Gaussian pulse mixing by stacked semiconductor layers

The nonlinear scattering of two Gaussian pulses with different central frequencies incident at slant angles on the periodic stack of binary semiconductor layers has been modelled in the self-consistent problem formulation taking into account the dynamics of charges. The effects of the pump pulse length and central frequencies, and the stack physical and geometrical parameters on the properties of the emitted combinatorial frequency waveforms are analysed and discussed.

Solar phase functions of cometary nuclei

Relatively few measurements of the solar phase function of cometary nuclei exist, despite the importance of this parameter in determining accurate sizes and its use in modeling surface properties. We make use of robotic telescopes and servicemode observing to monitor cometary nuclei over months at a time, combining intensive observations at a single epoch with regular short light-curve segments to efficiently account for brightness changes due to both nucleus rotation and changing solar phase angle. We present our latest results on comets 8P/Tuttle, 14P/Wolf, 67P/Churyumov- Gerasimenko and 110P/Hartley 3.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.

A First Step Towards Cost Functions for Quantum-dot Cellular Automata Designs

Quantum-dot cellular automata (QCA) is potentially a very attractive alternative to CMOS for future digital designs. Circuit designs in QCA have been extensively studied. However, how to properly evaluate the QCA circuits has not been carefully considered. To date, metrics and area-delay cost functions directly mapped from CMOS technology have been used to compare QCA designs, which is inappropriate due to the differences between these two technologies. In this paper, several cost metrics specifically aimed at QCA circuits are studied. It is found that delay, the number of QCA logic gates, and the number and type of crossovers, are important metrics that should be considered when comparing QCA designs. A family of new cost functions for QCA circuits is proposed. As fundamental components in QCA computing arithmetic, QCA adders are reviewed and evaluated with the proposed cost functions. By taking the new cost metrics into account, previous best adders become unattractive and it has been shown that different optimization goals lead to different “best” adders.

Rational Partial Choice Functions and Their Application to Belief Revision.

Necessary and sufficient conditions for choice functions to be rational have been intensively studied in the past. However, in these attempts, a choice function is completely specified. That is, given any subset of options, called an issue, the best option over that issue is always known, whilst in real-world scenarios, it is very often that only a few choices are known instead of all. In this paper, we study partial choice functions and investigate necessary and sufficient rationality conditions for situations where only a few choices are known. We prove that our necessary and sufficient condition for partial choice functions boils down to the necessary and sufficient conditions for complete choice functions proposed in the literature. Choice functions have been instrumental in belief revision theory. That is, in most approaches to belief revision, the problem studied can simply be described as the choice of possible worlds compatible with the input information, given an agent’s prior belief state. The main effort has been to devise strategies in order to infer the agents revised belief state. Our study considers the converse problem: given a collection of input information items and their corresponding revision results (as provided by an agent), does there exist a rational revision operation used by the agent and a consistent belief state that may explain the observed results?

Performance assessment of cascade control loopswith non-Gaussian disturbances using entropy information

Cascade control is one of the routinely used control strategies in industrial processes because it can dramatically improve the performance of single-loop control, reducing both the maximum deviation and the integral error of the disturbance response. Currently, many control performance assessment methods of cascade control loops are developed based on the assumption that all the disturbances are subject to Gaussian distribution. However, in the practical condition, several disturbance sources occur in the manipulated variable or the upstream exhibits nonlinear behaviors. In this paper, a general and effective index of the performance assessment of the cascade control system subjected to the unknown disturbance distribution is proposed. Like the minimum variance control (MVC) design, the output variances of the primary and the secondary loops are decomposed into a cascade-invariant and a cascade-dependent term, but the estimated ARMA model for the cascade control loop based on the minimum entropy, instead of the minimum mean squares error, is developed for non-Gaussian disturbances. Unlike the MVC index, an innovative control performance index is given based on the information theory and the minimum entropy criterion. The index is informative and in agreement with the expected control knowledge. To elucidate wide applicability and effectiveness of the minimum entropy cascade control index, a simulation problem and a cascade control case of an oil refinery are applied. The comparison with MVC based cascade control is also included.