Miracle-dz/UniFAR-FTUs huggingface dataset:
Converter-Level Reliability of Wind Turbine With Low Sample Rate Mission Profile

The thermal dynamics of power semiconductors and power capacitors are closely related to the reliability and affect the cost of power electronic converter. However, the component loading in a wind turbine system is disturbed by many factors of the power converter, which presents various time constants from microseconds to hours. To determine the system availability in such system is a challenge and need detailed analysis. In the case of a mission profile with 1-hour sample rate, a simplified circuit model, loss model, and thermal model of the active power switches and passive capacitors are needed and described. According to the long-term electrothermal profile, the percentile lifetime of a single component can be predicted. The Weibull function based time-to-failure distribution can then be used to link from component-level to converter-level reliability. From analysis of a 2 MW wind turbine system, it can be seen that the dc-link capacitor bank dominates the converter-level reliability.
Exploration of cross-linked polyethylene material morphological structure and its dielectric properties studied by the effect of pulse voltage

Over the past few decades, an increasing interest in using polymer in the power distribution cables. Earlier studies shown that cross-linked polyethylene (XLPE) have excellent mechanical and dielectric properties. However, recent publications relate that the judgment of reliability in polymer matrix depend upon the water or electrical trees. The origination and progression of trees in the polymer morphology leads to conjecture role. The tremendous performance of the XLPE insulation materials is mostly dependable on the cleanliness. But the inclusion of impurities in the insulating materials leads to premature aging phenomena and constitutes a foremost concern for the power utilities. This has been studied in the context of presence of contaminants in the insulation and shield region, but studies applicable to contaminants such as silicon, sulphur, selenium and phosphorous have been limited. The results of a study of the polarization behavior in the insulation containing various impurities, using the proposed research techniques of pulse voltage method. This eminent method was convincingly used for the surveillance of XLPE contaminated insulation successfully.
Compressive Sensing Based Algorithm for Rotational Motion Estimation and Compensation for ISAR Imaging

Inverse Synthetic Aperture Radar Imaging (ISAR imaging) is a tool for obtaining a radar image of a target from the received signal after illuminating the target. Typically, the targets of interest are non cooperative, this means that their relative motion with respect to the observing radar is not known a priori. Compensation for the unknown target motion is essential for imaging quality. In this paper we present an algorithm that uses Compressive Sensing(CS) as a leverage to reconstruct the Range Profiles (RP) with finer resolution followed by a technique to estimate the rotational motion parameters using the contribution of all of the scatterers. We call the algorithm CS-RP-RMC.
Abstract 18643: Role for RUNX2 in Pulmonary Arterial Hypertension

Introduction: Pulmonary arterial hypertension (PAH) is a vascular remodeling disease characterized by enhanced pulmonary artery smooth muscle cells (PASMC) proliferation, calcification and suppressed apoptosis. Numerous biological pathways have been implicated in the sustainability of this phenotype, including P53/P21, Bcl-2 along with the STAT3/NFAT axis. Several studies have demonstrated that miR-204 is downregulated in PAH. Interestingly, miR-204 directly regulates the expression of the transcription factor RUNX2, which is implicated in many features characterizing PAH, including modulation of the BMPR2, P53/P21, and Bcl2 pathways. Hypothesis: Thus, we hypothesized that a miR-204 dependent upregulation of RUNX2 in PAH promotes PASMC proliferation, resistance to apoptosis and calcification. Methods/Results: Using human explanted lungs, fresh distal PAs and freshly isolated PASMC from both control and PAH patients (n=5 to 12), we demonstrated a significant (p Conclusions: Taken together, our study uncovers a new miR-204-RUNX2 axis contributing to a P53/P21-dependent pro-proliferative and Bcl2-dependent anti-apoptotic phenotype in PAH-PASMC.
Early-Stage Design Guidelines for Net-Zero-Energy Office Buildings in Tropical Climate

The rapid growth of energy consumption in buildings has imposed increasing pressure on environmental protection. Energy consumption in buildings for heating, cooling, and lighting must be reduced in all Indian cities to meet the goals set by the latest directives of the Smart Cities Mission, which require the energy consumption to be reduced by 20% and the use of renewable energy sources to be increased by 20%. These goals can be achieved by constructing net-zero-energy buildings (nZEBs) in the future. In this study, the design approaches required for constructing nZEBs in tropical climate were analyzed through nZEB case studies in a tropical region. These strategies were then applied and tested on an energy-efficient office building in Gurgaon through energy simulation. A calibrated reference model was generated to apply passive and active techniques as well as analyze the simulation results. Finally, renewable energy sources were implemented to provide energy to the building or directly into the grid, which resulted in an nZEB. The case studies and simulation indicate that for every 34–44 m² of floor area, photovoltaic panels with a power output of 1 kW are required in nZEBs.
Numerical Study of the Influence of Geometry on the Primary Fluid Instability in the Couette-Taylor

This work concerns the numerical study which aims at the influence of geometry on the primary instability of water (Pr=0.802),with physical properties are temperature dependent confined between two concentric cylinders horizontal and closed at the ends by two walls and which are adiabatic, a temperature gradient is imposed (Δ T=100°), the hot inner cylinder (T<inf>1</inf>=100°) is rotated with an angular velocity (Ω<inf>1</inf>≠0), while the colder outer cylinder (T<inf>2</inf>=100°) is fixed (Ω<inf>2</inf>=0). Three radii ratios and three aspect ratio are used (η<inf>1</inf>=0.827, η<inf>2</inf>=0.727, η<inf>3</inf>=0.627), (Γ<inf>1</inf>=37.36, Γ<inf>2</inf>=18.86, Γ<inf>3</inf>=4.67) respectively. The control parameters are the Grashof number which is fixed (Gr=10<sup>3</sup>), and varies Taylor number (2022.4<Ta<2851.9). The problem is solved by the finite volume method with numerical schemes of second-order spatiotemporal precision. The results show the first instability of the fluid (completely spiral), characterized by a variety of critical Taylor numbers, whose evolution is studied according to the ratio of radius and aspect, these two parameters accelerate or delay the attenuation of the critical Taylor number, thus the number of cells (n) occupying the entire in gap and a function of the aspect ratio.
Linear Prediction Residual-Based Constant-Q Cepstral Coefficients for Replay Attack Detection

This paper proposes a new linear prediction residual-based constant-Q cepstral coefficients (LPR-CQCC) feature for replay attack detection (RAD). The main contribution of the proposed feature is to modify conventional constant-Q cepstral coefficients (CQCC) using linear prediction residual (LPR) signal instead of the original/raw speech signal. Since the LPR signal has a distortion obtained from playback devices, leading to the a difference between actual and replayed speech signal, it is expected that extracting the proposed feature based on LPR signal may provide a promising result for RAD task. To further improve the detection performance, LPR-CQCC was also combined with original CQCC and Gammatone-scale relative phase (Gammatone-scale RP) in order to fuse the complementary advantages based on different systems at score-level. Based on Gaussian mixture model-based classifier, the results on ASVspoof 2017 database version 1 exhibited that LPR-CQCC outperformed baseline CQCC on the development set and was very close to CQCC on the evaluation set. Moreover, the score combination of the proposed feature and CQCC/Gammatone-scale RP performed better than the system which uses individual features.
Valid Kick Recognition in Smart Trunks based on Hidden Markov Model Using Doppler Radar

Smart Trunk Opener (STO) is a concept of opening and closing a vehicle's trunk or door without using keys, automatic and hands-free. STO system provides a flexible approach to the user by recognizing and classifying the hand movement or leg kick of the user and transmits the intention of the authorized user to open or close the trunk or door. However, such STO system using conventional approaches are typically sensitive to vibrations, unintentional people walking close to the trunk. In this paper, we propose to use 2D-Doppler spectrogram feature extraction method to classify valid kicks using 24-GHz Doppler radar while rejecting false alarms.
An Intelligent Irradiance Equalization Approach based on Fuzzy Logic for Small Reconfigurable PV Architecture

When the panels in a photovoltaic generator (PVG) are subjected to Partial Shading (PS), some bypass diodes may turn on, and consequently multiple peaks in the Power -Voltage (P-V) characteristic are produced which reduces the output power. For an optimal GPV power extraction, one of emerging PS mitigation techniques is to reconfigure the PVG by altering the electrical connections between panels according to prevailing conditions. This paper presents a reconfigurable PV architecture based on irradiance equalization row approach for a PVG connected on Total cross tied (TCT) scheme. The proposed technique uses fuzzy logic as an optimization tool to obtain uniform shade dispersion throughout the PVG, either keeping the same number of panels in each row or forming rows with different number of PV panels. To evaluate the effectiveness of proposed fuzzy logic controller (FLC), extensive simulations with different shade patterns are carried out on Matlab/Simulink. Thorough analysis with the help of characteristics curves and the results obtained for the full PV chain conversion are performed. Results of simulation show that the proposed reconfiguration method exhibit superior results as compared to fixed TCT interconnection and enhanced irradiance equalization (IEq) method in terms of efficiency, power loss and response time.
Design and Implementation of a Remote Monitoring Human Health System

Information technology of the Internet of things is actively implemented in all spheres of human life. One of the main directions, in addition to IIoT and Smart City, is Smart healthcare. Wearable devices, including wristbands, not only play an important role in human health monitoring, but also have great potential in driving safety. The article describes the development of a hardware-software complex for monitoring the health status of a vehicle driver, which uses portable devices to control the physiological parameters of a person and transfers data to a mobile terminal or uploads it to a cloud server for data analysis.
An IoT Based Smart Water Quality Monitoring System using Cloud

The Internet of Things (IoT) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators and connectivity which enables these things to connect and exchange data. The number of IoT devices has increased 31% year-over-year to 8.4 billion in 2017 and it is estimated that there will be 30 billion devices by 2020. Water pollution is a major environmental problem in India. The largest source of water pollution in India is untreated sewage. Other sources of pollution include agricultural runoff and unregulated small scale industry that results in polluting, most of the rivers, lakes and surface water in India. In this paper, An IoT Based Smart Water Quality Monitoring System using Cloud and Deep Learningis proposedto monitor the quality of the water in water-bodies. In conventional systems, the monitoring process involves the manual collection of sample water from various regions, followed by laboratory testing and analysis. This process is ineffective, as this process is arduous and time-consuming and it does not provide real-time results. The quality of water should be monitored continuously, to ensure the safe supply of water from any water bodies and water resources. Hence, the design and development of a low-cost system for real-time monitoring of water quality using the Internet of Things (IoT) is essential. Monitoring water quality in water bodies using Internet of Things (IoT) helps in combating environmental issues and improving the health and living standards of all living things. The proposed system monitors the quality of water relentlessly with the help of IoT devices, such as, NodeMCU. The in-built Wi-Fi module is attached in NodeMCU which enables internet connectivity transfers the measured data from sensors to the Cloud. The prototype is designed in such a way that it can monitor the number of pollutants in the water. Multiple sensors are used to measure various parameters to assess the quality of water from water bodies. The results are stored in the Cloud, deep learning techniques are used to predict whether the water suitable or not.
Design of Portable Galvanic Skin Response Sensor for Pain Sensor

This paper describes the design of a portable galvanic skin response (GSR) sensor for pain sensors. GSR sensors can measure electricity change in the skin. The signal results obtained are used to collect autonomic nerve responses as a function parameter of sweat ties. This response can increase the electrical conductance or increase the resistance in the palm. GSR is a psychological change in the skin resulting from the changes in sweat gland activity; at the time, the sweat glands will be active when the body is in a state of pain. This prototype was made to measure the conductivity of the skin, and the activity based on light blows to the arms. This prototype was made to measure the conductivity of the skin, and the activity based on light blows to the arms. Measurement was delayed by 1 minute to let the body rest before receiving physical impulse. The total time taken for the measurement is 4 minutes. This prototype uses an Arduino Uno microcontroller as an analog to digital signal processor, and the results will be sent to a PC using Bluetooth HC-05 via a serial plotter for display. The purpose of this research is to develop a prototype portable GSR sensor for pain sensors in the human body. The additional value of this research is to use Bluetooth as a data transfer to make it easier to use. The results of this study succeeded in developing a prototype portable galvanic skin response with good sensor sensitivity by verifying the data using a commercial galvanic sensor module.
An 8-bit RRAM based Multiplier for Hybrid Memory Computing

Hybrid-memory computing can reconcile the speed of in-memory computing and the power of near-memory. An RRAM based multiplier for hybrid-memory computing is proposed to balance the efficiency and flexibility. It is implemented in standard RRAM model and 65nm CMOS technology. The simulation results show that the proposed RRAM based multiplier achieves a calculation speed of 1.6us and 1.32 mW from 1V power supply.
Study of Control for Grid Connected Transformerless Photovoltaic System with Improved Modulation Method

This paper discusses the modulation method to suppress the ground current for the single-phase grid-connected photovoltaic inverters. Hybrid modulation (HPWM) is a commonly modulation method for the single-phase inverters. But its application in the non-isolated photovoltaic grid-connected inverters will lead to large ground current. This paper establishes the common-mode model for the parallel single-phase gridconnected inverters with a front-end DC-DC boost converter. The expression of the ground current is given. An improved hybrid modulation method is proposed. The influence of the modulation mode and the circuit parameters on the inter-phase circulating current is also analyzed. The MPPT control algorithm is selected and the control loop for the system is designed based on PI control and repetitive control. The simulation results are given to verify the design based on Matlab\Simulink.
Statistical Methods for Fast LOS Detection for Ranging and Localization

Received signal strength indication (RSSI) data is often used for ranging and localization algorithms, where the data may be obtained using Bluetooth Low Energy (BLE) radios. In the BLE protocol, when a device is in advertising mode, it is possible to obtain RSSI values. However, these RSSI values are noisy and can often fluctuate due to multipath effects, which reduces the accuracy and reliability of ranging and localization. Additionally, the effectiveness of RSSI ranging degrades when there is an absence of line of sight (LOS) or when devices are in rich scattering environments. Therefore, the detection of LOS plays a very significant role in indoor localization and room reconstruction.In this paper, we present algorithms to detect whether there is LOS present between a transmit-receive pair being used for ranging. Our focus in this paper is fast detection with a minimum number of samples. We use measurements such as the energy distance and Mahalanobis distance, and benchmark our results against the Neyman-Pearson detector. Numerical simulations are used to validate our algorithms.
Efficient moving targets chirp rate estimation method in synthetic aperture radar

Synthetic aperture radar (SAR) combined with moving targets indication has attracted much attention in both civil and military applications. As an important issue, the chirp rate of moving targets estimation plays a key role in moving targets imaging and then recognition. Conventionally, the searching-based method introduces the hard tradeoff between estimation accuracy and computation complexity. Different from this, a novel method is proposed by not searching but computing. And the geometry information in the transformed domain is exploited to reduce the computation complexity from O(MN) to O(2N). Simulation and real data experiments demonstrate the effectiveness of the proposed method.
The Voltage Sag Mitigation Method Based on Hybrid SMES-BES Dynamic Voltage Restorer(DVR)

The research of voltage sag mitigation method is an important task. A new method using hybrid energy storage (HES) strategy is proposed, which consists of Superconducting Magnetic Energy Storage (SMES), Battery Energy Storage (BES) and Dynamic Voltage Restorer (DVR). In addition, a power allocation optimization strategy for HES-based DVR is proposed and validated using the life cycle cost (LCC) theory. LCC is the objective function, which can be used to establish the optimization model. The constraint conditions of the model include the power of the throughput, the state of charge (SOC) and the power allocation optimization strategy. Finally, the cost analysis and the compensation performance are implemented using the SEMI F47 curve for the single BES, single SMES and SMES-BES device. The compared results show the superiority and effectiveness of the proposed method on the voltage sag mitigation.
Compact Wilkinson Power Divider Using Composite Right/Left-Handed Transmission Line on CMOS Process

This paper shows the design and analysis of CMOS Wilkinson power divider using composite right/left-handed transmission lines. The effects of lossy components in the circuit are also analyzed. The circuit is then implemented on a 0.18-μm CMOS process. The return losses are less than −16 dB, the isolation is better than −26 dB, and the insertion losses are better than −5.15 dB at the frequencies of interest. Its chip size is 0.5 mm2 (9.9e−5λo2) including testing pads.
Supercomputer Networks in the Datacenter: Benchmarking the Evolution of Communication Granularity from Macroscale down to Nanoscale

In the Datacenter, a supercomputer network refers to the interconnections between the clustered processing nodes within a single supercomputer. In this paper, we primarily aim to describe how in supercomputers, as they evolve, the granularity of this inter-node communication continues to scale down, as a direct result of the processing nodes scaling down from full-sized clustered computers (and servers) to interconnected processor cores and even smaller reconfigurable logic cells. Hence, we start by first describing our exploration of the four generations of supercomputing and how they have evolved over the years from macroscale packet-switched coarse-grained cluster computing and grid computing, to conventional supercomputing, and then to fine-grained supercomputer Networks-on-Chip (NoC), and finally, to emerging fine-grained nanoscale NoC FPGA (Field Programmable Gate Arrays) supercomputer-on-chip as we see today. Apart from this, in this work, we also aim to demonstrate and analyze the results of benchmarking the Mandelbrot Set performance on a 3rd generation supercomputer, which is the Adapteva's 16-core Epiphany supercomputer NoC. On the basis of our study we can come to an inference that the next-generation supercomputing-on-chip will more likely depend on the fine-tuning between multi-core NoCs and high-end FPGA co-processors built into these NoCs.
An Area and Delay Efficient Logarithmic Multiplier

Multiplication is an ubiquitous operation in growing set of media processing applications (graphics, audio, video, and image). Many of these applications, however, possess an inherent quality of error resilience. Thus the multipliers, that are not very precise but return an approximate value, can be utilized in such applications. Such units, it may be anticipated, may result in area savings while also resulting in reduced power consumption. In recent years, logarithmic number system (LNS) has been increasingly used as an alternative to the binary number system as it converts multiplication to addition resulting in simplified hardware. However, they suffer from inherent error and any efforts in improving their accuracy would help find their increased usage in arithmetic computations with efficient hardware. In this paper, a method that combines Mitchell’s approximation with a hardware pruning technique that leads to an area efficient multiplier architecture without compromise on precision. Simulations carried out prove that the proposed multiplier architectures are efficient both in area and delay when compared to existing designs.
A Comparative Study of Support Vector Machine and Naive Bayes Classifier for Sentiment Analysis on Amazon Product Reviews

This paper represents a comparison between two machine learning approaches for analyzing the sentiment of the customers’ reviews on Amazon products. Eventually, reviews of a product help the customers to understand the product quality. Incorporating multiple product review factors, including product quality, content, time of the review related to product durability, and historically older positive customer reviews will affect product rankings accordingly. Conversely, the manual approach on a large scale comment is time-consuming leading to an inefficient and unproductive way. In this era of artificial intelligence, machine learning is the most convenient way to train the neural network. So it would be much easier to go through thousands of comments if a model were adopted to polarize those reviews and learn from them. In this research work, firstly, the sentiment of the consumer has been analyzed by the Naive Bayes classifier. Meanwhile, the support vector machine(SVM) has classified the sentiments of the users in binary categories. Hence, the data has been passed through the network model after the preprocessing method named term frequency(TF) and inverse document frequency(IDF) lead to evaluate the feature. To sum up, the goal of this research is to find comparatively better machine learning approaches among SVM and Naive Bayes classifier based on statistical measurement.