A Genetic Programming Approach to Rainfall-Runoff Modelling

Planning for sustainable development of water resources relies crucially on the data available. Continuous hydrologic simulation based on conceptual models has proved to be the appropriate tool for studying rainfall-runoff processes and for providing necessary data. In recent years, artificial neural networks have emerged as a novel identification technique for the modelling of hydrological processes. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. This paper introduces genetic programming, which is an evolutionary computing method that provides a transparent and structured system identification, to rainfall-runoff modelling. The genetic-programming approach is applied to flow prediction for the Kirkton catchment in Scotland (U.K.). The results obtained are compared to those attained using two optimally calibrated conceptual models and an artificial neural network. Correlations identified using data-driven approaches (genetic programming and neural network) are surprising in their consistency considering the relative size of the models and the number of variables included. These results also compare favourably with the conceptual models.     

Renewal type bootstrap for Markov chains

In this paper we treat a renewal type of bootstrap for atomic Markov chains under minimal moment conditions on renewal times, i.e.Er ²<∞. Three main results are: a) if a Markov chain satisfies the CLT for the mean then it also satisfies a bootstrap CLT; b) if a Markov chain satisfies a uniform CLT over classes of functions then, it also satisfies bootstrap uniform CLT with minimal condition on envelope functionF; c) we establish second order correctness for this procedure. All results are for “in probability” bootstrap and constitute the final word in this setting.     

Integrated reconfiguration and age-based preventive maintenance decision making

The use of manufacturing system reconfiguration in conjunction with maintenance operations has not been previously reported in the literature. This research attempts to incorporate reconfiguration into Preventive Maintenance (PM) actions for improved system performance in terms of reduced total cost. This paper presents an Integrated Reconfiguration and Age-Based Maintenance (IRABM) policy and applies it to a parallel-serial manufacturing system. The expected total cost of implementing the IRABM policy is estimated and minimized through a simulation-based heuristic optimization procedure. Using this method, it is possible to systematically identify the conditions under which the integration of reconfiguration into maintenance is cost effective. In addition, numerical examples demonstrate that the manufacturing system could have a higher probability of fulfilling production requirements at a lower cost under the IRABM policy compared to the conventional age-based PM policy. The influences of the input parameters associated with reconfiguration, production, and reliability on the performance of IRABM policy also are studied.     

DATA CLUSTERING USING A REORGANIZING NEURAL NETWORK

A new approach, designed for clustering of arbitrary distributed patterns, is presented. This study is concerned with the use of a self-organizing neural network as a frame for data clustering. The nearest network nodes in feature space are treated as prototypes, assigned to the corresponding cluster. The rules for dead-node shifting and simple adjustment of coordinates of the active nodes are introduced. The performance of the proposed self-organizing neural network is examined on the benchmark synthetic and the real-world problem.     

Maximum likelihood estimation from interval censored recurrent event data

In this paper, we consider the recurrent failures of several repairable units, which can only be observed at periodic inspection times. A unit is not aging over the period between a failure and its detection. The failure times are interval censored by the periodic assessment times. The observed data consists of censoring intervals of failure times and the unobserved data are the actual ages of the units at the failure times. We formulate the likelihood function and use several iterative algorithms to find the maximum likelihood estimate (MLE) of the parameters. The complete Expectation–Maximization (EM) algorithm, the EM gradient, full Newton–Raphson (NR), and the Simplex method are used. We derive recursive equations to calculate the expected values required in the algorithms. We estimate the parameters for four failure datasets, assuming that the failures follow a non-homogeneous Poisson process (NHPP). Three datasets are obtained from a hospital for the components of general infusion pump, and the fourth dataset is simulated. Since the estimation could take a long time, we compare the performance of the algorithms in terms of the required number of iterations to converge, the total execution time, and the precision of the estimated parameters. We also use Monte Carlo and Quasi-Monte Carlo simulation as the substitutes for the recursive procedures in the Expectation step of the EM gradient and compare the results.     

Adaptive neuro-fuzzy wheel slip control

Due to complex and nonlinear dynamics of a braking process and complexity in the tire–road interaction, the control of automotive braking systems performance simultaneously with the wheel slip represents a challenging problem. The non-optimal wheel slip level during braking, causing inability to achieve the desired tire–road friction force strongly influences the braking distance. In addition, steerability and maneuverability of the vehicle could be disturbed. In this paper, an active neuro-fuzzy approach has been developed for improving the wheel slip control in the longitudinal direction of the commercial vehicle. The dynamic neural network has been used for prediction and an adaptive control of the brake actuation pressure, during each braking cycle, according to the identified maximum adhesion coefficient between the wheel and road surface. The brake actuation pressure was dynamically adjusted on the level that provides the optimal level of the longitudinal wheel slip vs. the brake pressure selected by driver, the current vehicle speed, the brake interface temperature, vehicle load conditions, and the current value of longitudinal wheel slip. Thus the dynamic neural network model operates (learn, generalize and predict) on-line during each braking cycle, fuzzy logic has been integrated with the neural model as a support to the neural controller control actions in the case when prediction error of the dynamic neural model reached the predefined value. The hybrid control approach presented here provided intelligent dynamic model – based control of the brake actuation pressure in order to keep the longitudinal wheel slip on the optimum level during a braking cycle.     

Iterative algorithm to compute the maximal and stabilising solutions of a general class of discrete-time Riccati-type equations

In this article an iterative method to compute the maximal solution and the stabilising solution, respectively, of a wide class of discrete-time nonlinear equations on the linear space of symmetric matrices is proposed. The class of discrete-time nonlinear equations under consideration contains, as special cases, different types of discrete-time Riccati equations involved in various control problems for discrete-time stochastic systems. This article may be viewed as an addendum of the work of Dragan and Morozan (Dragan, V. and Morozan, T. (2009), ‘A Class of Discrete Time Generalized Riccati Equations’, Journal of Difference Equations and Applications, first published on 11 December 2009 (iFirst), doi: 10.1080/10236190802389381) where necessary and sufficient conditions for the existence of the maximal solution and stabilising solution of this kind of discrete-time nonlinear equations are given. The aim of this article is to provide a procedure for numerical computation of the maximal solution and the stabilising solution, respectively, simpler than the method based on the Newton–Kantorovich algorithm.     

Phase-synchroniser based on gm-C all-pass filter chain with sliding mode control

Phase-synchronisers have many applications in VLSI circuit designs. They are used in CMOS RF circuits including phase (de)modulators, phase recovery circuits, multiphase synthesis, etc. In this article, a phase-synchroniser based on g_m-C all-pass filter chain with sliding mode control is presented. The filter chain provides good controllable delay characteristics over the full range of phase and frequency regulation, without deterioration of input signal amplitude and waveform, while the sliding mode control enables us to achieve fast and predetermined finite locking time. IHP 0.25 µm SiGe BiCMOS technology has been used in design and verification processes. The circuit operates in the frequency range from 33 MHz up to 150 MHz. Simulation results indicate that it is possible to achieve very fast synchronisation time period, which is approximately four time intervals of the input signal during normal operation, and 20 time intervals during power-on.     

IR-change and colour changes of long-oil air drying alkyd paints as a result of UV irradiation

In this study the formation of photo-oxidative species was studied by monitoring the changes in the FT-IR spectra and colour changes. To stabilize long-oil air drying alkyd paint, separate and synergistic influence of two stabilizers were used Uvasorb^®S28 as the UV-absorber and Uvasorb^®HA29 as the stabilizer which inhibits the degradation of some groups. In the presence of UV radiation (450 h, λ > 300 nm), the alkyd paint undergoes photodegradation with gradual change of its colour. The photochemical degradation of the alkyd paint is associated with an increase in the hydroxyl content and broadening of the absorption in the carbonyl region. Analysis of the colour changes in alkyd surface during photodegradation was carried out by measuring CIEL*a*b* colour components (L*, a*, b* and ΔE*). Overall, ΔE* colour change correlates well with photodegradation of alkyd paint by relative increase of the concentration of carbonyl and hydroxyl groups determinated by FT-IR measurements.