The study of rotor blade aerodynamic performances of wind turbine has been presented in this thesis. This study was focused on aerodynamic effects changed by blade surface distribution as well as grid solution along the airfoil. The details of numerical calculation from Fluent were described to help predict accurate blade performance for comparison and discussion with available data. The direct surface curvature distribution blade design method for two-dimensional airfoil sections for wind turbine rotors have been discussed with the attentions to Euler equation, velocity diagram and the factors which affect wind turbine performance and applied to design a blade geometry close to an existing wind turbine blade, Eppler387, in order to argue that the blade surface drawn by direct surface curvature distribution blade design method contributes aerodynamic efficiency. The FLUENT calculation of NACA63-215V showed that the aerodynamic characteristics agreed well with the available experimental data at lower angles of attack although it was discontinuities in the surface curvature distributions between 0.7 and 0.8 in x/c. The discontinuities were so small that the blade performance could not be affected. The design of Eppler 387 blade performed to reduce drag force. The discontinuities of surface distribution matched the curve of the pressure coefficients. It was found in the curvature distribution that the leading edge pressure side had difficulties to connect to Bezier curve and also the trailing edge circle was never be tangent to the lines of trailing edge pressure and suction sides due to programming difficulties.

Aerodynamics of wind turbines

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

/pdf/machine-learning-for-the-diagnosis-of-parkinson-s-disease-a-gfmgb6ry9x.pdf

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.

Tidal EnergyLike conventional hydroelectric dams, power plants are built on river estuaries and hold back huge amounts of tidal water twice a day which generates electricity when released. India is expected to have 9,000 MW of tidal energy potential. Wave EnergyThis is generated by the movement of a device either floating on the surface of the ocean or moored to the ocean floor. Current EnergyIt is very similar to the wind above the oceans. Underwater turbines, large propellers tethered to the seabed, are moved with the marine currents to generate electricity. According to the Intergovernmental Panel on Climate Change (IPCC), given the scale of open ocean currents, there is a promise of significant project scale growth when technologies harness lower-velocity currents.

What is ocean energy

Etat de developpement des energies renouvelables au Royaume Uni : geothermie, solaire, hydraulique, energie des vagues et des marees

Renewable energy sources in the UK

Parkinson disease detection using energy direction features based on EMD from voice signal

One of the main problem in process industries is to control process parameters due to their nonlinear behaviour. Control in the level of liquid in a nonlinear spherical tank is challenging due to variation in the cross section area with height. This paper is about the controlling of liquid level in a nonlinear Dual Spherical Tank Liquid Level System (DSTLLS) using Gain Scheduled PI controller (GPIC).The performance indices like ISE, IAE and time domain calculations similar to rise time, peak time and settling time are also calculated in this paper, for different servo and regulatory tracking cases. This research paper gives details about implementation and performance analysis of real time GPIC for DSTLLS by using LabVIEW.

https://www.enggjournals.com/ijet/docs/IJET14-06-05-208.pdf

Real Time Implementation of Gain Scheduled Controller Design for Higher Order Nonlinear System Using LabVIEW

In recent years, information about a person can be extracted just from the person’s voice signals using certain computational systems. Additionally, it can then be determined whether a person is affected or not by a severe medical condition like Parkinson’s Disease (PD). This shows that there is a certain distinct connection between PD and speech impairment. Moreover, it is known that early detection of the disease can result in a better medical diagnosis and treatment. In this research paper, a versatile range of classification based machine learning and deep learning algorithms, employed with different dimensionality reduction (DR) techniques are used. A comparative study of their accuracies in differentiating a healthy human from one who is afflicted by the disease is performed. The time complexities of the algorithms have also been observed to understand the effect of DR techniques. These algorithms have been trained on the Parkinson’s speech dataset obtained from UCI machine learning repository.

Evaluation of Machine learning and Deep learning algorithms combined with dimentionality reduction techniques for classification of Parkinson’s Disease

In recent days, deep learning has been widely used in signal and information processing. Among the deep learning algorithms, Convolution Neural Network (CNN) has been widely used for image recognition and classification because of its architecture, high accuracy and efficiency. This paper proposes a method that uses the CNN on audio samples rather than on the image samples in which the CNN method is usually used to train the model. The one-dimensional audio samples are converted into two-dimensional data that consists of matrix of Mel-Frequency Cepstral Coefficients (MFCCs) that are extracted from the audio samples and the number of windows used in the extraction. This proposed CNN model has been evaluated on the TIDIGITS corpus dataset. The paper analyzes different convolution layer architectures with different number of feature maps in each architecture. The three-layer convolution architecture was found to have the highest accuracy of 97.46% among the other discussed architectures.

Experimental Evaluation of CNN Architecture for Speech Recognition

Background/Objectives: PID controllers are one of the first solutions often considered in the control of process industries. It has always been very difficult to treat the level anomalies n the real time processes. Particularly the non linear systems and processes have been a challenge in relation with their dynamics and flow properties. Methods/Statistical analysis: Dual Spherical Tank Liquid Level System (DSTLLS) has the characteristics of nonlinearity due to the dynamic behaviour and area of cross section of tank. One of the major problems in process industries is to control of liquid level for Second Order System Plus Delay (SOSPD) system because the presence of time delay in the system can lead to destabilization of the system. This simulation aims at portraying the performance of the designed Internal Model Control (IMC) PID Controller. Using the black box modelling technique the SOSPD is mathematically modelled experimentally, assuming the system to be a Single Input Single Output (SISO) model. Findings: The aim of this paper is to design a PID controller using IMC tuning method for a (DSTLLS) whose modelling has been done in real time. This simulation briefly explains about stabilizing problem for SOSPD system using an IMC PID controller. The designed controller performance is analysed in terms of performance indices like Integral Squared Error (ISE) and Integral Absolute Error (IAE) and time domain specifications like Rise time, Settling time and Peak time. Conclusion: The validation of the performance of the designed controller is performed under MATLAB environment. There can different controllers which can be also experimented on the same mathematical model for different configurations of the system, namely MISO and MIMO.

Design and Simulation of Internal Model Controller for a Real Time Nonlinear Process

In this paper a real time Single Spherical Tank Liquid Level System (SSTLLS) has been chosen for investigation. This paper describes the design and development of a Multi Model PI Controller (MMPIC) using classical controller tuning techniques for a single spherical nonlinear tank system. System identification of these different regions of nonlinear process are done using black box modeling, which is identified to be nonlinear and approximated to be a First Order Plus Dead Time (FOPDT) model. A proportional and integral controller is designed using LabVIEW and Chen-Hrones-Reswick (CHR), Zhuang-Atherton (ZA), and Skogestad’s Internal Model Controller (SIMC) tuning methods are implemented in real time. The paper provides the details about the data acquisition unit, shows the implementation of the controller, and comparision of the results of PI tuning methods used for an MMPI Controller.

Nithya Venkatesan

Papers

Real Time Implementation of Gain Scheduled Controller Design for Higher Order Nonlinear System Using LabVIEW

Evaluation of Machine learning and Deep learning algorithms combined with dimentionality reduction techniques for classification of Parkinson’s Disease

Experimental Evaluation of CNN Architecture for Speech Recognition

Design and Simulation of Internal Model Controller for a Real Time Nonlinear Process

Experimental Validation of a Multi Model PI Controller for a Non Linear Hybrid System in LabVIEW