Quality Of Life And Functioning Among Community-based Individuals Of Schizophrenia In Hong Kong

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An adaptive palette reordering method for compressing color-indexed image

Center for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

In vivo manganese-enhanced MRI for visuotopic brain mapping

This study explored the feasibility of localized manganese-enhanced MRI (MEMRI) via 3 different routes of Mn(2+) administrations for visuotopic brain mapping of retinal, callosal, cortico-subcortical, transsynaptic and horizontal connections in normal adult rats. Upon fractionated intravitreal Mn(2+) injection, Mn enhancements were observed in the contralateral superior colliculus (SC) and lateral geniculate nucleus (LGN) by 45-60% at 1-3 days after initial Mn(2+) injection and in the contralateral primary visual cortex (V1) by about 10% at 2-3 days after initial Mn(2+) injection. Direct, single-dose Mn(2+) injection to the LGN resulted in Mn enhancement by 13-21% in V1 and 8-11% in SC of the ipsilateral hemisphere at 8 to 24 hours after Mn(2+) administration. Intracortical, single-dose Mn(2+) injection to the visual cortex resulted in Mn enhancement by 53-65% in ipsilateral LGN, 15-26% in ipsilateral SC, 32-34% in the splenium of corpus callosum and 17-25% in contralateral V1/V2 transition zone at 8 to 24 hours after Mn(2+) administration. Notably, some patchy patterns were apparent near the V1/V2 border of the contralateral hemisphere. Laminar-specific horizontal cortical connections were also observed in the ipsilateral hemisphere. The current results demonstrated the sensitivity of MEMRI for assessing the neuroarchitecture of the visual brains in vivo without depth-limitation, and may possess great potentials for studying the basic neural components and connections in the visual system longitudinally during development, plasticity, pharmacological interventions and genetic modifications.published_or_final_versio

Macro-approach of cell formation problem with consideration of machining sequence

Cellular Manufacturing System (CMS) which is based on the concept of Group Technology (GT) has been recognized as an efficient and effective way to improve the productivity in the factory. In recent years, there has been much effort done for continuing to improve CMS. Most researches concentrated on distinguishing the part families and machine cells either simultaneously or individually by considering of minimizing intercellular and intracellular part movements. However, fewer researches have studied the impact of the sequencing of machine cells. In light of this, the main aim of this present work is to study the impact of the sequencing of allocating the machine cells in minimizing intercellular part movement. The problem scope, which is also called as machine-part grouping problem (MPGP) together with the background of cell layout problem (CLP), has been identified. A mathematical model is formulated and part incidence matrix with operational sequence is often used. Since MPGP has been proved as an NP complete, genetic algorithm (GA) is employed as cell formation algorithms in solving this problem. © 2004 IEEE.published_or_final_versio

Online fault detection and isolation of nonlinear systems

This paper describes an online fault detection scheme for a class of nonlinear dynamic systems with modelling uncertainty and inaccessible states. Only the inputs and outputs of the system can be measured. The faults are assumed to be functions of the state, instead of the output and the input of the system. A nonlinear online approximator using dynamic recurrent neural network is utilised to monitor the faults in the system. The construction and the learning algorithm of the online approximator are presented. The stability, robustness and sensitivity of the fault detection scheme under certain assumptions are analysed. An example demonstrates the efficiency of the proposed fault detection scheme.published_or_final_versio

Modelling of nonlinear stochastic dynamical systems using neurofuzzy networks

Though nonlinear stochastic dynamical system can be approximated by feedforward neural networks, the dimension of the input space of the network may be too large, making it to be of little practical importance. The Nonlinear Autoregressive Moving Average model with eXogenous input (NARMAX) is shown to be able to represent nonlinear stochastic dynamical system under certain conditions. As the dimension of the input space is finite, it can be readily applied in practical application. It is well known that the training of recurrent networks using gradient method has a slow convergence rate. In this paper, a fast training algorithm based on the Newton-Raphson method for recurrent neurofuzzy network with NARMAX structure is presented. The convergence and the uniqueness of the proposed training algorithm are established. A simulation example involving a nonlinear dynamical system corrupted with the correlated noise and a sinusoidal disturbance is used to illustrate the performance of the proposed training algorithm.published_or_final_versio

Optimization of machine flexibility in an ion plating cell

In 1970s, manufacturing system performance was heavily depended on productivity. Manufacturers only concentrated on increasing productivity by increasing the number of workforce. The concept of flexibility began to introduce into the industry since 1990s, manufacturers realized that flexibility was a better solution to improve productivity by responding the changing environment in manufacturing system. However, limited researches on machine flexibility in Ion Plating (IP) industry were studied, most of them have focused on product development and quality of coating. The aim of this paper is to determine the optimal level of machine flexibility in an Ion Plating Cell (IPC) to improve the entire system performance. A Machine Loading Sequencing (MLS) model based on multi-objectives Genetic Algorithms (GA) was developed. In the case study, industrial data of a precious metal finishing company has been input into the proposed Machine Loading Sequencing Genetic Algorithm (MLSGA) model. Different level of machine flexibility will be assigned into different machines to determine the optimum while the overall system performance (i.e. on-time delivery, quality of product and production cost) has been maximized. The results demonstrated that the machine flexibility level in IPC should be zero under the recent IP technology. However, when the quality of coating is improved, machine flexibility should be introduced. © 2004 IEEE.published_or_final_versio

Support vector recurrent neurofuzzy networks in modeling nonlinear systems with correlated noise

Good generalization results are obtained from neurofuzzy networks if its structure is suitably chosen. To select the structure of neurofuzzy networks, the authors proposed a construction algorithm that is derived from the Support Vector Regression. However, the modeling errors are assumed to be uncorrelated. In this paper, systems with correlated modeling errors are considered. The correlated noise is modeled separately by a recurrent network. The overall network is referred to as the support vector recurrent neurofuzzy networks. The prediction error method is used to train the networks, where the derivatives are computed by a sensitivity model. The performance of proposed networks is illustrated by an example involving a nonlinear dynamic system corrupted by correlated noise.published_or_final_versio

Fault estimation for a class of nonlinear dynamical systems

In this paper, model based fault estimation for a class of nonlinear dynamical systems is investigated. The state of the system is assumed unavailable, and a nonlinear observer is used to estimate the state. In the observer, neurofuzzy network is used as the approximator to estimate faults. The network is trained on-line and the convergence of the proposed learning algorithm is established. Abrupt fault and incipient fault are analyzed in the paper and they can be estimated accurately using neurofuzzy network with the proposed learning algorithm.published_or_final_versio

The Chinese classroom paradox: A cross-cultural comparison of teacher controlling behaviors

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