Neural Network Compensation Control for Output Power Optimization of Wind Energy Conversion System Based on Data-Driven Control

Due to the uncertainty of wind and because wind energy conversion systems (WECSs) have strong nonlinear characteristics, accurate model of the WECS is difficult to be built. To solve this problem, data-driven control technology is selected and data-driven controller for the WECS is designed based on the Markov model. The neural networks are designed to optimize the output of the system based on the data-driven control system model. In order to improve the efficiency of the neural network training, three different learning rules are compared. Analysis results and SCADA data of the wind farm are compared, and it is shown that the method effectively reduces fluctuations of the generator speed, the safety of the wind turbines can be enhanced, the accuracy of the WECS output is improved, and more wind energy is captured

Results of Fitted Neural Network Models on Malaysian Aggregate Dataset

This result-based paper presents the best results of both fitted BPNN-NAR and BPNN-NARMA on MCCI Aggregate dataset with respect to different error measures.  This section discusses on the results in terms of the performance of the fitted forecasting models by each set of input lags and error lags used, the performance of the fitted forecasting models by the different hidden nodes used, the performance of the fitted forecasting models when combining both inputs and hidden nodes, the consistency of error measures used for the fitted forecasting models, as well as the overall best fitted forecasting models for Malaysian aggregate cost indices dataset

Nonlinear Dynamic System Identification in the Spectral Domain Using Particle-Bernstein Polynomials

System identification (SI) is the discipline of inferring mathematical models from unknown dynamic systems using the input/output observations of such systems with or without prior knowledge of some of the system parameters. Many valid algorithms are available in the literature, including Volterra series expansion, Hammerstein–Wiener models, nonlinear auto-regressive moving average model with exogenous inputs (NARMAX) and its derivatives (NARX, NARMA). Different nonlinear estimators can be used for those algorithms, such as polynomials, neural networks or wavelet networks. This paper uses a different approach, named particle-Bernstein polynomials, as an estimator for SI. Moreover, unlike the mentioned algorithms, this approach does not operate in the time domain but rather in the spectral components of the signals through the use of the discrete Karhunen–Loève transform (DKLT). Some experiments are performed to validate this approach using a publicly available dataset based on ground vibration tests recorded from a real F-16 aircraft. The experiments show better results when compared with some of the traditional algorithms, especially for large, heterogeneous datasets such as the one used. In particular, the absolute error obtained with the prosed method is 63% smaller with respect to NARX and from 42% to 62% smaller with respect to various artificial neural network-based approaches

Structural and typological analyses of special lexical units in the sphere of information technologies in English

This article represents structural and typological analyses of special lexical units in the sphere of information technologies in English. 68 lexical units were classified into three groups due to typological analysis: abbreviations, monosyllabic terms, terminological units. According to the structural analysis, they were divided into such groups as multicomponent combinations, one-component models, two-component models. In addition, lexical units were categorized by subject areas: general, software engineering, graph theory, algorithm theory, mathematical statistics, data science, virtual reality, IT management and computer networks. Judging by results of typological analysis, terminological units are the most frequent type of lexical units. The most popular structure is two-component model. Finally, Computer Network and Data Science turned out to include more terminological units than any other subject area of information technology

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone’s health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models

Structural Organization and Chemical Activity Revealed by New Developments in Single-Molecule Fluorescence and Orientation Imaging

Single-molecule (SM) fluorescence and its localization are important and versatile tools for understanding and quantifying dynamical nanoscale behavior of nanoparticles and biological systems. By actively controlling the concentration of fluorescent molecules and precisely localizing individual single molecules, it is possible to overcome the classical diffraction limit and achieve \u27super-resolution\u27 with image resolution on the order of 10 nanometers. Single molecules also can be considered as nanoscale sensors since their fluorescence changes in response to their local nanoenvironment. This dissertation discusses extending this SM approach to resolve heterogeneity and dynamics of nanoscale materials and biophysical structures by using positions and orientations of single fluorescent molecules. I first present an SM approach for resolving spatial variations in the catalytic activity of individual photocatalysts. Quantitative colocalization of chemically triggered molecular probes reveals the role of structural defects on the activity of catalytic nanoparticles. Next, I demonstrate a new engineered optical point spread function (PSF), called the Duo-spot PSF, for SM orientation measurements. This PSF exhibits high sensitivity for estimating orientations of dim fluorescent molecules. This dissertation also discusses a new amyloid imaging method, transient amyloid binding (TAB) microscopy, for studying heterogeneous organization of amyloid structures, which are associated with various aging-related neurodegenerative diseases. Continuous transient binding of dye molecules to amyloid structures generates photon bursts for SM localization over hours to days with minimal photobleaching, yielding about 40% more localizations than standard immunolabeling. Finally, I augment TAB imaging to simultaneously measure positions and orientations of fluorescent molecules bound to amyloid surfaces. This new method, termed single-molecule orientation localization microscopy (SMOLM), robustly and sensitively measures the in-plane (xy) orientations of fluorophores (approximately 9 degree precision in azimuthal angle) near a refractive index interface and reveals structural heterogeneities along amyloid fibrillar networks that cannot be resolved by SM localization alone