Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Effective Dust Deposition Analysis of PV Modules Based on Finite Element Simulation for Candidate Site Determination

For a photovoltaic (PV) power generation system, the shading effect of PV panels caused by dust deposition is extremely unfavorable. The deposition of dust results in a severe reduction of power generation output, since the efficiency of PV panels is affected by the shading irradiance and blocking the cooling. In this study, a numerical simulation method is proposed to model the dust accumulation on PV panels to detect the effects on PV power generation caused by different wind directions and wind speeds. Due to the high accuracy of numerical simulation, and the short calculation cycle, the proposed method provides a certain prediction for the soiling management of PV panels in the wind-sand environment. Through simulations and experiments, the impacts of dust accumulation on the performance of PV panels with different wind directions are studied in detail with the wind speed changing from 4.43 m/s to 6.48 m/s and the dust particle size of $10~\mu \text{m}$ to $100~\mu \text{m}$ , which are based on the environment of Liverpool, England in a year. Besides, for PV arrays, the turbulences of the dust distribution around the PV panels are also analyzed. The data collected from experiments and simulations are used to verify the effectiveness of the proposed strategy.
A Circumferential Coupled Dipole-Coil Magnetic Coupler for Autonomous Underwater Vehicles Wireless Charging Applications

Inductive power transfer is a practical approach to recharging the autonomous underwater vehicles (AUVs). The performance of the magnetic coupler determines the system transfer capacity. The annular magnetic coupler is widely utilized in the underwater wireless charging system. However, this magnetic coupler has limitations on magnetic field distribution. The flux linkage occupies the center of the AUV, and hence, the electronics inside the AUV are susceptible to electromagnetic interference. Additionally, the weight of the annular magnetic coupler greatly increases the burden on AUV power supply. In this paper, the magnetic coupler structure evolution is analyzed and a dipole-coil-based magnetic coupler with a novel circumferential coupling manner is presented. The magnetic flux is perfectly confined by the coupling manner, and the magnetic coupler weight is reduced by dimension optimization. To implement the magnetic coupler, the Fe-based nanocrystalline soft magnetic material is taken into consideration. To validate the proposal, a wireless charging system is built. Experimental results show that the system transfers 630W under water with a DC-DC efficiency of 89.7%.
A Novel Approach to Detect Outer Retinal Tubulation Using U-Net in SD-OCT Images

Optical Coherence Tomography (OCT) has become a basic non-invasive tool in diagnosing and following different types of eye diseases. This technique can produce high-resolution cross-sectional images of retinal layers. Outer retinal tubulation (ORT) is one of the detectable biomarker by SD-OCT. ORTs defined as hyporeflective, tubular structures with hyperreflective borders or reversed within the retina and appear in many retinal diseases, including age-related macular degeneration (AMD). Our aim is to develop an automatic method that can efficiently characterize ORT biomarker. Detection of this biomarker can be challenging because of its variable size, location, and reflectivity. In this paper, we present a fully convolutional U-Net based architecture to detect ORT. The proposed approach is evaluated using a dataset annotated by ophthalmologists. One of the main challenges was the limited amount of training data that we resolve with real-time augmentation during training and using nested cross-validation. Our method achieved near human performance reaching an overall object-based recall score of 0.847 and Dice score of 0.579 on the test set.
CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds

Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as “pay as you go” provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a server’s CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a server’s capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA).
Analyzing Stress Situations for Blind People

Detecting stress in the daily life of blind people is a challenging task. In this context, our work focuses on the analysis and detection of stress in the daily life of blind people. In this paper, we present a major step related to data collection in order to detect physiological signs of stress using a network of sensors implemented on a white cane. We present an overview of our system which final objective is to identify the situations that cause stress and predict them in order to assist the blind person with navigation directions along his walking path. The goal of these directions is to anticipate and avoid stress situations.
Case Dependent Feature Selection using Mean Decrease Accuracy for Convective Storm Identification

Weather forecasting is one of the most critical information that closely related to real-life because of its influences in human society, especially high-impact weather. Advanced observation devices allow increase forecasting accuracy, but they also remain problems to solve: analyze data precise and fast, and reflect knowledge of experts. Recently, machine learning-based approaches have been presenting solutions for those problems. On the other hands, the approaches need to consider several conditions, such as model and feature selection. In this paper, we focused on the feature selection for the convective storm identification by using the random forest and the mean decrease accuracy method. By derived variable importance index, it is possible to determine correlation coefficient related features are more significant than others.
Floating Complementary JFET Differential Stage with Increased Rejection of input Common Mode Signal and Power-Supply Noises

The circuit solutions that provide increase of common-mode rejection ratio (CMRR) and power-supply noises rejection ratio (PSRR) on a basic parameters of input "floating" complementary differential stage (FCDS) are proposed in the article. This subclass of differential stages is different by low noise level, high radiation hardness and ability of work at low temperatures through the use of junction field- effect transistors. The modified FCDS circuit simulating with CJFET transistors models, consider influence of cryogenic temperatures in LTspice simulation software results are showed. They show that CMRR and PSRR are improved by 2-3 orders of magnitude.
An Algorithm for Texture Removal and Defect Extraction

It is challenging to inspect a metal part with textures because the conventional defect detection methods cannot effectively distinguish between texture features and defect features, which can easily lead to misjudgment of defects. In this paper, we propose a new method for detecting surface defects which can effectively eliminate the texture features' impact on defect detection. By computing the average energy of directional length and average gray scale energy, the distribution of the energy difference in the whole image is obtained. According to the distribution of the energy difference, textures' impact on defects detection can be effectively removed and the surface defects can then be extracted. Experiments were performed to validate the proposed method and the results demonstrate the proposed method can effectively remove surface textures and accurately detect surface defects.
Advanced GNSS-R Signals Processing With GPUs

Global navigation satellite system reflectometry (GNSS-R) is a group of techniques that uses satellite navigation signals as signals of opportunity for remote sensing applications. In GNSS-R, large amounts of data are acquired and have to be processed. Computation time is typically the bottleneck for ground and airborne experiments. This article presents an efficient solution for off-line GNSS-R processing data taking advantage of graphics processing units (GPUs). After comparing to the typically used CPU languages, such as MATLAB and C++, the advantage of using parallel processing on the GPU is clear. GPU-based computation can reduce the processing time by as much as 95% of the acquisition time of the data. An implementation taking advantage of a home-use GPU is proposed for the data processing units. Thanks to its efficiency, even real-time processing experiments are feasible.
Performance Analysis of a Single Neuron based Adaptive PID and Conventional PI Controller based PMBLDC Motor Drive

Permanent Magnet Brushless DC (PMBLDC) Motor Drives have been considered as highly demanded motor drives for the next decades. The motor control and its speed estimation are difficult due to trapezoidal field pattern. Based on Single Neuron, an Adaptive PID (SNAPID) Controller is designed to control motor speed in this paper. Effectiveness of the controller is tested through simulation in C++ environment. The obtained results are compared with those obtained with fixed PI controller. It is seen that proposed controller works satisfactorily meeting the high performance drive requirement.
A Deep Neural Network Object Detection Method Using Multiscale Poisson Fusion

The deep neural network method has been widely used in the field of video object detection. However, the detection performance of such detectors are very sensitive to scaling change of targets. To improve detecting performance, it’s necessary to use a large number of sample data at different scales to train the model, but generally we could not get real multi-scale data of one scene. Aiming at Faster-RCNN, a state-of-the-art object detector, this paper proposes a deep neural network object detection method based on multi-scale Poisson fusion, its augment samples using Poisson method, which extracts sample objects from the original video frames and uses the Laplacian differential operator to calculate the divergence of sample objects according to the divergence of target area. After multi scaling at the sample objects, the Poisson equation is used to merge the scaling objects into the background at the target area, thus generating a large number of sample data at different scales. The experiment proves that the multi-scale sample data which generated by Possion fusion method can effectively perform the fine-tuning training for Faster-RCNN and improve its performance when detecting objects at different scales.
Unbalanced Voltage Mitigation using D2VC with Proportional Resonant Controller in αβ-Frame

This paper proposes a control algorithm for unbalanced voltage compensation using the Dynamic Direct Voltage Controller (D2VC). An advantage of this device is the possibility of a online direct voltage control in distribution systems, rather than on-load tap changers or other indirect control schemes. The proposed algorithm is based on the instantaneous positive and negative sequence components in αβ-frame, dismissing the use of a Unified Three-Phase Processor (UTSP), consequently avoiding its complexity and excessive computational efforts, and the compensation reference signals are obtained using proportional plus resonant (PR) controllers. There is no need of using any synchronism algorithm, e.g. PLL, which means that the D2VC’s performance is independent of the dynamics of this type of synchronization circuit. Simulation results carried out in time domain using PSCAD/EMTDC are presented to show the effectiveness of the proposed solution.
Control System of a Quadrotor UAV with an Optimized Backstepping Controller

Quadrotor is an inherently nonlinear, coupled and unstable system which require an efficient controller to perform accurate tracking without losing the target trajectory. This paper concerns the development of an optimal backstepping controller (OBS) for the quadrotor attitude. A mathematical modeling for the quadrotor dynamics was derived by taking into account its nonlinear dynamics. Ant colony optimization algorithm (ACO) was used to select the optimal parameters of the BS controller. However, the obsession behind the use of this optimization algorithm is the development of a reliable flight controller ensuring optimal performances. In order to highlight the accuracy of the proposed approach, we carried out numerical experiments using two trajectory scenarios.
Structural and Electronic Properties of B8, VB8, CrB8, and MnB8Nanoclusters: A DFT Study

In this research, density functional theory (DFT) calculations were performed using widely accepted B3LYP/SDD theory to investigate the structural and electronic properties of both pristine B8and transition metal (TM) (V, Cr, Mn) adsorbed B8structures. Several properties like ionization potential, total energy, zero point energy, dipole moment etc. were enhanced and HOMO-LUMO energy gap was also increased due to the adsorption of TM atoms on B8nanocluster. In terms of the binding energy, TM adsorbed structures can be ordered as $\text{CrB}_{8} > \text{MnB}_{8} > \text{VB}_{8}$ which indicates that the most stable TM adsorbed structure in our case is CrB8. Binding energy investigation for stability of the structures has been also validated by the analysis of thermodynamic parameters such as Gibbs free energy ($\Delta \mathrm{G}$), enthalpy ($\Delta \mathrm{H}$) and entropy ($\Delta \mathrm{S}$). Finally, global indices with chemical potential were also calculated.
A Method to Determine if a Test Case is Necessary using Support Vector Machine

Communication software used for Next Generation Networks (NGN) is required to have high reliability, so it incorporates many methods to improve quality and to prevent service interruptions during operation for public networks. However, the accumulation of quality improvement methods has resulted in problems with longer development times and increasing costs. The authors are working to solve such problems by automating development processes. In this paper, we propose a method to determine if a test case is necessary using support vector machine. We vectorize know-how regarding creation of verification test cases from experts who understand the requirement specifications. The support vector machine learns the vector as the training data. Then this is used to automate determination if a test case is necessary. We evaluated the effectiveness of the method through practical experiments, automatical determination if a test case of particular requirements is necessary.
First Principle Study of Pristine and Zn Doped B6 Nanocluster

2D nanomaterial becomes one of the most dynamic areas of nanomaterial researches due to their encouraging application and the discovery of Borophene opens up a vast field of research interest. Recently metal boride clusters are highly investigated because of their impressive properties and inspired by this, the geometric, electronic and optical properties of the Zn doped B6 nanoclusters have theoretically explored by implementing density functional theory (DFT) calculations. The adsorption phenomena of Zn on B6 nanoclusters is explored with the analysis of Mulliken charge distribution, adsorption energy and thermodynamic parameters. Based on our results of adsorption energies and change of enthalpy (AH), it is inferred that physisorption occurs for doping Zn atom in B6 structure. Electronic properties are investigated by dipole moment, global indices and HOMO-LUMO energy gap (Eg) analysis. For pristine B6 the value of HOMO-LUMO gap is 0.792ev whereas for ZnB6 and Zn2B6 it is 2.198ev and 0.086ev respectively. It indicates the value of Eg decreases drastically after doping Zn atom bi-pyramidally in B6 which is also pictured by density of state (DOS) spectra. Furthermore, the DOS graphs refer to orbital hybridization for these structures. UV-Vis spectra refer eminent blue shift for the doped clusters and circular dichroism (CD) spectra indicate that B6 cluster turns into optically active material for doping Zn atom.
Network Constraint Compliant Choice of Renewable Energy Source Sitting

The renewable energy sources are being incorporated into the existing power system for a clean, cheap and feasible source of power generation. The system operator must inspect that the sites allocated for incorporating renewable energy sources (RES) can provide maximum utilization of RES. The paper proposes an approach to find out the suitable sites for integrating RES on the basis of maximum power that can be injected at these sites without violating transmission network constraints. The approach leads to the identification of the best and the worst sites or buses on the basis of transmission constraints. Furthermore, the paper gives an idea about the nature of the different buses in response to RES incorporation. It depicts the maximum quantum of the power that can be integrated on each bus without violating the power flow limits of any transmission line. It identifies the transmission lines that are the cause of concern for integrating RES. The proposal is demonstrated on IEEE 30 bus and IEEE 118 bus systems.
Evaluation of Control Strategies within Hybrid DC/AC Microgrids using Typhoon HIL

Hardware-in-Loop (HIL) based simulation improves effectiveness of system simulation process by enabling interaction with part of the physical components. Hybrid ac/dc microgrids comprise of both dc and ac distribution, and comprise of different sources, loads and storage, interfaced to either bus. A critical factor related to successful microgrid implementation is management of energy interaction amongst the different components. The control systems for each of the different elements need to be critically validated in such a configuration. This paper presents the use of Typhoon HIL as the validation tool for control systems for all the constituents within hybrid dc-ac microgrid. The modelled Micro Grid consists of Distributed Energy Resources (DERs) such as a PV plant, Diesel Generator, Fuel Cell, Wind Farm, Micro-turbines, Battery Energy Storage System (BESS) and Converters. The simulation of Bidirectional Converter, connected between ac and dc buses, and its operation has been validated first using Matlab Simulink, and then compared to Typhoon HIL 402 based simulation.
An Implementation and Evaluation of Fast Embedded Model Predictive Control

This work studies hardware and software aspects concerning the implementation and the performance evaluation of a fast real-time embedded Model Predictive Control (MPC). This study is carried out using a low cost and a low power off-the-shelf high performance embedded computing platform (ARM Cortex M) and an off-the-shelf generic embedded MPC software implementation, with a general purpose QP (quadratic programming) solver (MATLAB). As an example, two oscillating masses system subject to input and output constraints is used. Throughout MPC evaluation is done using a hardware-in-the-loop (HIL) experiments, achieving a couple of milliseconds sampling rates.
Wind power system connected to the grid from Squirrel Cage Induction Generator (SCIG)

The main contribution of this paper is to present a methodology proposal for the development of active and reactive power control of a wind power generation system connected to the grid using the three-phase induction generator with a squirrel cage rotor (SCIG). The SCIG stator terminals are connected to electric grid before the inductive filter using an AC/DC/AC power converter topology, called back-to-back, while the rotor terminals are short circuited, so that the SCIG’s active and reactive power control techniques are presented. The control of grid side converter (GSC) is presented, responsible for the power control and also for keeping the DC bus voltage constant. The control of induction generator side converter (IGSC) is presented also, in which vector control is used in rotor variables, so that the SCIG is controlled through the torque references and magnetizing current. The studied system was mathematically modeled and simulated using Matlab/Simulink software.
Ultra-Fast Detection and Location of Busbar Faults Employing IMG

This paper presents a method for ultra-fast detection and location of busbar faults. The proposed algorithm is based on the output values of online Improved Morphological Gradient (IMG) that is applied to the superimposed component of current signals. Algorithm only performs fault detection and location using current signals of current transformers (CTs) that are placed in busbar protection zones and can be used in any type of busbar structure. To evaluate the performance of the proposed algorithm, a 400 kV substation model has been simulated by EMTP/ATP-Draw software. The simulation results indicate that the proposed algorithm has high security against external faults, and is able to find the location of the internal busbar fault.
UAV-assisted Real-time Data Processing using Deep Q-Network for Industrial Internet of Things

Industrial internet of things (IIoT) enables edge computing technology to provide communication between the machines that produce a large amount of data and locate at the edge network. A task scheduling is implemented in the edge node. Furthermore, the real-time data can achieve with the lowest latency that allowed by the edge node near the edge network. However, a mobile machine such as an autonomous guided vehicle can interfere in this situation. Because the vehicle also needs service by the edge node. Over that, quality of service (QoS) performance can decrease. Therefore, this paper deploys an unmanned aerial vehicle (UAV) as an edge node to provide service to the edge network through optimizing the trajectory of UAV, where the edge network request task using a Deep Q-Network (DQN) Learning. The result shows that using machine learning, notably the DQN algorithm, can increase the number of the machine that can be provided service. Subsequently, the real-time data can achieve either the interrupt occurs at the edge node.
Stacking Multiple Differential Pairs for a NEF<1 Amplifier aimed at Electroneurographic Signal Recording

A low-noise, electroneurography (ENG) signal recording micro-power amplifier, for a single-channel implantable medical device, is presented. The amplifier is powered by a standard medical grade 3.7Vnom secondary battery, and stacks twelve differential pairs maximizing current-reuse and operating them close to saturation with a reduced VDS. The amplifier was implemented in a 0. 6μm technology, with a total gain of ~80dB, 200Hz-4kHz bandwidth, and a current consumption of just 16. 5μA, with a measured input referred noise of 330nVrms in the band of interest. This result corresponds to a Noise Efficiency Factor NEF=0.84, below the classic limit of 1. On the other hand, the die area of the amplifier is 6mm2, because very large input transistors and decoupling capacitors are necessary to reduce flicker noise. Thus finally, chopper technique is proposed to reduce flicker noise without resorting to such huge transistors. A new version of the current-reuse amplifier was designed with ~1/80th gate and capacitor area, and almost the same NEF.