Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel atmospheric turbulence-degraded image restoration algorithm based on support vector regression

A novel method based on support vector regression is presented for atmospheric turbulence-degraded image restoration. Firstly, an operation with a sliding window is employed to the images to analyze the correlation between pixels of clear image and 8 neighbors of corresponding pixels of degraded image. After feature selection, we get training samples. Secondly, an appropriate kernel function is employed to map the training samples into a higher space. Through linear learning machine in kernel feature space, we get non-linear function. Then the relationship between clear images and degraded images is constructed via regression analysis of the training samples by a support vector machine. Thus the model for turbulence-degraded image restoration is constructed here. Finally, the degraded images to be tested are restored by this model. The experimental results show that the proposed method has lower NMSE and higher PSNR and runs faster than classical image restoration methods such as Wiener Filter, Iterative Blind Deconvolution and etc.
GENERALIZED FORMULATIONS OF MAXWELL'S EQUATIONS FOR NUMERICAL VLASOV–MAXWELL SIMULATIONS

When solving numerically approximations of the Vlasov–Maxwell equations, the source terms in Maxwell's equations coming from the numerical solution of the Vlasov equation do not generally satisfy the continuity equation which is required for Maxwell's equations to be well-posed. Hence it is necessary to introduce generalized Maxwell's equations which remain well-posed when there are errors in the sources. Different such formulations have been introduced previously. The aim of this paper is to perform their mathematical analysis and verify the existence and uniqueness of the solution.
An Adaptive Prediction Algorithm Based on Maximum Correntropy Criterion

The traditional cost function, minimization mean square prediction error is a second order statistic, and it is based on the error Gaussian distribution and linear assumption. But chaotic signals are non-Gaussian, so the optimization criterion is not suitable. Then we present using the robust optimization criterion, maximum correntropy to replace the popular minima mean square error criterion minimization error. In simulation, the algorithm shows an improved performance to a common three-order Volterra prediction.
A Hermite finite element method for incompressible fluid flow

We describe some Hermite stream function and velocity finite elements and a divergence‐free finite element method for the computation of incompressible flow. Divergence‐free velocity bases defined on (but not limited to) rectangles are presented, which produce pointwise divergence‐free flow fields (∇·uh≡0). The discrete velocity satisfies a flow equation that does not involve pressure. The pressure can be recovered as a function of the velocity if needed. The method is formulated in primitive variables and applied to the stationary lid‐driven cavity and backward‐facing step test problems. Copyright © 2009 John Wiley & Sons, Ltd.
Multiple Resonant Voltage Control Scheme for Three-Phase UPS Inverters

A three-phase inverter for uninterruptible power supply (UPS) is required to generate high quality voltage under various loads with fast dynamic response, high tracking accuracy, and good robustness. To fulfill the aforementioned requirements, a plug-in multiple resonant control (MRSC) strategy is propounded for a three-phase UPS inverter to compensate AC signals and their harmonics. The proposed control scheme takes advantages of both multiple resonant controllers and the deadbeat controller, where the deadbeat controller furnishes rapid response and sufficient robustness, while the multiple resonant controllers offer precise recompense of the periodical signal. This paper also provides a design approach and gain tuning rules for the proposed control scheme. An experimental study on the plug-in MRSC scheme is carried out. The test verifies that the MRSC strategy is able to produce high-quality voltages with good robustness.
Classification of Driver Cognitive Load based on Physiological Data: Exploring Recurrent Neural Networks

In-vehicle systems can lead to high cognitive load that impairs driving performance. Interfaces that can detect and adapt to cognitive load accordingly may alleviate these effects. Previous research explored machine learning models to classify drivers’ cognitive load based on physiological signals but most conducted training and testing on data from the same participants (i.e., within-driver partitioning), which raises generalizability and practical feasibility concerns. In this paper, we explored the performance of widely-used models by training and testing them on data from different subjects (i.e., across-drivers partitioning), and further compared them with a more recent model that is effective for time-series data, the recurrent neural network (RNN). A driving simulator dataset was used to classify 2 levels of cognitive load (external cognitive secondary task vs. no task). All models performed better with within-driver partitioning. RNN outperformed other models with mean accuracies of 88.1% and 85.6% with within-driver and across-drivers partitioning, respectively.
Self-Calibrating Loss Models for Real-Time Monitoring of Power Modules Based on Artificial Neural Networks

This paper proposes a self-calibrating device loss model for real-time monitoring of power modules that applies artificial neural networks (ANNs). Combining loss estimating ANNs with junction temperature data in thermal observers allows continuous learning and self-calibration of the ANN on the basis of the loss estimation error of the observer. With the proposed ANNs, device loss characteristics can be modeled with superior accuracy over state-of-the-art parametric loss models. The learning-based self-calibration process introduces unique features: The device loss model can be applied to any power device technology without offline calibration and prior knowledge. Aging dependent changes of the losses are accurately captured and could even be used for device degradation diagnosis. The real-time generation of loss data allows flexible and effective converter efficiency measurements during operation. Overall, the proposed technology strongly reduces or even omits offline loss calibration of power device prior to operation. Furthermore, the application of the highly accurate device loss models in thermal monitoring systems allows monitoring thermal cycles with high precision. This is crucial for safe converter operation at thermal limits as well as accurate prediction of the remaining useful lifetime of power modules. Within this paper the proposed self-calibrating device loss model is described in detail and its static and dynamic performance is evaluated using measurements of an Infineon Hybridpack 2 IGBT module as an example.
Design and Evaluation of a Portable Emergency Ventilator with a New Titration Method

Mechanical ventilation is considered to be effective treatment in various emergency incidents to help patients breathe normally and emit carbon dioxide. However, mechanical ventilation is always replaced by bag-valve-mask ventilation in out-of-hospital cardiac arrest, pre-hospital care and temporary ventilation therapy. In order to release hands and improve ventilation quality, a portable emergency ventilation with two ventilation modes was designed. A commercial ventilator (ResMed stellar 150 portable ventilator) and a prototype ventilator were connected to a Michigan A13 test-lung to compare their performances. A new titration method was proposed for the volume controlled intermittent positive pressure ventilation mode using a relationship between pulse width modulation and a flow parament. The designed ventilator could provide precise control in inspiratory time, expiratory time and peak inspiratory pressure in comparative experiment. The new method reduced the titration time in emergency ventilation and suggested a new method to research the relationship between mechanical parameters of lung and flow paraments.
Event Driven Communication of BLE composite Sensors for Monitoring Temperature of Hot Press Forming

This paper presents an event driven communication method of Bluetooth Low Energy (BLE) composite sensors for energy saving. The aim of the composite sensor is to monitor the temperatures only while the hot press is forming. The composite sensor consists of an acceleration sensor and several temperature sensors. The acceleration sensor is to detect the forming start time and then the temperature sensor measures the forming temperature. This paper discusses the event detection method of the composite sensor and analyzes the expected battery lifetime.
Data-Driven Gap Metric Fault Diagnosis Technology With Its Application to DC-DC Converter

This paper investigates a data-driven gap metric fault detection and isolation method for buck DC-DC converters with component faults. First, the averaged state space model of a buck DC-DC converter and its component (inductor, capacitor and load resistance) fault models are established. Second, a data-driven gap metric using subspace identification is utilized to detect the occurred component faults. Third, to isolate these faults, the concept of fault cluster is firstly developed and then the definition of fault isolation under gap matric is proposed. Based on it, a fault isolation condition is presented by solving its fault cluster center model and radius. Finally, the simulation and experiment are reported to demonstrate the effectiveness of the used method.
Low Space Harmonic Content Windings (LSHWs) Applied to Improve the Pareto Front in Design Optimization of Electric Machines

In design optimization of high performance electric machines where the power density is increasingly a key metric, it is often desirable to maximize the average torque output within a given volume. However, if a smooth output is also required, the torque ripple may become a hurdle as it tends to increase with the average torque for conventional windings. A direct benefit of low space harmonic content windings (LSHWs), featuring the suppression or elimination of targeted non-working orders in the winding magnetomotive force (MMF), is the torque ripple reduction. This paper provides a detailed analysis of torque ripple pairs and the space-time harmonic order mapping, with concise results that applies to both distributed windings and fractionalslot concentrated windings (FSCWs) of any slot/pole combination. Metamodel-based optimizations are performed to compare two LSHWs with a conventional winding. Improved Pareto fronts are validated, which confirms higher torque producing capabilities at various torque ripple thresholds for LSHWs.
Informed Sampling of Prioritized Experience Replay

Experience replay an essential role as an information-generating mechanism plays in reinforcement learning systems that use neural networks as function approximators. It enables the artificial learning agents to store their past experiences in a sliding-window butter, effectively recycling them in the process of a continual re-training of a neural network. The intermediary process of experience caching opens a possibility for an agent to optimize the order in which the experiences are sampled from the butter. This may improve the default standard, i.e., the stochastic prioritization based on Temporal-Difference error (or TD-error), which focuses on experiences that carry more Temporal-Difference surprise for the approximator. A notion of informed prioritization is proposed, a method relying on fast on-line confidence estimates of approximator predictions in order to be able to dynamically exploit the benefits of TD-error prioritization only when its prediction confidence about the selected experiences increases. The presented informed-stochastic prioritization method of replay butter sampling, implemented as a part of standard staple Deep Q-learning algorithm outperformed the vanilla stochastic prioritization based on TD-error in 41 out of 54 trialed Atari games.
Analytics 2.0 for Precision Education Driven by Knowledge Map

Aiming to solve learning difficulties caused by information explosion in the current era of education informatization, this paper proposes a precise teaching model driven by knowledge graphs. Knowledge graphs are essentially learning tools that effectively build a correct and complete curriculum knowledge system and accurately promote personalized learning paths. The main research contents are: (1) In view of the problem that the general knowledge graphs currently in use are not applicable in the field of education, we define the knowledge graph ontology structure of special courses based on Bloom’s teaching target system; (2) In view of the diversification of various teaching data sources, a simple knowledge graph representing structured data is used as a heuristic condition to extract a complete teaching sequence between knowledge points through self-expansion. (3) In view of the problem faced when trying to navigate relevant knowledge points in personalized learning, this paper exploits the internal relationship between knowledge point loopholes and ability achievements, and constructs an accurate path recommendation model based on quantitative data analysis. Taking the C language programming course as experimental data, this paper verifies the effectiveness of this model by quantitative means, which can significantly provide accurate teaching quality and realize the "multi-directional adaptation" among teachers, courses and students.
Effective Music Genre Classification using Late Fusion Convolutional Neural Network with Multiple Spectral Features

Music genre classification is getting more and more attention amid the growing content consumption for music. Music Information Retrieval researchers have proposed various structures based on Convolutional Neural Networks that mainly achieve state-of-the-art results in the music genre classification tasks. Using multiple musical features as model inputs can improve classification accuracy. Therefore, this study proposes a new Convolutional Neural Network model using three musical features for music genre classification: Short-Time Fourier Transform, Mel-Spectrogram, and Mel-Frequency Cepstral Coefficient.
Transfer Learning for Medical Image Classification on Multiple Datasets using PubMedCLIP

Transfer learning is a key solution to deal with the problem of data scarcity, where learning process leverages knowledge learned from similar tasks. In this study, we employ PubMedCLIP as a pre-trained model for the medical image classification tasks. We evaluate multiple datasets of different body regions rather simultaneously. Moreover, we investigate different amounts of training data to evaluate the effectiveness of PubMedCLIP in the case of limited training data. The experimental results show that transfer learning using PubMedCLIP can achieve good results in across a diverse range of body-region datasets. To the best of our knowledge, this is the first study that employs PubMedCLIP for transfer learning.
Sensitivity Reliability Analysis of Power Distribution Networks Using Fuzzy Logic

This paper proposes a combined method utilizing both the reliability block diagram analytical technique and the Monte Carlo simulation method to estimate the reliability of power systems. Since the reliability of collected data is associated with noise and erroneous data, performing the sensitivity analysis is indispensable. Sensitivity analysis utilizing fuzzy logic specifies these uncertainties and their effects on the reliability calculations. The proposed method is applied to the Roy Billiton Test System Bus-2 to confirm its applicability. The obtained results have verified the sensitivity analysis's importance in drawing an accurate picture of reliability evaluation and a crucial tool for distribution power utilities to identify the susceptible parameters that seriously erode the system's complete reliability.
A Review On Lexical Based Malicious Domain Name Detection Methods

In 2019, a study conducted by Palo Alto Networks revealed 20 domain names that are largely cybersquatted by attackers. However, media never stopped reporting phishing and identity theft attacks held by third party entities that rely on domain names to mislead Internet users. Domain names are listed in public lists based on their behavior. These lists objectively evaluate the reputation of a domain name. Black lists contain domain names that have previously committed suspicious acts, whereas white lists include the most popular and trustworthy domain names. For a long time, this listing technique has been used as a reactive approach that has the major limitation of responding lately to attacks. Nowadays techniques tend to be much more proactive, they operate before any attack occurs. In this paper, we give a literature review of proactive malicious domain name detection techniques that use only lexical features of domain names. These features are available, privacy preserving and they highly improve detection results. This review covers twelve recent works that report highly good performance classified according to a new taxonomy of malicious domain name detection methods. Moreover, it introduces a new criterion for comparing performance based on targeted maliciousness and discusses limitations of existing work and new emerging research directions.
Model Predictive Control of Three-Level Shunt Active Power Filter Connected to a Photovoltaic System

This paper presents a model predictive control (MPC) for a three-level neutral-point clamped (NPC) inverter operating as a shunt active power filter (SAPF) with a photovoltaic (PV) system. In this proposal, the PV system is used to associate three-level NPC inverter for converting the dc voltage to three phase ac voltages by employing MPC. At the same time, The SAPF injects compensation current at the point of common coupling to cancel the current harmonics. In addition, this work introduces a fuzzy logic maximum power point tracking (MPPT) to the DC-DC boost converter for extracting maximum power from the photovoltaic array. The performance of the conventional perturbation and observation (P&O) and fuzzy logic MPPT algorithm is compared. The effectiveness of the proposed strategy has been verified in MATLAB/Simulink simulation platform. From the simulation results, it is found that the developed system provides excellent performance to achieve reference currents tracking, balance in the DC-link capacitor voltage, as consequence a reduction in the harmonics significantly.
Arrangement Strategy of Antenna Array Based on Cruciform Growth Algorithm for Microwave Wireless Power Transmission

As the most promising wireless power transmission (WPT) technology, microwave wireless power transmission (MPT) system has the advantages of long transmission distance and great environmental adaptability. To address the problem of power mismatch and efficiency reduction caused by non-uniform power distribution at the receiver, a new arrangement strategy of antenna array based on the cruciform growth (CG) algorithm is proposed in this paper. This arrangement strategy equalizes the power of the receiving antenna and makes all the rectifiers work within the optimal input power range, which improves the conversion efficiency of the receiver and simplifies the process of DC power integration. In order to verify the proposed method, a 36.16dBm/5.8GHz receiver platform with 55.1 % conversion efficiency of the rectifier and antenna is built in the laboratory.
Integral Sliding Mode Control of Synchronous Reluctance Machine based Electric Vehicle Powered by Battery/Supercapacitor Hybrid Source

This paper presents an integral sliding mode control of a synchronous reluctance motor (SynRM) operating as part of an urban electric vehicle. On the sources side of the electric vehicle, an energy management strategy (EMS) based on a frequency decoupling strategy is applied to supervise a battery-super-capacitor hybrid power system (HPS). These two sources are linked in parallel to the DC bus through two bidirectional DC-DC converters, allowing for independent control of each source's power flow. On the traction side, an integral sliding mode control is design for robust stabilization and disturbance rejection of the synchronous reluctance motor (SynRM) drive system. In order to verify the efficiency of the proposed control technique and evaluate the performance of the sources energy management strategy, a comprehensive simulation of electric vehicle was carried out using Matlab/Sim ulink software.
Research on Improved Modeling Method of CLLLC Resonant Converter

CLLLC resonant converters can realize bidirectional power flow and global soft switching, and are widely used in electric vehicle charging and DC microgrids. In this paper, static modeling method of CLLLC circuit are improved respectively. Since the state variable of the resonant converter contains high-frequency switching components and the average value of each switching period is 0, the condition of the small ripple assumption in the traditional state space averaging method cannot be satisfied. In this paper, the traditional Extended Describing Function (EDF) method is improved, and the corresponding state variables are represented by the superposition of sine and cosine components, and the weighted average of each mode is taken for each state variable. In the case that the switching frequency is less than the resonant frequency, in order to simplify the calculation process of the steady-state value of the state variable in the modeling and maintain high precision at the same time, this paper proposes a state variable optimization calculation method. Where, the equivalent output load introduces the information of switching frequency and resonant frequency, then the output voltage waveform is simplified and approximated to obtain more accurate output equivalent load. Thereafter, the optimized FHA model is obtained, which has the same simplified structure as the Fundamental Harmonic Approximation (FHA) method, and the problem of low accuracy solution of state quantities when it works under under-resonance is solved. Finally, the theoretical analysis and comparison are verified by simulation and experiments.
Estimation of Blood Pressure by Applying Principal Component Analysis Through the Decomposition of Pearson and Spearman Correlation Matrices

For the training of blood pressure predictive models, it is necessary to determine the optimal number of predictors when the data set is of high dimensionality. Applying the appropriate dimensionality reduction technique according to the dataset will reduce the number of components and improve the performance of the predictive models. This work proposes the dimensionality reduction of the data set through the explorations of linear and nonlinear relationships of photoplethysmography signals by applying principal component analysis through the decomposition of Pearson and Spearman correlation matrices. The differences between the explained and cumulative variances of the principal components are minimal by applying Pearson and Spearman correlations. The predictive models trained with the first 5 principal components obtained better results for the estimation of blood pressure, with minimal loss of information with respect to the original data set.