Miracle-dz/UniFAR-FTUs huggingface dataset:
AC Grid Emulations for Advanced Testing of Grid-Connected Converters—An Overview

High penetration of distributed generations and active loads have enabled the power electronics converter to become a vital component in the modern power grid system, and the broad employment of grid-connected converters at various power levels is making the grid impedance and characteristics complicated. Consequently, in order to validate more advanced features such as the reliability and stability performances of the grid-connected converters, it is becoming an emerging need to emulate the grid behaviors from more aspects. This article serves to foster and investigate the state-of-the-art techniques in the field of ac grid emulation from the perspective of multiple spatial-scales and multiple time-scales. Four major concepts used for grid emulation with featured principles, including concept I (analog simulation with under-scaled components), concept II (grid characteristics in the real-time simulator), concept III (grid characteristics in the converters structure), and concept IV (grid characteristics in the converters controller), are summarized, respectively, in this article. The practical implementation regarding to the circuit topology and the power supply for the grid emulation system are also discussed. Finally, the future trends and conclusions in the field of ac grid emulation are provided.
Medium Voltage Soft-Switching DC/DC Converter With Series-Connected SiC MOSFETs

Nowadays, due to the growing demands of data centers, the traditional line frequency transformer based data center power distribution system (DC-PDS) is becoming bulky and inefficient. Although SST-based DC-PDS dramatically improves the system efficiency, the traditional multicell SST with input-series output-parallel architectures has limitations. Using high-voltage devices to develop single-cell SST improves system performance, however, its application is limited by high-voltage power devices. As an alternative solution, achieving high blocking voltage through the series connection of low-voltage devices brings several advantages. Nevertheless, the series connection brings about two main problems, namely the voltage imbalance among devices and the increment of switching loss. This article proposes a medium voltage series resonant converter (MVSRC) with series-connected SiC MOSFETs. By adding snubber capacitor to each SiC MOSFET and working at LLC resonant soft-switching mode, the voltage imbalance is reduced and switching loss is decreased. Due to snubber capacitors, there are differences between MVSRC and traditional LLC converter, hence the analytical model of MVSRC is established. Moreover, the voltage imbalance model is established and the voltage imbalance sensitivity is defined to guide the snubber parameter selections. Then, a design example of the 99% efficient 5-kV/400-V prototype is presented. Last, the experimental results show that the voltage imbalance is reduced effectively and the switching loss is decreased dramatically, verifying the validity of proposed methods and the accuracy of analysis.
Structural Change in Labor Market Influenced by Artificial Intelligence: Theoretical and Empirical Analysis

The aim of the paper is to study the effects of the artificial intelligence technologies on labor market in global economic environment. The results of theoretical and empirical analysis demonstrate negative effect of scientific progress, technological changes and development of artificial intelligence on employment in the industrial sector. Policy implications concerning labor market interventions in accordance with the challenges of the Fourth Industrial Revolution and introduction of artificial intelligence technologies in all the fields of economic activity are developed.
A receding horizon control strategy for constrained differential-drive robots moving in static unknown environments

In this paper, a reference tracking problem for wheeled differential-drive robots subject to obstacle avoidance requirements is considered. By resorting to feedback-linearization techniques, the differential-drive nonlinear dynamics is first recast as two simple integrators under state-dependent input constraints; then, an ad-hoc switching receding horizon controller is derived with the aim to efficiently handle time-varying constraints and non-convex obstacle-free regions. Effectiveness and benefits of such an approach are finally discussed by means of experimental results obtained using an E-puck2 differential-drive robot.
Energy Optimisation through Path Selection for Underwater Wireless Sensor Networks

This paper explores energy-efficient ways of retrieving data from underwater sensor fields using autonomous underwater vehicles (AUVs). Since AUVs are battery-powered and therefore energy-constrained, their energy consumption is a critical consideration in designing underwater wireless sensor networks. The energy consumed by an AUV depends on the hydrodynamic design, speed, on-board payload and its trajectory. In this paper, we optimise the trajectory taken by the AUV deployed from a floating ship to collect data from every cluster head in an underwater sensor network and return to the ship to offload the data. The trajectory optimisation algorithm models the trajectory selection as a stochastic shortest path problem and uses reinforcement learning to select the minimum cost path, taking into account that banked turns consume more energy than straight movement. We also investigate the impact of AUV speed on its energy consumption. The results show that our algorithm improves AUV energy consumption by up to 50% compared with the Nearest Neighbour algorithm for sparse deployments.
Towards Classifying Parts of German Legal Writing Styles in German Legal Judgments

The main tool of a lawyer is their language. Legal prose is bound by writing styles, especially in Germany. These styles ensure that, i.a. judgments are written in a structured and comprehensive way. The writing style used for German judgements is called Urteilsstil and consist of several subcomponents. These subcomponents should be classifiable with the help of argumentation mining techniques. However, this classification is currently not possible because an annotated corpus, that considers such special structure of German legal text, is not available. This paper explores possibilities for classifying two subcomponents of the Urteilsstil by utilising argumentation mining. Furthermore, the creation of a new corpus for legal classification is proposed.
Gait analysis of people relying on mobility aids by using laser range finder

Gait analysis provides an effective way for the elderly to monitor abnormal gait, predict fall risk, and evaluate rehabilitation training. The laser range finder mounted on the mobility aids is used to analyze gait characteristics in this paper. The leg data segment recognition method based on Kalman filtering and difference measure is proposed to estimate legs position. The system is developed that detects gait cycle by integrating the zero-point constraint and the crests-based detection method and extracts spatiotemporal gait characteristics. The experimental results demonstrate the effectiveness of the methods.
Circularly Polarized Two Port MIMO Cylindrical DRA for 5G Applications

A two-port circularly polarized multiple inputs multiple outputs (MIMO) cylindrical dielectric resonator antenna (cDRA) is proposed for 5G Applications. The orthogonal orientation of antenna elements allows us to obtain the isolation of more than 15 dB. The Z-shaped slot incorporated on the ground plane produces the circular polarization and introduction of cDRA of optimized dimensions and aperture coupled feeding helps to obtain wide impedance bandwidth and high efficiency on operating bands. The proposed antenna has three resonating bands of 2.9–3.2 GHz, 3.44–3.64 GHz, and 4.75-5.5 GHz. 3-dB axial ratio bandwidths within the operating bands are 7.54 % and 6.21 % respectively. The MIMO antenna performance is justified by computing the envelope correlation coefficient (ECC) and diversity gain (dB). Measured and simulated results are in harmony.
Highly Isolated Compact Tri-Band MIMO Antenna with Trapezoidal Defected Ground Plane for 5G Communication Devices

In this paper a compact tri-band MIMO antenna has been proposed for 5G wireless communication devices. The isolation of −20 dB has been obtained between the two input ports using meander line structures and five vias incorporating at different places of the structure. Two rectangular slots have been etched on the radiating patch to further improve antenna performance. The designed antenna provides multiband characteristic in S, C and X frequency bands. Different characteristic parameters such as Envelope Correlation Coefficient, diversity gain, electric field distribution and radiation pattern are also presented and discussed. The overall antenna volumetric size is $42 \times 34 \times 1\ {{\mathrm {mm}}}^{3}$.
Classification of the Qualitative Concepts of Observability, Controllability and Identifiability of Dynamical Systems

In this paper, methods for evaluating the quality of observability and controllability of linear dynamic systems are reviewed. The classical concepts representing the structural characteristics of systems, like controllability, observability and identifiability are introduced. The concepts of measure, degree and qualities of the observability and controllability of the state variables are investigated. The concepts of estimability as properties of algorithms are considered. The philosophical and linguistic concepts of measure, degree and quality are analyzed, their relationship is revealed. Based on the analysis, a systematization of concepts characterizing the properties of linear dynamic models is carried out.
Direct Force Feedback using Gaussian Process based Model Predictive Control

Many robotic applications require control of the applied forces or moments. Model predictive control allows for the direct or indirect control of forces, while taking constraints into account. However, challenges arise when the robot environment that affects the force is highly variable, uncertain and difficult to model. Learning supported model predictive control makes it possible to combine the advantages of optimal control, such as the explicit consideration of constraints, with the advantages of machine learning, such as adaptive data-based modeling. In this paper Gaussian processes are used to model the contact forces that are applied in model predictive force control. The Gaussian process learns the static output mapping describing the interaction of the robot with the environment. It is shown that stability guarantees can be derived in a similar way as in classical predictive control. A proof-of-concept experimental implementation of a direct hybrid position force controller for a lightweight robot shows real-time feasibility.
HTMLPhish: Enabling Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis

Recently, the development and implementation of phishing attacks require little technical skills and costs. This uprising has led to an ever-growing number of phishing attacks on the World Wide Web. Consequently, proactive techniques to fight phishing attacks have become extremely necessary. In this paper, we propose HTMLPhish, a deep learning based data-driven end-to-end automatic phishing web page classification approach. Specifically, HTMLPhish receives the content of the HTML document of a web page and employs Convolutional Neural Networks (CNNs) to learn the semantic dependencies in the textual contents of the HTML. The CNNs learn appropriate feature representations from the HTML document embeddings without extensive manual feature engineering. Furthermore, our proposed approach of the concatenation of the word and character embeddings allows our model to manage new features and ensure easy extrapolation to test data. We conduct comprehensive experiments on a dataset of more than 50,000 HTML documents that provides a distribution of phishing to benign web pages obtainable in the real-world that yields over 93% Accuracy and True Positive Rate. Also, HTMLPhish is a completely language-independent and client-side strategy which can, therefore, conduct web page phishing detection regardless of the textual language.
DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach

With the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases. A unique dataset is prepared from four publicly available sources containing the posteroanterior (PA) chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our proposed DL-CRC framework leverages a data augmentation of radiograph images (DARI) algorithm for the COVID-19 data by adaptively employing the generative adversarial network (GAN) and generic data augmentation methods to generate synthetic COVID-19 infected chest X-ray images to train a robust model. The training data consisting of actual and synthetic chest X-ray images are fed into our customized convolutional neural network (CNN) model in DL-CRC, which achieves COVID-19 detection accuracy of 93.94% compared to 54.55% for the scenario without data augmentation (i.e., when only a few actual COVID-19 chest X-ray image samples are available in the original dataset). Furthermore, we justify our customized CNN model by extensively comparing it with widely adopted CNN architectures in the literature, namely ResNet, Inception-ResNet v2, and DenseNet that represent depth-based, multi-path-based, and hybrid CNN paradigms. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities.
Key Frame Extraction Algorithm of Motion Video Based on Priori

Key frame extraction technology is one of the core technologies of content-based video retrieval. For video types with complex content, various scenes, and rich actions, the performance of existing key frame extraction methods is not ideal. Based on the Visual Geometry Group (VGG), this article proposes an image saliency extraction model assisted by deep prior information, and uses a large-scale data set for training on the server to obtain a trained model, and then integrates multiple features. The saliency extraction algorithm is combined with the image saliency extraction model assisted by deep prior information, and a saliency extraction algorithm based on multi-feature fusion and deep prior information is proposed. A new method for extracting key frames of motion video is introduced in detail. Taking into account that sports videos in real applications are susceptible to interference from various factors, resulting in poor picture quality, this article constructs a new visual attention model for moving targets in sports videos, which integrates images. The combination of multiple features of the bottom-level features and the skin color confidence map of the moving target overcomes the problem that a single feature cannot fully express the moving target. Since the processing object in this article is for the moving target in the video of the sports room, the extracted moving target can provide samples for video post-processing. The experimental results show that the proposed key frame extraction algorithm can quickly grasp the pedestrian information in the motion video and provide effective processing samples for the motion target for video post-processing.
Effect of Varying the Genetic Algorithm Parameters and Operators on the Optimum PMUs Placement

Phasor measurement units (PMUs) play a significant role in future smart grids for real-time control and monitoring. This reflects greater reliability and better stability and further increases the operating efficiency of power grids. It is better to determine the optimum placement of PMUs in power systems using the heuristic algorithm, which can solve the problem more efficiently and precisely in less time, compared with traditional techniques. This paper investigates the effect of varying GA parameters and operators on the overall solution of the problem of PMUs placement. Different population sizes, selection functions, and crossover and mutation rates are studied. As a case study, the IEEE-30 bus system is considered for the analysis. MATLAB–SIMULINK is used for the modelling and simulation.
Output-Capacitorless Tri-Loop Digital Low Dropout Regulator Achieving 99.91% Current Efficiency and 2.87 fs FOM

This article presents an output-capacitorless digital low-dropout regulator (OCL-DLDO) for fine-grained on-chip power delivery and management in system-on-chip devices. The proposed OCL-DLDO incorporates three distinct feedback loops: asynchronous, coarse, and fine loops. The asynchronous loop is activated during drastic load transients, rapidly restoring the output voltage and achieving a small voltage undershoot even without any output capacitor. The coarse loop, which is equipped with adaptive gain adjustment logics based on least-mean-square algorithm, adaptively adjusts the connection of power switches for fast and accurate voltage regulation. Lastly, the fine loop, which is implemented with shift-registers-based control, takes over the control in steady-states for low output voltage ripples and quiescent current. The proposed OCL-DLDO was fabricated in 65 nm CMOS process with an active area of 0.041 mm2. The measurement results show that the proposed OCL-DLDO achieves a peak current efficiency of 99.91% and a figure-of-merit as low as 2.87 fs when driving 25 mA of load current.
End-to-End TCP Congestion Control for Mobile Applications

This paper addresses the re-engineering of congestion control for TCP applications over networks with coupled wireless links. Using queueing delay as a congestion measure, we show that optimal TCP congestion control can be achieved by developing window-control oriented implicit primal-dual solvers for intended network utility maximization problem. Capitalizing on such an idea, we prove the existence of scalable, easy-to-deploy, yet optimal end-to-end congestion control schemes for networks with wireless links, given that the wireless access point appropriately schedules packet transmissions. A class of so-called QUIC-TCP congestion control algorithms are developed. Relying on a Lyapunov method, we rigorously establish the global convergence/stability of the proposed QUIC-TCP to optimal equilibrium in the network fluid model. Numerical results corroborate the merits of the proposed schemes in IPv6-based Internet environments.
Dynamic Compensation Pricing Scheme for Demand Resources Based on Deep Reinforcement Learning

As demand response is playing a more and more important role in power system operation, the pricing of those kinds of service becomes critical to the economic operation. In this paper, a pricing method based on deep reinforcement learning is proposed to maximize the cumulative gain of the load aggregator. The responsive behavior of demand resources is affected by the price generated by the load aggregator which dynamically adjusts its compensation price in an optimal way to maximize its return in a longer time horizon. Experimental simulation is conducted to illustrate the effectiveness of the proposed pricing model.
The Association between Team Satisfaction and Work-Family Conflict: The Mediating Role of Flexible Work Options and Job Demands

This study examined the link between team satisfaction and work-family conflict and explored the mediating effects of flexible work options and job demands in this relationship. It consisted of a survey among employees from five Irish companies. The sample consisted of 220 employees, a response rate of 53%. Overall, findings showed that team satisfaction had a negative association with work-family conflict and negatively predicted work-family conflict; the Beta Coefficient for this regression was -. 17, t(199) = -2.547, p < . 05). Similarly, team satisfaction demonstrated direct effects = -. 25, SE = . 09, p < . 01, 95%CI = -. 43 and -. 07; and total effects = -. 33, SE = 0.95, p < . 001, 95% CI = -. 52 and -. 14 on work-family conflict. The study further found an indirect effect of team satisfaction on work-family conflict through pace of the tasks which is an aspect of job demands; indirect effect = -. 06, SE = . 032, 95% (Bootstrap CI) = -. 13 and -. 01. Practical implications and future research lines are discussed.
Driver Identification Leveraging Single-turn Behaviors via Mobile Devices

Drivers’ identities are essential information that can facilitate a broad range of applications. For example, by understanding who is driving the vehicle when an accident happens, insurance companies could determine the liability and payment in a car accident claim case with high confidence. Another example, pick-up service companies could track the identities of their drivers to ensure that authorized drivers are driving esteemed clients to their destinations. While there are existing studies that can utilize video cameras and dedicated sensors to identify drivers, they either have privacy issues or require additional hardware, which is not practical enough for daily uses. In this paper, we devise a low-cost driver identification system, which can determine drivers’ identities by using sensors readily available in wearable devices. Our system captures the unique driving behaviors during pervasive but momentary driving events (i.e., turning at intersections) with motion sensors, which are widely integrated into commodity wearable devices (e.g., smartphones and activity trackers). Toward this end, we extensively analyze people’s driving behaviors and identify the critical turning events that capture people’s unique behavioral patterns for driver identification. We design a fine-grained turning segmentation method that divides sensor data into critical turning stages (i.e., before, during, and after-turn stages), which provide multiple dimensions of turning behavioral metrics facilitating driver identification. The system extracts unique turning behavior features from time and frequency domains to enable driver identification based on drivers’ turning behaviors at different types of turns. Extensive experiments are conducted with 12 drivers and various types of turns in real-road conditions. The results demonstrate that our system can identify drivers with high accuracy and low falsepositive rate based on one single turning event.