Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

A novel continuous forward algorithm for RBF neural modelling

A continuous forward algorithm (CFA) is proposed for nonlinear modelling and identification using radial basis function (RBF) neural networks. The problem considered here is simultaneous network construction and parameter optimization, well-known to be a mixed integer hard one. The proposed algorithm performs these two tasks within an integrated analytic framework, and offers two important advantages. First, the model performance can be significantly improved through continuous parameter optimization. Secondly, the neural representation can be built without generating and storing all candidate regressors, leading to significantly reduced memory usage and computational complexity. Computational complexity analysis and simulation results confirm the effectiveness.

Integrated structure selection and parameter optimisation for eng-genes neural models

The eng-genes concept involves the use of fundamental known system functions as activation functions in a neural model to create a 'grey-box' neural network. One of the main issues in eng-genes modelling is to produce a parsimonious model given a model construction criterion. The challenges are that (1) the eng-genes model in most cases is a heterogenous network consisting of more than one type of nonlinear basis functions, and each basis function may have different set of parameters to be optimised; (2) the number of hidden nodes has to be chosen based on a model selection criterion. This is a mixed integer hard problem and this paper investigates the use of a forward selection algorithm to optimise both the network structure and the parameters of the system-derived activation functions. Results are included from case studies performed on a simulated continuously stirred tank reactor process, and using actual data from a pH neutralisation plant. The resulting eng-genes networks demonstrate superior simulation performance and transparency over a range of network sizes when compared to conventional neural models. (c) 2007 Elsevier B.V. All rights reserved.

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

Developing Approaches for Solving a Telecommunications Feature Subscription Problem

Call control features (e.g., call-divert, voice-mail) are primitive options to which users can subscribe off-line to personalise their service. The configuration of a feature subscription involves choosing and sequencing features from a catalogue and is subject to constraints that prevent undesirable feature interactions at run-time. When the subscription requested by a user is inconsistent, one problem is to find an optimal relaxation, which is a generalisation of the feedback vertex set problem on directed graphs, and thus it is an NP-hard task. We present several constraint programming formulations of the problem. We also present formulations using partial weighted maximum Boolean satisfiability and mixed integer linear programming. We study all these formulations by experimentally comparing them on a variety of randomly generated instances of the feature subscription problem.

Self-reported teamwork in family health team practices in Ontario:Organizational and cultural predictors of team climate

Objective: To determine the organizational predictors of higher scores on team climate measures as an indicator of the functioning of a family health team (FHT). Design: Cross-sectional study using a mailed survey. Setting: Family health teams in Ontario. Participants: Twenty-one of 144 consecutively approached FHTs; 628 team members were surveyed. Main outcome measures: Scores on the team climate inventory, which assessed organizational culture type (group, developmental, rational, or hierarchical); leadership perceptions; and organizational factors, such as use of electronic medical records (EMRs), team composition, governance of the FHT, location, meetings, and time since FHT initiation. All analyses were adjusted for clustering of respondents within the FHT using a mixed random-intercepts model. Results: The response rate was 65.8% (413 of 628); 2 were excluded from analysis, for a total of 411 participants. At the time of survey completion, there was a median of 4 physicians, 11 other health professionals, and 4 management and clerical staff per FHT. The average team climate score was 3.8 out of a possible 5. In multivariable regression analysis, leadership score, group and developmental culture types, and use of more EMR capabilities were associated with higher team climate scores. Other organizational factors, such as number of sites and size of group, were not associated with the team climate score. Conclusion: Culture, leadership, and EMR functionality, rather than organizational composition of the teams (eg, number of professionals on staff, practice size), were the most important factors in predicting climate in primary care teams.

An optimal algorithm for low power multiplierless FIR filter design using chebychev criterion

In this paper, we propose a novel finite impulse response (FIR) filter design methodology that reduces the number of operations with a motivation to reduce power consumption and enhance performance. The novelty of our approach lies in the generation of filter coefficients such that they conform to a given low-power architecture, while meeting the given filter specifications. The proposed algorithm is formulated as a mixed integer linear programming problem that minimizes chebychev error and synthesizes coefficients which consist of pre-specified alphabets. The new modified coefficients can be used for low-power VLSI implementation of vector scaling operations such as FIR filtering using computation sharing multiplier (CSHM). Simulations in 0.25um technology show that CSHM FIR filter architecture can result in 55% power and 34% speed improvement compared to carry save multiplier (CSAM) based filters.

On-off based charging strategies for EVs connected to a Low Voltage distributon network

The development of appropriate Electric Vehicle (EV) charging strategies has been identified as an effective way to accommodate an increasing number of EVs on Low Voltage (LV) distribution networks. Most research studies to date assume that future charging facilities will be capable of regulating charge rates continuously, while very few papers consider the more realistic situation of EV chargers that support only on-off charging functionality. In this work, a distributed charging algorithm applicable to on-off based charging systems is presented. Then, a modified version of the algorithm is proposed to incorporate real power system constraints. Both algorithms are compared with uncontrolled and centralized charging strategies from the perspective of both utilities and customers. © 2013 IEEE.

Advances in Learning Bayesian Networks of Bounded Treewidth

This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Issues in fe modelling of FRP strengthened masonry arch bridges

Externally bonding of FRP composites is an effective technique for retrofitting historical masonry arch structures. A major failure mode in such strengthened structures is the debonding of FRP from the masonry. The bond behaviour between FRP and masonry thus plays a crucial role in these structures. Major challenges exist in the finite element modelling of such structures, such as modelling of mixed Mode-I and Mode-II bond behaviour between the FRP and the curved masonry substrate, modelling of existing damages in the masonry arches, consideration of loading history in the unstrengthened and strengthened structure etc. This paper presents a rigorous FE model for simulating FRP strengthened masonry arch structures. A detailed solid model was developed for simulating the masonry and a mixed-mode interface model was used for simulating the FRP-to-masonry bond behaviour. The model produces results in very close agreement with test results.

MIXED-MODEL PRODUCTION SYSTEM DESIGN FOR AIRCRAFT ASSEMBLY

With the advancement of flexible fixture and flexible tooling, mixed production has become possible for aircraft assembly as the manufacturing processes of different aircraft/sub-assembly models are similar. However, it is a great challenge to model the problem and provide a practical solution due to the low volume and complex constraints of aircraft assemblies. To tackle this problem, this work proposes a methodology for designing the mixed production system, and a new scheduling approach is proposed by combined backward and forward scheduling methods. These methods are validated through a real-life industrial case study. Simulation results show that the number of workstations and the cycle time for making a fuselage can be reduced by 50% and 39% respectively with the newly designed mixed-model system.

Self-assembly of hybrid organic–inorganic polyoxovanadates: functionalised mixed-valent clusters and molecular cages’

Herein we report the intra- and inter-molecular assembly of a {V5O9} subunit. This mixed-valent structural motif can be stabilised as [V5O9(L1–3)4]5−/9− (1–3) by a range of organoarsonate ligands (L1–L3) whose secondary functionalities influence its packing arrangement within the crystal structures. Variation of the reaction conditions results in the dodecanuclear cage structure [V12O14(OH)4(L1)10]4− (4) where two modified convex building units are linked via two dimeric {O4VIV(OH)2VIVO4} moieties. Bi-functional phosphonate ligands, L4–L6 allow the intramolecular connectivity of the {V5O9} subunit to give hybrid capsules [V10O18(L4–6)4]10− (5–7). The dimensions of the electrophilic cavities of the capsular entities are determined by the incorporated ligand type. Mass spectrometry experiments confirm the stability of the complexes in solution. We investigate and model the temperature-dependent magnetic properties of representative complexes 1, 4, 6 and 7 and provide preliminary cell-viability studies of three different cancer cell lines with respect to Na8H2[6]·36H2O and Na8H2[7]·2DMF·29H2O.

Developing a best practice model for partnership practice between specialist palliative care and intellectual disability services: a mixed methods study

Background: The lack of access to good quality palliative care for people with intellectual disabilities is highlighted in the international literature. In response, more partnership practice in end-of-life care is proposed. 
Aim: This study aimed to develop a best practice model to guide and promote partnership practice between specialist palliative care and intellectual disability services. 
Design: A mixed methods research design involving two phases was used, underpinned by a conceptual model for partnership practice. 
Setting/participants: Phase 1 involved scoping end-of-life care to people with intellectual disability, based on self-completed questionnaires. In all, 47 of 66 (71.2%) services responded. In Phase 2, semi-structured interviews were undertaken with a purposive sample recruited of 30 health and social care professionals working in intellectual disability and palliative care services, who had provided palliative care to someone with intellectual disability. For both phases, data were collected from primary and secondary care in one region of the United Kingdom. 
Results: In Phase 1, examples of good practice were apparent. However, partnership practice was infrequent and unmet educational needs were identified. Four themes emerged from the interviews in Phase 2: challenges and issues in end-of-life care, sharing and learning, supporting and empowering and partnership in practice. 
Conclusion: Joint working and learning between intellectual disability and specialist palliative care were seen as key and fundamental. A framework for partnership practice between both services has been developed which could have international applicability and should be explored with other services in end-of-life care.