Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Feature Sets for Day-Ahead Load Demand Forecasting Using Deep Neural Network

The performance of load demand forecasting plays a vital role in economic operation and planning in the power sector. There are several methodologies in the literature for predicting load. However, there is still an essential need to develop more accurate load forecast method. The performance of these methods can be improved by using an effective machine learning methods by selecting informative feature sets. In this paper, at first, we choose the effective time lags based feature by using auto-correlation and cross-correlation. Then, more robust features have been extracted by using Principal Component Analysis (PCA) and Autoencoder (AE) based Deep Neural Network (DNN). Extracted features are provided as an input to the Artificial Neural Network (ANN) model. ANN with Levenberg-Marquardt (LM) training algorithm has been used for day-ahead load forecasting (DALF) using the extracted features. The proposed method is AE based DNN for features extraction followed by ANN with LM training algorithm. The proposed method has been compared with ANN and PCA-ANN. The performance evaluation for DALF has been analyzed on two different substations of New England Independent System Operator (NE-ISO) dataset. Each dataset is analyzed for two separate cases. The performance of the proposed approach is better than ANN and PCA-ANN method.
Object Reconstruction with Deep Learning: A Survey

Object reconstruction is one of the most crucial branches of computer vision. With the development of deep learning, many tasks have achieved remarkable improvements in computer vision. 3D reconstruction with deep learning also has attracted much attention in recent years. Deep learning methods based on CNN-based and GAN-based architectures have been adopted for 3D object prediction. In addition, researchers utilize different inputs such as RGB and depth images to achieve prediction based on different problem. In this paper, we provide a detailed overview of recent advances in 3D object reconstruction. The reviewed approaches are categorized into three groups depending on the input modality: RGB-based, depth-based and other-input-based. Particularly, we introduce the various methods and indirectly classify the shape representation. As a survey, we discuss the strong and weak points of exciting approaches.
Finite Control Set Model Based Predictive Control of Grid-Tied Six-Switch Converter Applied to Induction Generator

Finite Control Set Model Based Predictive Control (FCS-MPC) established itself as an effective technique for the control of power electronic converters, with fast dynamics and the absence of voltage modulators. However, there are still converters in the literature which did not exploit the advantages of FCS-MPC, as the Six-Switch Converter (SSC), a multiport converter which can replace the back-to-back converter in power generation with 50% of the switch count. As a result, this paper investigates the use of FCS-MPC in the SSC for grid-tied power generation with the squirrel-cage induction generator. Two Predictive Current Control (PCC) approaches are presented: a Concentrated PCC (PCC-C), in which a centralized cost function is employed for control decision evaluation; and a Decoupled PCC (PCC-D), with independent cost functions for the grid and generator control. Simulation results show PCC-D provided better torque ripple factor and better grid current distortion, when compared to the concentrated approach. Also, PCC-D eliminated the diverging bus capacitor voltage offset of PCC-C, and the superior performance was obtained with a lower computational cost.
Gene Regulatory Networks Analysis for Subtypes of Muscle-Invasive Bladder Cancer Based on Pathway

Recently, the Erdafitinib was approved by the U.S. Food and Drug Administration, which is the first targeted therapy drug for genetic alteration in metastatic urothelial carcinoma. Cancer genomics research is greatly encouraged. Currently, a great number of gene regulatory networks between different states have been constructed, which can reveal the difference of biological states. However, they have not yet been applied to the subtypes of bladder cancer. Furthermore, gene regulatory networks are not constructed on their pathway, which provide insights for multi-target gene therapy. In this paper, we proposed a new method that construct gene regulatory networks under different conditions. Specifically, gene regulatory networks under different molecular subtypes of bladder cancer based on pathway are constructed. Besides, the regulatory differences between molecular subtypes are analyzed. We find that many hub genes in differential networks are related to cancer or even bladder cancer and these genes are significant diversity in subtypes of bladder cancer. In addition, attention should be paid to the links between hub genes and others, especially when the hub genes are the (candidate) target of drugs.
Analysis of Neutral-Point Voltage Balancing in Three-Ports Active Neutral-Point-Clamped Converter

This paper investigates the neutral-point (NP) voltage balancing problem from the perspective of the space vector modulation for different operating conditions of the recently proposed Three-ports Active Neutral-Point-Clamped (ANPC-3P) topology, which can integrate one energy storage system per inverter leg. Some scenarios to reduce the low-frequency voltage ripple in the NP are analyzed and the effects on the NP current due to the integration are reported. Theoretical propositions were validated by simulation results.
Power Demand Prediction Based on Mixed Driving Cycle Applied to Electric Vehicle Hybrid Energy Storage System

The use of multiple energy sources as power supply of an electric vehicle allows to improve its performance by increasing its autonomy and extending life cycle of on-board battery pack, which is the most expensive element of this type of automobile. In this work, it is proposed the use of computational intelligence techniques in the management of a hybrid energy storage system based on battery and supercapacitor, both embedded in an electric vehicle. For this purpose, a methodology for prediction and separation of the fractions of power demand, using an artificial neural network (ANN) based on the NonLinear Autoregressive Model with Exogenous Inputs (NARX), is presented. A mixed driving cycle (MDC), composed by the combination of characteristics of different standard driving cycles, is used for NARX ANN training. This proposed MDC ANN training allows a better performance of power demand prediction, when compared to a single driving cycle ANN training approach, since it imposes a more diversified speed profile. From the simulations carried out and the adjustments of the network parameters, a very small error was found in relation to the predicted signals. Based on the obtained results, it is possible to conclude that the proposed method is effective and promising for the calculation of power demand in electric vehicles.
A New DC-DC Double Zeta Quadratic Converter

This paper presents a study of a new dc-dc non-isolated high gain converter called Double Zeta Quadratic Converter. From a single basic switching cell of a family of quadratic converters, the topological states, the waveforms of voltage and current, and the output characteristic graph of the converter are presented in this paper. To prove the advantages of this new topology as the high static gain and high efficiency, the raised curves are illustrated. Besides, the experimental results of the proposed converter are presented in this paper.
A hybrid RC-droop control strategy for power sharing and voltage restoration in islanded DC microgrids

This paper investigates the performance assessment of droop controlled converters in a low inertia, low voltage DC microgrid scenario. It employs a generalized DC distribution network consisting of both current-controlled and voltage-controlled voltage source converters and a mix of constant impedance, constant current and constant power loads. Since in large distribution networks, lack of inertia is the cause of instability, virtual impedance based methods and capacitor control techniques have been proposed. A RC-based droop control strategy is employed in this work in order to impart inertia to the system thereby improving the transient performance of the network. The converters considered are both grid-feeding and grid-forming in order to analyze the response in the presence of dissimilar electrical energy sources. The grid-feeding converters, both voltage and current-controlled, are interconnected via communication layer and are governed by the consensus laws for multi-agent systems. The networked control systems aids in achieving proper load current sharing and desired voltage regulation. This is essential for the operation of critical loads. The study presents a comparative assessment of the response of RC-droop and conventional droop in the varied microgrid conditions. The cyber-physical system is modeled in MATLAB/Simulink and tested against various operating situations and cyber adversities to test the efficacy of the control law.
An Unit-less Mathematical Model for Analysis and Design of Class-E Resonant Converters

Considering sustainable policies for energy systems, resonant converters are suitable systems to realize the desirable flow of energy in several applications, such as: renewable sources, energy harvesting systems, electric and hybrid vehicles. However, analysis and design of resonant converters are more complex than their pulse-width modulation counterparts, which leads to simplifying assumptions that result in limitations in the size of the designed components. This paper proposes a result-oriented methodology for analysis and design Class-E converters by means of an unit-less mathematical model that is independent of circuit parameters, such as, inductances and capacitances. The system is modeled in a generalized representation and normalized gain curves can be obtained to design the converter. A 500 kHz Class-E resonant converter was simulated and implemented to validate the theoretical approach.
Modeling and Simulation of the Battery Energy Storage System for Analysis Impact in the Electrical Grid.

With increasing use of intermittent renewable energy sources, energy storage is needed to maintain the balance between demand and supply. The renewable energy sources, e.g. solar and wind energy sources, are characterized by their intermittent generation, causing fluctuations in power generation, and, similarly, demand may vary. There may be fluctuations in power generation, and, similarly, demand may vary. Then, for these new sources become completely reliable as primary energy sources, energy storage is a crucial factor. This work uses real-time simulation to analyze the impact of battery-based energy storage systems on electrical systems. The simulator used is the OPAL-RT/5707™ real-time simulator, from OPAL-RT Technologies company. The simulated system consists of a three-phase inverter connected to a BESS (battery energy storage system) and to the electrical grid with variable loads. The obtained results from real-time simulations prove its effectiveness for this type of analysis and open the possibility to perform PHIL (power hardware in the loop) simulations.
DWT: Decoupled Workload Tracing for Data Centers

Workload tracing is the foundational technology that many applications hinge upon. However, recent paradigm shift to-ward cloud computing has caused tremendous challenges to traditional workload tracing. Existing solutions either require a dedicated offline cluster or fail to capture the full-spectrum workload characteristics. This paper proposes DWT, a novel framework that leverages fast online instruction tracing, and uses synthetic data offline for memory access pattern reconstruction, thereby capturing the full workload characteristics while obviating the need of dedicated clusters. Experiment results show that the stack distance profiles generated from synthetic address traces match well with the original ones across all SPEC CPU 2017 programs and representative cloud applications, with correlation coefficient R^2 no less than 0.9. The page-level access frequencies also match well with those of the original programs. This decoupled tracing approach not only removes the roadblocks on workload characterization for data centers, but also enables new applications such as efficient online resource management.
Bypassing MRI Pre-processing in Alzheimer's Disease Diagnosis using Deep Learning Detection Network

Although promising results have been achieved in the area of Alzheimer's Disease diagnosis via hippocampal atrophy analysis, most of these solutions are heavily dependent on various MRI pre-processing techniques to obtain a good result. Besides, most recent works using deep learning methods such as Convolutional Neural Network (CNN) solve Alzheimer's Disease diagnosis based on a classification problem, leaving a research gap to use deep learning object detection method on Alzheimer's Disease diagnosis. In this study, we make two contributions to solve this problem. Firstly, we are the first group to propose an Alzheimer's Disease diagnosis solution without requiring any MRI pre-processing technique. Secondly, we introduce recent deep learning object detection architectures such as Faster R-CNN, SSD and YOLOv3 into the area of Alzheimer's Disease diagnosis. As a side product of our research, we provide a new Deep Learning Alzheimer's Disease/Normal Control (AD/NC) object detection benchmark dataset which includes 500 raw, unprocessed screening instances per class from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset for future object detection research. This dataset consists of a collection of T1 weighted sagittal MRI dicom slices in MP-Rage series in DICOM 16-bit and PNG 16-bit image format, annotated with their respective class label and bounding box in Pascal VOC format. We call this benchmark dataset UTM-ADNI-RA W*. In this study, without using any MRI preprocessing technique, we managed to obtain a detection accuracy of 0.998 for YOLOv3, 0.982 for SSD and 0.988 for Faster R-CNN in AD/NC territory while surpassing 0.75 IoU threshold across all three deep learning architectures.
Prediction of Short-Term Electricity Consumption by Artificial Neural Networks Levenberg-Marquardt Algorithm in Hormozgan Province, Iran

The prediction of short-term electricity consumption is of vital importance for designing and management of energy production and storage systems. Forecasting the next 24 h of electrical load allows operators to prevent power blackout and optimize their resources. In this paper, we present a more accurate prediction methodology for short-term energy consumption utilizing optimized artificial neural networks (ANNs) for Hormozgan. Variant architectures were developed for hourly load prediction. For the determination of best combinations of learning algorithms, layers and neurons, the main training algorithm of Levenberg-Marquardt was used. In the proposed implementation of the network, data groups are modeled by Levenberg-Marquardt backpropagation algorithm containing two layers and 20 neurons. Weather conditions, holidays, weekends, etc. cause increase and decrease in electricity consumption and temperature is known as the meteorological variable with the highest effect. Therefore, a total of five kinds of parameters were considered in this model. The evaluation of performance of the model was based on mean absolute, mean absolute percentage error and daily peak forecast error. Using the proposed framework, the error values in the forecast in the order of 2.83% have been achieved in the third training. The Hormozgan province load data are used to train and validate the forecast prediction.
A Transient and Steady-State Power Signature Feature Extraction Using Different Prony's Methods

The extraction of features that defines the electric power signature of a load or an appliance is one of the most relevant stages for the Non-Intrusive Load Monitoring (NILM) problem. In general, harmonic content, damping and transient switching features are normally used to classify different loads connected or disconnected in a consumer unit. In this sense, this work compares five different approaches of the Prony's method (Polynomial, Least Squares, Total Least Squares, Matrix Pencil, and IIR-based) with the aim of simplifying the order of the model and provide an analytic solution to the problem of estimating the harmonic content (frequency, phase, and amplitude) with the exponential damping factor for current signals. Using the components from Prony's method as steady-state and transient features, an Ensemble classifier is used to perform the classification of the different waveforms of two publicly available databases (COOLL and LIT). With that, we show that the proposed method achieves 98% for the COOLL dataset and 97% for the LIT database, using the Matrix Pencil approach.
Considerations on Communication Infrastructures for Cooperative Operation of Smart Inverters

The presence of distributed generation systems spread over low-voltage electrical networks is boosting the development of control methodologies aiming at coordinating and cooperatively managing the existing smart inverters. Although low-bandwidth data transmission links are constantly described to be required for a considerable number of centralized and decentralized control methodologies, there is a gap in literature concerning the plain understanding of the features of the related communication protocols available for such application. Thus, this paper brings considerations on some of the most relevant communication protocols that can be applied to the cooperative control of multiple smart inverters, taking into account the recent updates on interoperability requirements recommended by the IEEE 1547-2018 standard. The communication infrastructure, topology and features of a low-bandwidth data transmission link are discussed in this paper focusing on the SunSpec, DNP3 and SEP2 protocols. Yet, some critical comments are made regarding the practical interoperability of commercial inverters, also bordering cyber security matters.
A Non-iterative Three Phase Distribution System Power Flow Analysis

A novel non-iterative three phase power flow has been proposed in this paper for distribution systems. Due to high R/X ratio and unbalance in load of distribution systems, the linear DC power flow is not applicable as resistance can’t be neglected. Moreover, voltage magnitudes play vital role in distribution systems. So, the aim of this paper is to develop a linear power flow for unbalanced distribution system which takes into account of both resistance and voltage magnitude along with reactance and voltage angle unlike DC power flow. The proposed method is implemented and tested on 19-bus, and 25- bus unbalanced radial distribution systems and is bench marked against standard backward forward sweep power flow analysis. Though the method is developed for unbalanced radial distribution systems, it is still valid for weakly mesh connected system as well.
A Comparative Study of Haar Wavelet-Based Numerical Solution and Exact Solution of Differential Equation

Wavelet is relatively new but an emerging theory in the field of mathematics and signal processing which is being used in different engineering and mathematical tasks. From many existing wavelets, the Haar wavelet is one of the popular in mathematics and engineering due to its simplicity and compact support. In my study, the Haar wavelet operational matrix method is used to solve 2nd order ordinary differential equations as well as two-dimensional partial differential equations. Comparing to the conventional methods Haar wavelet method is easy to find the required integral to solve differential equations. By making the block pulse operational matrix and Haar wavelet matrix, Haar wavelet operational matrix can be formed. Using the Haar wavelet the differential equation can be decomposed in the Haar series, and the integral can be calculated to get the numerical solution. The numerical solution of the Haar wavelet-based method is compared to the exact solution, and it gives very little error.
Evaluation of Power Quality Impacts due to Photovoltaic Penetration in Distribution Grids via Time-Domain Simulation

In this paper, the problem of estimating the maximum penetration of distributed photovoltaic power in a distribution grid for mantaining acceptable power quality is considered. We evaluate power quality supplied by the grid in terms of root mean square value and total harmonic distortion of bus voltages, which are two important parameters taken into account by the utility in management of grid supply quality. A simple simulation model is proposed for evaluating these parameters as functions of the photovoltaic penetration factor. The model is used in a simulation of an hypotethical distribution feeder in order to validate the proposed method of estimating penetration factor limits.
A Systematic Review on Clone Node Detection in Static Wireless Sensor Networks

The recent state of the art innovations in technology enables the development of low-cost sensor nodes with processing and communication capabilities. The unique characteristics of these low-cost sensor nodes such as limited resources in terms of processing, memory, battery, and lack of tamper resistance hardware make them susceptible to clone node or node replication attack. The deployment of WSNs in the remote and harsh environment helps the adversary to capture the legitimate node and extract the stored credential information such as ID which can be easily re-programmed and replicated. Thus, the adversary would be able to control the whole network internally and carry out the same functions as that of the legitimate nodes. This is the main motivation of researchers to design enhanced detection protocols for clone attacks. Hence, in this paper, we have presented a systematic literature review of existing clone node detection schemes. We have also provided the theoretical and analytical survey of the existing centralized and distributed schemes for the detection of clone nodes in static WSNs with their drawbacks and challenges.
Automated Deployment of IoT Networks in Outdoor Scenarios using an Unmanned Ground Vehicle

Latest research in the integration of robotics and Internet of Things (IoT) systems paved the way for the development of new and potentially disruptive technologies, with applications in critical domains such as surveillance, environmental monitoring, smart agriculture and ambient intelligence in general. In this work, a robotic-aided IoT system for the automatic deployment of sensor networks is developed. Specifically, an Unmanned Ground Vehicle (UGV) is used to look for the best suitable deploying positions of the IoT nodes in extensive environments, such as in outdoor settings, based on the Received Signal Strength Indicator (RSSI) value measured between IoT devices. The system exploits accurate robot localization information obtained by integrating odometry, inertial and Global Position System (GPS) measurements. IoT nodes implement an IPv6 protocol, with IEEE 802.15. 4 (6TiSCH) technology and make use of multi-hop communications, so as to guarantee an IoT network topology that provides wide coverage. Experimental results carried out in a real outdoor context are presented to demonstrate the feasibility of the system. It is shown that compared to a manual deployment, the proposed robotic-aided approach leads to higher performance, thus increasing communication efficiency between nodes and attaining stable IoT network topology.