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Study of ppLN crystals for sum frequency generation of CW laser demonstrating 20 nm tunability in the visible range from 616 to 636 nm, through temperature tuning
This work presents the study of custom-made nonlinear ppLN crystals enabling the generation of CW laser around 626 nm demonstrating over 500mW over a 20 nm tunability range using temperature tuning, through SFG of tunable 1 and 1.5µm lasers.
Journal Article
The forest of wool and steel
Tomura is startled by the hypnotic sound of a piano being tuned in his school. It seeps into his soul and transports him to the forests, dark and gleaming, that surround his beloved mountain village. From that moment, he is determined to discover more. Under the tutelage of three master piano-tuners -- one humble, one cheery, one ill-tempered -- Tomura embarks on his training, never straying too far from a single, unfathomable question: do I have what it takes? Set in small-town Japan, this warm and mystical story is for the lucky few who have found their calling -- and for the rest of us who are still searching. It shows that the road to finding one's purpose is a winding path, often filled with treacherous doubts and, for those who persevere, astonishing moments of revelation.
BOOK
Recent Advances in Tunable Metasurfaces and Their Application in Optics
Metasurfaces can be opportunely and specifically designed to manipulate electromagnetic wavefronts. In recent years, a large variety of metasurface-based optical devices such as planar lenses, beam deflectors, polarization converters, and so on have been designed and fabricated. Of particular interest are tunable metasurfaces, which allow the modulation of the optical response of a metasurface; for instance, the variation in the focal length of a converging metalens. Response tunability can be achieved through external sources that modify the permittivity of the materials constituting the nanoatoms, the substrate, or both. The modulation sources can be classified into electromagnetic fields, thermal sources, mechanical stressors, and electrical bias. Beside this, we will consider optical modulation and multiple approach tuning strategies. A great variety of tunable materials have been used in metasurface engineering, such as transparent conductive oxides, ferroelectrics, phase change materials, liquid crystals, and semiconductors. The possibility of tuning the optical properties of these metamaterials is very important for several applications spanning from basic optics to applied optics for communications, depth sensing, holographic displays, and biochemical sensors. In this review, we summarize the recent progress on electro-optical magnetic, mechanical, and thermal tuning of metasurfaces actually fabricated and experimentally tested in recent years. At the end of the review, a short section on possible future perspectives and applications is included.
Journal Article
Band gaps of crystalline solids from Wannier-localization–based optimal tuning of a screened range-separated hybrid functional
Accurate prediction of fundamental band gaps of crystalline solid-state systems entirely within density functional theory is a long-standing challenge. Here, we present a simple and inexpensive method that achieves this by means of nonempirical optimal tuning of the parameters of a screened range-separated hybrid functional. The tuning involves the enforcement of an ansatz that generalizes the ionization potential theorem to the removal of an electron from an occupied state described by a localized Wannier function in a modestly sized supercell calculation. The method is benchmarked against experiment for a set of systems ranging from narrow band-gap semiconductors to large band-gap insulators, spanning a range of fundamental band gaps from 0.2 to 14.2 electronvolts (eV), and is found to yield quantitative accuracy across the board, with a mean absolute error of ∼0.1 eV and a maximal error of ∼0.2 eV.
Journal Article
Review of State-of-the-Art Microwave Filter Tuning Techniques and Implementation of a Novel Tuning Algorithm Using Expert-Based Hybrid Learning
Present-day demand and supply of connectivity necessitate the rapid production of Microwave (MW) filter units. The production of these filters is then followed by the step of utmost importance in the assembly line, viz., the ‘tuning of the filter’, as tuning is crucial to meeting the selectivity requirements of the band. Since the advent of filters, tuning has always been done manually, and hence it is considered a bottleneck by experts in the field. Thus, the need to automate the system is highly implied. The goal of the current work is to outline various MW filter tuning techniques that have been advocated by the community of researchers. The limitations of the said research works and their comparative analysis are also encapsulated in tabular form in the present paper. The paper ends with the implementation of an Expert-Based Hybrid Deep Learning Algorithm to fully automate the filter tuning process.
Journal Article
Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis
Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization.
Journal Article
Design, construction and tuning of an RF deflecting cavity for the REGAE facility
Extraordinary emittance requirements in the nm range (normalized) and pulse lengths down to a level of ∼10 fs for REGAE bunches demand both operation at low bunch charges on the sub-pC scale and a very careful beam handling. The S-band RF deflecting cavity is intended for diagnostics of the longitudinal bunch parameters. For the first time a deflecting structure, specially developed and optimized for bunch rotation has been realized for the REGAE RF deflector. The developed cavity provides a minimized level of aberrations in the distribution of the deflecting field combined with an improved RF efficiency. The main steps in the cavity design, construction and tuning are described.
Journal Article
Hybrid data-driven fuzzy active disturbance rejection control for tower crane systems
•Model-free VRFT applied to ADRC combined with fuzzy control is proposed.•Least-squares algorithm specific to VRFT is replaced with Grey Wolf Optimizer.•The fuzzy control system stability is employed in the design approaches.•Model-free optimal tuning of controllers for tower crane systems is done.•Experimentally validated model-free controllers are offered.
This paper proposes the Virtual Reference Feedback Tuning (VRFT) of a combination of two control algorithms, Active Disturbance Rejection Control (ADRC) as a representative data-driven (or model-free) control algorithm and fuzzy control, in order to exploit the advantages of data-driven control and fuzzy control. The combination of Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control (PDTSFC) tuned by Virtual Reference Feedback Tuning results in two novel data-driven algorithms referred to as hybrid data-driven fuzzy ADRC algorithms. The main benefit of this combination is the automatic optimal tuning in a model-free manner of the parameters of the combination of Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control called ADRC-PDTSFC. The second benefit is that the suggested combination is time saving in finding the optimal parameters of the controllers. However, since Virtual Reference Feedback Tuning generally works with linear controllers to solve a certain optimization problem and the fuzzy controllers are essentially nonlinear, this paper replaces the least-squares algorithm specific to Virtual Reference Feedback Tuning with a metaheuristic optimization algorithm, i.e. Grey Wolf Optimizer. The fuzzy control system stability is guaranteed by including a limit cycle-based stability analysis approach in Grey Wolf Optimizer algorithm to validate the next solution candidates. The hybrid data-driven fuzzy ADRC algorithms are validated as controllers in terms of real-time experiments conducted on three-degree-of-freedom tower crane system laboratory equipment. To determine the efficiency of the new hybrid data-driven fuzzy ADRC algorithms, their performance is compared experimentally with that of two control algorithms, namely Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control, whose parameters are optimally tuned by Grey Wolf Optimizer in a model-based manner using the nonlinear process model.
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Journal Article
Deep Learning-Based Model for Financial Distress Prediction
Predicting bankruptcies and assessing credit risk are two of the most pressing issues in finance. Therefore, financial distress prediction and credit scoring remain hot research topics in the finance sector. Earlier studies have focused on the design of statistical approaches and machine learning models to predict a company's financial distress. In this study, an adaptive whale optimization algorithm with deep learning (AWOA-DL) technique is used to create a new financial distress prediction model. The goal of the AWOA-DL approach is to determine whether a company is experiencing financial distress or not. A deep neural network (DNN) model called multilayer perceptron based predictive and AWOA-based hyperparameter tuning processes are used in the AWOA-DL method. Primarily, the DNN model receives the financial data as input and predicts financial distress. In addition, the AWOA is applied to tune the DNN model's hyperparameters, thereby raising the predictive outcome. The proposed model is applied in three stages: preprocessing, hyperparameter tuning using AWOA, and the prediction phase. A comprehensive simulation took place on four datasets, and the results pointed out the supremacy of the AWOA-DL method over other compared techniques by achieving an average accuracy of 95.8%, where the average accuracy equals 93.8%, 89.6%, 84.5%, and 78.2% for compared models.
Journal Article