Experimental Analysis of Power Quality in a Power Substation Used in a Residential and Educational Sector

The article describes the experimental measurements made at a low-voltage residential and educational power substation, in a point of common coupling. Two groups of experiments were carried out, in normal conditions and during the COVID-19 pandemic. Measurements were made using a power quality analyzer and include phase RMS voltages and line currents, total harmonic distortion and unbalance of phase voltages and line currents, neutral current, active, reactive and apparent powers, power factors and displacement power factors, Fresnel diagrams, and harmonic spectra. Measurements indicate significant differences of power quality indicators between the two measurement groups. © 2023 IEEE.

Vaccine Supply Forecasting and Optimization using Deterministic and Probabilistic Approaches

When the pandemic was at its peak, it was a quite difficult task for the government to schedule vaccine supply in various districts of a state. This task became further difficult when vaccines were required to be supplied to various Covid Vaccination Centers (CVCs) at a granular level. This is because there was no data regarding the trend being acquired at each CVC and the population distribution is non-uniform across the district. This led to the arousal of an ambiguous situation for a certain period and hence mismanagement. Now that we have sufficient data across each CVC, we can work on a time series analysis of vaccine requirements in which we can essentially forecast the number of administered doses and optimize the wastage at all atomic CVC levels. © 2023 IEEE.

Forecasting of COVID-19 Outbreak in India: A Time Series Analysis

The World Health Organization (WHO) declared the status of coronavirus disease 2019 (COVID-19) to a global pandemic on March 11, 2020. Since then, numerous statistical, epidemiological and mathematical models have been used and investigated by researchers across the world to predict the spread of this pandemic in different geographical locations. The data for COVID-19 outbreak in India has been collated on daily new confirmed cases from March 12, 2020 to April 10, 2021. A time series analysis using Auto Regressive Integrated Moving Average (ARIMA) model was used to investigate the dataset and then forecast for the next 30-day time-period from April 11, 2021, to May 10, 2021. The selected model predicts a surge in the number of daily new cases and number of deaths. An investigation into the daily infection rate for India has also been done. © 2023 The authors and IOS Press.

Forecasting Overtime Budgets for Naval Fleet Maintenance Facilities Using Time-Series Analysis During Transient System States

In 2019, predictive models were initially developed to attempt to better predict an annual budget for staffing overtime hours within a Royal Canadian Navy (RCN) fleet maintenance facility. The H20.ai open-source framework was used, and models were implemented in the R programming language. Model validation at the time showed the predicted hours were within 5% error rate compared to the actual data. However, when it came to re-apply the process to fiscal year 2020/2021 data, the impact of the COVID-19 pandemic on factors such as the workforce and the logistics supply chain, changed the system dynamics sufficiently that the autoML algorithms had difficulty generating accurate estimates. Therefore, it was decided to examine how times series forecasting methods would predict overtime hours at the fleet maintenance facility. Since historical daily data were readily available, the open-source Prophet model developed by Facebook was used because it can incorporate multiple seasonal patterns, as well as variable holiday effects. The models were tested on fiscal years 2019/2020 and 2020/2021, which showed over 90% accuracy in predicting the total overtime hours. The revised approach in this follow-on study was used to provide financial comptrollers with a prediction for fiscal year 2021/2022. © 2023, Springer Nature Switzerland AG.

A Tri-Model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study

The impact of COVID-19 and the pressure it exerts on health systems worldwide motivated this study, which focuses on the case of Greece. We aim to assist decision makers as well as health professionals, by estimating the short to medium term needs in Intensive Care Unit (ICU) beds. We analyse time series of confirmed cases, hospitalised patients, ICU bed occupancy, recovered patients and deaths. We employ state-of-the-art forecasting algorithms, such as ARTXP, ARIMA, SARIMAX, and Multivariate Regression models. We combine these into three forecasting models culminating to a tri-model approach in time series analysis and compare them. The results of this study show that the combination of ARIMA with SARIMAX is more accurate for the majority of the investigated regions in short term 1-week ahead predictions, while Multivariate Regression outperforms the other two models for 2-weeks ahead predictions. Finally, for the medium term 3-weeks ahead predictions the Multivariate Regression and ARIMA with SARIMAX show the best results. We report on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), R-squared ((Formula presented.)), and Mean Absolute Error (MAE) values, for one-week, two-week and three-week ahead predictions for ICU bed requirements. Such timely insights offer new capabilities for efficient management of healthcare resources. © 2023 by the authors.

Month-Wise Investigation on Residential Load Consumption Impact during COVID-19 Period on Distribution Transformer and Practical Mitigation Solution

This paper investigates the month-wise impact of COVID-19 conditions on residential load due to people's presence at home during office hours in Memphis city, Tennessee, USA. The energy consumption patterns of four consumers are analyzed based on the data available from pre-COVID to continuing COVID-19 situations. It is observed that the energy consumptions from April 2020 to June 2021 for all families have increased during office hours than that of pre-COVID months. Therefore, the impacts of the increased loads on distribution transformer are analyzed in terms of hottest spot, top-oil temperature, and loss of transformer life. Moreover, an experimental setup is made to produce the harmonics in currents of non-linear residential load which has detrimental effects on temperature rise of distribution transformer. In addition, this work proposes implementation of harmonic filter at the load side considering the impact of harmonics produced by loads to reduce the temperature rise due to the increased load consumption and presence of harmonics in currents produced by the load. The rise in temperatures and the loss of life of distribution transformer with and without the proposed solutions are simulated in MATLAB to show the efficacy of the proposed solution. Moreover, cost value analysis among different methods, which can be implemented to reduce the adverse impact on distribution transformer, are provided to rank the available methods. © 2023 by the authors.

Development of practice support system for violin bowing

Recently, there has been an increase in the demand for playing musical instruments at home, primarily because of the restrictions enforced due to the COVID-19 pandemic. We propose a system that can automatically estimate the angle between the bow and the string of a violin during playing from the playing sound and provide feedback to the player to realize a violin bowing practice support system. In this study, the automatic estimation of the angle performed using harmonics structure of the violin tone. The angle between the bow and the string were estimated using the ratio values between the harmonics level in the low frequency band and the level in the total frequency band. It was confirmed that there was a significant difference in the ratio between the sound played at a right angle and the sound played at an acute angle of approximately 20-30 degrees from the right angle. We are developing a practice support system using this method. The system judges the bowing angle to be good or bad and provides feedback to the player using LEDs. In full paper, we will describe the bowing skill identification method on the system in detail and report the evaluation results of the system. © 2022 IEEE.

Improving the quality of the power supply during the period of covid-19 using plug-in electric vehicles and artificial intelligence

COVID-19, corona virus disease, has been ravaging the world since the last quarter of 2019. To address this threat, the World Health Organization (WHO) has established a list of priority medical equipment that must be used by hospitals and clinics. These equipments are in fact non-linear harmonic-producing loads that have a detrimental effect on the devices and degrade the quality of the power supply. In this paper a parallel active filter: a neuronal harmonic compensation strategy based on plug-in electric vehicles (PEVs) connected to the charging stations in the parking lot of hospitals and clinics and artificial intelligence is proposed in order to improve the quality of power supply thus protecting medical equipment in order to save lives. This strategy will be simulated in MATLAB and the results will be presented as evidence of its effectiveness. © 2022 IEEE.

COVID-19 Pandemic vis-à-vis Zamboanga Peninsula Aviation: A Predictive Analysis

The use of time-series analysis to examine aviation data trends through time comes crucial in planning its future. The prophet is an additive model that fits non-linear patterns. It functions best with historical data from various seasons and time series with significant seasonal impacts. This research looked closely into the aviation data in Zamboanga Peninsula, Jolo, and Tawi-Tawi to give a clearer picture of its impact on the sector and forecast passenger and aircraft movement in the coming months to see whether the impact of the opening in the aviation industry can be sustained. The final data comprise 51 data points for flight arrivals and departures and 51 data points for passenger arrivals and departures. Data show the decline in passengers and aircrafts arriving and departing in major airports in Zamboanga Peninsula, Jolo, and Tawi-Tawi during the pandemic. However, an increasing trend was observed years after the pandemic hit the region. Findings during the training and testing phase revealed that different models attained varied results;however, there are models which attained a higher degree of accuracy as depicted in the RMSE and R2. This indicates that predicting passenger and aircraft movement using models with higher accuracy is similar to real data thus, it is viable in predicting future values. Forecasting results further show a gradually increasing trend of aircraft and passenger arrivals in the major airports in Zamboanga Peninsula, Jolo, and Tawi-Tawi despite some observed smaller forecasted values. © 2022 IEEE.

Analysis of Pandemic Data in Brazil The relationship between vaccinated and hospitalized in Recife

This paper consist in a time series analysis of Covid-19 vaccination data and hospitalization data for suspected or confirmed Severe Acute Respiratory Syndrome cases for Covid-19 among the citizens of Recife city, located in Pernambuco, in the northeast region of Brazil. This study aims to demonstrate the effectiveness of vaccination to combat severe cases of Covid-19 in a population. For this, population vaccination data against Covid-19 and data on severe cases were arranged in time series for correlation study. The results show that the adherence in residents to the vaccination against Covid-19 can directly reflect a reduction of serious cases, even in front of challenges such as the relaxation of restrictive measures, the pandemic politicization, the high contagion of the variants, and the spread of misinformation about vaccines. The research also demonstrates that establishing a technological policy to combat pandemics may make a difference by generating consistent and reliable data for science in the race for answers, bringing speed and organization in the population immunization. © 2022 IEEE Computer Society. All rights reserved.

Modelling the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis

Since early 2020, strict restrictions on non-essential movements were imposed globally as countermeasures to the rapid spread of COVID-19. The various containment and closures strategies, taken by the majority of countries, have directly affected travel behavior. This paper aims to investigate and model the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis. Driving and walking data, as well as confinement policies were collected from February 2020 to February 2021 for twenty-five European countries and were implemented into Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) time-series models. Results reveal a significant number of models in order to estimate mobility during pandemic almost in every country of the study. School closing was found to be the most important exogenous factor for describing driving or walking, while “Stay at home” orders had not a significant effect on the evolution of people movements. In addition, countries which suffered the most due to the pandemic indicated a strong correlation with the restrictive measures. No time-series models were found to describe the countries which implemented weak confinement policies. © 2022 Marianthi Kallidoni, Christos Katrakazas, George Yannis.

Time Series Analysis of COVID-19 Cases in Humboldt County

The time series of COVID-19 daily cases in the U.S is analyzed by utilizing the county-level temporal data, from January 22, 2020 to October 18, 2021. Autocorrelation and partial autocorrelation show that time series of daily cases in Humboldt county has a 7-day seasonal pattern. Visualization and augmented Dickey-Fuller test show that time series of daily cases in Humboldt county is non-stationary. The seven-order difference reveals that the time series is stationary. There is a moderate positive correlation between daily cases and fully vaccination rate. Clustering analysis describes 33 counties have similar daily case pattern with Humboldt County by standard deviation of 0.003. This analysis can be used for future time-series forecasting and planning. © 2021 IEEE.

Machine Learning Based Time Series Analysis for COVID-19 Cases in India

The World Health Organization declared the Coronavirus Infection, or COVID-19, to be widespread. One of the most appropriate methodologies for COVID-19 is time series analysis. The most appropriate technique for COVID-19 is time series analysis. It can be applied to Recognizing Information Patterns and Predicting Insights. The paper summarises the components of time series using the COVID-19 dataset for India as an example of one of the most important methodologies in predictive analytics. Time series models are chosen because they can predict future outcomes, comprehend prior outcomes, provide strategy recommendations, and much more. These common goalrists of temporal arrangement modelling do not differ significantly from those of cross-sectional or board data modelling. Machine Learning may be a well-known fact that it is an excellent technique for imagining, discourse, and standard dialect management for a large clarified accessible dataset. The results for confirmed, recovered, and death cases are presented in this study. © 2022 IEEE.

Accent Classification of Native and Non-Native Children using Harmonic Pitch

To combat the Covid-19 outbreak, the education system shifted away from the classroom to distinct e-learning on digital platforms, which made effective use of voice-based recognition systems, especially for preliterate children. Children’s speech recognition systems face multiple challenges owing to their immature vocal tracts, and they demand more intelligence due to the fact that children with diverse accents utter words differently. Accent refers to a unique style of pronouncing a language, particularly one associated with a specific nation, place, or socio-economic background. This paper aims to extract reliable acoustic and prosodic speech cues of accent for classification of native and non-native preschool children using harmonic pitch estimation along with Mel Frequency Cepstral Coefficients (MFCCs) to train the k-Nearest Neighbour (k-NN) classifier. The experimental results reveal that the proposed robust model outperforms various feature extractors in accent classification of native and non-native children in terms of accuracy & F-Measure and more discriminate against noisy environments. © 2022 IEEE.

Coverage Prediction of Novel Coronavirus Vaccine Based on Deep Learning Time Series Analysis

Since the beginning of 2020, COVID-19 has swept the world, bringing many inconveniences and even threats to human life. Through medical scientists' constant study, the vaccine was finally developed earlier this year. According to mathematical and medical modelling, if novel coronavirus transmission is counted as three (i.e., one patient can infect three), 70% of the vaccinations will be required for protection to be substantially achieved. To tackle the issue of vaccine coverage prediction, this paper proposed three-time series analysis models, which can be utilized to analyze and predict the COVID-19 Vaccine coverage worldwide with the application of machine learning. For a long time, statistical methods have mostly solved time series prediction problems (AR, AM, ARMA, ARIMA). Mathematicians try to constantly refine these techniques to constrain stationary and non-stationary time series, but the results are often not very good. In this paper, we propose a method based on deep learning, using CNN-LSTM, VAE-LSTM, DeepAR, and other models to analyze and predict the data of vaccine coverage rate. The experimental results demonstrated that the RMSE of LSTM, CNN-LSTM, VAE-LSTM and DeepAR are 9.295522e+07, 1.028151e+07, 1.857031e+06 and 1.961001e+07 separately. © 2021 IEEE.

Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams

Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data. [ FROM AUTHOR] Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

FFT Spectrum Spread With Machine Learning (ML) Analysis of Triaxial Acceleration From Shirt Pocket and Torso for Sensing Coughs While Walking

Early detection of respiratory distress, marked by coughing associated with pandemics such as Covid, severe acute respiratory syndrome, and influenza, has become important for early public health preparedness. Recognizing respiratory distress from data pooled from accelerometers and other sensors common in phones/wearables can be a useful tool in tracking diseases in larger populations. However, detecting low-/medium-intensity coughs, which are a precursor to influenza/Covid, are harder to detect in the presence of human activity especially walking. In this letter, we study spectrum-spread features of triaxial accelerometer signals measured from the human torso during coughs. In particular, we analyze the vestigial sideband like spurs that cough-induced motion of the torso produces alongside walking signal between 0.2 and 2 Hz and propose the use of its spectral spread square metric in discerning coughs during walking action in test subjects of different sizes. Unlike prior works on time-domain measurements or spectral summation (units: g) in multiple bands, this work uses bandwidth, i.e., spectrum-spread features of acceleration signals (units: Hz(2)) to detect low to medium intensity coughs from a single accelerometer worn on the chest or shirt pocket or stomach. Acceleration signals measured at these points in five test subjects of varying heights, age, and weight show its median square spectral spread increase prominently along Y (up-down) and Z axes (front-back) from between 0.016-0.0167 Hz(2) to between 0.023-0.026 Hz(2) with a cough-detection threshold observed at 0.02 Hz(2) for all axes. Using a machine learning (ML) classification model with these spectral spread features results in cough detection accuracy of 92.5, 92.2, and 91.5% with k-nearest neighbors (kNN), and 94.3, 96.1, and 93.6% using Support Vector Machine (SVM) ML models for all three torso points especially shirt pocket where phones are commonly worn.