Variety Of Input Conditions Research Articles

Performance Comparison of ANN-Based Model and Empirical Correlations for Void Fraction Prediction of Subcooled Boiling Flow in Vertical Upward Channel

The accurate prediction of void fraction parameter in subcooled boiling flow is very important for nuclear safety since it has significant influences on the mass flow rate, the onset of two-phase flow instability, and the heat transfer characteristics in a nuclear reactor core. Many different models and empirical correlations have been established over a variety of input conditions; however, this classical approach could lead to unsatisfactory prediction due to the uncertainties of model parameter and model forms. To cope with these limitations, Artificial Neural Network (ANN) is a powerful machine learning tool for modeling and solving non-linear and complicated physical problems. Therefore, this work is aim at developing an ANN-based model to predict the local void fraction of subcooled boiling flows. The comparison results of the performance between the ANN-based model and empirical correlations for the void fraction prediction of subcooled boiling in vertical upward channel showed the potential use of ANN-based model in the Computational Fluid Dynamics (CFD) codes to accurately simulate the subcooled boiling phenomena.

Dynamic analysis and performance assessment of the Inertial Sea Wave Energy Converter (ISWEC) device via harmonic balance

Given the particular energy-maximising performance objective, wave energy converter (WEC) systems are prone to exhibit highly nonlinear behaviour. We present, in this paper, a detailed dynamic analysis and control synthesis for the Inertial Sea Wave Energy Converter (ISWEC) system, deriving and considering a comprehensive associated nonlinear model. In particular, we adopt a harmonic balance (HB) method to achieve this objective, producing the so-called amplitude-frequency curves (AFC) for the corresponding ISWEC nonlinear model, derived via a Lagrangian approach. We demonstrate that the system can present a variety of different behaviours which are completely neglected by its linear model counterpart. Leveraging both the efficiency, and convenient representation of the HB method, we synthesise so-called ‘passive’ (i.e. proportional) energy-maximising controllers using a variety of input conditions. We provide a comparison of the obtained control parameters with those arising from standard linear modelling, showing a consistent improvement in performance by effectively considering the relevant nonlinear ISWEC dynamics.

Optimal integrated energy systems design incorporating variable renewable energy sources

The effect of variability in renewable input sources on the optimal design and reliability of an integrated energy system designed for off-grid mining operation is investigated via a two-stage approach. Firstly, possible energy system designs are generated by solving a deterministic non-linear programming (NLP) optimization problem to minimize the capital cost for a number of input scenarios. Two measures of reliability, the loss of power supply probability (LPSP) and energy index of reliability (EIR), are then evaluated for each design based on the minimization of the external energy required to satisfy load demands under a variety of input conditions. Two case studies of mining operations located in regions with different degrees of variability are presented. The results show that the degree of variability has an impact on the design configuration, cost and performance, and highlights the limitations associated with deterministic decision making for high variability systems.

Discriminative Syntax-Based Word Ordering for Text Generation

Word ordering is a fundamental problem in text generation. In this article, we study word ordering using a syntax-based approach and a discriminative model. Two grammar formalisms are considered: Combinatory Categorial Grammar (CCG) and dependency grammar. Given the search for a likely string and syntactic analysis, the search space is massive, making discriminative training challenging. We develop a learning-guided search framework, based on best-first search, and investigate several alternative training algorithms.The framework we present is flexible in that it allows constraints to be imposed on output word orders. To demonstrate this flexibility, a variety of input conditions are considered. First, we investigate a “pure” word-ordering task in which the input is a multi-set of words, and the task is to order them into a grammatical and fluent sentence. This task has been tackled previously, and we report improved performance over existing systems on a standard Wall Street Journal test set. Second, we tackle the same reordering problem, but with a variety of input conditions, from the bare case with no dependencies or POS tags specified, to the extreme case where all POS tags and unordered, unlabeled dependencies are provided as input (and various conditions in between). When applied to the NLG 2011 shared task, our system gives competitive results compared with the best-performing systems, which provide a further demonstration of the practical utility of our system.

Optical pulse propagation in dynamic Fabry–Perot resonators

We present a simple and intuitive model based on the impulse response of linear electrical systems for describing the propagation of optical pulses through a dynamic Fabry–Perot resonator whose refractive index changes with time. Our model shows that the adiabatic wavelength conversion process in resonators results from a scaling of the round-trip time with index changes. For pulses longer than the cavity round-trip time, we find that more energy can be transferred to the new wavelength when the input pulses are slightly detuned from the cavity resonance and the refractive index does not change too rapidly. In fact, the optimum duration of index changes scales with the photon lifetime of the resonator. We describe the evolution of the shape and spectrum of picosecond pulses inside a resonator under a variety of input conditions and with the magnitude and duration of index variations. We also apply our general theory to the case of pulses whose widths are shorter than the round-trip time and derive an analytical expression for the output field under quite general conditions. This analysis reveals a shifting of the spectral comb as well as compression of the temporal pulse train that depends on the both the magnitude and sign of the index change. Our results should find applications in the area of optical signal processing using resonant photonic structures.

A neural circuit for angular velocity computation

In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly tunable wing steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuromechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob.

Evaluation of methods for approximating shapes used to synthesize 3D solid textures

In modern computer graphics applications, textures play an important role in conveying the appearance of real-world materials. But while surface appearance can often be effectively captured with a photograph, it is difficult to use example imagery to synthesize fully three-dimensional (3D) solid textures that are perceptually similar to their inputs. Specifically, this research focuses on human perception of 3D solid textures composed of aggregate particles in a binding matrix. Holding constant an established algorithm for approximating particle distributions , we examine the problem of estimating particle shape . We consider four methods for approximating plausible particle shapes—including two methods of our own contribution. We compare the performance of these methods under a variety of input conditions using automated, perceptually motivated metrics, as well as a psychophysical experiment. In the course of assessing the relative performance of the four algorithms, we also evaluate the reliability of the automated metrics in predicting the results of the experiment.

Parametric speech codec for representing synthetic speech in the presence of background noise

A system and method are provided for processing audio and speech signals using a pitch and voicing dependent spectral estimation algorithm (voicing algorithm) to accurately represent voiced speech, unvoiced speech, and mixed speech in the presence of background noise, and background noise with a single model. The present invention also modifies the synthesis model based on an estimate of the current input signal to improve the perceptual quality of the speech and background noise under a variety of input conditions. The present invention also improves the voicing dependent spectral estimation algorithm robustness by introducing the use of a Multi-Layer Neural Network in the estimation process. The voicing dependent spectral estimation algorithm provides an accurate and robust estimate of the voicing probability under a variety of background noise conditions. This is essential to providing high quality intelligible speech in the presence of background noise.

On the luminosity function, lifetimes, and origin of blue stragglers in globular clusters

We compute theoretical evolutionary tracks of blue stragglers created by mergers. Two formation scenarios are considered: mergers of primordial binaries, and stellar collisions. These two scenarios predict strikingly different luminosity functions, which are potentially distinguishable observationally. Tabulated theoretical luminosity functions and lifetimes are presented for blue stragglers formed under a variety of input conditions. We compare our results with observations of the blue straggler sequences in 47 Tucanae and M3. In the case of 47 Tuc, the luminosity function and the formation rate are compatible with the hypothesis that the blue stragglers formed through the collision of single stars. Mergers of primordial binaries are only marginally cosistent with the data, and a significant enhancement of the collision cross section by binary-single-star encounters appears to be ruled out. In the case of M3, we find that the innermost blue stragglers have a luminosity function significantly different from that of the outer stragglers, thus confirming earlier suggestions that there are two distinct populations of blue stragglers in this cluster. The inner stragglers are preferentially brighter and bluer, as would be expected if they were made by collisions, but there are so many of them that the collision rate would need to be enhanced by interactions involving wide binaries. The luminosity function of the outer stragglers is almost identical to the predictions of mergers from primordial binaries and is inconsistent with the collision hypothesis.

An introduction to the application of system identification techniques to elements of the neuromuscular system

A simplified account of the organisation of the neuromuscular system is presented. An important component of this system, the 'muscle spindle', is a complex sensor that responds to imposed length changes and is thought to provide information which is important in the control of movement and in the maintenance of posture. The structure of the muscle spindle and its response to various inputs are described. The multi-input and multi-output nature and non-linear behaviour of the muscle spindle are emphasised, both in terms of continuous and pulse- coded quantities. The methods of pre-processing of pulse-coded signals from nerves, and the identification techniques currently applied by neurophysiologists for the estimation of the parameters of linearised models of the muscle spindle, are presented and assessed. New developments in the identification of point-process systems are shown to be applicable to some aspects of muscle-spindle behaviour. This approach, based upon point-process parameters, is applied to an analysis of the output signal of a muscle spindle under a variety of input conditions and to the estimation of a transfer function of the muscle spindle subjected to a point-process input. A description of one non-linearity and the effects of an additional input are also presented.

Time-dependent kinetics. V: Time course of drug levels during enzyme induction (one-compartment model)

Equations were derived to describe the time course of drug levels during auto- and heteroinduction under a variety of input conditions. These equations were based on a pharmacokinetic theory of induction which assumes that metabolic clearance increases exponentially to a maximum value and that the rate of this increase is governed by the degradation rate constant of the induced enzyme (k'). Closed form solutions could be obtained only for intravenous single-dose (case I) and multiple-dose (case IV) administration. For each of the other cases, constant-rate intravenous infusion (case III), oral single-dose administration (case II), and multiple-dose administration (case V), an exact solution (not closed form) and an approximation (closed form) were derived. Two sets of equations were derived for each of the five cases to take into consideration the possibility of a latency term (lambda). Plots of drug amount X (or concentration C) vs. time (t) were constructed. In case I, a log X vs. t plot was convex, the slope increasing with time. In case II, X increased, reached a peak, and decayed as in case I. In case III (lambda greater than 5 ln 2V/Q) C reached a preinduction steady state before decreasing to a lower (induced) steady state. The behavior of C vs. t for cases IV and V was similar to that for case III. Determination of parameters was attempted in case III. Nonlinear least-square fitting of generated data with 3-9% error yielded reasonable estimates of k'.