level Video Advertising: An Optimization Framework

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.

Approximation of dynamical time-variant systems by continuous-time recurrent neural networks

This paper studies the approximation ability of continuous-time recurrent neural networks to dynamical time-variant systems. It proves that any finite time trajectory of a given dynamical time-variant system can be approximated by the internal state of a continuous-time recurrent neural network. Given several special forms of dynamical time-variant systems or trajectories, this paper shows that they can all be approximately realized by the internal state of a simple recurrent neural network.

A framework for developing an agent-based collaborative service-support system in a manufacturing information network

Abstract A framework for the development of a collaborative service-support system, which is an object-based tool designed to provide a service to the manufacturing firms within an enterprise information network, is presented here. The service is carried out by virtual agents (VAs), which are software programs designed to accomplish specific tasks, just like ‘real’ human agents with specialized skills. This proposed system is equipped with the ‘push delivery’ feature, which automatically directs updated information straight to the user without having to be requested every time. The new feature of this collaborative service-support system is the incorporation of the task-management system, which is characterized by the combined capabilities of a rule-based inference mechanism and the object-oriented technology, in order to achieve the decomposition of a job into individual tasks that are to be automatically undertaken by the relevant virtual agents. The virtual agents can act cooperatively and collaboratively, to achieve the given goal, under the control of a task-control subsystem. This proposed service-support system enables the easy accessing and use of accurate and updated manufacturing information on the network, and therefore enhances the organizational productivity of the companies involved. In this paper, the detailed architecture and the components included in the proposed system are described.

A multimedia communication framework for the selection of collaborative partners in global manufacturing

Nowadays multimedia technology can develop all kinds of signals (i.e. sound, text, pictures, moving graphics, videos, etc) that can be transported from one place to another via a computer network. There is no doubt that multimedia technology can offer the global manufacturing enterprise a very powerful means to communicate with worldwide manufacturing partners. However, the utilization of multimedia communication does not always improve the effectiveness of collaboration among the manufacturing partners. It is, therefore, very important for a global manufacturing enterprise to ensure that the developed multimedia communication system can provide a tangible improvement to global manufacturing, rather than further complicating it. This paper proposes a collaborative framework to deal with multimedia communications, which can simplify the enterprises collaboration in an effective manner. The focus of this framework is on a key area, that of the global manufacturing partners selection. The proposed framework is generated from the CIM-OSA (Open System Architecture for CIM) approach and modelling. It is constructed and operated using the principles of communication, collaboration and coordination for global manufacturing, in conjunction with an object-oriented paradigm. The paper will particularly address information access difficulties, framework modelling and the collaboration of information flow.

Iterative learning control for a class of nonlinear discrete-time systems with multiple input delays

This article, addresses the robust iterative learning control (ILC) problem for nonlinear discrete time-delay systems. The derivation of convergence and robustness for the proposed ILC rule is based on two-dimensional (2D) linear inequalities. For a class of nonlinear discrete-time systems with multiple input delays, it is shown that the ILC tracking errors are bounded in the presence of state, output disturbances and initial state uncertainty. As these disturbances and uncertainty satisfy required conditions, the ILC tracking errors even can be driven to zero. Two numerical examples are used to validate the proposed ILC method.

Coordination and monitoring in an intelligent global manufacturing service system

Abstract Recent years have seen significant changes made in terms of enterprise strategy and manufacturing paradigms particularly for companies working together to remain world competitive in the volatile market. As the development of manufacturing is becoming less and less limited by national borders, a number of global manufacturing (GM) networks have been established, taking advantage of the quickly evolving computer networking and information technologies. Study indicates that while a number of frameworks related to global systems have been described in contemporary publications, the detailed structure and formulation of the central-monitoring mechanism of such a partnership system has not received as much attention as it deserves. This paper presents the framework of a GM service network that is characterized by its coordinating as well as monitoring capabilities. The main feature of the presented system is its rule-based reasoning capability to convert a job request from clients into basic tasks which are to be carried out by a group of virtual agents (VAs) equipped with various defined capabilities. This paper also presents a prototype program that has been developed and tested in an emulated GM environment, thereby validating the application of agent-based systems in enterprise networks.

Repetitive Learning Control of Nonlinear Continuous-Time Systems Using Quasi-Sliding Mode

In this brief, a quasi-sliding mode (QSM)-based repetitive learning control (RLC) method is proposed for tackling multi-input multi-output nonlinear continuous-time systems with matching perturbations. The proposed RLC method is able to perform rejection of periodic exogenous disturbances as well as tracking of periodic reference trajectories. It ensures a robust system stability when it is subject to nonperiodic uncertainties and disturbances. In this brief, an application to a robotic manipulator is used to illustrate the performance of the proposed QSM-based RLC method. A comparative study with the conventional variable structure control (VSC) technique is also included

Multidimensional Latent Semantic Analysis Using Term Spatial Information

In this paper, we consider the problem of in-depth document analysis. In particular, we propose a novel document analysis method, named multidimensional latent semantic analysis (MDLSA), which enables us to mine local information efficiently from a document with respect to term associations and spatial distributions. MDLSA works by first partitioning each document into paragraphs and building a term affinity graph, which represents the frequency of term cooccurrence in a paragraph. We then conduct a 2-D principal component analysis to achieve an optimal semantic mapping. This analysis involves finding the leading eigenvectors of the sample covariance matrix of a training set to characterize the lower dimensional semantic space. A hybrid document similarity measure is designed to further improve the performance of this framework. Our algorithm is examined in two document applications: retrieval and classification. Experimental results demonstrate that the proposed technique outperforms current algorithms with respect to accuracy and computational efficiency.

Further results on iterative learning control with convergence conditions for linear time-variant discrete systems

This article is concerned with some further results on iterative learning control (ILC) algorithms with convergence conditions for linear time-variant discrete systems. By converting two-Dimensional (2-D) ILC process of the linear time-variant discrete systems into 1-D linear time-invariant discrete systems, this article presents convergent ILC algorithms with necessary and sufficient conditions for two classes of linear time-variant discrete systems. Main results in (Li, X.-D., Ho, J.K.L., and Chow, T.W.S. (2005), ‘Iterative Learning Control for Linear Time-variant Discrete Systems Based on 2-D System Theory’, IEE Proceedings, Control Theory and Applications, 152, 13–18 and Huang, S.N., Tan, K.K., and Lee, T.H. (2002), ‘Necessary and Sufficient Condition for Convergence of Iterative Learning Algorithm’, Automatica 38, 1257–1260) are extended and generalised.