Sampled-data fuzzy controller for continuous nonlinear systems

The sampled-data fuzzy control of nonlinear systems is presented. The consequents of the fuzzy controller rules are linear sampled-data sub-controllers. As a result, the fuzzy controller is a weighted sum of some linear sampled-data sub-controllers that can be implemented by a microcontroller or a digital computer to lower the implementation cost. Consequently, a hybrid fuzzy controller consisting of continuous-time grades of memberships and discrete-time sub-controller is obtained. The system stability of the fuzzy control system is investigated on the basis of Lyapunov-based approach. The sampling activity introduces discontinuity to complicate the system dynamics and make the stability analysis difficult. The proposed fuzzy controller exhibits a favourable property to alleviate the conservativeness of the stability analysis. Furthermore, linear matrix inequality-based performance conditions are derived to guarantee the system performance of the fuzzy control system. An application example is given to illustrate the merits of the proposed approac

Developing a Nasal Organotypic Model to Investigate the Effects of the Nasal Microbiome on Susceptibility to Pathogens

The microbiota is essential to the functioning of the immune system. The nasal milieu secretes immune molecules that can be influenced by diverse bacteria. Hence commensals that enhance anti-viral responses may confer resistance to respiratory viral infection. Our collaborators have identified 7 microbial state types (CST) defined by indicator species in the nose and recently, through analyses of nasal immune molecules, we have categorized the nasal immune profile types into 8 groups (IPT). Although the IPTs correlated with certain CSTs, the influence of the nasal microbiome on susceptibility to respiratory pathogens is still unknown. Defining this complex relationship requires a relevant in vitro model which recapitulates key aspects of the in vivo nasal epithelium (pseudostratification, mucociliary differentiation), can sustain stable bacterial communities in a relevant environment (air-interfaced), and can support infection with respiratory pathogens (e.g., Staphylococcus aureus, influenza, SARS-CoV-2). Conventionally cultured cells lack innate protective features such as mucus and cilia and do not express physiological levels of innate immune mediators or pathogen entry receptors. These epithelial characteristics are crucial to reconstruct the complexity of microbiome-host-pathogen interactions in a controlled in vitro model. We have previously developed a nasal model capable of being infected by SARS-CoV-2, however due to variability in the source of cells, maintenance of culture consistency was difficult to achieve. Some obstacles included nonviable cells at isolation, fibroblast contaminations, and early death of differentiated cells. Hypothesis: In this study, we aim to optimize our current nasal model to provide consistent cell cultures to support bacterial co-cultures and SARS-CoV-2 infection studies

A stepwise based fuzzy regression procedure for developing customer preference models in new product development

Fuzzy regression methods have commonly been used to develop consumer preferences models which correlate the engineering characteristics with consumer preferences regarding a new product; the consumer preference models provide a platform whereby product developers can decide the engineering characteristics in order to satisfy consumer preferences prior to developing the products. Recent research shows that these fuzzy regression methods are commonly used to model customer preferences. However, these approaches have a common limitation in that they do not investigate the appropriate polynomial structure which includes significant regressors with only significant engineering characteristics; also, they cannot generate interaction or high-order regressors in the models. The inclusion of insignificant regressors is not an effective approach when developing the models. Exclusion of significant regressors may affect the generalization capability of the consumer preference models. In this paper, a novel fuzzy modelling method is proposed, namely fuzzy stepwise regression (F-SR), in order to develop a customer preference model which is structured with an appropriate polynomial which includes only significant regressors.Based on the appropriate polynomial structure, the fuzzy coefficients are determined using the fuzzy least square regression. The developed fuzzy regression model attempts to obtain a better generalization capability using a smaller number of regressors. The effectiveness of the F-SR is evaluated based on two design problems, namely a tea maker design and a solder paste dispenser design. Results show that better generalization capabilities can be obtained compared with the fuzzy regression methods commonly-used for new product development. Also, smaller-scale consumer preference models with fewer engineering characteristics can be obtained. Hence, a simpler and more effective product development platform can be provided

An Improved GA Based Modified Dynamic Neural Network for Cantonese-Digit Speech Recognition

Author name used in this publication: F. H. F. Leung2007-2008 > Academic research: refereed > Chapter in an edited book (author)published_fina

Classification of epilepsy using computational intelligence techniques

AbstractThis paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural network (ANN) and support vector machines (SVMs) combined with supervised learning algorithms, and k-means clustering (k-MC) combined with unsupervised techniques are employed to classify the three seizure phases. Different techniques to combine binary SVMs, namely One Vs One (OvO), One Vs All (OvA) and Binary Decision Tree (BDT), are employed for multiclass classification. Comparisons are performed with two traditional classification methods, namely, k-Nearest Neighbour (k-NN) and Naive Bayes classifier. It is concluded that SVM-based classifiers outperform the traditional ones in terms of recognition accuracy and robustness property when the original clinical data is distorted with noise. Furthermore, SVM-based classifier with OvO provides the highest recognition accuracy, whereas ANN-based classifier overtakes by demonstrating maximum accuracy in the presence of noise

The association of types of training and practice settings with doctors’ empathy and patient enablement among patients with chronic illness in Hong Kong

Background: The increase in non-communicable disease (NCD) is becoming a global health problem and there is an increasing need for primary care doctors to look after these patients although whether family doctors are adequately trained and prepared is unknown. Objective: This study aimed to determine if doctors with family medicine (FM) training are associated with enhanced empathy in consultation and enablement for patients with chronic illness as compared to doctors with internal medicine training or without any postgraduate training in different clinic settings. Methods: This was a cross-sectional questionnaire survey using the validated Chinese version of the Consultation and Relational Empathy (CARE) Measure as well as Patient Enablement Instrument (PEI) for evaluation of quality and outcome of care. 14 doctors from hospital specialist clinics (7 with family medicine training, and 7 with internal medicine training) and 13 doctors from primary care clinics (7 with family medicine training, and 6 without specialist training) were recruited. In total, they consulted 823 patients with chronic illness. The CARE Measure and PEI scores were compared amongst doctors in these clinics with different training background: family medicine training, internal medicine training and those without specialist training. Generalized estimation equation (GEE) was used to account for cluster effects of patients nested with doctors. <b>Results</b> Within similar clinic settings, FM trained doctors had higher CARE score than doctors with no FM training. In hospital clinics, the difference of the mean CARE score for doctors who had family medicine training (39.2, SD = 7.04) and internal medicine training (35.5, SD = 8.92) was statistically significant after adjusting for consultation time and gender of the patient. In the community care clinics, the mean CARE score for doctors with family medicine training and those without specialist training were 32.1 (SD = 7.95) and 29.2 (SD = 7.43) respectively, but the difference was not found to be significant. For PEI, patients receiving care from doctors in the hospital clinics scored significantly higher than those in the community clinics, but there was no significant difference in PEI between patients receiving care from doctors with different training backgrounds within similar clinic setting. Conclusion: Family medicine training was associated with higher patient perceived empathy for chronic illness patients in the hospital clinics. Patient enablement appeared to be associated with clinic settings but not doctors’ training background. Training in family medicine and a clinic environment that enables more patient doctor time might help in enhancing doctors’ empathy and enablement for chronic illness patients