scholarly journals Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR

2021 ◽  
Vol 15 (4) ◽  
pp. 1-28
Author(s):  
Ajitkumar Sureshrao Shitole ◽  
Manoj Himmatrao Devare
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
Local System ◽  
Sensor Data ◽  
Physical Location ◽  
Squared Error ◽  
And Storage ◽  
Auto Regression

This study shows an enhancement of IoT which gets sensor data and performs real-time face recognition to screen physical areas to find strange situations and send an alarm mail to the client to make remedial moves to avoid any potential misfortune in the environment. Sensor data is pushed onto the local system and GoDaddy Cloud, whenever the camera detects a person to optimize the Physical Location Monitoring System by reducing the bandwidth requirement and storage cost onto the Cloud using edge computation. The study reveals that Decision Tree (DT) and Random Forest give reasonably similar macro average f1-score to predict a person using sensor data. Experimental results show that DT is the most reliable predictive model for the Cloud datasets of three different physical locations to predict a person using timestamp with an accuracy of 83.99%, 88.92%, and 80.97%. This study also explains multivariate time series prediction using Vector Auto Regression that gives reasonably good Root Mean Squared Error to predict Temperature, Humidity, Light Dependent Resistor, and Gas time series.

scholarly journals Correlation Determination between COVID-19 and Weather Parameters Using Time Series Forecasting: A Case Study in Pakistan

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Humera Batool ◽  
Lixin Tian
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Detailed Examination ◽  
Percentage Error ◽  
Mortality Data ◽  
Squared Error ◽  
Temperature And Humidity ◽  
Weather Parameters

Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan’s COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection.

Real-Time Wellhead Bending Moment Measurement Using Motion Reference Unit (MRU) Sensors and Machine Learning

2018 ◽  
Author(s):  
Rohit Srikonda ◽  
Rune Haakonsen ◽  
Massimiliano Russo ◽  
Peri Periyasamy
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Real Time ◽  
Mean Squared Error ◽  
Bending Moment ◽  
Sensor Data ◽  
Fe Model ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Tension Time

In order to facilitate real-time monitoring of accumulated wellhead fatigue damage, it is necessary to measure the wellhead bending moment in real-time. This paper presents a novel method to estimate the wellhead bending moment in realtime using acceleration and inclination data from the motion reference unit (MRU) sensors installed on BOP and LRJ, riser tension data and a trained neural network model. The method proposed in this paper is designed with a Recursive Neural Network (RNN) model to be trained to estimate the wellhead bending moment in real-time with high accuracy based on motion MRU sensor data and riser tension time series of a few previous cycles. In addition to the power of modeling complex nonlinearities, RNNs provide the advantage of better capturing the dynamic effects by learning to recognize the patterns in the sensor data and riser tension time series. The RNN model is trained using virtual sensor data and wellhead bending moment from a finite element (FE) model of the drilling riser subjected to irregular wave time domain analyses based on a training matrix with limited number of significant height (Hs) and peak period (Tp) combinations. Once trained, tested and deployed, the RNN model can make real-time estimation of the wellhead bending moment based on MRU sensor data and riser tension time series. The RNN model can be an efficient and accurate alternative to a physical model based on the indirect method for real-time calculation of wellhead bending moment using real-time sensor data. A case study is presented to explain the training procedures for the RNN model. A set of test cases that are not included in the training dataset are used to demonstrate the accuracy of the RNN model using Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE) and coefficient of determination (R2) as a metrics.

scholarly journals Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series

2021 ◽  
Author(s):  
Ginno Millán ◽  
Román Osorio-Comparán ◽  
Gastón Lefranc
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
High Speed ◽  
Mean Squared Error ◽  
Computer Network ◽  
Accurate Estimation ◽  
Minimum Length ◽  
Squared Error ◽  
In Series ◽  
Speed Computer

<div>This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behaviour, standard deviation, and Mean Squared Error using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few</div><div>points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed computer network.</div>

A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

2020 ◽  
Author(s):  
Meenakshi Narayan ◽  
Ann Majewicz Fey
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Real Time ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Force Sensor ◽  
Sensor Data ◽  
Series Data ◽  
Model Free ◽  
Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

scholarly journals Compound Autoregressive Network for Prediction of Multivariate Time Series

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yuting Bai ◽  
Xuebo Jin ◽  
Xiaoyi Wang ◽  
Tingli Su ◽  
Jianlei Kong ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
Prediction Algorithm ◽  
Short Term ◽  
Advanced Control ◽  
Multiple Variables ◽  
Nonlinear Autoregressive Model ◽  
Interactive Relation ◽  
Nonlinear Autoregressive

The prediction information has effects on the emergency prevention and advanced control in various complex systems. There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time-series impact on each other to make the prediction more difficult. Then, a solution of time-series prediction for the multivariate was explored in this paper. Firstly, a compound neural network framework was designed with the primary and auxiliary networks. The framework attempted to extract the change features of the time series as well as the interactive relation of multiple related variables. Secondly, the structures of the primary and auxiliary networks were studied based on the nonlinear autoregressive model. The learning method was also introduced to obtain the available models. Thirdly, the prediction algorithm was concluded for the time series with multiple variables. Finally, the experiments on environment-monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term.

Multiple convolutional neural networks for multivariate time series prediction

2019 ◽  
Vol 360 ◽  
pp. 107-119 ◽  
Author(s):  
Kang Wang ◽  
Kenli Li ◽  
Liqian Zhou ◽  
Yikun Hu ◽  
Zhongyao Cheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Networks ◽  
Time Series Prediction ◽  
Multivariate Time Series
Sign in / Sign up

Export Citation Format

Share Document