scholarly journals Hysteresis Compensation in Force/Torque Sensors Using Time Series Information

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4259 ◽  
Author(s):  
Ryuichiro Koike ◽  
Sho Sakaino ◽  
Toshiaki Tsuji
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Signal Processing ◽  
Linear Regression ◽  
Mathematical Models ◽  
High Precision ◽  
Previous History ◽  
Force Sensor ◽  
Force Sensing ◽  
Hysteresis Compensation

The purpose of this study is to compensate for the hysteresis in a six-axis force sensor using signal processing, thereby achieving high-precision force sensing. Although mathematical models of hysteresis exist, many of these are one-axis models and the modeling is difficult if they are expanded to multiple axes. Therefore, this study attempts to resolve this problem through machine learning. Since hysteresis is dependent on the previous history, this study investigates the effect of using time series information in machine learning. Experimental results indicate that the performance is improved by including time series information in the linear regression process generally utilized to calibrate six-axis force sensors.

scholarly journals Attacking Data Transforming Learners at Training Time

2019 ◽  
Vol 33 ◽  
pp. 3167-3174
Author(s):  
Scott Alfeld ◽  
Ara Vartanian ◽  
Lucas Newman-Johnson ◽  
Benjamin I.P. Rubinstein
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Ordinary Least Squares ◽  
General Purpose ◽  
Training Time ◽  
Target Values ◽  
Gradient Based ◽  
Deployment Time ◽  
Compositional Learning

While machine learning systems are known to be vulnerable to data-manipulation attacks at both training and deployment time, little is known about how to adapt attacks when the defender transforms data prior to model estimation. We consider the setting where the defender Bob first transforms the data then learns a model from the result; Alice, the attacker, perturbs Bob’s input data prior to him transforming it. We develop a general-purpose “plug and play” framework for gradient-based attacks based on matrix differentials, focusing on ordinary least-squares linear regression. This allows learning algorithms and data transformations to be paired and composed arbitrarily: attacks can be adapted through the use of the chain rule—analogous to backpropagation on neural network parameters—to compositional learning maps. Bestresponse attacks can be computed through matrix multiplications from a library of attack matrices for transformations and learners. Our treatment of linear regression extends state-ofthe-art attacks at training time, by permitting the attacker to affect both features and targets optimally and simultaneously. We explore several transformations broadly used across machine learning with a driving motivation for our work being autogressive modeling. There, Bob transforms a univariate time series into a matrix of observations and vector of target values which can then be fed into standard learners. Under this learning reduction, a perturbation from Alice to a single value of the time series affects features of several data points along with target values.

scholarly journals Prediction and Forecasting of Air Quality Index in Chennai using Regression and ARIMA time series models

2021 ◽  
Vol 9 ◽  
Author(s):  
Geetha Mani ◽  
◽  
Joshi Kumar Viswanadhapalli ◽  
Albert Alexander Stonie ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Air Quality ◽  
Linear Regression ◽  
Quality Index ◽  
Air Quality Index ◽  
Model Parameters ◽  
Sensor Output ◽  
Model Accuracy ◽  
Life On Earth

Air is one of the most fundamental constituents for the sustenance of life on earth. The meteorological, traffic factors, consumption of non-renewable energy sources, and industrial parameters are steadily increasing air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of air quality in our environment needs to be monitored continuously. The Air Quality Index (AQI), which indicates air quality, is influenced by several individual factors such as the accumulation of NO2, CO, O3, PM2.5, SO2, and PM10. This research paper aims to predict and forecast the AQI with Machine Learning (ML) techniques, namely linear regression and time series analysis. Primarily,Multi Linear Regression (MLR) model, supervised machine learning, is developed to predict AQI. NO2, Ozone(O3), PM 2.5, and SO2 sensor output collected from Central Pollution Control Board (CPCB) – Chennai region, India feed as input features and optimized AQI calculated from sensor's output set as a target to train the regression model. The obtained model parameters are validated with new and unseen sensor output. The Key Performance Indices(KPI) like co-efficient of determination, root mean square error and mean absolute error were calculated to validate the model accuracy. The K-cross-fold validation for testing data of MLR was obtained as around 92%. Secondly, the Auto-Regressive Integrated Moving Average (ARIMA) time series model is applied to forecast the AQI. The obtained model parameters were validated with unseen data with a timestamp. The forecasted AQI value of the next 15 days lies in a 95 % confidence interval zone. The model accuracy of test data was obtained as more than 80%.

Mathematical foundations

2019 ◽  
pp. 1-40
Author(s):  
Max A. Little
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Signal Processing ◽  
Mathematical Models ◽  
Applied Mathematics ◽  
Statistical Machine Learning ◽  
Mathematical Concepts ◽  
The Subject ◽  
Mathematical Foundations

Statistical machine learning and signal processing are topics in applied mathematics, which are based upon many abstract mathematical concepts. Defining these concepts clearly is the most important first step in this book. The purpose of this chapter is to introduce these foundational mathematical concepts. It also justifies the statement that much of the art of statistical machine learning as applied to signal processing, lies in the choice of convenient mathematical models that happen to be useful in practice. Convenient in this context means that the algebraic consequences of the choice of mathematical modeling assumptions are in some sense manageable. The seeds of this manageability are the elementary mathematical concepts upon which the subject is built.

Development of a Compliant-Mechanism-Based Compact Three-Axis Force Sensor for High-Precision Manufacturing

2015 ◽  
Author(s):  
Guangbo Hao ◽  
Marc Murphy ◽  
Xichun Luo
Keyword(s):  
High Precision ◽  
Parallel Mechanism ◽  
Compliant Mechanism ◽  
Experimental Testing ◽  
Force Sensor ◽  
Force Sensing ◽  
Linear Matrix ◽  
Precision Manufacturing ◽  
Decoupling Method ◽  
Compliant Parallel Mechanism

This paper develops a light-weight compact three-axis force senor for high-precision manufacturing application. This sensor uses a cubic three-axial translational compliant parallel mechanism to undergo the loading on its end-effector thereby producing voltages through strain gauges on the deformed beams. The cubic compliant parallel mechanism and sensor system are described at first. Force sensing theoretical analysis is then presented followed by the initial experimental testing and analysis. A linear matrix based multi-axis loading decoupling method is also proposed so that the sensed force can maximally reflect the actual applied force in each axis. The work in this paper is expected to lay a foundation for further investigation into the online force sensing in the high-precision machine tool.

scholarly journals Analysis of psychometric data using statistical and machine learning methods

2021 ◽  
Author(s):  
Krishnapriya Subramanian
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Well Being ◽  
Time Series Model ◽  
Learning Methods ◽  
Logistics Regression ◽  
Machine Learning Methods ◽  
Psychometric Data ◽  
Simulation Results

The objective of this thesis is to analyse the psychometric data using statistical and machine learning methods. Psychological data are analysed to predict illness and injury of athletes. Regression technique, one of the statistical processes for estimating the relationship among variables is used as basis of this thesis. We apply the linear regression, time series and logistics regression to predict illness and well-being. Our linear regression simulation results are mainly used, to understand the data well. By reviewing the results of linear regression, time series model is developed which predicts sickness one day ahead. The predicted values of this time series model are continuous. However, logistic regression can be used, to provide a probabilistic approach to predict the future levels as a categorical value. Hence we have developed a binomial logistics regression model, when observation variable is the type of dichotomous. Our simulation results show that this prediction model performs well. Our empirical studies also show that our method can act as early warning system for athletes.

Multisensor crop yield estimation with machine learning

2020 ◽  
Author(s):  
Laura Martínez Ferrer ◽  
Maria Piles ◽  
Gustau Camps-Valls
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Crop Yield ◽  
Gaussian Processes ◽  
Vegetation Index ◽  
Maximum Temperature ◽  
Yield Estimation ◽  
Study Results ◽  
Non Linear

<p>Providing accurate and spatially resolved predictions of crop yield is of utmost importance due to the rapid increase in the demand of biofuels and food in the foreseeable future. Satellite based remote sensing over agricultural areas allows monitoring crop development through key bio-geophysical variables such as the Enhanced Vegetation Index (EVI), sensitive to canopy greenness, the Vegetation Optical Depth (VOD), sensitive to biomass water-uptake dynamics, and Soil Moisture (SM), which provides direct information of plant available water. The aim of this work is to implement an automatic system for county-based crop yield estimation using time series from multisource satellite observations, meteorological data and available in situ surveys as supporting information. The spatio-temporal resolution of satellite and meteorological observations are fully exploited and synergistically combined for crop yield prediction using machine learning models. Linear and non-linear regression methods are used: least squares, LASSO, random forests, kernel machines and Gaussian processes. Here we are not only interested in the prediction skill, but also on understanding the relative relevance of the covariates. For this, we first study the importance of each feature separately and then propose a global model for operational monitoring of crop status using the most relevant agro-ecological drivers.</p><p> </p><p>We selected the Continental U.S. and a four-year time series dataset to perform the research study. Results reveal that the three satellite variables are complementary and that their combination with maximum temperature and precipitation from meteorological stations provides the best estimations. Interestingly, adding information about crop planted area also improved the predictions. A non-linear regression model based on Gaussian processes led to best results for all considered crops (soybean, corn and wheat), with high accuracy (low bias and correlation coefficients ranging from 0.75 to 0.92). The feature ranking allowed understanding the main drivers for crop monitoring and the underlying factors behind a prediction loss or gain.</p>

scholarly journals Analysis of psychometric data using statistical and machine learning methods

2021 ◽  
Author(s):  
Krishnapriya Subramanian
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Well Being ◽  
Time Series Model ◽  
Learning Methods ◽  
Logistics Regression ◽  
Machine Learning Methods ◽  
Psychometric Data ◽  
Simulation Results

The objective of this thesis is to analyse the psychometric data using statistical and machine learning methods. Psychological data are analysed to predict illness and injury of athletes. Regression technique, one of the statistical processes for estimating the relationship among variables is used as basis of this thesis. We apply the linear regression, time series and logistics regression to predict illness and well-being. Our linear regression simulation results are mainly used, to understand the data well. By reviewing the results of linear regression, time series model is developed which predicts sickness one day ahead. The predicted values of this time series model are continuous. However, logistic regression can be used, to provide a probabilistic approach to predict the future levels as a categorical value. Hence we have developed a binomial logistics regression model, when observation variable is the type of dichotomous. Our simulation results show that this prediction model performs well. Our empirical studies also show that our method can act as early warning system for athletes.

scholarly journals Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B)

2021 ◽  
Vol 1 ◽  
Author(s):  
Agbassou Guenoupkati ◽  
Adekunlé Akim Salami ◽  
Mawugno Koffi Kodjo ◽  
Kossi Napo
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Performance Metrics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Short Term ◽  
Machine Learning Models

Time series forecasting in the energy sector is important to power utilities for decision making to ensure the sustainability and quality of electricity supply, and the stability of the power grid. Unfortunately, the presence of certain exogenous factors such as weather conditions, electricity price complicate the task using linear regression models that are becoming unsuitable. The search for a robust predictor would be an invaluable asset for electricity companies. To overcome this difficulty, Artificial Intelligence differs from these prediction methods through the Machine Learning algorithms which have been performing over the last decades in predicting time series on several levels. This work proposes the deployment of three univariate Machine Learning models: Support Vector Regression, Multi-Layer Perceptron, and the Long Short-Term Memory Recurrent Neural Network to predict the electricity production of Benin Electricity Community. In order to validate the performance of these different methods, against the Autoregressive Integrated Mobile Average and Multiple Regression model, performance metrics were used. Overall, the results show that the Machine Learning models outperform the linear regression methods. Consequently, Machine Learning methods offer a perspective for short-term electric power generation forecasting of Benin Electricity Community sources.

TecVolSA: InSAR and Machine Learning for Surface Displacement Monitoring in South America

2021 ◽  
Author(s):  
Sina Montazeri ◽  
Homa Ansari ◽  
Francesco De Zan ◽  
René Mania ◽  
Robert Shau ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Signal Processing ◽  
South America ◽  
Surface Deformation ◽  
Supervised Machine Learning ◽  
Research Centre ◽  
Prototype System ◽  
Data Archives ◽  
Deformation Velocity

<p>TecVolSA (Tectonics and Volcanoes in South America) is a project dedicated to the development of an intelligent Earth Observation (EO) data exploitation system for monitoring various geophysical activities in South America. Three partners from the German Aerospace Center (DLR) and the German Research Centre for Geosciences (GFZ) are involved to combine their expertise in signal processing, geophysics and Artificial Intelligence (AI).</p><p>The first milestone of the project is to perform interferometric processing on tens of terabytes of SAR data to generate deformation products. Efficient algorithms have been designed to accommodate big data processing. Employing these algorithms, five-year data archives of Sentinel-1 have been processed thus far. The data archives span an area of over 770,000 km² surrounding the central volcanic zone of the Andes. Products in the form of surface deformation velocity and displacement time series are generated as point-wise measurements. To ensure highly accurate deformation estimates, two novel techniques have been utilized: large-scale atmospheric correction and covariance-based phase estimation for distributed scatterers.</p><p>The second milestone is automatic mining of the wealth of the deformation products to gain insights about anthropogenic and geophysical signals in the region. Here two challenges are faced: the variety of crustal deformation processes as well as the sheer volume of the data. A closer analysis of the estimated deformation velocity verifies the presence of various signals including tectonic movements, volcanic unrest and slope-induced deformations. Such variety requires the classification of the observed signals. Furthermore, the dataset includes displacement time series and velocity estimates of over 750 million data points. This data volume necessitates the incorporation of AI for efficient mining of the products. The aforementioned challenges are met by combining geophysical and signal processing expertise of the project partners, and translating them to the AI algorithms.</p><p>The use of AI in EO is a growing topic with numerous successful applications. However, compared to the well-established AI applications of cartography and ground cover classification, there is not enough training data available for the analysis of tectonic and volcanic signals. Therefore, there is a need for synthetic data generation. GFZ produces geophysical models for the simulation of a diverse database that is used for the training of neural networks to autonomously discover significant events in deformation products.</p><p>DLR employs supervised machine learning techniques based on simulated data to automatically detect volcanic deformation from InSAR products. Apart from this application, signals which are not attributed to volcanic deformation are automatically clustered for further studies by expert geologists. For this approach, we depend on InSAR and geometrical feature engineering as well as advanced unsupervised learning algorithms. In the presentation, examples of clustering similar points in terms of temporal progression and a prototype system for the automatic detection of volcanic deformations will be illustrated.</p><p>Our system is being developed with scalability and transferability in mind. South America serves as a generic and challenging case for this development, as it reveals manifold geophysical and anthropogenic signals. Our ultimate goal is to apply the developed AI-assisted system for global processing.</p><p> </p>

scholarly journals MACHINE LEARNING IN THE PROBLEM OF FORECASTING THE TIME SERIES OF MUF OF SHORT-WAVE RADIO CHANNELS

2021 ◽  
Vol 7 (8(62)) ◽  
pp. 29-31
Author(s):  
NIKITA ALEXANDROVICH KONKIN ◽  
ANASTASIA DMITRIEVNA PASOVA
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Short Wave ◽  
Machine Learning Algorithms ◽  
Filtration Method ◽  
Wave Radio ◽  
Processing Techniques ◽  
Long Term Prediction

The article is devoted to the creation of an algorithm for long-term prediction of the values of the MPR. The paper analyzes the influence of various methods of processing raw values of the maximum applicable frequency on the results of machine learning algorithms, such as linear regression and XGBoost. As processing techniques, the Savitsky -Goley filtration method and the isolated forest algorithm were used to determine emissions for the daily course of the MPR.

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