Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Power Management of Multiple PV, Battery, and Droop Units in an Islanded Microgrid

In this paper, a decentralized power management strategy is proposed for multiple photovoltaic (PV), battery, and droop units in an islanded microgrid. The control strategy is developed to handle any combination of these units without modifying their control systems. This provides a more comprehensive and generalized approach to coordinate the three types of units, in comparison to the techniques in the literature that consider only two types, mainly PV and battery systems, or consider only a single unit of each type. The operation of each unit is autonomously coordinated to maintain the balance between generation and consumption, while ensuring controlled charging/discharging of the batteries in the microgrid. To achieve this coordination, the voltage and the power control loops, within each of the PV and battery units, are configured to follow the specifically designed multi-segment power/frequency (P/f) characteristic curves. These characteristics are designed to independently adapt to the microgrid operating conditions, without relying on any external communications and centralized management systems. Accordingly, the control system for each unit is able to autonomously and seamlessly switch in real-time between power control and frequency regulation based on the available PV power, the state-of-charge of the batteries, and the total load demand in the microgrid. The strategy is designed and implemented using multi-loop controllers, in contrast to the commonly adopted approach of using discrete operating modes and switching logics. The proposed strategy is validated using a microgrid simulated in PSCAD/EMTDC with detailed switching models of the power electronic converters.
A Home Service-Oriented Question Answering System With High Accuracy and Stability

With the development of deep learning, neural network-based (NN-based) methods have been applied in question answering (QA) widely and achieved significant progress. Although an NN-based QA system can obtain better performance and save manual efforts, the system is likely to suffer attacks from the external perturbation, due to its character of being black boxes. Limited in the area of home service, we present an innovative method for constructing an NN-based QA system. In our method, the accuracy can be further increased, and the stability can be enhanced in the meantime. Inspired by observing the process of performing home services, the tool information (tool names and tool sequences) is integrated with question terms, as a way of extending the question representation. The conception of attribution (word importance) is introduced to gauge the word importance since NN-based models can be easily affected by the uninformative question terms. In order to optimize the model parameters effectively, the reinforcement learning is employed and both factors on accuracy and stability are regarded as rules in designing rewards. A few state-of-the-art methods are adopted to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the model ability to produce effective answers in QA can be further improved with our method, and the model stability on perturbations can be enhanced with our method.
An Investigation into the Effects of Weak Interfaces on Fracture Height Containment in Hydraulic Fracturing

Hydraulic fracturing is an effective method for developing unconventional reservoirs. The fracture height is a critical geometric parameter for fracturing design but will be limited by a weak interface. Fracture containment occurs when fracture propagation terminates at layer interfaces that are weaker than the surrounding rock. It always occurs in multilayer formation. Therefore, the mechanism of fracture height containment guides fracture height control in hydraulic fracturing. In order to study the fracture containment mechanism, this paper first calculates the propagation behaviour of the fracture in 3D under the influence of a weak interface through a block discrete element method and analyzes the geometric characteristics of the fracture after it meets the weak interface. Then, the induced stress of the hydraulic fracture on the weak interface is calculated by fracture mechanics theory, and the mechanism of blunting at the fracture tip is explained. Then, two kinds of interface slippage that can lead to blunting of the fracture tip are discussed. Based on the behavior of shear slippage at the interface, a control method for multilayer fracturing in thin sand-mud interbed and pay zone fracturing in shale is proposed. The results show that the fracture height is still limited by the weak interface in the formation without the difference of in-situ stress and rock properties. Interface slippage is the main factor impeding fracture propagation. Fracture height containment can be adjusted and controlled by changing the angle between the hydraulic fracture, the interface, and the stress state to strengthen and stiffen the interface. This study has a certain guiding significance for fracture height control in the design of hydraulic fracturing of shale or thin sand-mud interbed reservoirs.
A Radio Grid Monitoring Method Based on Independent Component Analysis with Empirical Mode Decomposition Denoising

Due to the rapid development of wireless communication networks, electromagnetic environment are becoming much more intricate. Traditional radio monitoring systems are facing challenges. Grid monitoring is considered to be a promising architecture for monitoring systems. However, the layouts and corresponding signal processing methods of the grid monitoring systems are needed to be studied. In this paper, we propose a novel two-layer overlapping structure for grid monitoring systems, and apply Independent Component Analysis (ICA) as a competitive blind source separation algorithm. Empirical Mode Decomposition (EMD) is introduced to denoise the monitored signals. A complete processing procedure of monitored signals is implemented. Simulation based on Matlab software is conducted to verify the performance of the proposed scheme.
Photovoltaic Applications for Lighting Load Energy Saving: Case Studies, Educational Building

Renewable energy is generally defined as energy that comes from natural resources such as sunlight, wind, rain, tides, waves and geothermal heat energy. Rapid deployment of renewable energy and energy efficiency is resulting in significant energy security, climate change mitigation, and economic benefits. In this Work one of the Renewable Energies is being focused which is solar energy that it can be used in different two ways Solar Thermal or Solar Photovoltaic; as Solar power (Electricity) is produced in two ways: Direct Transformation from PV or Concentration of solar energy to create steam in order to drive a turbine. Photovoltaic (PV) devices are discovered with their types based on the type of silicon used, specifically; (Mono-crystalline Silicon Cells, Poly-crystalline Silicon Cells, Amorphous thin-film Silicon Cells); Also, different factors that can affect the efficiency of solar panel mounting systems are discussed. PV Systems (Stand-Alone System or a Grid Connected System) with their configurations is discovered and discussed briefly. As Smart Grid is an Electrical Grid in which it includes varieties of operational and Energy Measurements such as Smart Meters, Smart Appliances, Renewable Energy Resources, and Efficiency Energy Resources from here PV Systems as one the Renewables resources in smart grid is introduced. The Study is for Supplying Building (A) – BUE with power and loads was assumed to be 921364.7059 Wh/day and the no. of modules required was 2000 PV module. In order to install 2000 PV Module, it requires 4000m2 Area and the building roof area doesn’t support to install all this number of modules so it is assumed to be installed in another area land beside the BUE location. A MATLAB Program Code was Generated to estimate the Monthly Averaged Global Solar Radiation at BUE Location and also another Code was generated to Draw the (I–V) Curve for the Selected PV Module.
High-Speed Reconfigurable Free-Space Optical Interconnects with Carrierless-Amplitude-Phase Modulation and Space-Time-Block Code

High-speed interconnects are highly demanded in data centers and high-performance computing, and the use of optical interconnects to replace electrical interconnects has been proposed and widely studied. For medium-range board-to-board communications, free-space based optical interconnects provide the additional advantage of reconfigurability and flexibility, compared with traditional waveguide or fiber-based counterparts. However, the bit rate and interconnection range in free-space optical interconnects are typically limited. In this paper, we propose a carrierless-amplitude-phase (CAP) modulated and space-time coded free-space optical interconnect scheme to overcome these limitations. Up to 80 Gb/s (2 × 40 Gb/s) interconnection with reconfigurability and flexibility is experimentally demonstrated for a maximum error-free distance exceeding 50 cm, showing about 65% improvement over previous demonstrations. The receiver sensitivity of the space-time coded system is measured as well, and results show that the space-time-block code (STBC) is capable of improving the receiver sensitivity and suppressing the inter-channel crosstalk. The impact of air turbulence, which exists in typical interconnect environments, is also experimentally investigated and results show that its impact on the proposed scheme is negligible. The proposed reconfigurable free-space optical interconnects with CAP and STBC can be further scaled up to provide higher speed and further extended interconnection range.
VSC-MTDC Operation Adjustments for Damping Inter-Area Oscillations

Application of voltage source converter based multi-terminal high-voltage direct current (VSC-MTDC) grids operation adjustment for damping inter-area oscillations are addressed and analyzed in this paper. The new operation adjustment method is proposed to meet short-term solution requirements and accommodate critical operation conditions where the conventional damping stabilizer by using active power modulation cannot provide sufficient damping. Based on the sensitivities with respect to the active power injections of VSCs and the interior point algorithm, an operation optimization method is presented to obtain a proper VSC-MTDC operation scheme to achieve better damping performance for an ac/VSC-MTDC system. For some critical operation conditions in which an inter-area oscillation has been detected, an operation rescheduling method is proposed as an emergency oscillation control. Time-domain simulations are performed on a New England system with a five-terminal MTDC grid. A comparative study with a conventional damping stabilizer is also included in the case study. Both the theoretical analysis and the time-domain simulations have verified the effectiveness and superiority of the two proposed methods.
Analysis of Reproducibility of Noninvasive Measures of Sympathetic Autonomic Control Based on Electrodermal Activity and Heart Rate Variability

Heart rate variability (HRV) and electrodermal activity (EDA) are useful tools for assessing the central and peripheral dynamics of the sympathetic nervous system and detecting the effects of numerous systemic diseases and life-challenging situations. However, the indices of HRV and EDA are highly influenced by mental stress, environmental conditions, body position, and other physiological conditions that introduce variability. In this paper, we assessed the five-day reproducibility of HRV and EDA measures of sympathetic control, for <inline-formula> <tex-math notation="LaTeX">$\text {N}=20$ </tex-math></inline-formula> subjects undergoing 70° head-up tilt test (HUT) and Stroop task tests. We made the assessment in highly controlled conditions without environmental causes of variability, to have a good baseline understanding of the consistency of the various indices of HRV and EDA. Therefore, we assessed intra-subject variation (using the coefficient of variation, CV) and consistency (using the intra-class correlation coefficient, ICC) of the test-to-baseline differences produced by both tests on the studied measures. The low-frequency component of HRV (HRVLF), and its normalized variant was computed as HRV measures of sympathetic control. For EDA, the skin conductance level, frequency of non-specific skin conductance responses, spectral index (EDASympn), and time-varying index (TVSymp) were computed. TVSymp (<inline-formula> <tex-math notation="LaTeX">$\text {ICC} = 0.85$ </tex-math></inline-formula>) and HRV indices exhibited higher consistency during the HUT (ICC <inline-formula> <tex-math notation="LaTeX">$\ge0.8$ </tex-math></inline-formula>), compared to other EDA measures, and HRVLF was the least variable measure (<inline-formula> <tex-math notation="LaTeX">$\text {CV} = 85.4$ </tex-math></inline-formula>%). EDA indices exhibited higher consistency (except for the EDASympn) during the Stroop task (ICC <inline-formula> <tex-math notation="LaTeX">$\ge 0.79$ </tex-math></inline-formula>) when compared to HRV, and TVSymp was the least variable measure (<inline-formula> <tex-math notation="LaTeX">$\text {CV} = 97.2$ </tex-math></inline-formula>%). Remarkably, TVSymp proved to be a reproducible measurement (low variation and high consistency) in both scenarios. These results are the necessary groundwork for studying the use of EDA and HRV in real-world conditions, as reproducibility of the indices has remarkable importance in clinical practice.
Behavioral Implementation of SVD on FPGA

Implementing Singular-Value Decomposition (SVD) in real time is a requirement in wireless communications. Pure-software solutions are not likely to provide a satisfactory computing speed; thus, hardware support is needed. Field-Programmable Gate Arrays (FPGA) can provide a cost-effective alternative to full-custom hardware as long as the digital design is tuned toward the specific reconfigurable architecture. In this paper, we describe techniques to generate a behavioral implementation of SVD that can be mapped easily onto FPGA, thereby reducing the effort of the design and coding. The computing performance and hardware complexity are in line or better than prior-art.
Development of a Hybrid Analytical Model for a Fast Computation of Magnetic Losses and Optimization of Coaxial Magnetic Gears

This paper focuses on a two-dimensional (2-D) analytical model for a fast computation of magnetic losses due to eddy currents in the permanent magnets as well as iron losses in the ferromagnetic parts within coaxial magnetic gears. The magnetic field distribution is computed in yokes and permanent magnets by solving both Maxwell's equations, whereas for pole pieces, the magnetic field is computed by coupling the previous analytical model with a reluctance network model. Both the eddy current losses and iron losses are determined from this hybrid analytical model. The iron loss model takes into account the temporal and spatial variations of flux density. The eddy current loss model takes into account the magnet splitting. Results of this magnetostatic eddy current loss model are then compared to the results obtained with a 2-D magnetodynamic finite element model. A verification of validity limits is also proposed. The final function of this analytical model is to ensure integration into a set of models in the aim of a global mechatronic optimization of magnetic gears, for their insertion into multimegawatt wind turbines. A preliminary bi-objective, mass-efficiency optimization protocol is subsequently proposed along with an analysis of the computation time reduction via the presented models.
Vehicle Longitudinal Acceleration Determination from Mobile Phone Sensor: An IoT System Solution for Intelligent Transportation

Many methods have been proposed to calibrate or normalize the inference of automobile acceleration from measured mobile phone accelerometers. The method proposed in this paper uses simplifying assumptions and geometrical projection to yield an accurate solution to the problem. The proposed method has been tested and implemented successfully along with recommendations laid out for future approaches using the intelligent Internet of Things $(\mathrm {I}^{2}\mathrm {o}\mathrm {T})$.
Coarse-To-Fine Segmentation Refinement and Missing Shape Recovery for Halibut Fish

Image processing and analysis techniques have drawn increasing attention since they enable a non-extractive and non-lethal approach to fisheries survey. To measure the fish size and length accurately, a reliable segmentation result is required. However, there are two major challenges about the segmentation. One is that images may be blurred due to the spray of water on the camera lens, and the other is that some part of the fish body is out of the camera view. In this paper, we present an innovative and effective contour-based segmentation refinement and missing shape recovery method from an arbitrary initial segmentation. The refinement is processed from coarse level to fine level. At the coarse level, a weighted affine transform is estimated to align the entire fish contour of the initial segmentation with trained representative contours. At the finer and finest levels, we iteratively refine the contour segments to represent the poorly segmented or shape missing image. The proposed method shows promising results on segmentation and length measurement for both water drop images and images with part of the fish out of the camera view.
Learning Doppler with Deep Neural Networks and its Application to Intra-Cardiac Echography

Cardiac ablation therapy is an effective treatment for atrial fibrillation and ventricular tachycardia that relies on the creation of electrically isolating scars, e.g. through heat. The ability to reliably visualize and assess the formation of these lesions during the procedure would greatly enhance the therapy's success rate and safety. Tissue Doppler echography enables measurement of tissue strain, and could therefore be used to monitor and quantify the stiffening of developing lesions. In tissue Doppler, the tradeoff between spatiotemporal resolution and estimation accuracy/precision is balanced by manually tweaking the fast-and slow-time range gates, with the optimal settings varying across measurements and desired clinical objectives. Convolutional neural networks have shown remarkable performance at learning to execute a large variety of signal and image processing tasks. In this work, we show how a deep neural network can be trained to robustly fulfil Doppler imaging functionality, which we term DopplerNet.
Share Price Trend Prediction Using CRNN with LSTM Structure

The stock market plays an important role in the entire financial market, and the prediction of stock price volatility is one of the most attractive research issues. In this paper, we will use the historical information of stocks to predict future stock prices and use deep learning to achieve them. This paper uses deep learning to predict the future trend of stock prices. Since the trend of stocks is usually related to the previous stock price, this paper proposes a Convolutional Recurrent Neural Network (CRNN)-based architecture, ConvLSTM, in which long and short-term memory is used in RNN (Long short). -term memory, LSTM) architecture. LSTM improves the long-term dependence of traditional RNN and effectively improves the accuracy and stability of prediction. This paper collects a total of ten stock historical data to test and achieve an average error rate of 3.449 RMSE.
Adaptive Artificial Neural Network-Coupled LDPC ECC as Universal Solution for 3-D and 2-D, Charge-Trap and Floating-Gate NAND Flash Memories

Adaptive artificial neural network (ANN)-coupled low-density parity-check (LDPC) error-correcting code (ECC) (ANN-LDPC ECC) is proposed to increase acceptable errors for various NAND flash memories. The proposed ANN-LDPC ECC can be the universal solutions for 3-D and 2-D, charge-trap and floating-gate NAND flash memories. In 3-D NAND flash, lateral charge migration, vertical charge de-trap, inter floating-gate capacitive coupling noise, and inter word-line variations cause errors. On the other hand, in 2-D NAND flash, the charge de-trap and the harsh inter floating-gate capacitive coupling of adjacent word-lines and bit-lines cause errors. To solve these reliability problems, the proposed ANN automatically and adaptively compensates for complex memory cell errors. Moreover, the proposed ANN-LDPC can reproduce the dynamic endurance and data-retention time dependence of errors. In addition, this paper evaluates the impacts of the chip-to-chip variations on the proposed ANN-LDPC. The proposed ANN-LDPC is implemented in the storage controller and can precisely and adaptively estimate bit-error rate (BER) and log-likelihood ratio (LLR). By using the precise LLR, LDPC decoder effectively corrects errors. As a result, ANN-LDPC extends the acceptable data-retention time by over 76 $\times $ and 45 $\times $ compared with conventional Bose–Chaudhuri–Hocquenghem (BCH) ECC in 3-D and 2-D triple-level cell (TLC) NAND flash memories, respectively.
Determining Disaster Risk Management Priorities through a Neural Network-Based Text Classifier

Community participation and involvement plays a big role in disaster risk reduction. This paper made use of the feedback from public on how local communities can be better prepared in times of disaster. Main goal of this study is to automatically assign qualitative responses into its appropriate category in disaster management using bidirectional recurrent neural network. In building the BRNN model, data corpus was split into training set (85%) and testing set (15%), which achieved acceptable average accuracy rate of 81.67%, 81.17% precision, 81.67% recall and 80.81% f-measure. Output of the classifier showed that the top four priority needs of the respondents in DRR fall under the categories of education and training; communication and coordination; dissemination of information alerts and warnings/ early warning system; and role of local authority. The validated results generated is a useful feedback to concerned agencies, specifically in the Province of Albay in enhancing their existing disaster management plans. Future work may add trained data to achieve higher performance results. Using other hyperparameters in the configuration of neural networks may also be considered for better evaluation result of the classification model.
Micromagnetic Model Simulations Considering Write Head, Spin–Torque Oscillator, and Double-Layered Medium Altogether

Micromagnetic model simulations were performed to investigate spin–torque oscillators (STOs) integrated into the main pole–trailing shield (MP–TS) gap of the write head (integrated STO). First, the oscillation states of a tilted STO were investigated in a model comprising the write head, STO, and medium soft underlayer (SUL). We found that inserting the STO into a non-parallel MP–TS gap resulted in more stable oscillation. Second, we performed full micromagnetic simulations of the write head, STO, SUL, and recording layer (RL), and the oscillation of the tilted STO was investigated. We found that the RL affected the STO oscillation to some extent; however, the effect was smaller than that from the write head. We also discuss the calculation times for various structures with and without the RL, and with/without medium motion.