In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis The accurate prediction of stock price movements could play an important role in helping investors improve stock returns The complexity in predicting these trends lies in the inherent noise and volatility in daily stock price movement The Bayesian regularized network assigns a probabilistic nature to the network weights, allowing the network to automatically and optimally penalize excessively complex models The proposed technique reduces the potential for overfitting and overtraining, improving the prediction quality and generalization of the network Experiments were performed with Microsoft Corp and Goldman Sachs Group Inc stock to determine the effectiveness of the model The results indicate that the proposed model performs as well as the more advanced models without the need for preprocessing of data, seasonality testing, or cycle analysis

A Bayesian regularized artificial neural network for stock market forecasting

The paper proposed an improved bacterial chemotaxis optimization (IBCO), which is then integrated into the back propagation (BP) artificial neural network to develop an efficient forecasting model for prediction of various stock indices. Experiments show its better performance than other methods in learning ability and generalization.

Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network

Extensive calculations based on density functional theory have been carried out to understand the origin of magnetism in undoped ZnO thin films as found in recent experiments. The observed magnetism is confirmed to be due to Zn, instead of O, vacancy. The main source of the magnetic moment, however, arises from the unpaired 2p electrons at O sites surrounding the Zn vacancy with each nearest-neighbor O atom carrying a magnetic moment ranging from 0.490 to 0.740 B. Moreover, the study of vacancy-vacancy interactions indicates that in the ground state, the magnetic moments induced by Zn vacancies prefer to ferromagnetically couple with the antiferromagnetic state lying 44 meV higher in energy. Since this is larger than the thermal energy at room temperature, the ferromagnetic state can be stable against thermal fluctuations. Calculations and discussions are also extended to ZnO nanowires that have larger surface to volume ratio. Here, the Zn vacancies are found to lead to the ferromagnetic state too. The present theoretical study not only demonstrates that ZnO samples can be magnetic even without transition-metal doping but also suggests that introducing Zn vacancy is a natural and an effective way to fabricate magnetic ZnO nanostructures. In addition, vacancy mediated magnetic ZnO nanostructures may have certain advantages over transition-metal doped systems in biomedical applications.

https://absimage.aps.org/image/MAR08/MWS_MAR08-2007-004197.pdf

Zinc Vacancy induced magnetism in ZnO thin films and nanowires

The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

/pdf/recent-advances-on-active-noise-control-open-issues-and-1wg2ydx57i.pdf

Recent Advances on Active Noise Control: Open Issues and Innovative Applications

Vehicle system dynamics integration encompasses interdisciplinary challenges innovations in various aspects related to vehicle system/subsystems/components dynamic characteristics, modeling and validation, vehicle dynamics state measurement and estimation, vehicle/chassis control systems, coordination of power management and dynamics/stability control, etc [1-4]. These are becoming more critical due to the increasing concern and rapid development in electric and hybrid vehicles, which tend to exhibit different vehicle dynamics/stability and control characteristics when compared to conventional vehicles.

Mechanical systems and signal processing

The hum noise generated from the distribution transformers is annoying and is significant when the transformer is installed near the residential area. This hum noise consists of harmonics of 100 Hz, when the power line frequency is 50 Hz. A new type of active noise control method has been proposed in this paper to combat such low-frequency harmonically related noise. The method is based on generation of required harmonics from the power line signal consisting of 50 Hz frequency using two adaptive filters. Through simulation study it has been shown that the proposed technique is able to accurately generate the mix of noise frequency components with the frequency change. The method also tracks the change in power line frequency and generates the corresponding anti-noise to nullify the noise.

http://data.mecheng.adelaide.edu.au/avc/publications/public_papers/2013/ICIEA_debi_manuscript.pdf

Active control of transformer noise by using power line signal as reference

Filter proportionate normalized least mean square algorithm for a sparse system

The conventional narrowband active noise control (ANC) is a popular method for reducing narrowband noise generated from rotating machines like engines, fans, blowers, and power transformers. The narrowband active noise control works efficiently only when the internal reference frequency of the controller and the frequency of the primary noise remains the same. Any change in the frequency of the primary noise from that of the reference is termed as frequency mismatch (FM), which degrades the narrowband ANC performance. In this paper, a filtered-x weighted-frequency Fourier linear combiner least mean square (FX-WFLC-LMS) algorithm is developed for narrowband ANC system. This algorithm is capable of adapting to both frequency and amplitude variations in the primary noise. To reduce the computational burden of the proposed FX-WFLC-LMS algorithm, a computationally efficient filtered-error weighted-frequency Fourier linear combiner least mean square (FE-WFLC-LMS) algorithm is also proposed. The comparative performance of these proposed algorithms is evaluated through extensive simulation and real-time experiments. It was found that both these proposed algorithms are capable of correcting any amount of frequency mismatch and are suitable for narrowband ANC systems.

An Efficient Narrowband Active Noise Control System for Accommodating Frequency Mismatch

In this paper, zinc oxide (ZnO) film has been deposited on Al substrate by chemical wet and dry technique which is just a simple modified version of the dip coating method. In this method, it is possible to precisely control the immersion and withdrawal speed, with drying as well as annealing at the same time. The polycrystalline nature of ZnO has been confirmed by X-ray diffraction (XRD) analysis. The XRD analysis clearly indicates that some percentage of Al diffuses into the ZnO matrix at its interface region; hence it affects mobility of the sample. Hall measurement indicates the ZnO semiconductor as n-type. I–V characteristic of the sample shows that the contact is Ohmic and it can be used as a sensor at low potential value. The mobility decreases with increase in temperature. The simulation study carried out for I–V and mobility through simulation using ATLAS (SILVACO) software confirms that the experimental and simulation results are in close agreement with respect to the I–V characteristic and the mobility.

Experimental and simulated study of electrical behaviour of ZnO film deposited on Al substrate for device applications

In this paper, Ar–H2 plasma is studied by the nonintrusive optical emission spectroscopy method. The emission spectra of excited species of hydrogen and argon are observed and measured. The effect of an increase in hydrogen gas flow in the Ar–H2 plasma is studied experimentally. The variation in peak intensity and broadening of $\text{H}_{\alpha }$ line with increased hydrogen gas flow rates is examined. It is noticed that the peak intensity of $\text{H}_{\alpha }$ line is very sensitive to hydrogen gas flow rate while the broadening of the line is found to be constant. The broadening of $\text{H}_{\alpha }$ line is observed to be a Lorentzian profile. In addition to this observation, the electron temperature was computed using Boltzmann’s plot method. It is learned that the theoretical and the experimentally evaluated electron densities are not in close agreement with each other. Therefore, the estimation of electron density from the electron temperature will not always be a reliable method in the case of such atmospheric plasma. The saturation effect of electron density and the quenching of Ar I emission lines are also observed and discussed.

Debi Prasad Das

Papers

Active control of transformer noise by using power line signal as reference

Filter proportionate normalized least mean square algorithm for a sparse system

An Efficient Narrowband Active Noise Control System for Accommodating Frequency Mismatch

Experimental and simulated study of electrical behaviour of ZnO film deposited on Al substrate for device applications

Optical Emission Spectroscopy Study of Ar–H 2 Plasma at Atmospheric Pressure