Search results

The purpose of this paper is to reveal the multi‐scale relation between power law distribution and correlation of stock returns and to figure out the determinants underlying capital markets.

The multi‐scale relation between power law distribution and correlation is investigated by comparing the original series with the special series. The eliminating intraday trend series approach developed by Liu et al. is utilized to analyze the effects of power law decay change on correlation properties, and shuffling series originated by Viswanathan et al. for the impacts of special type of correlation on power‐law distribution.

It is found that the accelerating decay of power law has an insignificant effect on correlation properties of returns and the empirical results indicate that time scale may also be an important factor maintaining power law property of returns besides correlation. When time scale is under critical point, the effects of correlation are crucial, and the correlation of nonlinear long‐range presents the strongest influence. However, for time scale beyond critical point, the impact of correlation begins to diminish or even finally disappear and then the power law property shows complete dependence on time scale.

The 5‐min high frequency data of the Shanghai market as the empirical benchmark is insufficient to depict the relation over the entire time scale in the Chinese stock market.

The paper identifies the determinants of market dynamics to apply them to risk management through analysis of multi‐scale relations, and supports endeavors to introduce time parameter into further risk measures and control.

The paper provides the empirical evidence that time scale is one of the key determinants of market dynamics by analyzing the multi‐scale relation between power law distribution and correlation.

Mathematical bios and heartbeat series show an inverse relation between frequency and power; the time series of differences between successive terms of cardiac and mathematical chaos shows a direct relation between frequency and power. Other statistical analyses differentiate these biotic series from stochastically generated 1/f noise. The time series of complex biological and economic processes as well as mathematical bios show asymmetry, positive autocorrelation, and extended partial autocorrelation. Random, chaotic and stochastic models show symmetric statistical distributions, and no partial autocorrelation. The percentage of continuous proportions is high in cardiac, economic, and mathematical biotic series, and scarce in pink noise and chaos. These findings differentiate creative biotic processes from chaotic and stochastic series. We propose that the widespread 1/f power spectrum found in natural processes represents the integration of the fundamental relation between frequency and energy stated in Planck's law. Natural creativity emerges from determined interactions rather than from the accumulation of accidental random changes.

A three-dimensional finite element (FE) model is developed to design a vibrating bimorph piezoelectric cantilever beam with lead zirconate titanate (PZT-5H) for energy harvesting. The paper aims to discuss these issues.

A parametric study of electric power generated as a function of the dielectric constant, transverse piezoelectric strain constant, length and thickness of the piezoelectric material, is conducted for a time-harmonic surface pressure load. Transversely isotropic elastic and piezoelectric properties are assigned to the bimorph layers with brass chosen as the substrate material in the three-dimensional FE model. Using design of experiments, a study was conducted to determine the sensitivity of power with respect to the geometric and material variables.

The numerical analysis shows that a uniform decrease in thickness and length coverage of the piezoelectric layers results in a nonlinear reduction in power amplitude, which suggests optimal values. The piezoelectric strain coefficient, d31 and the thickness of PZT-5H, tp, are the most important design parameters to generate high electric energy for bimorph vibration harvesting device.

The work demonstrates that, through a sensitivity analysis, the electro-mechanical piezoelectric coupling coefficient (d31) and the thickness of the piezoelectric strips (tp) are the most important parameters which have a significant effect on power harvested.

EACH September the eyes of the aeronautical World turn towards the S.B.A.C. Air Display and Exhibition with interest unequalled by any other event. It is fitting that the Display is now held each year at the airfield of the Royal Aircraft Establishment, one of the world's most prominent aeronautical research centres. This interest becomes increasingly keen too, as the preview day comes closer, because new prototypes of unorthodox designs often appear a short time before the Show to illustrate the results of years of careful planning, development and research of the particular company. These designs often mould the path of progress for smaller countries without the economic resources to forge the way ahead alone. Most British citizens are very proud of their country's place in aviation today, both in the military and civil fields. This is understood by most foreigners because it is clear that Britain has won a place in aeronautical development second to none.

The purpose of this study is to eliminate voltage harmonics and instantly measure the positive sequence fundamental voltage during unbalanced grid conditions, the dual second-order generalized integrator-phase locked loop used in series hybrid filter structures is often used in grid synchronisation in three-phase networks. The preferred series active hybrid power filter simultaneously compensates for voltage balancing and current harmonics generated by non-linear loads.

This paper examines the use of renewable energy–based microgrid (MG) to support linear and non-linear loads. It is capable of synchronising with both the utility and the diesel generator unit. Power is transferred from the grid throughout a stable grid situation with minimum renewable energy generation and maximum load demand. It synchronises with diesel generator set to supply the load and form an AC MG during outages and minimum renewable power generation. In islanded and grid-connected mode, the voltage and power quality issues of the MG are controlled by static synchronous compensator and series hybrid filter.

Because of the presence of non-linear loads, reactive loads in the distribution system and the injection of wind power into the grid integrated system result power quality issues like current harmonics, voltage fluctuations, reactive power demand, etc.

The voltage at the load (linear and non-linear) is regulated, and the power factor and total harmonic distortions were improved with the help of the series hybrid filter.

The purpose of this study is to build a high accuracy thrust model under various small turbojet engine shaft speeds by using robust, ordinary, linear and nonlinear least squares estimation methods for target drone applications.

The dynamic shaft speeds from the test experiment of a target drone engine is conducted. Then, thrust values are calculated. Based on these, the engine thrust is modeled by robust linear and nonlinear equations. The models are benefited from quadratic, power and various series expansion functions with several coefficients to optimize the model parameters.

The error terms and accuracy of the model are given using sum of squared errors, root mean square error (RMSE) and R-squared (R²) error definitions. Based on the multiple analyses, it is seen that the RMSE values are no more than 17.7539 and the best obtained result for robust least squares estimation is 15.0086 for linear at all cases. Furthermore, the R² value is found to be 0.9996 as the highest with the nonlinear Fourier series expansion model.

The motivation behind this paper is to propose robust nonlinear thrust models based on power, Fourier and various series expansion functions for dynamic shaft speeds from the test experiments.

This paper aims to improve the device performance from the perspective of reducing ohmic contact resistance; the effects of different electrode structures and alloying parameters on the series resistance and power-current-voltage of laser diodes (LDs) have been investigated in this paper.

Four groups of p-GaAs side metal electrodes with different metal layer arrangements and thicknesses are fabricated for the investigated LDs. The investigated p-GaAs side electrodes are based on Ti/Pt/Au material and the n-GaAs side metal electrodes all have a same structure of Ni/Ge/Ni/Au/Ti/Pt/Au. The LDs with different electrodes were alloyed at 380°C for 60 s and 420°C for 80 s.

The experimental results show that the series resistance decreases by 14%–20%, the output power increases by 2%–2.2% and the conversion efficiency increases by 1.69%–2.16% for the LDs prepared with optimized alloying parameters (420°C for 80 s). The laser diode with p-GaAs side Ti/Pt/Au electrode of 30/70/100 nm has the best device characteristics under both annealing conditions.

The utilization of this improvement on ohmic contact property in electrode is not only very important for upgrading high-power LDs but also helpful for GaAs-based microelectronic devices such as HBT and monolithic microwave integrated circuit.

THE study of the vibration of a straight elastic bar is a very old one. The vibrations may be classified as lateral (or transverse) torsional and longitudinal. The first two are the more important ones and also the more difficult. We shall consider the lateral and torsional vibrations of a bar clamped at one end and free at the other, which may rotate like the blades of an airscrew. Both cases, rotation or none, are important problems of civil engineering, marine and aeronautical engineering and the theory of elasticity.

This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning.

In this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model.

The LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability.

The proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

Studies a two‐dimensional natural convection in a porous, square cavity using a regular asymptotic development in powers of the Rayleigh number. Carries the approximation through to the 34th order. Analyses convergence of the resulting series for the Nusselt number in both monocellular and multicellular cases, providing insight in the validity regions of the power series.