In the world, wind power is rapidly becoming a generation technology of significance. Unpredictability and variability of wind power generation is one of the fundamental difficulties faced by power system operators. Good forecasting tools are urgent needed under the relevant issues associated with the integration of wind energy into the power system. This paper gives a bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting. Based on the assessment of wind power forecasting models, further direction for additional research and application is proposed.

A review on the forecasting of wind speed and generated power

http://pierrepinson.com/31761/Literature/foley2012.pdf

Current methods and advances in forecasting of wind power generation

With the continuous increase of wind power penetration in power systems, the problems caused by the volatile nature of wind speed and its occurrence in the system operations such as scheduling and dispatching have drawn attention of system operators, utilities and researchers towards the state-of-the-art wind speed and power forecasting methods These methods have the required capability of reducing the influence of the intermittent wind power on system operations as well as of harvesting the wind energy effectively In this context, combining different methodologies in order to circumvent the challenging model selection and take advantage of the unique strength of plausible models have recently emerged as a promising research area Therefore, a comprehensive research about the combined models is called on for how these models are constructed and affect the forecasting performance Aiming to fill the mentioned research gap, this paper outlines the combined forecasting approaches and presents an up-to date annotated bibliography of the wind forecasting literature Furthermore, the paper also points out the possible further research directions of combined techniques so as to help the researchers in the field develop more effective wind speed and power forecasting methods

A review of combined approaches for prediction of short-term wind speed and power

As financial time series are inherently noisy and non-stationary, it is regarded as one of the most challenging applications of time series forecasting. Due to the advantages of generalization capability in obtaining a unique solution, support vector regression (SVR) has also been successfully applied in financial time series forecasting. In the modeling of financial time series using SVR, one of the key problems is the inherent high noise. Thus, detecting and removing the noise are important but difficult tasks when building an SVR forecasting model. To alleviate the influence of noise, a two-stage modeling approach using independent component analysis (ICA) and support vector regression is proposed in financial time series forecasting. ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signals without having any prior knowledge of the mixing mechanism. The proposed approach first uses ICA to the forecasting variables for generating the independent components (ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise and served as the input variables of the SVR forecasting model. In order to evaluate the performance of the proposed approach, the Nikkei 225 opening index and TAIEX closing index are used as illustrative examples. Experimental results show that the proposed model outperforms the SVR model with non-filtered forecasting variables and a random walk model.

Financial time series forecasting using independent component analysis and support vector regression

Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model

https://faculty.kfupm.edu.sa/ri/srehman/papers/support vector machine.pdf

Support vector machines for wind speed prediction

Two dimensional (2-D) direction-of-arrival (DOA) elevation and azimuth angles estimation for uncorrelated sources using L-shaped array is presented in this letter. The key points of the proposed method are the following: 1) The proposed scheme obtains the cross-correlation matrix between the subarrays data to construct a data matrix with free noise; 2) employ only linear operations on the data matrix hence low computational complexity; 3) it has better angle estimation with no failure in practical mobile elevation angle range (70 $^{\circ}$ –90 $^{\circ}$ ). Simulation results demonstrate that the proposed method has better performance and lower computational complexity compared to the existing schemes.

Two-Dimensional DOA Estimation Using Cross-Correlation Matrix With L-Shaped Array

Postnatal teeth, wisdom teeth, and exfoliated deciduous teeth can be harvested for dental stem cell (DSC) researches. These mesenchymal stem cells (MSCs) can differentiate and also consider as promising candidates for dental and oral regeneration. Thus, the development of DSC therapies can be considered a suitable but challenging target for tissue regeneration. Epigenetics describes changes in gene expression rather than changes in DNA and broadly happens in bone homeostasis, embryogenesis, stem cell fate, and disease development. The epigenetic regulation of gene expression and the regulation of cell fate is mainly governed by deoxyribonucleic acid (DNA) methylation, histone modification, and noncoding RNAs (ncRNAs). Tissue engineering utilizes DSCs as a target. Tissue engineering therapies are based on the multipotent regenerative potential of DSCs. It is believed that epigenetic factors are essential for maintaining the multipotency of DSCs. A wide range of host and environmental factors influence stem cell differentiation and differentiation commitment, of which epigenetic regulation is critical. Several lines of evidence have shown that epigenetic modification of DNA and DNA-correlated histones are necessary for determining cells' phenotypes and regulating stem cells' pluripotency and renewal capacity. It is increasingly recognized that nuclear enzyme activities, such as histone deacetylases, can be used pharmacologically to induce stem cell differentiation and dedifferentiation. In this review, the role of epigenetic in dental and oral regenerative medicine by different types of dental stem cells is discussed in two new and promising areas of medical and biological researches in recent studies (2010-2022).

/pdf/the-role-of-epigenetic-in-dental-and-oral-regenerative-2stomcev.pdf

The Role of Epigenetic in Dental and Oral Regenerative Medicine by Different Types of Dental Stem Cells: A Comprehensive Overview

In this paper, authors present their work on field-programmable gate array (FPGA) hardware implementation of proposed direction of arrival estimation algorithms employing LU factorization. Both $L$ and $U$ matrices were considered in computing the angle estimates. Hardware implementation was done on a Virtex-5 FPGA and its experimental verification was performed using National Instruments PXI platform which provides hardware modules for data acquisition, RF down-conversion, digitization, etc. A uniform linear array consisting of four antenna elements was deployed at the receiver. LabVIEW FPGA modules with high throughput math functions were used for implementing the proposed algorithms. MATLAB simulations of the proposed algorithms were also performed to validate the efficacy of the proposed algorithms prior to hardware implementation of the same. Both MATLAB simulation and experimental verification establish the superiority of the proposed methods over existing methods reported in the literature, such as QR decomposition-based implementations. FPGA compilation results report low resource usage and faster computation time compared with the QR-based hardware implementation. Performance comparison in terms of estimation accuracy, percentage resource utilization, and processing time is also presented for different data and matrix sizes.

FPGA Hardware Implementation of DOA Estimation Algorithm Employing LU Decomposition

Hardware implementation of the proposed direction of arrival (DOA) estimation algorithms based on Cholesky and LDL decomposition is presented in this paper. The proposed algorithms are implemented for execution on a field programmable gate array (FPGA) as well as a PC (running LabVIEW) for the multiple non-coherent sources located in the far-field region of a uniform linear array (ULA). Prototype testbeds built using the national instruments (NI) universal software radio peripheral (USRP) software defined radio (SDR) platform and Xilinx Virtex-5 FPGA are originally constructed for the experimental validation of the proposed algorithms. The results from LabVIEW simulations and real-time hardware experiments demonstrate the effectiveness of the proposed algorithms. Specifically, the implementation of the proposed algorithms on a Xilinx Virtex-5 FPGA using the LabVIEW software clarifies their efficiency in terms of computation time and resource utilization, which make them suitable for the real-time practical applications. Moreover, the performance comparison with the QR decomposition-based DOA algorithms as well as similar FPGA-based implementations reported in the literature is conducted in terms of the estimation accuracy, computation speed, and FPGA resources consumed.

/pdf/fpga-based-hardware-implementation-of-computationally-2dt6tsd4ky.pdf

Ahmed A. Hussain

Papers

Support vector machines for wind speed prediction

Two-Dimensional DOA Estimation Using Cross-Correlation Matrix With L-Shaped Array

The Role of Epigenetic in Dental and Oral Regenerative Medicine by Different Types of Dental Stem Cells: A Comprehensive Overview

FPGA Hardware Implementation of DOA Estimation Algorithm Employing LU Decomposition

FPGA-Based Hardware Implementation of Computationally Efficient Multi-Source DOA Estimation Algorithms