Miracle-dz/UniFAR-FTUs huggingface dataset:
High Power Quality Voltage Control of Smart Transformer-Fed Distribution Grid

Unlike the conventional Uninterruptible Power Supply (UPS), which generally is connected to the scheduled critical loads (data centers, chemical plants and hospitals), the Smart Transformer (ST) experiences a complicated distribution grid with high penetration of Distributed Generator (DG) and nonlinear loads, leading to ST-fed distribution grid suffering from more power quality issues, due to harmonic interaction among DG, nonlinear loads and ST. Based on the CIGRE LV 50Hz distribution grid model, this paper systematically studies the impact of distribution grid integrated with DGs and nonlinear loads on grid voltage power quality by comparing various voltage FeedForward (FF) (reference and measured voltage FF), various current FeedBack (FB) (filter inductor and capacitor current FB), and various current controller (proportional and proportional plus resonant controller). In order to satisfy the standard EN50160 requirement on voltage quality, an improved voltage control strategy is proposed to improve grid voltage quality. The simulation results based on the CIGRE model clearly validate the effectiveness and feasibility of the proposed voltage control strategy.
Chernobyl : a decade : proceedings of the Fifth Chernobyl Sasakawa Medical Cooperation Symposium, Kiev, Ukraine, 14-15 October 1996

Health Status in the Ukraine After the Accident. Health conditions of children in the Kiev region 10 years after the Chernobyl nuclear power plant accident: based on the results of health examinations made by the Chernobyl Sasakawa Diagnostic Center (Kiev Regional Hospital No.2) (A.I. Avramenko). Summary of the 10-year observation of thyroid disorders among Ukrainian children who were exposed to ionizing radiation after the Chernobyl disaster: tasks for the future (N.D. Tronko). Reports on the Chernobyl Sasakawa Health and Medical Cooperation Project. Chernobyl Sasakawa Health and Medical Cooperation Project: structure and scope (K. Kiikuni). Chernobyl Sasakawa Health and Medical Cooperation Project: materials and methods (Y. Shibata et al. ). Findings of the Chernobyl Sasakawa Health and Medical Cooperation Project: 137 Cs concentration among children around Chernobyl (V.F. Sharifov et al. ). Hematological findings of the Chernobyl Sasakawa Health and Medical Cooperation Project (I.V. Karevskaya et al. ). Findings of the Chernobyl Sasakawa Health and Medical Cooperation Project: thyroid nodules and cancer (G.D. Panasyuk et al. ). Relationship between thyroid abnormality and absorbed dose for children living in the Chernobyl area (V.B. Masyakin et al. ). Findings of the Chernobyl Sasakawa Health and Medical Cooperation Project: abnormal thyroid echogenity and autoimmune thyroid diseases around Chernobyl (A.S. Saiko et al. ). Findings of the Chernobyl Sasakawa Health and Medical Cooperation Project: goiter and iodine around Chernobyl (N.V. Nikiforova et al. ). Comments by Japanese Specialists. Comments on thyroid-related studies by the Chernobyl Sasakawa Health and Medical Cooperation Project (S. Yamashita). Comments on the two reports from the Gomel Specialized Medical Dispensary (Y. Shibata). Chernobyl Sasakawa Health and Medical Cooperation Project: achievements in 5 years and future prospects (K. Fujimura et al. ). Pediatric thyroid diseases around Chernobyl: morphological aspects of the Chernobyl Sasakawa Health and Medical Cooperation Project (M. Ito et al. ). Role of the ret oncogene activation in thyroid carcinoma (T. Motomura) Chernobyl and Japan. Opening words (S. Hinohara). Radiation health effects research in Japan (I. Shigematsu). WHO contribution to the medical follow-up of the Chernobyl accident (W.E. Kreisel) Selection of the cohort for long-term clinical follow-up and assessment of radiation risks for thyroid diseases under the joint medical research project conducted by Sasakawa Memorial Health Foundation and MRRC of RAMS (V.K. Ivanov et al. ). Thyroid cancer in children: comparison among cases in Belarus, Ukraine, Japan and other countries (S. Nagataki, K. Ashizawa). Discussion on the international thyroid problems after the Chernobyl accident in the Ukraine: present studies and future joint projects (S. Nagataki). Appendix A. Statistical Tables.
Thermal Uncertainty Simulation on LED Lighting Boards of Heavy Duty Transportation Vehicles

Lighting of heavy duty vehicles like trucks, buses and trains has been mainly implemented with fluorescent lamps and inverters, and is being now upgraded to LED (Light Emitting Diode) new technology. Given the normative lighting requirements (measured in lux), added to the aggressive environment of automotive application and its specifications, the high flux, improved efficiency mid and high power LED's are becoming the most practical and smart solution for city and coach transportation. One of the relevant factors on PCB design of LED lighting is LED junction temperature, which depends on the type of power supply employed, pad temperature and construction factors. This paper aims to shows the uncertainty on junction temperature measurements through Monte Carlo method. This technique uses randomly generated input variables in a mathematically built model (plant), to obtain the combined uncertainty output estimation. After the evaluation of all uncertainty factors that influence the LED and its switched power supply's thermal behavior, the model is extensively simulated showing that the significance of the junction temperature variation is finally taken, in terms of the input manufacturing and environmental parameters
Feature Selection Based on Graph Representation

Best features subset identification is an important preprocessing step in Machine Learning and Data Mining. Therefore, many feature selection algorithms have been proposed in the literature. Generally, there are three major approaches of feature selection: Filters, Wrappers and Embedded. In this paper, we propose a new feature selection approach for numerical datasets, which is based on graph representation where the node degree used as criterion to select the best subset of features among the whole features space. The experimental results show the effectiveness of the proposed algorithm in terms of execution time and achieved performance.
The Influence of Technological Factors on Students’ Academic Performance

this paper focuses on the influence of technological factors on students' academic performance (SAP) in blended and traditional learning domains. Its purpose is to identify the most important and significant factors of technology that influence SAP. The method used is a survey study with a total sample size of 216 participants of experimental group and control group. The results have shown that the influence of use of technology on SAP is significant. These predictors of technological factors contribute by 65% and 98% of explained variance in improving SAP in blended learning compared to traditional learning respectively.
A Fast Message-Delivery Mechanism to Support Broadcast in Vehicular Ad hoc Networks

Traffic-related problems such as accidents, traffic jams and pollution are being addressed with the use of Intelligent Transportation Systems (ITS). ITS creates a network connecting vehicles and enabling the exchange of useful information to improve safety and provide efficient content delivery. In congested areas, however, the communication efficiency of a vehicular ad-hoc network (VANET) is greatly affected due to the number of generated messages. Packet collision and transmission errors are likely to increase as the messages are carried over radio waves. Hence, to improve coverage, redundant relay nodes are usually considered in the literature. However, such schemes increase channel contention and, consequently, the overall delay to cover a zone of interest (ZoI). This paper proposes a mechanism that explores the characteristics of the IEEE 802.11p standard to improve message dissemination to support broadcast tasks in VANETs. Simulation results show that the proposed scheme reduced delivery delay by 63.5% and presented 15 times less retransmissions when compared to a 802.11-based scheme. Even in congested scenarios, it presented a stable behavior in terms of delivery delay.
Power Quality Improvement in Single Phase Solar PV-APF Grid Tied System Using Robust Least-Mixed-Norm (RLMN) Algorithm

This paper presents a grid coupled solar photovoltaic generation system (PVGS) which contains solar photovoltaic (PV) array, P&O (Perturb and Observe) based MPT (maximum power tracking), single-phase converters and its integration into the utility grid. A robust least mixed norm (RNMN) stochastic adaptive algorithm is used for grid tied solar photovoltaic generation system (PVGS) for improving the power quality, using on convex function of the error norms combined the least mean square (LMS) and sign-error LMS algorithms. The benefits of the PVGS are: reactive current and harmonic elimination, reactive power compensation and delivering PV power into the utility. The proposed PVSG system works in two modes, 1) PV-VSC, 2) VSC (Voltage Source Converter) is an active power filter (APF) (i.e. solar power is zero). The VSC feeds the existing MPT power from PV to nonlinear loads and the grid, with auxiliary services such as harmonics compensation with its high utilization factor. The proposed RLMN algorithm is used with reduced steady state error (SSE) and higher convergence rate. The effectiveness of the control algorithm of PVGS under different modes and environmental conditions is verified by using test results on an prototype of PVGS in the laboratory.
Applicability of Solid-State Transformers in Today’s and Future Distribution Grids

Solid-state transformers (SSTs) are power electronic converters that provide isolation between a medium-voltage and a low-voltage (LV) system using medium-frequency transformers. The power electronic stages enable full-range control of the terminal voltages and currents and hence of the active and reactive power flows. Thus, SSTs are envisioned as key components of a smart grid. Various SST concepts have been proposed and analyzed in literature concerning technical aspects. However, several issues could potentially limit the applicability of SSTs in distribution grids. Therefore, this paper discusses four essential challenges in detail. It is found that SSTs are less efficient than low-frequency transformers (LFTs), yet their prospective prices are significantly higher. Furthermore, SSTs are not compatible with the protection schemes employed in today’s LV grids, i.e., they are not drop-in replacements for LFTs. The limited voltage control range typically required in distribution grids can be provided by competing solutions, which do not involve power electronics (e.g., LFTs with tap changers), or by hybrid transformers, where the comparably inefficient power electronic stage processes only a fraction of the total power. Finally, potential application scenarios of SSTs (ac-dc, dc-dc, weight/space limited applications) are discussed. All considerations are distilled into an applicability flowchart for SST technology.
Kinetic Energy Attenuation Method for Posture Balance Control of Humanoid Biped Robot under Impact Disturbance

For the situation of the robot's foot soles rotating around their edges, the current posture balance control methods only used linear controllers and ignored the system's nonlinearity. For this problem, we propose a posture balance control method, which actively attenuate kinetic energy of the robot. Based on simplified dynamics model established for the foot rotating situation, our control law is deduced to adjust the amplitude of the ground contact force (GCF) and the angular acceleration of the robot foot rotation simultaneously. Then the posture balance controller against the impact force disturbance is designed. Stability of the foot rotation phase space is analyzed for our controller and other two existing controllers. Also, the situation of no balance controller is analyzed as a presentation of the system's passive characteristic. Then area of the stable region is compared between these three controllers. Simulations under frontal impact disturbances are also conducted. The results demonstrate that, our controller is able to resist stronger impact disturbance and make the robot recover its balance in a shorter time. This indicates that, compared with other posture control methods, the proposed control method makes better use of the robot joint motion to attenuate the robot's kinetic energy.
AutoCOT - AutoEncoder Based Cooperative Training for Sparse Recommendation

Currently, data sparseness problem caused by large amount of data has resulted in low recommendation quality of traditional recommendation algorithms. Aiming at this problem, this paper proposes an auto-encoder recommendation algorithm based on cooperative training (AutoCOT) that combines the auto-encoder framework with cooperative training (COT) model, which can not only better learn the non-linear relationship of data but alleviate the data sparseness problem, especially in large amount of user and item data. The experiments show that, on Movielen datasets, AutoCOT performs better in coverage, precision and recall rate when compares with the traditional collaborative filtering algorithms and pure auto-encoder recommendation algorithms.
Optimized Twitter Cyberbullying Detection based on Deep Learning

Cyberbullying is a crime in which a perpetrator targets a person with online harassment and hate. Many cyberbullying detection approaches have been introduced, but they were largely based on textual and user features. Most of the research found in the literature aimed to improve detection by introducing new features. Although, as the number of features increases, the feature extraction and selection phases become harder. In addition, another drawback of such improvements is that some features—for example, user age—can be easily fabricated. In this paper, we propose optimised Twitter cyberbullying detection based on deep learning (OCDD), a novel approach to address the above challenges. Unlike prior work in this field, OCDD does not extract features from tweets and feed them to a classifier; rather, it represents a tweet as a set of word vectors. In this way, the semantics of words is preserved, and the feature extraction and selection phases can be eliminated. As for the classification phase, deep learning will be used, along with a metaheuristic optimisation algorithm for parameter tuning.
Research of Low Inductance Loop Design in GaN HEMT Application

High electron mobility transistor (HEMT) is one popular research topic in the field of power electronic devices. Gallium Nitride (GaN) HEMT has the advantages of high slew rate and low operation loss. In high frequency application, parasitic impedance introduced from PCB layout can have huge impact on operation efficiency and reliability. Minimizing the loop inductance is important. In this paper, three different low inductance loop design methods are analyzed and compared in details. Numeral comparison based on finite elements analysis (FEA) is carried out. Double pulse test is carried to verify the idea. Simulation and experimental results of switching transient are given for comparison. Low inductance loop design is further corroborated by the switching loss and switching time characterization.
Power Management IC Design for Microwatts Vibration Piezoelectric Energy Harvesting Based on Parallel-SSHI

Vibration energy harvesting is an emerging technology for future self-powering devices. This paper presents an autonomous piezoelectric energy harvesting power management system. It is based on Parallel-SSHI (synchronized switch harvesting on inductor) technique in a $\mathbf{0.18}-\mu\mathbf{m}$ CMOS process. Comparing with the low efficiency of passive rectifier, the peak efficiency can reach 83% with the design of charge transfer logic circuit in this paper. Through internal high-precision currentreference, external load can be changed to support the needs of different systems. This design can provide an input power in the range from a few to several hundreds of microwatts, and the output voltage can reach 4.5V.
Frequency Support and Stability Analysis for an Integrated Power System with Wind Farms

In order to handle the challenges associated with large capacities wind farms integrated in the power grid, different techniques such as optimization algorithms, artificial intelligence and others are employed and studied. Recently, many researches focus on the frequency regulation problem by showing the role of kinetic energy stored in the rotor side of wind turbine in supporting the grid frequency. The main challenge of the inertia emulation scheme is to release the maximum possible amount of kinetic energy without violating operating limits of the wind turbines and keeping in mind the grid integration standards for wind farms. However, no selection criteria for the control parameters has yet been given. The objective in this paper is to consider the constraints and limitations on the rotor speed and buffered power in DFIG wind turbines and also to highlight the influence of the controller parameters on the stability of the power grid and the operation of the wind farms. The stability of the power system with high penetration of wind farms is analyzed based on Kuramoto theory and a sufficient stability condition is derived and mathematically proved. Finally the stability condition is verified in a test power system with a large integration of wind power.
Improved Voltage Controlled Three Phase Voltage Source Inverter Using Model Predictive Control for Standalone System

This article presents a new method for controlling the output voltage of the voltage source inverter (VSI) for stand-alone systems using finite control set model predictive control (FCS- MPC). In the existing conventional method, such as sinusoidal pulse width modulation (SPWM), the output voltage is varied by varying the amplitude modulation ratio (ma), which can be problematic if the value of (ma) exceeds 1. The conventional SPWM method also suffers from slow transient response. To solve the aforementioned problems, FCS-MPC is proposed for stand-alone VSI. The proposed method precisely controls the output voltage by varying the weighting factor of the cost function in the FCS-MPC algorithm. The proposed technique enhances the transient response, improves the THD and provide stability under all tested conditions. Based on the single step prediction set, it has a low computational load with improved performance. The feasibility of the proposed method is verified by simulations in MATLAB/Simulink.
ISM Band 1:16 Wilkinson Power Divider

A 1:16 Wilkinson power divider (WPD) working from 1.3 GHz to 3 GHz is presented in this paper. Equal power division, phase and amplitude consistency is achieved among the output ports. The proposed design has a wideband with a 10 dB fractional bandwidth of 76%. The isolation loss within the bandwidth is better than 15 dB and the insertion loss is less than 2 dB. The size of the WPD has been minimized to 16×10.7 cm2, which was the main objective of this work. The compact size of the proposed design makes it a suitable candidate for feeding phased antenna arrays for wideband operations.
Host Overloading Detection Based on EWMA Algorithm in Cloud Computing Environment

Energy consumption and Service Level Agreement (SLA) in Cloud computing environment are important cloud management issues. Dynamic consolidation of the Virtual Machines (VMs) need effective and efficient distribution for VMs migration to hosts in data center. The process of VMs migration needs to evaluate host capability, VM placement and reallocation, which satisfy SLA criterions under a flexible service plan. Therefore, the plan is to select effective resource allocation to achieve cost minimization, reduce energy consumption and avoid SLA violation. We proposes Exponentially Weighted Moving Average (EWMA) algorithm to detect overloaded hosts, which deals with dynamic consolidation of VMs based on an analysis of historical data of the resource usage by VMs. It increases the accuracy in calculation of the upper threshold for host overloading and consequently increases accuracy in identification to deal with VMs migration issue.
A Maturity Model for Examination Management in University

The examination management plays an irreplaceable role on teaching quality in university education, therefore, we analyze the process of examination management. From the perspective of project management, we introduce and establish a model from two key dimensions: maturity index system and key process area. Thus, corresponding models for the two dimensions are proposed respectively. Onwards, based on samples from universities in Beijing, we conduct an empirical analysis to measure the performance using our proposed models. The evaluation results highlight the importance and the practicality of maturity model for examination management in university.
Detection of Violent Content in Cartoon Videos Using Multimedia Content Detection Techniques

Children are the most vulnerable to ideas presented in Cartoon videos and TV. Cartoons have become one of the most important source of entertainment, but it also introduce a lot of ideas that are not suitable for them. Violence is one of the unwanted feature that is prevalent in cartoons to put element of fantasy and enchantment. In order to stop children from viewing violent intense cartoons, the best strategy is to make them inaccessible. Therefore, some sort of filters should be placed at certain hubs to perform this task. The challenge is that how a filter will know that a particular cartoon video has violent content in it. The meta-data telling the world about the video does not inform that the video consists of violent material. Certain frames/snapshots/images of video, if analyzed using image processing techniques, can help in concluding that a particular video has intense material in it. The aim of this work is to classify social media videos especially related to animated cartoons with violent / nonviolent behaviors. It addresses the problem of content based image matching algorithms based on key point descriptors. The basic goal is to extract general information from an image without any specific query. First SIFT-descriptors are extracted from a large set of images. This set of descriptors are then defined as a means of providing fast and accurate comparisons between images and distinguish between violent and nonviolent images in combination with Machine Learning algorithms. The results are then compared for each classifier with varying parameters.
Tracking and Forecasting Dynamics in Crowdfunding: A Basis-Synthesis Approach

Crowdfunding is an emerging online fundraising mechanism for creators to launch campaigns (projects) to solicit funds or expand their influence. Tracking the dynamics, i.e., daily funding amounts can be of great help to campaign creators as well as contributors. Previous works on this subject either fit the fluctuations of time-series with predefined stochastic process or apply a regularization term to constrain learned tendencies, resulting in limited generalization abilities. Patterns of funding-amount sequences in crowdfunding are often exclusive and non-linear, making previous predictors suboptimal. To tackle this problem, we propose a novel method based on synthesized bases which can be composed into arbitrary patterns. Concretely, we build a large set of candidate basis from which we select based on reliability, diversity and latent structures. We use representations of sequences in this basis space as a predictor, and adopt a dual-graph to exploit neighbouring information to enhance its prediction quality. Experimental results demonstrate the effectiveness of our method.