Voice input for authentication

Recent years have seen a proliferation of systems designed to respond to voice, e.g., smart speakers, smartphones, and other voice-activated devices. Such systems are convenient and intuitive to use. However, they also open the possibility for malicious actors to gain control of sensitive information through fraudulent use of speech commands. For example, a malicious actor can spoof the voice recognition system through a recording of the user’s voice or by using a computer to synthesize the speech mimicking the user. The techniques of this disclosure thwart such attacks by providing a random phrase for the user to repeat and verifying the resulting audio and transcription for authenticity. Spoofed audio sounds choppy and exhibits sharp transitions, while authentic audio is smooth and lacks unnatural artifacts. A trained machine learning model is used to tell the difference

The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: A machine learning approach

Hybrid electric vehicles and portable electronic systems use supercapacitors for energy storage owing to their fast charging discharging rates, long life cycle, and low maintenance. Specific capacitance is regarded as one of the most important performance-related characteristics of a supercapacitor's electrode. In the current study, Machine Learning (ML) algorithms were used to determine the impact of various physicochemical properties of carbon-based materials on the capacitive performance of electric double-layer capacitors. Published experimental datasets from 147 references (4899 data entries) were extracted and then used to train and test the ML models, to determine the relative importance of electrode material features on specific capacitance. These features include current density, pore volume, pore size, presence of defects, potential window, specific surface area, oxygen, and nitrogen content of the carbon-based electrode material. Additionally, categorical variables as the testing method, electrolyte, and carbon structure of the electrodes are considered as well. Among five applied regression models, an extreme gradient boosting model was found to best correlate those features with the capacitive performance, highlighting that the specific surface area, the presence of nitrogen doping, and the potential window are the most significant descriptors for the specific capacitance. These findings are summarized in a modular and open-source application for estimating the capacitance of supercapacitors given, as only inputs, the features of their carbon-based electrodes, the electrolyte and testing method. In perspective, this work introduces a new wide dataset of carbon electrodes for supercapacitors extracted from the experimental literature, also giving an instance of how electrochemical technology can benefit from ML models.Comment: Manuscript and associated Supplementary Informatio

Preventive maintenance for heterogeneous industrial vehicles with incomplete usage data

Large fleets of industrial and construction vehicles require periodic maintenance activities. Scheduling these operations is potentially challenging because the optimal timeline depends on the vehicle characteristics and usage. This paper studies a real industrial case study, where a company providing telematics services supports fleet managers in scheduling maintenance operations of about 2000 construction vehicles of various types. The heterogeneity of the fleet and the availability of historical data fosters the use of data-driven solutions based on machine learning techniques. The paper addresses the learning of per-vehicle predictors aimed at forecasting the next-day utilisation level and the remaining time until the next maintenance. We explore the performance of both linear and non-liner models, showing that machine learning models are able to capture the underlying trends describing non-stationary vehicle usage patterns. We also explicitly consider the lack of data for vehicles that have been recently added to the fleet. Results show that the availability of even a limited portion of past utilisation levels enables the identification of vehicles with similar usage trends and the opportunistic reuse of their historical data

An improved algorithm to remove dc offsets from fault current signals

Fault current signals that are processed by digital relays consist of DC, fundamental, and harmonic components. Filtering algorithms are necessary to eliminate the DC and harmonic components from these signals. Several algorithms have been proposed for this task which vary in their accuracy, response time, and computational burden. The conventional Discrete Fourier Transform (DFT) can eliminate harmonics and is commonly used to estimate the fundamental frequency phasor. But its accuracy is lower as it does not filter the DC offset. Other algorithms including variants of DFT attempt to improve the accuracy and response time. This paper proposes a technique that takes into account the exponential variation of the DC offset and more accurately determines the fundamental component. The effectiveness of this method is evaluated by simulation on a 2-machine system and also compared against existing phasor measurement methods. Simulations confirm that the proposed method can more accurately estimate the fundamental component compared to the existing methods

A flexible protection scheme for an islanded Multi-Microgrid

Multi-Microgrids (MMGs) have been proposed to connect distributed generators (DG), microgrids (MG), and medium-voltage (MV) loads with the distribution system. A flexible protection scheme that enables an islanded MMG to continue operation during fault conditions is yet to be developed. In this paper, a protection scheme for an islanded MMG that utilises MG controllers and communication links is proposed. The MMG model used includes two MGs connected to the distribution system. Each MG consists of diesel, wind, and photovoltaic (PV) microsources. The effectiveness of the proposed protection scheme is evaluated by simulation

An improved protection strategy for microgrids

Microgrids (MG) enable the integration of low capacity renewable energy resources with distribution systems. A recently proposed protection scheme for MGs utilising undervoltage, High Impedance Fault (HIF) detection, directional protection modules, and communication links significantly reduces the fault clearing time compared to previous schemes. In this paper, the effect of replacing undervoltage protection with differential protection in a scheme that also contains HIF and directional protection modules is studied. The MG model used in this study includes a diesel, wind, and two photovoltaic (PV) microsources. The alternative protection schemes are evaluated by simulation. It is found that the protection scheme consisting of differential, HIF detection, and directional protection modules is more effective compared to the alternative in protecting the MG from some fault conditions such as the phase-A-to-ground, phase-B-to-C, and phase-B-to-C-to-ground

Protection issues in microgrids and multi-microgrids

Conventional power generation from fossil fuels contributes to the problems of climate change and low energy efficiency. These problems can be partly addressed by interconnecting distributed generators (DGs) with the distribution system to supply electric power from renewable energy resources such as photovoltaic (PV), wind, biomass, and fuel cells. One way of integrating a large number of DG sources with the distribution system is to interconnect them with low-voltage (LV) loads to form a microgrid (MG). Multi-microgrids (MMGs) increase the load capacity of the distribution system by integrating several MGs, DGs, and medium-voltage (MV) loads with the distribution system. This chapter discusses a number of challenges in the protection of active distribution systems, MGs, and MMGs. The existing protection schemes and coordination strategies to address these challenges are briefly reviewed in this chapter. This is followed by the presentation of a new protection scheme for MMGs. The existing protection scheme for MMGs originally proposed by the EU More Microgrids Project disconnects all the DG sources when fault conditions occur. As a result, this protection scheme prevents the continued operation of the MMG during fault conditions. The MMG also experiences varying fault current levels due to contributions from DGs and MGs compared to single grid-connected MGs. A new adaptive overcurrent protection scheme for MMGs containing MGs with varying generation and load is proposed in this chapter. It automates the setting of relay characteristics to deal with the variations of generation and load as well as adjust the tripping time delays based on the fault current levels. It also addresses the problems due to the presence of DGs such as the blinding of protection, failed reclosing, and false tripping. The effectiveness of this protection scheme is evaluated by simulation for the MMG scenarios containing different combinations of net load (NL-) and net generator (NG-) MGs

The NetMob23 Dataset: A High-resolution Multi-region Service-level Mobile Data Traffic Cartography

Digital sources have been enabling unprecedented data-driven and large-scale investigations across a wide range of domains, including demography, sociology, geography, urbanism, criminology, and engineering. A major barrier to innovation is represented by the limited availability of dependable digital datasets, especially in the context of data gathered by mobile network operators or service providers, due to concerns about user privacy and industrial competition. The resulting lack of reference datasets curbs the production of new research methods and results, and prevents verifiability and reproducibility of research outcomes. The NetMob23 dataset offers a rare opportunity to the multidisciplinary research community to access rich data about the spatio-temporal consumption of mobile applications in a developed country. The generation process of the dataset sets a new quality standard, leading to information about the demands generated by 68 popular mobile services, geo-referenced at a high resolution of $100\times100$ $m^2$ over 20 metropolitan areas in France, and monitored during 77 consecutive days in 2019

Echocardiographic nomograms and Z-scores for term Indian neonates

Background : The availability of nomograms is crucial for the correct interpretation of pediatric and neonatal echocardiograms. Echocardiographic Z-score applications/websites use Western nomograms as reference, which may not be an appropriate standard for gauging Indian neonates. Currently available Indian pediatric nomograms either have not included neonates or have not been specifically designed for neonates. This gross underrepresentation of neonates renders available nomograms unreliable for use as standards for comparison. Objectives : The objective of this study was to collect normative data for the measurement of various cardiac structures using M-Mode and two-dimensional (2D) echo in healthy Indian neonates and to derive Z-scores for each measured parameter. Methods : Echocardiograms were performed on healthy term neonates (within first 5 days of life). Birth weight and length were recorded, and body surface area was calculated using Haycock's formula. Twenty M-mode and 2D-echo parameters were measured (including left ventricular dimensions, atrioventricular valves, and semilunar valves' annuli sizes, pulmonary artery and branches, aortic root, and arch). Results : We studied 142 neonates (73 males) with a mean age of 1.83 ± 1.12 days and mean birth weight of 2.89 ± 0.39 Kg. Regression equations with linear, logarithmic, exponential and square root models were tested to select the best model of fit for the relationship between birth weight and each echocardiographic parameter. Scatter plots and nomogram charts with Z-scores were prepared for each echocardiographic parameter. Conclusions : Our study provides nomograms with Z-scores for term Indian neonates weighing between 2 kg and 4 kg at birth, within first 5 days of life, for a set of echocardiographic parameters that are frequently used in clinical practice. This nomogram has poor predictability for babies at extremes of birth weight. There is a need for further indigenous studies to include neonates at extremes of weight, both term, and preterm