New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification

In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP

Discovering explicit Reynolds-averaged turbulence closures for turbulent separated flows through deep learning-based symbolic regression with non-linear corrections

This work introduces a novel data-driven framework to formulate explicit algebraic Reynolds-averaged Navier-Stokes (RANS) turbulence closures. Recent years have witnessed a blossom in applying machine learning (ML) methods to revolutionize the paradigm of turbulence modeling. However, due to the black-box essence of most ML methods, it is currently hard to extract interpretable information and knowledge from data-driven models. To address this critical limitation, this work leverages deep learning with symbolic regression methods to discover hidden governing equations of Reynolds stress models. Specifically, the Reynolds stress tensor is decomposed into linear and non-linear parts. While the linear part is taken as the regular linear eddy viscosity model, a long short-term memory neural network is employed to generate symbolic terms on which tractable mathematical expressions for the non-linear counterpart are built. A novel reinforcement learning algorithm is employed to train the neural network to produce best-fitted symbolic expressions. Within the proposed framework, the Reynolds stress closure is explicitly expressed in algebraic forms, thus allowing for direct functional inference. On the other hand, the Galilean and rotational invariance are craftily respected by constructing the training feature space with independent invariants and tensor basis functions. The performance of the present methodology is validated through numerical simulations of three different canonical flows that deviate in geometrical configurations. The results demonstrate promising accuracy improvements over traditional RANS models, showing the generalization ability of the proposed method. Moreover, with the given explicit model equations, it can be easier to interpret the influence of input features on generated models

Unsteady aerodynamic modelling of horizontal axis wind turbine performance

The present work presents a study of unsteady aerodynamic modelling of horizontal axis wind turbine performance. The unsteady aspects addressed in this work include effects of variations in turbine inflow velocity due to operation in yawed flow, in the atmospheric boundary layer, in a wind tunnel, and due to the tower wake. In each case, the basis for the analysis is a prescribed wake vortex model, the development and enhancement of which has been the main focus of the work. A high resolution model has been developed to meet the requirement for adequate representation of the tower shadow effects. A near wake dynamic model has been enhanced with appropriate modifications and integrated into the prescribed wake scheme to produce a hybrid method capable of predicting the detailed high resolution unsteady response in the tower shadow region. The azimuthal interval used within the shadow region can be reduced to 0.5° whilst the computational cost introduced by the high resolution near wake model is almost negligible. A low order source panel method and the prescribed wake model have been combined into a coupled scheme capable of assessing the basic effect of wind tunnel walls on wind turbine flow and performance. The wind tunnel walls are discretised into a series of panels on which source singularities are placed. The source strengths are related to the turbine bound and wake vorticities via their induced velocities. The geometry of the turbine wake is obtained by superposition of the contribution of the disturbance velocities due to the source panels upon the prescribed wake. This new wake structure modifies the wind turbine aerodynamic performance in turn

Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL). More precisely, the proximal policy optimization (PPO) method is used to control the mass flow rate of four synthetic jets symmetrically located on the upper and lower sides of a cylinder immersed in a two-dimensional flow domain. The learning environment supports four flow configurations with Reynolds numbers 100, 200, 300 and 400, respectively. A new smoothing interpolation function is proposed to help the PPO algorithm to learn to set continuous actions, which is of great importance to effectively suppress problematic jumps in lift and allow a better convergence for the training process. It is shown that the DRL controller is able to significantly reduce the lift and drag fluctuations and to actively reduce the drag by approximately 5.7%, 21.6%, 32.7%, and 38.7%, at $Re$=100, 200, 300, and 400 respectively. More importantly, it can also effectively reduce drag for any previously unseen value of the Reynolds number between 60 and 400. This highlights the generalization ability of deep neural networks and is an important milestone to active flow control

Variable pitch approach for performance improving of straight-bladed VAWT at rated tip speed ratio

This paper presents a new variable pitch (VP) approach to increase the peak power coefficient of the straight-bladed vertical-axis wind turbine (VAWT), by widening the azimuthal angle band of the blade with the highest aerodynamic torque, instead of increasing the highest torque. The new VP-approach provides a curve of pitch angle designed for the blade operating at the rated tip speed ratio (TSR) corresponding to the peak power coefficient of the fixed pitch (FP)-VAWT. The effects of the new approach are exploited by using the double multiple stream tubes (DMST) model and Prandtl’s mathematics to evaluate the blade tip loss. The research describes the effects from six aspects, including the lift, drag, angle of attack (AoA), resultant velocity, torque, and power output, through a comparison between VP-VAWTs and FP-VAWTs working at four TSRs: 4, 4.5, 5, and 5.5. Compared with the FP-blade, the VP-blade has a wider azimuthal zone with the maximum AoA, lift, drag, and torque in the upwind half-cycle, and yields the two new larger maximum values in the downwind half-cycle. The power distribution in the swept area of the turbine changes from an arched shape of the FP-VAWT into the rectangular shape of the VP-VAWT. The new VP-approach markedly widens the highest-performance zone of the blade in a revolution, and ultimately achieves an 18.9% growth of the peak power coefficient of the VAWT at the optimum TSR. Besides achieving this growth, the new pitching method will enhance the performance at TSRs that are higher than current optimal values, and an increase of torque is also generated

Geometric nonlinear dynamic response of wind turbines with different power performance

As the size of wind turbine blades increases, the influence of geometric nonlinearity on aerodynamic, structural and design of blades becomes more and more serious. In this work, the efficient aero-elastic calculation of large flexible blades is studied. In order to solve the problem of efficient aeroelastic caculation of large flexible blades, this work applied the geometrically exact beam theory based on Legendre spectral finite element and coupled with the blade element momentum theory to establish the aero-elastic analysis model of large flexible blades. This model can efficiently calculate the deformation and load on the blade under aerodynamic loading and fully consider the influence of geometric nonlinearity caused by deformation on aeroelastic ability. Taking NREL 5MW and IEA 15MW wind turbines as examples, the linear and nonlinear dynamic responses of these two wind turbine blades are calculated. The result shows that the neglect of nonlinear effect will bring error. From 5MW wind turbine to 15MW wind turbine, the numerical error increased by 27.88%. The influence of geometric nonlinearity of blades on dynamic responses is analysed, which is of great significance to improve the design level of large-scale wind turbines

Real-Time Detection of Application-Layer DDoS Attack Using Time Series Analysis

Distributed denial of service (DDoS) attacks are one of the major threats to the current Internet, and application-layer DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. Consequently, neither intrusion detection systems (IDS) nor victim server can detect malicious packets. In this paper, a novel approach to detect application-layer DDoS attack is proposed based on entropy of HTTP GET requests per source IP address (HRPI). By approximating the adaptive autoregressive (AAR) model, the HRPI time series is transformed into a multidimensional vector series. Then, a trained support vector machine (SVM) classifier is applied to identify the attacks. The experiments with several databases are performed and results show that this approach can detect application-layer DDoS attacks effectively

Development of a CFD-Based Wind Turbine Rotor Optimization Tool in Considering Wake Effects

In the present study, a computational fluid dynamic (CFD)-based blade optimization algorithm is introduced for designing single or multiple wind turbine rotors. It is shown that the CFD methods provide more detailed aerodynamics features during the design process. Because high computational cost limits the conventional CFD applications in particular for rotor optimization purposes, in the current paper, a CFD-based 2D Actuator Disc (AD) model is used to represent turbulent flows over wind turbine rotors. With the ideal case of axisymmetric flows, the simulation time is significantly reduced with the 2D method. The design variables are the shape parameters comprising the chord, twist, and relative thickness of the wind turbine rotor blades as well as the rotational speed. Due to the wake effects, the optimized blade shapes are different for the upstream and downstream turbines. The comparative aerodynamic performance is analyzed between the original and optimized reference wind turbine rotor. The results show that the present numerical optimization algorithm for multiple turbines is efficient and more advanced than conventional methods. The current method achieves the same accuracy as 3D CFD simulations, and the computational efficiency is not significantly higher than the Blade Element Momentum (BEM) theory. The paper shows that CFD for rotor design is possible using a high-performance single personal computer with multiple cores

Foundations of offshore wind turbines: a review

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