Design, implementation and testing of an intelligent knowledge-based system for the supervisory control of a hot rolling mill

This paper describes the design, implementation and testing of an intelligent knowledge-based supervisory control (IKBSC) system for a hot rolling mill process. A novel architecture is used to integrate an expert system with an existing supervisory control system and a new optimization methodology for scheduling the soaking pits in which the material is heated prior to rolling. The resulting IKBSC system was applied to an aluminium hot rolling mill process to improve the shape quality of low-gauge plate and to optimise the use of the soaking pits to reduce energy consumption. The results from the trials demonstrate the advantages to be gained from the IKBSC system that integrates knowledge contained within data, plant and human resources with existing model-based systems. (c) 2005 Elsevier Ltd. All rights reserved.

A supramolecular polymer based on tweezer-type pi-pi stacking interactions: molecular design for healability and enhanced toughness

An elastomeric, healable, supramolecular polymer blend comprising a chain-folding polyimide and a telechelic polyurethane with pyrenyl end groups is compatibilized by aromatic pi-pi stacking between the pi-electron-deficient diimide groups and the pi-electron-rich pyrenyl units. This interpolymer interaction is the key to forming a tough, healable, elastomeric material. Variable-temperature FTIR analysis of the bulk material also conclusively demonstrates the presence of hydrogen bonding, which complements the pi-pi stacking interactions. Variable-temperature SAXS analysis shows that the healable polymeric blend has a nanophase-separated morphology and that the X-ray contrast between the two types of domain increases with increasing temperature, a feature that is repeatable over several heating and cooling cycles. A fractured sample of this material reproducibly regains more than 95% of the tensile modulus, 91% of the elongation to break, and 77% of the modulus of toughness of the pristine material.

Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

A typology of internet users based on comparative affective states: evidence from eight countries

Purpose - The role of affective states in consumer behaviour is well established. However, no study to date has empirically examined online affective states as a basis for constructing typologies of internet users and for assessing the invariance of clusters across national cultures. Design/methodology/approach - Four focus groups with internet users were carried out to adapt a set of affective states identified from the literature to the online environment. An online survey was then designed to collect data from internet users in four Western and four East Asian countries. Findings - Based on a cluster analysis, six cross-national market segments are identified and labelled "Positive Online Affectivists", "Offline Affectivists", "On/Off-line Negative Affectivists", "Online Affectivists", "Indistinguishable Affectivists", and "Negative Offline Affectivists". The resulting clusters discriminate on the basis of national culture, gender, working status and perceptions towards online brands. Practical implications - Marketers may use this typology to segment internet users in order to predict their perceptions towards online brands. Also, a standardised approach to e-marketing is not recommended on the basis of affective state-based segmentation. Originality/value - This is the first study proposing affective state-based typologies of internet users using comparable samples from four Western and four East Asian countries.

Context-based interaction design for smart homes

This paper presents the notion of Context-based Activity Design (CoBAD) that represents context with its dynamic changes and normative activities in an interactive system design. The development of CoBAD requires an appropriate context ontology model and inference mechanisms. The incorporation of norms and information field theory into Context State Transition Model, and the implementation of new conflict resolution strategies based on the specific situation are discussed. A demonstration of CoBAD using a human agent scenario in a smart home is also presented. Finally, a method of treating conflicting norms in multiple information fields is proposed.

Nano structures through self-assembly of protected hydrophobic amino acids: encapsulation of rhodamine B dye by proline-based nanovesicles

The development of novel molecules for the creation of nanometer structures with specific properties has been the current interest of this research. We have developed a set of molecules from hydrophobic omega- and alpha-amino acids by protecting the -NH(2) with Boc (t-butyloxycarbonyl) group and -CO(2)H with para-nitroanilide such as BocHN-Xx-CONH-(p-NO(2))center dot C(6)H(4), where Xx is gamma-aminobutyric acid (gamma-Abu), (L)-isoleucine, alpha-aminoisobutyric acid, proline, etc. These molecules generate various nanometer structures, such as nanofibrils, nanotubes and nanovesicles, in methanol/water through the self-assembly of bilayers in which the nitro benzene moieties are stacked in the middle and the Boc-protected amino acids parts are packed in the outer surface. The bilayers can be further stacked one over the other through hydrophobic interactions to form multilayer structure, which helps to generate different kinds of nanoscopic structures. The formation of the nanostructures has been facilitated through the participation of various noncovalent interactions, such as hydrophobic interactions, hydrogen bonding and aromatic p-stacking interactions. Fluorescence microscopy and UV studies reveal that the nanovesicles generated from pro-based molecule can encapsulate dye molecules which can be released by addition of acid (at pH 2). These single amino acid based molecules are both easy to synthesize and cost-effective and therefore offer novel scaffolds for the future design of nanoscale structures.

Cross-layer design for MIMO systems over spatially correlated and keyhole Nakagami-m fading channels

Cross-layer design is a generic designation for a set of efficient adaptive transmission schemes, across multiple layers of the protocol stack, that are aimed at enhancing the spectral efficiency and increasing the transmission reliability of wireless communication systems. In this paper, one such cross-layer design scheme that combines physical layer adaptive modulation and coding (AMC) with link layer truncated automatic repeat request (T-ARQ) is proposed for multiple-input multiple-output (MIMO) systems employing orthogonal space--time block coding (OSTBC). The performance of the proposed cross-layer design is evaluated in terms of achievable average spectral efficiency (ASE), average packet loss rate (PLR) and outage probability, for which analytical expressions are derived, considering transmission over two types of MIMO fading channels, namely, spatially correlated Nakagami-m fading channels and keyhole Nakagami-m fading channels. Furthermore, the effects of the maximum number of ARQ retransmissions, numbers of transmit and receive antennas, Nakagami fading parameter and spatial correlation parameters, are studied and discussed based on numerical results and comparisons. Copyright © 2009 John Wiley & Sons, Ltd.