Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Odd F-Weibull Distribution: Properties and Application of the Monthly Nigerian Naira to British Pound Exchange Rate Data

This study developed a novel generalized family of probability distributions known as the Odd F generalized family of distribution. The compound Odd F-Weibull distribution was introduced from the Odd F generalized family. This distribution is an alternative to various current distributions, such as the Beta-Weibull distribution, Generalized Modified Weibull, Log-Logistic-Weibull, Frechet-Weibull and Exponentiated Exponential-Weibull distributions. We obtained the distributions of quantile function, moments and information-generating function. Moreover, the parameters of its estimates were derived using the maximum likelihood approach. Furthermore, monthly Nigerian Naira to British Pound exchange rate data was analyzed to verify the viability of developed model. The findings demon-strate that the Odd F-Weibull model outperformed better than competitive distributions by having minimal corrected akaike information and Bayesian information criteria values.
Feature Selection based False Data Detection Scheme using Machine Learning for Power System

An electrical power grid is a conglomerate system that requires meticulous monitoring to ensure uninterrupted, secured and reliable grid operation by incorporating state estimation to ensure a better estimate of the power grid state through assessment of meter quantification. The state estimator operates on real-time inputs that are data and status information. Thereby, it becomes necessary to automatize and digitize the electric grid by enhancing the widespread installation of Remote Terminal (RTUs) and Phasor Measurement Units (PMUs) for improvising real-time wide-area system monitoring and control. However, the challenge of anomaly detection of the data obtained from the PMUs still exists as the PMUs data comprises different types of anomalies arising from both physical and cyber systems. This work proposes a machine learning-based scheme to detect the anomaly in the data. Principal Component Analysis algorithm is used as the feature selection algorithm to attain the important characteristics in the data and then a supervised classification algorithm is used to obtain the erroneous data in the PMU data streams.
Data-driven-based Stable Object Grasping for a Triple-fingered Under-actuated Robotic Hand

This paper proposes a data-driven-based stable object grasping method to grasp and realize object force/torque equilibrium for a triple-fingered under-actuated robotic system. Controlling the fingertip force of an under-actuated hand for precision grasp has been a challenging task in the robotic field due to the high nonlinearity of the under-actuated system as well as the inaccuracy in model-based controllers. By attaching a force sensor to the suitable location inside the robotic hand, necessary information for stable grasping, which is the detection of contact between fingertip and object and the magnitude of the fingertip force, is obtained using the proposed data-driven methods on internal sensor data in real-time. The obtained information is then used as feedback for the stable grasp controller to realize grasped object force/torque equilibrium.
Positive Definite Toeplitz Completions

The problem of completing a partially prescribed Toeplitz matrix to a positive definite Toeplitz matrix is considered. The main result is a characterization of the patterns of specified entries that guarantee the existence of a positive definite Toeplitz completion of a partial positive definite Toeplitz matrix. This characterization resolves affirmatively a conjecture of C. R. Johnson and L. Rodman.
Intracavity grating-confined all-epitaxial vertical-cavity surface-emitting laser based on selective interface Fermi-level pinning

An all-epitaxial GaAs-based vertical-cavity surface-emitting laser is demonstrated with an intracavity mode and current-confining grating. The grating uses selective Fermi-level pinning at a heterointerface to confine the current to the same regions as the optical mode. Despite the grating’s “coarseness,” an increase of efficiency is obtained in side-by-side comparison with devices that lack the grating.
The meaning of chains

This thesis investigates the mechanisms applying in the interpretation of syntactic chains. The theoretical background includes a translation of syntactic forms into semantic forms and a model theoretic explication of the meaning of semantic forms. Simplicity considerations apply to all three stages of the interpretation process: syntactic derivation, translation into semantic forms, interpretation of semantic forms. Three main results are achieved. The first is that trace positions can have semantic content beyond what is needed for the semantic dependency of trace and binder. This extra content is some or all of the lexical material of the head of the chain, as expected on the copy theory of movement. Two independent arguments support this conclusion. One, discussed in chapter 2, is based on the distribution of Condition C effects, where novel interactions between variable binding, antecedent contained deletion and Condition C are observed. The second, developed in chapter 3, is based on conditions on the identity of traces observed in antecedent contained deletion constructions. Both arguments lead to the same generalizations about what lexical material of the head is interpreted in the trace position. The second main result is that lambda calculus is superior to both standard predicate logic and combinatorial logic as the mathematical model for the semantic mechanism mediating the dependency of trace (or bound pronoun) and binder. Chapter 4 argues this on the basis of the distribution of focus and destressing in constructions with bound pronouns. The third main result is that quantification must be allowed to range over pointwise different choice functions. Chapter 5 shows that quantification over individuals is insufficient, and that pointwise different choice functions are required. The result entails that the syntactic difference of A-chains and A-bar chains predicts a semantic difference in the type of the variable involved, which is argued to explain weak crossover phenomena. Chapters 6 argues that the interpretation procedures developed in the preceeding chapters account for all cases. It is shown that only traces of the type of individuals arise, and that scope reconstruction is a phonological phenomenon. The latter result also supports the T-model of syntax.
Marine Objects Detection Using Deep Learning on Embedded Edge Devices

Artificial Intelligence techniques based on convolution neural networks (CNNs) are now dominant in the field of object detection and classification. The deployment of CNNs on embedded edge devices targeting real-time inference sets a challenge due to the limited computing resources and power budgets. Several optimization techniques such as pruning, quantization and use of light neural networks enable the real-time inference but at the cost of precision degradation. However, using efficient approaches to apply the optimization techniques at training and inference stages enable high inference speed with limited degradation of detection performance. In this paper, we revisit the problem of detecting and classifying maritime objects. We investigate different versions of the You Only Look Once (YOLO), a state-of-the-art deep neural network, for real-time object detection and compare their performance for the specific application of detecting maritime objects. The trained YOLO networks are efficiently optimized targeting three recent edge devices: Nvidia Jetson Xavier AGX, AMD-Xilinx Kria KV260 Vision AI Kit, and Movidius Myriad X VPU. The proposed deployments demonstrate promising results with an inference speed of 90 FPS and a limited degradation of 2.4% in mean average precision.
PROJETO DE TRANSFORMADOR MONOFÁSICO UTILIZANDO O ALGORITMO DE EVOLUÇÃO DIFERENCIAL

Universidade Federal de Uberlândia – Faculdade de E ng nharia Elétrica. Av. : João Naves de Ávila, no 2121, Bairro: Santa Mô nica, CEP: 38.408-100, Uberlândia-MG, Brasil. Universidade Federal de Uberlândia – Faculdade de E ng nharia Química. Av. : João Naves de Ávila, no 2121, Bairro: Santa Mô nica, CEP: 38.408-100, Uberlândia-MG, Brasil. Universidade Federal de Santa Catarina – Departamen to d Engenharia Elétrica. Campus Universitário Reitor João David Ferreira Lim a, Bairro: Trindade, CEP: 88.040-970, Florianópolis -SC, Brasil.
Implementation of a Priority Queue to Optimize Resources during Manual Verification of Fake News

Combating fake news on social media is a critical challenge in today's digital age, especially when misinformation is spread regarding vital matters such as the Covid-19 pandemic. Manual verification of all content is infeasible; hence, Artificial Intelligence is used to classify fake news. Our ensemble model uses multiple Natural Language Processing techniques to analyze the truthfulness of the text in tweets. We create custom parameters that analyze the consistency and truthfulness of domains contained in hyperlinked URLs. We then combine these parameters with the results of our deep learning models to achieve classification with greater than 99% accuracy. We have proposed a novel method to calculate a custom coefficient, the Combined Metric of Prediction Uncertainty (CMPU), which is a measure of how uncertain the model is of its classification of a given tweet. Using CMPU, we have proposed the creation of a priority queue following which the tweets classified with the lowest certainty can be manually verified. By manually verifying 3.93% of tweets, we were able to improve the accuracy from 99.02% to 99.77%.
Dynamic Event-triggered Fault Detection for Networked Singularly Perturbed Systems in Finite-frequency Domain Using Interval Observers

This paper investigates the fault detection problem in finite-frequency domain for networked singularly perturbed systems. A novel dynamic event-triggered scheme is introduced to mitigate the transmission burden of the network. Under the consideration of fault sensitivity and disturbance robustness, the addressed fault detection problem is converted into an auxiliary H− / H∞ filtering problem by properly augmenting the states of the original system and the interval observer. In addition, the residual generated by the interval observer can be directly utilized for fault detection decision-making instead of designing the residual evaluation function and threshold. Finally, simulation results of vehicle lateral dynamic systems are provided to illustrate the effectiveness and applicability of the proposed method.
Efficient split linear multistep methods for stiff ordinary differential equations

A new family of predictor-corrector schemes is designed for the numerical solution of stiff differential systems. Based on split Adams–Moulton formulas through sixth order, members of the new family achieve higher order and possess smaller error constants than corresponding split backward differentiation formulas of the same stepnumber, while maintaining similar stability properties. Some confirmation of this is obtained using a variable step implementation on test problems from the literature.
NOx prediction of gas turbine based on Dual Attention and LSTM

Aiming at the difficulty in predicting NOX emissions products of gas turbines in power plants, this paper analyzes the long-term operating parameter data during the combustion process, and proposes a dual attention long short-term memory (DA-LSTM) gas turbine NOX prediction model. Decompose time series by singular spectrum analysis (SSA) to remove noise in data acquisition, apply Light Gradient Boosting Machine (LightGBM) method to select features, and the dual attention mechanism is used to establish a time window to assign weights to input features and times. The model in this article is tested on the UCI public data set, and compare with related models. The simulation result shows the model can achieve effective prediction on NOX gas emissions during the combustion process.
LMI-based Optimization for the Position Feedback Control of Underactuated Robotic Systems via an Affine PD Controller: Case of the Pendubot

The set-point control problem of underactuated mechanical systems is a challenging issue and needs to be further investigated. This research work focuses on such problem for underactuated Lagrangian-type robotic systems, and then the objective lies in designing a suitable controller to change the robot’s current position to a desired state. Thus, our main idea is to control the robotic systems through an affine-type PD controller and by virtue of the LMI-based optimization techniques. Thus, we will develop LMI conditions on the controller gains to ensure the stability of the underactuated robot at the desired state. Then, to achieve this goal, we will consider the rescaled dynamical model describing the difference between the nonlinear model and its approximate linear dynamics, where we assume that this rescaled dynamics satisfies such some Lipschitz condition. Finally, the pendubot manipulator is adopted in order to verify the efficiency of the proposed controller.
Performance of Multiple Intelligent Reflecting Surfaces Assisted Communication

Intelligent Reflecting Surface (IRS) is an emerging passive device that implements passive beamforming by adjusting the amplitude and the phase of incident signal through a large number of passive reflection units on its surface. Many propagation paths can be constructed from a transmitter to an IRS, and then from the IRS to a receiver, thereby enhancing the received signal and improving the communication quality. In this paper, we evaluate the performance of wireless propagation paths without direct link assisted by multiple IRSs by leveraging stochastic geometry to model the locations of base stations (BSs) and IRSs. By analyzing the coverage probability and average rate of a typical user at a blind spot assisted by multiple IRSs which are modeled by stochastic geometry, we observe that increasing the number of IRSs will effectively improve the performance, however, the improvement will be smaller and smaller.
Towards Better Service Personalization: Reinforcement Learning with Guarantee of User Preference

There is a growing demand for personalized services in today's diverse society, requiring software to handle unique preferences for each user. Reinforcement learning (RL) is fre-quently used to offer personalized services because it is able to handle complicated preferences without the high design cost for their numerous combinations. The most representative weight sum method handles soft preferences well but cannot guarantee hard preferences and learning speed. This paper proposes Preference-Guaranteed Reinforcement Learning (PGRL) to solve this problem. In PGRL, a preference-guarantor that provides a preference-guaranteed action list for the agent to choose from is introduced into the original RL. It takes into account the previous performance and predicts future performance to guarantee hard preferences. A preliminary evaluation through two experiments (i.e., trip planning and automated braking) indicates that PGRL increases learning speed while guaranteeing hard preferences.
A note on residual-based empirical likelihood kernel density estimation

In general the empirical likelihood method can improve the performance of estimators by including additional information about the underlying data distribution. Application of the method to kernel density estimation based on independent and identically distributed data always improves the estimation in second order. In this paper we consider estimation of the error density in nonparametric regression by residual-based kernel estimation. We investigate whether the estimator is improved when additional information is included by the empirical likelihood method. In comparison to the residual-based kernel estimator we observe a change in the asymptotic bias of the empirical likelihood estimator already in rst order and in the asymptotic variance in second order.
Active Disturbance Rejection Control of Morphing Aircraft with PSO-Based Parameter Optimization

In order to solve the control problem of morphing aircraft under anti-jamming requirement, this paper proposed a new type of active disturbance rejection controller (ADRC) with optimized parameters. First, a longitudinal nonlinear dynamic model of a variable-swept-wing aircraft was established. Then, a set of control schemes with cascaded structure were proposed and summarized, and the decoupling control of speed and altitude was realized through the throttle opening and elevator deflection angle respectively. Next, for the controller of each loop, an extended state observer was designed to estimate all states in real time, the backstepping method was used to design the output feedback control law, and the dynamic inversion method was used to solve the control non-affine problem. Finally, the particle swarm optimization (PSO) algorithm was used to optimize controller parameters. The simulation results show that the PSO algorithm converges well when optimizing the controller parameters, and the parameter-optimized ADRC can track the command well in the case of configuration change and interference existence.
Wireless Charging in a Dynamic Environment for Electric Vehicles

Electrical vehicles require too much time to recharge their batteries, so to accommodate our busy schedule the conventional method of electric vehicle battery charging is replaced by “Dynamic Charging” The work presented involved the expansion of a novel type of wireless power transmission device to ensure high-efficiency battery charging stations for electric cars. A research project will look at the efficiency of traditional battery charging systems. In this paper, the finite element analysis is done by ANSYS simulation software. The static and dynamic modeling of the suggested wireless power transfer technique is the study's most important finding. A new model is created and describedthat takes into account both static and dynamic issues. This article will aid in the growth of future electric vehicle infrastructure.
Cavitation bubble dynamics in a liquid gap of variable height

We report on an experimental study of cavitation bubble dynamics within sub-millimetre-sized narrow gaps. The gap height is varied, while the position of the cavitation event is fixed with respect to the lower gap wall. Four different sizes of laser-induced cavitation bubbles are studied using high-speed photography of up to 430,000 frames per second. We find a strong influence of the gap height, H, on the bubble dynamics, in particular on the collapse scenario. Also, similar bubble dynamics was found for the same non-dimensional gap height η = H/Rx, where Rx is the maximum radius in the horizontal direction. Three scenarios are observed: neutral collapse at the gap centre, collapse onto the lower wall and collapse onto the upper wall. For intermediate gap height the bubble obtains a conical shape 1.4 < η < 7.0. For large distances, η > 7.0, the bubble no longer feels the presence of the upper wall and collapses hemispherically. The collapse time increases with respect to the expansion time for decreasing values of η. Due to the small scales involved, the final stage of the bubble collapse could not be resolved temporally and numerical simulations were performed to elucidate the details of the flow. The simulations demonstrate high-speed jetting towards the upper and lower walls and complex bubble splitting for neutral collapses.
Out of Band Emission Suppression and PAPR Analysis in OFDM systems

The sudden discontinuities that are present across the symbols in the Orthogonal Frequency Division Multiplexing (OFDM) system result in high frequency harmonics leading to poor adjacent channel leakage ratio (ACLR). This can lead to harmful interference to adjacent channels and in order to combat this effect, windowing techniques that smooth the transitions across the symbols are used. Since the spectrum emission mask (SEM) requirements are more stringent and may not be satisfied by windowing alone, this is followed by filtering to further restrict the out of band (OOB) emissions. In this paper, we analyse the performance metrics of the OFDM signal when it is subject to windowing and filtering methods. We also provide simulation results and an analytical expression for the peak to average power ratio (PAPR) of an OFDM signal when identical symbols are used across the subcarriers.
Review of Research on Robot Visual Slam

SLAM technology, which is called simultaneous localization and mapping technology, is to make the robot running in the environment self-locate according to the estimation of its position and posture and the existing map, and at the same time build the map incrementally on this basis, and finally achieve the purpose of building the map and positioning for the whole environment. Firstly, this paper summarizes the history and development of SLAM technology. Secondly, it introduces monocular vision SLAM and RGB_D SLAM through two kinds of vision SLAM algorithms. Finally, it expounds the content of cooperation between vision technology and single robot, and finds related problems, and looks forward to the future.
Graphene, Copper and Nickel based composites in EMI applications: A brief review

Electromagnetic inference (EMI) is one of the most troubling back issues on which research is ongoing with various new combinations. There are many composites that have been utilized to provide shielding against EMI. Therefore, this review focused on the graphene, copper and nickel-based composites which are been utilized. As a study limitation, iron was not considered in the review as the focus was to explore the other metals which can come up as an emerging metals for EMI applications. It was revealed by the review that copper and nickel when combined with graphene give better shielding effectiveness. The effectiveness was not only achieved in X-band but also in the higher frequency range which shows it is of utmost importance in EMI applications. Therefore, more attention is utilized to these metals to further enhance their effectiveness.
MedEstimate: Patient Treatment Recommendation Model

The quantity of digital data that may be used to successfully propose treatments has significantly expanded as a result of the extensive amounts of clinical data indicating patients' health states that have previously been gathered. The treatment recommender system is quickly becoming a vital platform for healthcare services. The system treatment recommendation model would predict the disease based upon the symptoms the user has entered and will predict the disease and recommends first-aid and medications based on the disease predicted. Without the correct first aid, a small injury or ailment might become more serious, and in certain situations, deaths can happen for a lack of prompt medical attention. First Aid not only facilitates a speedier recovery but also contributes to lifesaving. As a result, a Treatment Recommendation System is required to utilize the huge medical expertise of skilled physicians as well as cutting-edge medical technologies in modern facilities to the fullest.
A Statistical FDFD Simulator for the Generation of Labeled Training Data Sets in the Context of Humanitarian Demining using GPR

Due to years of warfare in Colombia, humanitarian demining is more important than ever. However, practice has shown that the detection of Improvised Explosive Devices is challenging and represents a tedious task, due to the lack of reliable sensors. However, developments in the field of Ground Penetrating Radar and machine learning are opening new possibilities. On the downside, supervised machine learning classifiers require large amounts of training data that are not available until today. Therefore, we introduce a 2D-simulator for the generation of randomly behaving ground scenes, which is capable of generating training data sets in a short amount of time.