On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

The decomposition of some RDX and HMX based materials in the one dimensional time to explosion apparatus. Part 2. Estimating the violence of the cook-off event

Various methods of assessment have been applied to the One Dimensional Time to Explosion (ODTX) apparatus and experiments with the aim of allowing an estimate of the comparative violence of the explosion event to be made. Non-mechanical methods used were a simple visual inspection, measuring the increase in the void volume of the anvils following an explosion and measuring the velocity of the sound produced by the explosion over 1 metre. Mechanical methods used included monitoring piezo-electric devices inserted in the frame of the machine and measuring the rotational velocity of a rotating bar placed on the top of the anvils after it had been displaced by the shock wave. This last method, which resembles original Hopkinson Bar experiments, seemed the easiest to apply and analyse, giving relative rankings of violence and the possibility of the calculation of a “detonation” pressure.

The decomposition of some RDX and HMX based materials in the one-dimensional time to explosion apparatus. Part 1. Time to explosion and apparent activation energy

A One-Dimensional Time to Explosion (ODTX) apparatus has been used to study the times to explosion of a number of compositions based on RDX and HMX over a range of contact temperatures. The times to explosion at any given temperature tend to increase from RDX to HMX and with the proportion of HMX in the composition. Thermal ignition theory has been applied to time to explosion data to calculate kinetic parameters. The apparent activation energy for all of the compositions lay between 127 kJ mol−1 and 146 kJ mol−1. There were big differences in the pre-exponential factor and this controlled the time to explosion rather than the activation energy for the process.

Neural network based control in food processing

A number of commonly encountered simple neural network types are discussed, with particular attention being paid to their applicability in automation and control when applied to food processing. In the first instance n-tuple networks are considered, these being particularly useful for high speed production checking operations. Subsequently backpropagation networks are discussed, these being useful both in a more familiar feedback control arrangement and also for such things as recipe prediction.

Implementation of microprocessor based self-tuning control on a heating system

This paper describes the implementation, using a microprocessor, of a self-tuning control algorithm on a heating system. The algorithm is based on recursive least squares parameter estimation with a state-space, pole placement design criterion and shows how the controller behaves when applied to an actual system.

Life histories of academics who become heads of department: socialisation, identity and career trajectory

Although the role of the academic head of department (HoD) has always been important to university management and performance, an increasing significance given to bureaucracy, academic performance and productivity, and government accountability has greatly elevated the importance of this position. Previous research and anecdotal evidence suggests that as academics move into HoD roles, usually with little or no training, they experience a problem of struggling to adequately manage key aspects of their role. It is this problem – and its manifestations – that forms the research focus of this study. Based on the research question, “What are the career trajectories of academics who become HoDs in a selected post-1992 university?” the study aimed to achieve greater understanding of why academics become HoDs, what it is like being a HoD, and how the experience influences their future career plans. The study adopts an interpretive approach, in line with social constructivism. Edited topical life history interviews were undertaken with 17 male and female HoDs, from a range of disciplines, in a post-1992 UK university. These data were analysed using coding, categorisation and theme formation techniques and developing profiles of each of the respondents. The findings from this study suggest that academics who become HoDs not only need the capacity to assume a range of personal and professional identities, but need to regularly adopt and switch between them. Whether individuals can successfully balance and manage these multiple identities, or whether they experience major conflicts and difficulties within or between them, greatly affects their experiences of being a HoD and may influence their subsequent career decisions. It is claimed that the focus, approach and analytical framework - based on the interrelationships between the concepts of socialisation, identity and career trajectory - provide a distinct and original contribution to knowledge in this area. Although the results of this study cannot be generalised, the findings may help other individuals and institutions move towards a firmer understanding of the academic who becomes HoD - in relation to theory, practice and future research.

Effective elements of cognitive behaviour therapy for psychosis: results of a novel type of subgroup analysis based on principal stratification

Background. Meta-analyses show that cognitive behaviour therapy for psychosis (CBT-P) improves distressing positive symptoms. However, it is a complex intervention involving a range of techniques. No previous study has assessed the delivery of the different elements of treatment and their effect on outcome. Our aim was to assess the differential effect of type of treatment delivered on the effectiveness of CBT-P, using novel statistical methodology. Method. The Psychological Prevention of Relapse in Psychosis (PRP) trial was a multi-centre randomized controlled trial (RCT) that compared CBT-P with treatment as usual (TAU). Therapy was manualized, and detailed evaluations of therapy delivery and client engagement were made. Follow-up assessments were made at 12 and 24 months. In a planned analysis, we applied principal stratification (involving structural equation modelling with finite mixtures) to estimate intention-to-treat (ITT) effects for subgroups of participants, defined by qualitative and quantitative differences in receipt of therapy, while maintaining the constraints of randomization. Results. Consistent delivery of full therapy, including specific cognitive and behavioural techniques, was associated with clinically and statistically significant increases in months in remission, and decreases in psychotic and affective symptoms. Delivery of partial therapy involving engagement and assessment was not effective. Conclusions. Our analyses suggest that CBT-P is of significant benefit on multiple outcomes to patients able to engage in the full range of therapy procedures. The novel statistical methods illustrated in this report have general application to the evaluation of heterogeneity in the effects of treatment.

A global blended tropopause based on ERA data, Part II: trends and tropical broadening

A new tropopause definition involving a flow-dependent blending of the traditional thermal tropopause with one based on potential vorticity has been developed and applied to the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA), ERA-40 and ERA-Interim. Global and regional trends in tropopause characteristics for annual and solsticial seasonal means are presented here, with emphasis on significant results for the newer ERA-Interim data for 1989-2007. The global-mean tropopause is rising at a rate of 47 m decade−1 , with pressure falling at 1.0 hPa decade−1 , and temperature falling at 0.18 K decade−1 . The Antarctic tropopause shows decreasing heights,warming,and increasing westerly winds. The Arctic tropopause also shows a warming, but with decreasing westerly winds. In the tropics the trends are small, but at the latitudes of the sub-tropical jets they are almost double the global values. It is found that these changes are mainly concentrated in the eastern hemisphere. Previous and new metrics for the rate of broadening of the tropics, based on both height and wind, give trends in the range 0.9◦ decade−1 to 2.2◦ decade−1 . For ERA-40 the global height and pressure trends for the period 1979-2001 are similar: 39 m decade−1 and -0.8 hPa decade−1. These values are smaller than those found from the thermal tropopause definition with this data set, as was used in most previous studies.

An overview of domestic energy models and a new approach with agent based modelling

Government targets for CO2 reductions are being progressively tightened, the Climate Change Act set the UK target as an 80% reduction by 2050 on 1990 figures. The residential sector accounts for about 30% of emissions. This paper discusses current modelling techniques in the residential sector: principally top-down and bottom-up. Top-down models work on a macro-economic basis and can be used to consider large scale economic changes; bottom-up models are detail rich to model technological changes. Bottom-up models demonstrate what is technically possible. However, there are differences between the technical potential and what is likely given the limited economic rationality of the typical householder. This paper recommends research to better understand individuals’ behaviour. Such research needs to include actual choices, stated preferences and opinion research to allow a detailed understanding of the individual end user. This increased understanding can then be used in an agent based model (ABM). In an ABM, agents are used to model real world actors and can be given a rule set intended to emulate the actions and behaviours of real people. This can help in understanding how new technologies diffuse. In this way a degree of micro-economic realism can be added to domestic carbon modelling. Such a model should then be of use for both forward projections of CO2 and to analyse the cost effectiveness of various policy measures.

An improvement in clear-air turbulence forecasting based on spontaneous imbalance theory: the ULTURB algorithm

Recent research has shown that Lighthill–Ford spontaneous gravity wave generation theory, when applied to numerical model data, can help predict areas of clear-air turbulence. It is hypothesized that this is the case because spontaneously generated atmospheric gravity waves may initiate turbulence by locally modifying the stability and wind shear. As an improvement on the original research, this paper describes the creation of an ‘operational’ algorithm (ULTURB) with three modifications to the original method: (1) extending the altitude range for which the method is effective downward to the top of the boundary layer, (2) adding turbulent kinetic energy production from the environment to the locally produced turbulent kinetic energy production, and, (3) transforming turbulent kinetic energy dissipation to eddy dissipation rate, the turbulence metric becoming the worldwide ‘standard’. In a comparison of ULTURB with the original method and with the Graphical Turbulence Guidance second version (GTG2) automated procedure for forecasting mid- and upper-level aircraft turbulence ULTURB performed better for all turbulence intensities. Since ULTURB, unlike GTG2, is founded on a self-consistent dynamical theory, it may offer forecasters better insight into the causes of the clear-air turbulence and may ultimately enhance its predictability.

This is me: digital identity and reputation on the Internet

The chapter reports on the ‘This Is Me’ project, that aimed to help students and the wider public to be aware of the impact that online material, particularly that on the Internet, has on their identity and reputation. The chapter explores practical aspects of Digital Identity, relating to issues such as employability, relationships and even death. For example, understanding the impact a photograph posted on a social networking website might have for different groups of people, ranging from friends or parents to future employers. As part of the ‘This is Me’ project stories were collected from students and others about Digital Identity matters, a grounded methodological approach based on action research was used to establish issues related to Digital Identity particularly relevant to those in academia. Drawing from these issues, resources were developed to help inform and educate people about how they can understand and control their own Digital Identity. A number of these resources are presented here, along with reflections on how they are used and can be adapted.

Incorporating creativity into teachers practice and self-concept of professional identity

The study explores what happens to teachers practice and ’ professional identity when they adopt a collaborative action research approach to teaching and involve external creative partners and a university mentor. The teachers aim to nurture and develop the creative potential of their learners through empowering them to make decisions for themselves about their own progress and learning directions. The teachers worked creatively and collaboratively designing creative teaching and learning methods in support of pupils with language and communication difficulties. The respondents are from an English special school, primary school and girls secondary school. A mixed methods methodology is adopted. Gains in teacher confidence and capability were identified in addition to shifts in values that impacted directly on their self-concept of what it is to be an effective teacher promoting effective learning. The development of their professional identities within a team ethos included them being able to make decisions about learning that are based on the educational potential of learners that they proved resulted in elevated standards achieved by this group of learners. They were able to justify their actions on established educational principles. Tensions however were revealed between what they perceived as their normal required professionalism imposed by external agencies and the enhanced professionalism experienced working through the project where they were able to integrate theory and practice.