Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Graphical Learning Method for Parameter Estimation in Large-Scale Distribution Networks

In distribution systems with growing distributed energy resources, accurate estimation of network parameters is crucial to feeder modeling, monitoring and management. Al-though existing state-of-the-art parameter estimation algorithms such as physics-informed graphical learning (GL) have accurate estimation, they can potentially suffer from scalability issues due to slow training in larger networks. In this paper, we propose an upgraded graphical learning method called fast graphical learning (FGL) to improve the computational efficiency and scalability while preserving the merits of GL. In FGL, we develop faster alternative algorithms to replace the fixed-point-iteration-based FORWARD and BACKWARD algorithms in GL. These alternative algorithms are based on fast power flow calculation of the current injection method and more efficient state initialization by the linearized power flow model. A comprehensive numerical study on IEEE test feeders and large-scale real-world distribution feeders shows that FGL improves the computational efficiency by as much as 60 times in larger distribution networks while attaining the accuracy of the state-of-art algorithms.
Monte Carlo Simulation of Electron Beam Irradiation of “Lightning and Ice Protection Composite Insulators”

In this study, Monte Carlo simulation of the electron beam irradiation process of composite insulators is used to simulate energy deposition and backscattered electrons (BEs) inside composite materials with complex geometries. This simulation provides a simulation idea for simulating the parameters and conditions set for the electron beam irradiation process of a complex material or device. The simulation simplifies the shape model of the insulation, the physical model of electron transport in solids combines the Mott cross section for electron elastic scattering and the software integrated dielectric function method for electron inelastic scattering, and presents the irradiated particle statistics in the form of a spectrogram, which, according to comparative and theoretical analysis, is more suitable for the irradiation treatment of insulators at 4000–5000 keV.
SS-DGD: Scalable Short-Entry Dual-Grain Coherence Directoris

As the number of cores in a chip multiprocessor increase, the directory area overhead becomes excessive. Current research shows that directory area can be reduced by tracking private entries with coarse-grain region entries. The insight is that contiguous memory blocks are often accessed by a single core. In order to indicate which blocks the region owner has cached, a bit vector format is applied in the dual-grain directory (DGD). However, this limits scalability and is incompatible with the latest scalable directories. In this paper, we propose a scalable short-entry dual-grain coherence directory (SS-DGD). In private region entries, a counter is used instead of the original bit vector. To reduce the directory area, we use a separate short-entry directory to store private block entries with a pointer format and region entries with counters. The detailed simulation-based study in 64-core CMPs shows that our proposal can reduce the area overhead by 29.9% compared with DGD.
Detecting PVC Beats by Beat-by-beat Analysis of ECG Signals Using Machine Learning Classifiers for Real-time Predictive Cardiac Health Monitoring

Premature Ventricular Contraction (PVC) episodes are redundant heartbeats that disrupt the normal rhythm of the heart. The use of wearable sensors for remote heart monitoring and the implementation of trusted artificial intelligence (AI) algorithms are improvements in the field of smart health (sHealth) using cyber-physical systems (CPS) for telemedicine systems. We detect PVC beats by analyzing electrocardiogram (ECG/EKG) data and perform automatic classification to achieve high accuracy in real-time. In this study, we used a number of PVC heartbeat recordings from the MIT BIH supraventricular arrhythmia database We divided the recordings into a training dataset, which contains 39 ECG data, and a test dataset, which contains the remaining 39 ECG data. Both datasets contain approximately 80,000 samples of normal heartbeats and 7,000 samples of ventricular ectopic We extract combination of signal-specific features and signal-independent features for feature selection and ranking. We apply four algorithms, receiver operator characteristic (ROC) and the area under the ROC curve (AUC) (ROCAUC), constant, quasi constant and duplicate feature removal (Univariate) (CQCDFR), analysis of variance (ANOVA), and root mean square deviation (RMSE) to select and rank the feature. For each algorithm, it has its own selection of signal-independent features, which we combine separately with signal-specific features and test their accuracy. Then, we train the top 10 ranked combined features of each algorithm separately and check the highest performance. We explored the random forest (RF) classifier and support vector machine (SVM) classifier. Compared with other algorithms, the performance of feature selection using ANOVA algorithm before feature ranking is the lowest. The ANOVA algorithm achieved the highest accuracy after picking out the top 10 features. We further separately evaluate the sensitivity, specificity, accuracy, precision and F1 score of the top-ranked features according to the best accuracy obtained by different feature selection algorithms. The classification ANOVA algorithm from RF selects the top 7 features with 97% accuracy, 97.5 sensitivity, 98.1% specificity, 98.1 Precision%, and 95.0% F1 Score. This method can accurately monitor cardiac disease in real-time and analyze ECG beats so that patients can get accurate feedback.
Ferromagnetic Quantum Dots Improving Dielectric Properties of Polypropylene Film in Magnetic Field

In this paper, the influence of magnetic field on dielectric properties of polypropylene (PP) films are investigated. The insulation degradation mechanism is analyzed. A performance optimization method based on Fe304 nano doping is proposed. After the doping modification, the conductivity decreases by 64.7% and the breakdown strength is enhanced to 124% at 12 T.
Battery Charging Strategies Design for Battery Swapping Stations: A Game Theoretic Approach

Battery Swapping Stations (BSSs) are rapidly ex-panding infrastructures for electric vehicles. However, the in-appropriate battery charging strategy of BSSs will lead to unnecessary charging costs. In this paper, we study the real-time optimal battery charging strategies for every BSSs in a system under a non-cooperative scenario and dynamic electricity pricing environment. We propose a non-cooperative game model to characterize the BSS charging competition. We prove the existence and uniqueness of Nash Equilibrium under arbitrary swapping demands and battery numbers, and an algorithm is proposed to solve the Equilibrium. Numerical results show that our proposed strategy outperforms the benchmark strategies in terms of overall profits.
Machine Learning Techniques for River Discharge Prediction Using ERA5 and GloFAS Data

Discharge has traditionally seen direct applications of itself for water management. From river embankments to dams, to design and model all, water discharge is required for long-term adaptation to climate change’s effects, including floods. Floods are one of the most common and economically costly natural disasters that, because of climate change, have only intensified in recent months. Predicting floods requires a multitude of factors and parameters, each having its own complexity to deal with, among which, river discharge is always used as a parameter. The advent of technology has led to the creation of many predicting models, but because of the continued advancement of artificial intelligence, new techniques are tested to find more accurate results. Although there are statistical models using which floods are predicted, models that utilize machine learning are increasingly being adopted as Academia is progressively moving towards ML techniques. This work includes numerous Machine Learning algorithms that have been applied to predict river discharge on a time-series dataset. We ran our experiments on the publicly available atmospheric, land, and oceanic climate variable database called ECMWF Reanalysis v5 (ERA5). The river discharge variable was obtained from GloFAS; both are made available by Europe's Copernicus Climate Data Store (CDS). We processed the data over time and the shape of the river basin to allow predictions at a more precise scale. We show that Machine Learning techniques are a good choice in terms of model complexity and performance on this dataset. The top three performing machine learning models are Support Vector Regression, Gradient Boost, and Random Forest. But the only neural network model that is used, Time Delay Neural Network outperforms all the other models.
Cross-Correlation for Streaming Seismic Time Series to Detect Events using Parallel and Real-time Methods

The design of real-time systems is becoming increasingly important. Such systems must process the input and provide the output in a timely fashion. Therefore, the tasks must be carefully scheduled and executed on the processor(s). A good example of a system that requires real-time action is the earthquake warning system because it is critical that the system instantly examines the events happening. In such case, no disaster will happen if the system’s response is in real-time. This paper describes real-time methods of seismic event detection and how to identify the correlation between seismic time-series in order to find the events' source and study the seismicity in New Mexico, USA using Spark Streaming framework.
Coronary Plaque Classification of Intravascular Ultrasound Images based on a Multi-Stage Deep Classifier Cascades

A method of multi-stage deep classifier cascades is proposed to classify coronary plaque from gray-scale IVUS images. The framework consists of one segmentation module and four classification modules, which are trained and evaluated successively. The pre-segmentation module aims to highlight potential plaque territory. A multi-level classification strategy is designed based on plaque histological composition and morphology. The IVUS images are labelled by clinical cardiologists and used to evaluate the effectiveness of multi-stage classifier. The result shows that proposed framework improves the accuracy of coronary plaque classification.
Neural Collaborative Filtering for Network Delay Matrix Completion

In network monitoring, delays are of great use when it comes to QoS or content distributed services. However, it is often impossible to have access to all the delay measurements within a network. This can be due to network failures or to established measurement policies. For these reasons, delay matrix completion techniques are important for an optimal network monitoring service. In this paper, we formulate the completion problem as a neural collaborative filtering problem by testing two different architectures, generalized matrix factorization and multi-layer perceptron. We evaluate these methods on two different datasets: a synthetic one generated by an autonomous system simulator, and a real-world dataset from Ripe Atlas platform. Finally, a comparative study is conducted between these neural collaborative filtering methods and standard approaches.
Machine Learning Models for Customer Churn Risk Prediction

Customer churn analysis in the industry is an important area of research due to its effect on profitability of business, measuring customer satisfaction, figuring out product promotions, and creating marketing strategies. In this paper we predict the possibility of churn of a given customer by advanced machine learning pipelines. In particular we perform a comprehensive comparison of feature selection methods, oversampling methods, and three machine learning methods the Logistic Regression, Random Forest and Deep Neural Networks on customer churn dataset. The conclusions we reach include (1) Information Value features performed significantly better than Principal Component Analysis features, (2) data oversampling provides model consistency, and (3) Random Forest is superior to Logistic Regression and Deep Neural Networks in the datasets where the number of instances is few.
LLC Resonant Converter with Partial-Power Auxiliary DC/DC Module for Wide-range Output Applications

This paper proposes LLC resonant converter with a partial-power auxiliary DC/DC module for wide-range output applications. In which, the LLC resonant converter operates near the resonant frequency to maintain high efficiency, while only the partial power is processed by the auxiliary DC/DC module to offer the wide-range output voltage capability. Therefore, the proposed topology achieves higher efficiency than the conventional two-stage structure. A hybrid control strategy combining PI control and current prediction control is presented to implement the proposed design. The proposed LLC resonant converter is validated by the experimental results, which can achieve 24-72V wide-range output voltage regulation without wide-range frequency control.
Employment of Artificial Intelligence Mechanisms for e-Health Systems in Order to Obtain Vital Signs Improving the Processes of Online Consultations and Diagnosis

A large number of web-based e-Health applications have been developed through time, allowing doctors to access different types of functionalities, like knowing which medication the patient has consumed or performing online consultations. Internet systems for healthcare can be improved by using artificial intelligence mechanisms for the process of detecting diseases and obtaining biological data, allowing medical professionals to have important information that facilitates the diagnosis process and the choice of the correct treatment for each particular person.The proposed research work aims to present an innovative approach when compared to traditional platforms, by providing online vital signs in real time and allowing the visualization of all historical data of a patient. It aims to defend the concept of promoting online consultations, providing complementary functionalities to the traditional methods for performing medical diagnoses through the use of software engineering practices. This investigation led to the conclusion that, in the future, many medical processes will most likely be done online, where this practice is considered extremely helpful for the analysis and treatment of contagious diseases, or cases that require constant monitoring.
Optimization Design of the Flat-Top High Magnetic Field Powered by Two Multiple-Capacitor Power Supplies

The flat-top high magnetic field (FTHMF) can meet the demands of some scientific experiments for the extreme environment with strong electromagnetic characteristics. Therefore, the FTHMF has become a vital research tool in physics, chemistry, and biology in recent years. The multiple-capacitor power supply (MCPS) consists of multiple capacitor modules output high voltage. Currently, an FTHMF with a single-coil magnet energized by the MCPS is achieved at Wuhan National High Magnet Field Center. To further improve the magnetic field strength and stability, this paper proposes an FTHMF system powered by two MCPSs with a dual-coil magnet. The operating characteristics of the system in the dual-coil case are analyzed, and an evolutionary algorithm obtains the parameters of the dual-coil magnet. The models of the designed system are built in MATLAB/Simulink and COMSOL Multiphysics to verify the effectiveness of the design method.
Gate Driver Power Supply for Medium Voltage SiC Mosfets with Air Core Transformer

Medium voltage SiC power MOSFETs have enabled higher efficiency and simplification of implementations for high voltage and high bandwidth applications. Driving these power devices requires gate drive power supplies with high galvanic isolation. Transformers provide the insulation but must have low interwinding capacitance to deal with the high dv/dt of the switching. In this paper, we present two air-core transformer solutions: single-turn primary and toroidal windings on a nonmagnetic former, and primary and secondary winding embedded in the PCB. Both alternatives can keep the interwinding capacitance < 5pF while providing enough coupling to deliver the needed power. High-frequency driving of the transformers results in compact solutions. Series-series compensation of these air core transformers has been designed for efficient power transfer and low voltage drop. Issues related to the design and compensation parameters are addressed in this paper. The performance has been verified experimentally.
Collision Parameter Identification Method for the Main Contact Mechanism of On-Load Tap-Changer

The on-load tap-changer is an indispensable and frequently operated core equipment for AC engineering transformers and DC engineering converter transformers. The core mechanism—the main contact mechanism has a complex structure and has a large contact collision force. Therefore, an accurate identification method of collision parameters is proposed in this paper, and an accurate dynamic analysis model is established on this basis to obtain accurate collision forces. First, the mapping model between the contact stiffness and the maximum damping coefficient is established, and the model only contains the restitution coefficient as the only unknown variable. Then, the collision test of the main contact mechanism is carried out, the restitution coefficient is determined, and the collision parameter database is established. Finally, accurate dynamic analysis model to obtain the collision analysis results corresponding to each set of data in the collision parameter database, and compare with the experimental results to determine the optimal collision parameter combination. The results show that the simulation error of the dynamic analysis model is within 12%. The method can accurately obtain the collision parameters of the complex structure of the tap changer, and establish an accurate dynamic analysis model.
A Novel High-Performance Tunneling Field-Effect Transistor Ternary Inverter

As the critical building block of the ternary system, massive research in recent years has mainly been focused on the ternary inverter. Particularly, the tunnel FET (TFET)-based ternary inverter is of great interest due to its small supply voltage (VDD) and energy-efficient signature. However, it is still problematic for the inverter to obtain stable intermediate state due to the input-voltage-dependent tunneling current. In the paper, a TFET-based ternary inverter with the equiprobable, distinctive and stable electrical states is demonstrated. Moreover, the formation mechanism of the stable intermediate state is investigated in detail. Finally, the impact of VDD on the voltage-transfer characteristics of the ternary inverter is also discussed deeply. This study shows the great application foreground of the TFET-based ternary inverter for the ternary system.
Effects of Beam Steering Angle in Vector Doppler Method with Plane Wave Imaging

The ultrasonic Doppler method is widely used for quantitative blood flow measurements. One of the shortcomings of the method is that only the axial velocity is estimated. To address such an issue, angle-independent velocity estimators have been investigated. The vector Doppler method is one of such methods and estimates velocity vectors by measuring axial velocities from different directions. Recently the vector Doppler method were implemented in high-frame-rate plane wave imaging with steered beams. The autocorrelation method is one of the methods widely used for estimation of axial velocities. The method estimates axial velocities from the phase of the complex correlation function of the complex beamformed ultrasonic signal and requires the center frequency of the received signal. We developed a method for estimation of center frequency and beam steering angle by evaluating two-dimensional autocorrelation and showed that the accuracy was improved significantly using the estimated steering angle in reception. In the present study, the effect of the beam steering angle was further investigated. The simulations with Field II showed that the standard deviations in estimated velocities were reduced further using the estimated steering angle in both transmission and reception.
Analysis of Knowledge Distillation on Image Captioning Models

Image Captioning involves generating a textual description of an image, in the most accurate way possible. It requires a combination of Computer Vision and Natural Language Processing techniques, which can both be enhanced individually to improve the overall performance of the model. Knowledge Distillation is a model compression technique, where a smaller network learns from the predictions of a larger network to find a more optimal convergence space. It effectively improves the performance of the smaller network, without any problems like over fitting. Typically, the performance of image captioning is measured in terms of the BLEU score and CIDER score. In this work, we have tested and recorded the performance of three different Image Captioning model architectures, in terms of a large network, small network and knowledge distilled small network to track and analyse the effects of Knowledge Distillation. The results are promising when compared with the state of art models.
Using Strategic Intelligence and Technology as building block for Educational Innovation: a conceptual framework towards the impact for outcome-based education

Today, technology is used for several purposes across the different sectors and organizations. In education, there has been discussions, both in theory and in practice, around what technology offers to the educational community, and how it can help the educators offer effective learning or yet outcome-based education for the stakeholders (teachers, students, society). To this vein, this study introduces a Strategic Intelligence and Technology in Education framework, called SITE, designed for contextual analysis of the impact of the new and emerging educational technologies and innovations in education. Theoretically, it describes an ensemble algorithm or procedures that can be implemented by educators to provide a more strategic-process-related and enhanced curriculum or educational learning models. It then provides an empirical discussion and implications of the results of the data analysis conducted using datasets about (i) students’ learning outcome and assessment, and (ii) the different types of innovation in education. The results show that while “technology” is more associated to “educational innovation” than other methods or services. There is also, on the other hand, the need for reimagining education to include indicators for outcome-based education through integration of multi-service or co-productive learning components or pathways, such as amalgamation of technology with strategic intelligence done in this paper.