Use of machine-learning for monitoring solar thermal plants

Abstract A machine-learning algorithm (MLA) was developed to assess the operational state of solar thermal plants, based on the data of only one temperature sensor, and the irradiance and ambient temperature data from the nearest weather station. A detailed requirements analysis of the situation results in the classification of a multivariate time series problem. Neural networks used in the field of data science are ideally suited for problems of this type. Data from the operational monitoring system, which runs a rule-based algorithm, were used to train the neural network using the software framework TensorFlow. It was shown that the chosen MLA can detect malfunctions such as heat loss due to gravity-driven circulation during night. However, further development towards a practical tool requires not only far more data for training and validation. It became clear that corresponding pressure data are needed to classify temperature transients and to attribute these classes to certain malfunctions.

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Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

Scientific Reports ◽

10.1038/s41598-021-92014-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Gabriel A. Colozza-Gama ◽

Fabiano Callegari ◽

Nikola Bešič ◽

Ana C. de J. Paviza ◽

Janete M. Cerutti

Keyword(s):

Machine Learning ◽

Sanger Sequencing ◽

Learning Algorithm ◽

Absolute Quantification ◽

Braf V600e Mutation ◽

Braf V600e ◽

Driver Genes ◽

Quantitative Classification ◽

Cancer Driver

AbstractSomatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice.

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Multi-Class Assessment Based on Random Forests

Education Sciences ◽

10.3390/educsci11030092 ◽

2021 ◽

Vol 11 (3) ◽

pp. 92

Author(s):

Mehdi Berriri ◽

Sofiane Djema ◽

Gaëtan Rey ◽

Christel Dartigues-Pallez

Keyword(s):

Higher Education ◽

Machine Learning ◽

Random Forests ◽

Learning Algorithm ◽

Teaching Staff ◽

Machine Learning Algorithm ◽

Process Data ◽

Training Courses ◽

Education Courses

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.

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A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641034 ◽

2020 ◽

Vol 64 (1) ◽

pp. 129-133

Author(s):

Katherine Darveau ◽

Daniel Hannon ◽

Chad Foster

Keyword(s):

Machine Learning ◽

Human Factors ◽

Human Error ◽

Data Science ◽

Aircraft Engine ◽

Rule Based ◽

Root Cause ◽

Textual Data ◽

Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

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Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

Sensors ◽

10.3390/s18010075 ◽

2017 ◽

Vol 18 (2) ◽

pp. 75 ◽

Cited By ~ 10

Author(s):

Ole Rindal ◽

Trine Seeberg ◽

Johannes Tjønnås ◽

Pål Haugnes ◽

Øyvind Sandbakk

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Automatic Classification ◽

Sensor Data ◽

Machine Learning Algorithm ◽

Micro Sensor ◽

Cross Country Skiing ◽

Cross Country ◽

Classical Cross

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Machine Learning Based Track Classification and Estimation using Kalman Filter

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a2616.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1700-1704

Keyword(s):

Machine Learning ◽

Kalman Filter ◽

Learning Algorithm ◽

Signal To Noise Ratio ◽

Minimum Mean Square Error ◽

Velocity Model ◽

Supervised Machine Learning ◽

Multiple Target ◽

Low Snr

Classification of target from a mixture of multiple target information is quite challenging. In This paper we have used supervised Machine learning algorithm namely Linear Regression to classify the received data which is a mixture of target-return with the noise and clutter. Target state is estimated from the classified data using Kalman filter. Linear Kalman filter with constant velocity model is used in this paper. Minimum Mean Square Error (MMSE) analysis is used to measure the performance of the estimated track at various Signal to Noise Ratio (SNR) levels. The results state that the error is high for Low SNR, for High SNR the error is Low

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Comparative Analysis of Machine Learning Algorithm for Classification of different Osteosarcoma types

10.1109/icccnt51525.2021.9579556 ◽

2021 ◽

Author(s):

Sanket Mahore ◽

Kalyani Bhole ◽

Shashikant Rathod

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Learning Algorithm ◽

Machine Learning Algorithm

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Classification of Chaotic Behaviors in Jerky Dynamical Systems

Complex Systems ◽

10.25088/complexsystems.30.1.93 ◽

2021 ◽

Vol 30 (1) ◽

pp. 93-110

Author(s):

Tianyi Wang ◽

Keyword(s):

Machine Learning ◽

Chaotic Systems ◽

Chaotic Behavior ◽

Learning Algorithm ◽

Model Systems ◽

Supervised Machine Learning ◽

Learning Program ◽

New Methods ◽

Over Time

Differential equations are widely used to model systems that change over time, some of which exhibit chaotic behaviors. This paper proposes two new methods to classify these behaviors that are utilized by a supervised machine learning algorithm. Dissipative chaotic systems, in contrast to conservative chaotic systems, seem to follow a certain visual pattern. Also, the machine learning program written in the Wolfram Language is utilized to classify chaotic behavior with an accuracy around 99.1±1.1%.

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