Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Simulation-based coyote optimization algorithm to determine gains of PI controller for enhancing the performance of solar PV water-pumping system

In this study, a simulation-based coyote optimization algorithm (COA) to identify the gains of PI to ameliorate the water-pumping system performance fed from the photovoltaic system is presented. The aim is to develop a stand-alone water-pumping system powered by solar energy, i.e., without the need of electric power from the utility grid. The voltage of the DC bus was adopted as a good candidate to guarantee the extraction of the maximum power under partial shading conditions. In such a system, two proportional-integral (PI) controllers, at least, are necessary. The adjustment of (Proportional-Integral) controllers are always carried out by classical and tiresome trials and errors techniques which becomes a hard task and time-consuming. In order to overcome this problem, an optimization problem was reformulated and modeled under functional time-domain constraints, aiming at tuning these decision variables. For achieving the desired operational characteristics of the PV water-pumping system for both rotor speed and DC-link voltage, simultaneously, the proposed COA algorithm is adopted. It is carried out through resolving a multiobjective optimization problem employing the weighted-sum technique. Inspired on theCanis latransspecies, the COA algorithm is successfully investigated to resolve such a problem by taking into account some constraints in terms of time-domain performance as well as producing the maximum power from the photovoltaic generation system. To assess the efficiency of the suggested COA method, the classical Ziegler-Nichols and trial-error tuning methods for the DC-link voltage and rotor speed dynamics, were compared. The main outcomes ensured the effectiveness and superiority of the COA algorithm. Compared to the other reported techniques, it is superior in terms of convergence rapidity and solution qualities

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

Hardware Development of Dual-Modality Tomography Using Electrical Resistance and Ultrasonic Transmission Tomography for Imaging Liquid and Gas

In decades, single-modality technique focuses on particular application such as liquid/gas, gas/solid, liquid/liquid and liquid/solid which has drawbacks in imaging complex flow with multiple components. This paper focuses on the development of dual-modality tomography system (DMT) integrating ultrasonic tomography and electrical resistance tomography (ERT) to visualize cross-sectional images of two-phase liquid/gas in vertical column. A combination of soft-field and hard-field tomography system measures different physical parameters of two-phase liquid/gas specific of two different material properties which are conductivity (σ) and acoustics impedance (Z). A DMT system is developed with 16 units of ultrasonic transceiver sensors, and 16 units of ERT electrode positioned alternately on a single-plane arrangement to perform measurement simultaneously. The reconstructed tomographic images obtained from measurement data from these two modalities are then fused into a single tomographic image by employing discrete wavelet transform (DWT)

Hardware Development of Dual-Modality Tomography Using Electrical Resistance and Ultrasonic Transmission Tomography for Imaging Liquid and Gas

In decades, single-modality technique focuses on particular application such as liquid/gas, gas/solid, liquid/liquid and liquid/solid which has drawbacks in imaging complex flow with multiple components. This paper focuses on the development of dual-modality tomography system (DMT) integrating ultrasonic tomography and electrical resistance tomography (ERT) to visualize cross-sectional images of two-phase liquid/gas in vertical column. A combination of soft-field and hard-field tomography system measures different physical parameters of two-phase liquid/gas specific of two different material properties which are conductivity (σ) and acoustics impedance (Z). A DMT system is developed with 16 units of ultrasonic transceiver sensors, and 16 units of ERT electrode positioned alternately on a single-plane arrangement to perform measurement simultaneously. The reconstructed tomographic images obtained from measurement data from these two modalities are then fused into a single tomographic image by employing discrete wavelet transform (DWT)

Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Identification Of Continuous-Time Model Of Hammerstein System Using Archimedes Optimization Algorithm

This thesis proposed a novel identification method known as the improved archimedes optimization algorithm (IAOA) for identifying the continuous-time Hammerstein model. Two modifications were employed to solve several demerits of the original archimedes optimization algorithm (AOA). The first modification was an alteration of the density decreasing factor to solve the imbalance of the exploration and exploitation phases. The second one was the introduction of safe updating mechanism to solve the local optima issue. Next, the proposed method was utilized in identifying the variables of the linear and nonlinear subsystems in a continuous-time Hammerstein model using the given input and output data. To verify the efficiency of the proposed method, a numerical example and two real-world experiments, namely the twin-rotor system and the electromechanical positioning system were carried out. The results were analysed in terms of the convergence curve of the fitness function, the variable deviation index, time-domain and frequency-domain responses of the identified model, and the Wilcoxon’s rank-sum test. The obtained results showed that the proposed method, yields solutions with better accuracy and consistency when compared with other well-known metaheuristics methods such as the Particle Swarm Optimizer, Grey Wolf Optimizer, Multi-Verse Optimizer, Archimedes Optimization Algorithm and a hybrid method named the Average Multi-Verse Optimizer and Sine Cosine Algorithm