Modelling In-Store Consumer Behaviour Using Machine Learning and Digital Signage Audience Measurement Data

2014 ◽  
pp. 123-133 ◽  
Author(s):  
Robert Ravnik ◽  
Franc Solina ◽  
Vesna Zabkar
Keyword(s):  
Machine Learning ◽  
Consumer Behaviour ◽  
Measurement Data ◽  
Digital Signage ◽  
Audience Measurement

Artificial Intelligence and Data Analytics for Virtual Flow Metering

2021 ◽  
Author(s):  
Anton Gryzlov ◽  
Liliya Mironova ◽  
Sergey Safonov ◽  
Muhammad Arsalan
Keyword(s):  
Machine Learning ◽  
Multiphase Flow ◽  
Real Time ◽  
Production Control ◽  
Measurement Data ◽  
Sufficient Accuracy ◽  
Operating Conditions ◽  
Machine Learning Techniques ◽  
Flow Metering ◽  
Oil Gas

Abstract Modern challenges in reservoir management have recently faced new opportunities in production control and optimization strategies. These strategies in turn rely on the availability of monitoring equipment, which is used to obtain production rates in real-time with sufficient accuracy. In particular, a multiphase flow meter is a device for measuring the individual rates of oil, gas and water from a well in real-time without separating fluid phases. Currently, there are several technologies available on the market but multiphase flow meters generally incapable to handle all ranges of operating conditions with satisfactory accuracy in addition to being expensive to maintain. Virtual Flow Metering (VFM) is a mathematical technique for the indirect estimation of oil, gas and water flowrates produced from a well. This method uses more readily available data from conventional sensors, such as downhole pressure and temperature gauges, and calculates the multiphase rates by combining physical multiphase models, various measurement data and an optimization algorithm. In this work, a brief overview of the virtual metering methods is presented, which is followed by the application of several advanced machine-learning techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore. The predictive capabilities of different types of machine learning instruments are explored using a model simulated production data. Also, the effect of measurement noise on the quality of estimates is considered. The presented results demonstrate that the data-driven methods are very capable to predict multiphase flow rates with sufficient accuracy and can be considered as a back-up solution for a conventional multiphase meter.

scholarly journals Identifying occupant presence in a room based on machine learning techniques by measuring indoor air conditions

2020 ◽  
Vol 172 ◽  
pp. 22005
Author(s):  
Lucia Hanfstaengl ◽  
Michael Parzinger ◽  
Uli Spindler ◽  
Ulrich Wellisch ◽  
Markus Wirnsberger
Keyword(s):  
Machine Learning ◽  
Boundary Conditions ◽  
Heat Dissipation ◽  
Measurement Data ◽  
Test Sample ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Automation System ◽  
Precise Control ◽  
Occupancy Detection

Knowing about the presence and number of people in a room can be of interest for precise control of heating, ventilation and air conditioning. To determine the number and presence of occupants cost-effectively, it is of interest to use already existing air condition sensors (temperature, humidity, CO2) of the building automation system. Different approaches and methods for determining presence have attracted attention in recent years. We propose an occupancy detection method based on a method of supervised machine learning. In an experiment, measurement data were recorded in a research apartment with controllable boundary conditions. The presence of people was simulated by artificial injection of water vapour, CO2 and heat dissipation. The variation of the number of artificial users, the duration of presence and the supply air volume flow of the ventilation resulted in a total of 720 combinations. By using artificial users, the boundary conditions were accurately defined, and different presence situations could be measured time-effectively. The data is evaluated with a method of supervised machine learning called random forest. The statistical model can determine precisely the number of people in over 93% of the cases in a disjoint test sample. The experiments took part in the Rosenheim Technical University of Applied Sciences laboratory.

scholarly journals Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

2020 ◽  
Vol 10 (11) ◽  
pp. 3980 ◽  
Author(s):  
Cung Lian Sang ◽  
Bastian Steinhagen ◽  
Jonas Dominik Homburg ◽  
Michael Adams ◽  
Marc Hesse ◽  
...  
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Measurement Data ◽  
Ultra Wideband ◽  
Machine Learning Techniques ◽  
Line Of Sight ◽  
Support Vector ◽  
Case Scenario ◽  
Worst Case ◽  
Worst Case Scenario

In ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight (LOS), non-line-of-sight (NLOS), and multi-path (MP) conditions is important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS, or MP). However, the major contributions in the literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. However, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the three mentioned classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental dataset. The dataset was collected in different conditions in different scenarios in indoor environments. Using the collected real measurement data, we compared three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results showed that applying ML methods in UWB ranging systems was effective in the identification of the above-three mentioned classes. Specifically, the overall accuracy reached up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it was 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we provide the publicly accessible experimental research data discussed in this paper at PUB (Publications at Bielefeld University). The evaluations of the three classifiers are conducted using the open-source Python machine learning library scikit-learn.

scholarly journals Ammonoid Taxonomy with Supervised and Unsupervised Machine Learning Algorithms

2021 ◽  
Author(s):  
Floe Foxon
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Clustering Algorithms ◽  
Measurement Data ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Unsupervised Machine Learning

Ammonoid identification is crucial to biostratigraphy, systematic palaeontology, and evolutionary biology, but may prove difficult when shell features and sutures are poorly preserved. This necessitates novel approaches to ammonoid taxonomy. This study aimed to taxonomize ammonoids by their conch geometry using supervised and unsupervised machine learning algorithms. Ammonoid measurement data (conch diameter, whorl height, whorl width, and umbilical width) were taken from the Paleobiology Database (PBDB). 11 species with ≥50 specimens each were identified providing N=781 total unique specimens. Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbours, and Support Vector Machine classifiers were applied to the PBDB data with a 5x5 nested cross-validation approach to obtain unbiased generalization performance estimates across a grid search of algorithm parameters. All supervised classifiers achieved ≥70% accuracy in identifying ammonoid species, with Naive Bayes demonstrating the least over-fitting. The unsupervised clustering algorithms K-Means, DBSCAN, OPTICS, Mean Shift, and Affinity Propagation achieved Normalized Mutual Information scores of ≥0.6, with the centroid-based methods having most success. This presents a reasonably-accurate proof-of-concept approach to ammonoid classification which may assist identification in cases where more traditional methods are not feasible.

scholarly journals Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6671
Author(s):  
Sharif Hossain ◽  
Christopher W.K. Chow ◽  
Guna A. Hewa ◽  
David Cook ◽  
Martin Harris
Keyword(s):  
Machine Learning ◽  
Drinking Water ◽  
Real Time ◽  
Learning Algorithm ◽  
Measurement Data ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Kernel Functions ◽  
Support Vector ◽  
Organic Nitrate

The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1.

When Data and Intuition Converge

2021 ◽  
pp. 65-84
Author(s):  
Jacob L. Nelson
Keyword(s):  
Measurement Data ◽  
Data Driven ◽  
News Industry ◽  
Audience Measurement ◽  
Relationship Of ◽  
The Relationship ◽  
The Way

How is it that, in an age of sophisticated audience data, there continue to be widespread uncertainty and inconsistency throughout the news industry surrounding what people want and expect from news? This chapter explores this question by examining the relationship of journalists with audience measurement data. While the previous chapter examined the differences within journalism’s imagined audiences, this chapter explores the origins of journalism’s imagined audiences. In doing so, it identifies the way these differences emerge—and, more importantly, how they persist—in an increasingly data-driven news culture. The author’s overarching argument is that audience measurement data are neither as straightforward nor comprehensive as the discourse surrounding them suggests. Instead, these data continue to leave ample room for interpretation, and the interpretations vary from one journalist to the next.

Quantitative Demonstration of a High-Fidelity Oil-Based Mud Resistivity Imager Using a Controlled Experiment

2021 ◽  
Vol 62 (4) ◽  
pp. 407-421
Author(s):  
Baris Guner ◽  
◽  
Ahmed E. Fouda ◽  
Wei-Bin Ewe ◽  
David Torres ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Estimation ◽  
Hybrid Approach ◽  
Measurement Data ◽  
Quantitative Information ◽  
Inversion Algorithm ◽  
Data Set ◽  
Test Fixture ◽  
Imaging Tool ◽  
Formation Resistivity

The objective of this paper is to describe and validate a new approach for acquiring images that provides both qualitative and quantitative information on the formation electrical properties using a high-resolution, oil-based mud imager (HROBMI) tool. This new multifrequency imaging tool is able to function at high frequencies (in the MHz range) in oil-based muds. To allow for the quantitative estimation of formation and mud properties from the HROBMI data, a hybrid machine-learning/inversion approach was implemented. In this hybrid approach, machine-learning models corresponding to different candidate mud properties are trained, and the resulting regression functions are stored. For a given measurement data set, predictions of these different models are used to quickly identify an optimum mud candidate. This information is then fed into an inversion algorithm that provides accurate quantitative information on the logging environment of the HROBMI. The accuracy of this algorithm has been verified using a test fixture that enables the change of formation properties in different mud environments. The measurements from the HROBMI are a function of the formation properties: resistivity and permittivity, frequency, and mud properties. The hybrid algorithm can untangle HROBMI data from multiple frequencies to obtain true formation resistivity images independent of the other parameters that affect the tool measurements. In addition, the algorithm provides formation permittivity images as well as a standoff image. The results have been provided from both the controlled experiments in the test fixture and from field logs.

Dynamic behavioral modeling of RF power amplifiers based on decomposed piecewise machine learning technique

2020 ◽  
pp. 1-7
Author(s):  
Jialin Cai ◽  
Justin B. King ◽  
Chao Yu ◽  
Baicao Pan ◽  
Lingling Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Amplifiers ◽  
Model Development ◽  
Measurement Data ◽  
Model Performance ◽  
Behavioral Modeling ◽  
Modeling Technique ◽  
Support Vector ◽  
Long Term Memory ◽  
Rf Power Amplifiers

Abstract Multi-device radio frequency power amplifiers (PAs) often exhibit strongly non-linear behavior in combination with long-term memory effects, leading to an extremely challenging model development cycle. This paper presents a new, dynamic, behavioral modeling technique, based on a combination of the real-valued decomposed piecewise method and concepts from the field of machine learning. The underlying theory of the proposed modeling technique is provided, along with a detailed modeling procedure. Experimental results show that the proposed decomposed piecewise support vector regression (SVR) model leads to significant performance improvements when compared with standard SVR models for both single transistor and multi-transistor PAs. Different model thresholds are used to test the proposed model performance for both PA types. For the single-transistor PA, modeled using only one partition, an approximately 10 dB normalized mean square error (NMSE) reduction is seen when compared with the standard SVR model. For the same PA, when utilizing two partitions, the reduction improves to 14 dB. When applied to a multi-device Doherty PA, the NMSE between model and measurement data is −50 dB, representing more than 10 dB improvement compared with the standard SVR model.

Sign in / Sign up

Export Citation Format

Share Document