scholarly journals Combining Radar and Optical Sensor Data to Measure Player Value in Baseball

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 64
Author(s):  
Glenn Healey
Keyword(s):  
Machine Learning ◽  
Optical Sensors ◽  
Optical Sensor ◽  
Doppler Radar ◽  
Major League Baseball ◽  
Three Dimensional ◽  
Sensor Data ◽  
Running Speed ◽  
Machine Learning Methods ◽  
Improved Model

Evaluating a player’s talent level based on batted balls is one of the most important and difficult tasks facing baseball analysts. An array of sensors has been installed in Major League Baseball stadiums that capture seven terabytes of data during each game. These data increase interest among spectators, but also can be used to quantify the performances of players on the field. The weighted on base average cube model has been used to generate reliable estimates of batter performance using measured batted-ball parameters, but research has shown that running speed is also a determinant of batted-ball performance. In this work, we used machine learning methods to combine a three-dimensional batted-ball vector measured by Doppler radar with running speed measurements generated by stereoscopic optical sensors. We show that this process leads to an improved model for the batted-ball performances of players.

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

Determining Rig State from Computer Vision Analytics

2021 ◽  
Author(s):  
Crispin Chatar ◽  
Suhas Suresha ◽  
Laetitia Shao ◽  
Soumya Gupta ◽  
Indranil Roychoudhury
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Graphics Processing Units ◽  
New Technology ◽  
Three Dimensional ◽  
Cost Effective ◽  
Temporal Information ◽  
Sensor Data ◽  
High Tech ◽  
Drilling Rig

Abstract For years, many companies involved with drilling have searched for the ideal method to calculate the state of a drilling rig. While companies cannot agree on a standard definition of "rig state," they can agree that as we move forward in drilling optimization and with further use of remote operations and automation, that rig state calculation is mandatory in one form or the other. Internally in the service company, many methods exist for calculating rig state, but one new technology area holds promise to deliver a more efficient and cost-effective option with higher accuracy. This technology involves vision analytics. Currently, detection algorithms rely heavily on data collected by sensors installed on the rig. However, relying exclusively on sensor data is problematic because sensors are prone to failure and are expensive to maintain and install. By proposing a machine learning model that relies exclusively on videos collected on the rig floor to infer rig states, it is possible to move away from the existing methods as the industry moves to a future of high-tech rigs. Videos, in contrast to sensor data, are relatively easy to collect from small inexpensive cameras installed at strategic locations. Consequently, this paper presents machine learning pipeline that is implemented to perform rig state determination from videos captured on the rig floor of an operating rig. The pipeline can be described in two parts. Firstly, the annotation pipeline matches each frame of the video dataset to a rig state. A convolutional neural network (CNN) is used to match the time of the video with corresponding sensor data. Secondly, additional CNNs are trained, capturing both spatial and temporal information, to extract an estimation of rig state from videos. The models are trained on a dataset of 3 million frames on a cloud platform using graphics processing units (GPU). Some of the models used include a pretrained visual geometry group (VGG) network, a convolutional three-dimensional (C3D) model that used three-dimensional (3D) convolutions, and a two-stream model that uses optical flow to capture temporal information. The initial results demonstrate this pipeline to be effective in detecting rig states using computer vision analytics.

scholarly journals Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 7
Author(s):  
Emilio Corcione ◽  
Diana Pfezer ◽  
Mario Hentschel ◽  
Harald Giessen ◽  
Cristina Tarín
Keyword(s):  
Machine Learning ◽  
Measurement Techniques ◽  
Gaussian Process Regression ◽  
Glucose Sensing ◽  
Sensor Data ◽  
Glucose Measurement ◽  
Sensor Calibration ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Major Interest

The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in the literature, with surface-enhanced infrared absorption (SEIRA) spectroscopy combining the sensitivity of plasmonic systems and the specificity of standard infrared spectroscopy. The challenge addressed in this paper is to determine the best method to estimate the glucose concentration in aqueous solutions in the presence of fructose from the measured reflectance spectra. This is referred to as the inverse problem of sensing and usually solved via linear regression. Here, instead, several advanced machine learning regression algorithms are proposed and compared, while the sensor data are subject to a pre-processing routine aiming to isolate key patterns from which to extract the relevant information. The most accurate and reliable predictions were finally made by a Gaussian process regression model which improves by more than 60% on previous approaches. Our findings give insight into the applicability of machine learning methods of regression for sensor calibration and explore the limitations of SEIRA glucose sensing.

Using Mobile Sensors to Study Personality Dynamics

2020 ◽  
pp. 1-13
Author(s):  
Brenton M. Wiernik ◽  
Deniz S. Ones ◽  
Benjamin M. Marlin ◽  
Casey Giordano ◽  
Stephan Dilchert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Personality Assessment ◽  
Mobile Sensors ◽  
Sensor Data ◽  
Practical Implementation ◽  
Personality Assessments ◽  
New Approach ◽  
Machine Learning Methods ◽  
Research Designs ◽  
Over Time

Abstract. Research interest in personality dynamics over time is rapidly growing. Passive personality assessment via mobile sensors offers an intriguing new approach for measuring a wide variety of personality dynamics. In this paper, we address the possibility of integrating sensor-based assessments to enhance personality dynamics research. We consider a variety of research designs that can incorporate sensor-based measures and address pitfalls and limitations in terms of psychometrics and practical implementation. We also consider analytic challenges related to data quality and model evaluation that researchers must address when applying machine learning methods to translate sensor data into composite personality assessments.

scholarly journals Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2397
Author(s):  
Bernd Zimmering ◽  
Oliver Niggemann ◽  
Constanze Hasterok ◽  
Erik Pfannstiel ◽  
Dario Ramming ◽  
...  
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Sensor Data ◽  
Support Vector ◽  
Neural Net ◽  
Generation Process ◽  
Self Organizing Map ◽  
Data Generation ◽  
Learning Methods ◽  
Machine Learning Methods

In the field of Cyber-Physical Systems (CPS), there is a large number of machine learning methods, and their intrinsic hyper-parameters are hugely varied. Since no agreed-on datasets for CPS exist, developers of new algorithms are forced to define their own benchmarks. This leads to a large number of algorithms each claiming benefits over other approaches but lacking a fair comparison. To tackle this problem, this paper defines a novel model for a generation process of data, similar to that found in CPS. The model is based on well-understood system theory and allows many datasets with different characteristics in terms of complexity to be generated. The data will pave the way for a comparison of selected machine learning methods in the exemplary field of unsupervised learning. Based on the synthetic CPS data, the data generation process is evaluated by analyzing the performance of the methods of the Self-Organizing Map, One-Class Support Vector Machine and Long Short-Term Memory Neural Net in anomaly detection.

scholarly journals Placement Prediction System

2021 ◽  
Vol 9 (VIII) ◽  
pp. 700-702
Author(s):  
Mr. Sreenivasa M
Keyword(s):  
Machine Learning ◽  
User Satisfaction ◽  
Educational Institution ◽  
Personality Testing ◽  
Prediction System ◽  
Project Implementation ◽  
Student Records ◽  
Machine Learning Methods ◽  
Use Of Time ◽  
Improved Model

Placement prediction system is a useful software for managers and students. An educational institution contains student records which is a wealth of information but is very large one person analyzes complete student records. To find out the placement status of each student at institution is a tedious task. Therefore, the limit of the system includes the use of time, which is minimal efficient and with little user satisfaction. The project implementation prediction plan predicts the reader placement using a variety of machine learning methods such as merging methods, regression strategies, decision solution etc. Based on student schools with the ability to measure, English skill, logical ability, technical personality testing. Improved model used predict the placement of students in the training and placement office (TPO)

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