A Multidimensional Extension of Balje Chart for Axial Flow Turbomachinery Using Artificial Intelligence-Based Meta-Models

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Gino Angelini ◽  
Alessandro Corsini ◽  
Giovanni Delibra ◽  
Lorenzo Tieghi
Keyword(s):  
Axial Flow ◽  
Principal Component ◽  
Correct Choice ◽  
Machine Learning Techniques ◽  
Operational Parameters ◽  
Kinematic Parameters ◽  
Fan Performance ◽  
Learning Techniques ◽  
Optimal Values ◽  
Specific Speed

Abstract The main intent of this work is the exploration of the rotor-only fan design space to identify the correlations between fan performance and enriched geometric and kinematic parameters. In particular, the aim is to derive a multidimensional “Balje chart,” where the main geometric and operational parameters are taken into account in addition to the specific speed and diameter, to guide a fan designer toward the correct choice of parameters such as hub solidity, blade number, hub-to-tip ratio (HR). This multidimensional chart was built using performance data derived from a quasi-3D in-house software for axisymmetric blade analysis and then explored by means of machine learning techniques suitable for big data analysis. Principal component analysis (PCA) and projection to latent structure (PLS) allowed finding optimal values of the main geometric parameters required by each specific speed/specific diameter pair.

A Multi-Dimensional Extension of Balje Chart for Axial Flow Turbomachinery Using Artificial Intelligence Based Meta-Models

2019 ◽  
Author(s):  
Gino Angelini ◽  
Alessandro Corsini ◽  
Giovanni Delibra ◽  
Lorenzo Tieghi
Keyword(s):  
Axial Flow ◽  
Principal Component ◽  
Correct Choice ◽  
Machine Learning Techniques ◽  
Operational Parameters ◽  
Kinematic Parameters ◽  
Fan Performance ◽  
Learning Techniques ◽  
Optimal Values ◽  
Specific Speed

Abstract The main intent of this work is the exploration of the rotor-only fan design-space to identify correlations between fan performance and enriched geometric and kinematic parameters. In particular, the aim is to derive a multidimensional “Balje chart”, where the main geometric and operational parameters are taken into account in addition to the specific speed and diameter, to guide a fan designer towards the correct choice of parameters such as hub solidity, blade number, hub-to-tip ratio. This multidimensional chart was built using performance data derived from a quasi-3D in-house software for axisymmetric blade analysis and then explored by means of machine learning techniques suitable for big data analysis. Principal Component Analysis (PCA) and Projection to Latent Structure (PLS) allowed finding optimal values of the main geometric parameters required by each specific speed/specific diameter pair.

scholarly journals Crack detection using tap-testing and machine learning techniques to prevent potential rockfall incidents

2021 ◽  
Author(s):  
Roya Nasimi ◽  
Fernando Moreu ◽  
John Stormont
Keyword(s):  
Crack Detection ◽  
Confusion Matrix ◽  
Principal Component ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Rock Surface ◽  
Acoustic Data ◽  
Learning Techniques ◽  
Different Characteristics ◽  
2D Space

Abstract Rockfalls are a hazard for the safety of infrastructure as well as people. Identifying loose rocks by inspection of slopes adjacent to roadways and other infrastructure and removing them in advance can be an effective way to prevent unexpected rockfall incidents. This paper proposes a system towards an automated inspection for potential rockfalls. A robot is used to repeatedly strike or tap on the rock surface. The sound from the tapping is collected by the robot and subsequently classified with the intent of identifying rocks that are broken and prone to fall. Principal Component Analysis (PCA) of the collected acoustic data is used to recognize patterns associated with rocks of various conditions, including intact as well as rock with different types and locations of cracks. The PCA classification was first demonstrated simulating sounds of different characteristics that were automatically trained and tested. Secondly, a laboratory test was conducted tapping rock specimens with three different levels of discontinuity in depth and shape. A real microphone mounted on the robot recorded the sound and the data were classified in three clusters within 2D space. A model was created using the training data to classify the reminder of the data (the test data). The performance of the method is evaluated with a confusion matrix.

scholarly journals Using Semantic Distance to Support Geometric Harmonisation of Cadastral and Topographical Data

2014 ◽  
Vol II-2 ◽  
pp. 15-22 ◽  
Author(s):  
M.J. Schulze ◽  
F. Thiemann ◽  
M. Sester
Keyword(s):  
Semantic Distance ◽  
Correct Choice ◽  
Machine Learning Techniques ◽  
Major Influence ◽  
Combine Data ◽  
Learning Techniques ◽  
Least Squares Adjustment ◽  
Data Infrastructures ◽  
Different Sources ◽  
Matching Probability

In the context of geo-data infrastructures users may want to combine data from different sources and expect consistent data. If both datasets are maintained separately, different capturing methods and intervals leads to inconsistencies in geometry and semantic, even if the same reality has been modelled. Our project aims to automatically harmonize such datasets and to allow an efficient actualisation of the semantics. The application domain in our project is cadastral and topographic datasets. To resolve geometric conflicts between topographic and cadastral data a local nearest neighbour method was used to identify perpendicular distances between a node in the topographic and an edge in the cadastral dataset. The perpendicular distances are reduced iteratively in a constraint least squares adjustment (LSA) process moving the coordinates from node and edge towards each other. The adjustment result has to be checked for conflicts caused by the movement of the coordinates in the LSA. <br><br> The correct choice of matching partners has a major influence on the result of the LSA. If wrong matching partners are linked a wrong adaptation is derived. Therefore we present an improved matching method, where we take distance, orientation and semantic similarity of the neighbouring objects into account. Using Machine Learning techniques we obtain corresponding land-use classes. From these a measurement for the semantic distance is derived. It is combined with the orientation difference to generate a matching probability for the two matching candidates. Examples show the benefit of the proposed similarity measure.

scholarly journals Classical and Deep Learning Paradigms for Detection and Validation of Key Genes of Risky Outcomes of HCV

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 73
Author(s):  
Nagwan M. Abdel Samee
Keyword(s):  
Hepatic Cirrhosis ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Second Phase ◽  
Selection Methods ◽  
Neural Network Approach ◽  
Learning Techniques ◽  
Key Genes ◽  
Two Phases

Hepatitis C virus (HCV) is one of the most dangerous viruses worldwide. It is the foremost cause of the hepatic cirrhosis, and hepatocellular carcinoma, HCC. Detecting new key genes that play a role in the growth of HCC in HCV patients using machine learning techniques paves the way for producing accurate antivirals. In this work, there are two phases: detecting the up/downregulated genes using classical univariate and multivariate feature selection methods, and validating the retrieved list of genes using Insilico classifiers. However, the classification algorithms in the medical domain frequently suffer from a deficiency of training cases. Therefore, a deep neural network approach is proposed here to validate the significance of the retrieved genes in classifying the HCV-infected samples from the disinfected ones. The validation model is based on the artificial generation of new examples from the retrieved genes’ expressions using sparse autoencoders. Subsequently, the generated genes’ expressions data are used to train conventional classifiers. Our results in the first phase yielded a better retrieval of significant genes using Principal Component Analysis (PCA), a multivariate approach. The retrieved list of genes using PCA had a higher number of HCC biomarkers compared to the ones retrieved from the univariate methods. In the second phase, the classification accuracy can reveal the relevance of the extracted key genes in classifying the HCV-infected and disinfected samples.

Identification of Losses in Turbomachinery With Machine Learning

2020 ◽  
Author(s):  
Gino Angelini ◽  
Alessandro Corsini ◽  
Giovanni Delibra ◽  
Marco Giovannelli
Keyword(s):  
Machine Learning ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Post Processing ◽  
Pressure Losses ◽  
Learning Techniques ◽  
Flow Features ◽  
Exploratory Data ◽  
Unsupervised Approach ◽  
Important Design

Abstract One of the issues of handling large CFD datasets and process them to derive important design correlations is the limitation in automating the post-processing of data. Machine learning techniques, developed to process large unlabelled dataset, can play a key role on this subject. In this work an unsupervised approach to isolate different flow features inside a 2D cascade is proposed and validated. The approach relies on machine learning methods and in particular on Exploratory Data Analysis (EDA) and Principal Component Analysis for the pre-processing of the data and on K-means clustering for the post-processing. The K-means algorithm was trained on a Design of Experiments (DoE) of over 140 cases of 2D linear cascade configurations to identify the boundary layer on the profiles and the wake downstream. Validation resulted in a perfect capability of identifying the regions of interest. Then a possible exploitation of this method is presented, to compute pressure losses downstream of the cascade and train an artificial neural network to make a regression able to extend data to all the possible combinations of geometrical and operating parameters of the cascade. The same algorithm was applied to 3D flow cascades of profiles with sinusoidal leading edges to stress its extrapolation capability in case of flow regimes not present in the training DoE.

A Pharmaco-Cybernetics Approach to Patient Safety

2016 ◽  
pp. 1445-1464
Author(s):  
Kevin Yi-Lwern Yap
Keyword(s):  
Machine Learning ◽  
Patient Safety ◽  
Cancer Patients ◽  
Adverse Drug Reactions ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Internet Applications ◽  
Case Examples ◽  
Learning Techniques

Pharmaco-cybernetics is an upcoming interdisciplinary field that supports our use of medicines and drugs through the combined use of computational technologies and techniques with human-computer-environment interactions to reduce or prevent drug-related problems. The advent of pharmaco-cybernetics has led to the development of various software, tools, and Internet applications that can be used by healthcare practitioners to deliver optimum pharmaceutical care and health-related outcomes. Patients are becoming more informed through health information on the Internet, which empowers them to better participate in the management of their own conditions. Focusing on patients with cancer, this chapter describes the use of a pharmaco-cybernetics approach to identify clinically relevant predictors of two debilitating adverse drug reactions, which are a cause of patient safety – chemotherapy-induced nausea and vomiting and febrile neutropenia. The early identification of such clinical predictors enables clinicians to prevent or reduce the occurrence of adverse drug reactions in cancer patients undergoing chemotherapy through appropriate management strategies. The computational methods used in this approach involve two unsupervised machine-learning techniques – principal component and multiple correspondence analyses. Using two case examples, this chapter shows the potential of machine-learning techniques for identifying patients who are at greater risks of these adverse drug reactions, thus enhancing patient safety. This chapter also aims to increase the awareness among healthcare professionals and clinician-scientists about the usefulness of such techniques in clinical patient populations, so that these can be considered as part of clinical care pathways to enhance patient safety and effectively manage cancer patients on chemotherapy.

A Pharmaco-Cybernetics Approach to Patient Safety

2014 ◽  
pp. 179-197
Author(s):  
Kevin Yi-Lwern Yap
Keyword(s):  
Machine Learning ◽  
Patient Safety ◽  
Cancer Patients ◽  
Adverse Drug Reactions ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Internet Applications ◽  
Case Examples ◽  
Learning Techniques

Pharmaco-cybernetics is an upcoming interdisciplinary field that supports our use of medicines and drugs through the combined use of computational technologies and techniques with human-computer-environment interactions to reduce or prevent drug-related problems. The advent of pharmaco-cybernetics has led to the development of various software, tools, and Internet applications that can be used by healthcare practitioners to deliver optimum pharmaceutical care and health-related outcomes. Patients are becoming more informed through health information on the Internet, which empowers them to better participate in the management of their own conditions. Focusing on patients with cancer, this chapter describes the use of a pharmaco-cybernetics approach to identify clinically relevant predictors of two debilitating adverse drug reactions, which are a cause of patient safety – chemotherapy-induced nausea and vomiting and febrile neutropenia. The early identification of such clinical predictors enables clinicians to prevent or reduce the occurrence of adverse drug reactions in cancer patients undergoing chemotherapy through appropriate management strategies. The computational methods used in this approach involve two unsupervised machine-learning techniques – principal component and multiple correspondence analyses. Using two case examples, this chapter shows the potential of machine-learning techniques for identifying patients who are at greater risks of these adverse drug reactions, thus enhancing patient safety. This chapter also aims to increase the awareness among healthcare professionals and clinician-scientists about the usefulness of such techniques in clinical patient populations, so that these can be considered as part of clinical care pathways to enhance patient safety and effectively manage cancer patients on chemotherapy.

Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques

2022 ◽  
pp. 209-232
Author(s):  
Xiang Li ◽  
Jingxi Liao ◽  
Tianchuan Gao
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Data Science ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Classification Models ◽  
Performance Measurements ◽  
Learning Techniques ◽  
Source Data ◽  
Bed And Breakfast

Machine learning is a broad field that contains multiple fields of discipline including mathematics, computer science, and data science. Some of the concepts, like deep neural networks, can be complicated and difficult to explain in several words. This chapter focuses on essential methods like classification from supervised learning, clustering, and dimensionality reduction that can be easily interpreted and explained in an acceptable way for beginners. In this chapter, data for Airbnb (Air Bed and Breakfast) listings in London are used as the source data to study the effect of each machine learning technique. By using the K-means clustering, principal component analysis (PCA), random forest, and other methods to help build classification models from the features, it is able to predict the classification results and provide some performance measurements to test the model.

scholarly journals Development of accurate classification of heavenly bodies using novel machine learning techniques

2021 ◽  
Vol 25 (10) ◽  
pp. 7213-7228
Author(s):  
Michał Wierzbiński ◽  
Paweł Pławiak ◽  
Mohamed Hammad ◽  
U. Rajendra Acharya
Keyword(s):  
Machine Learning ◽  
Outer Space ◽  
Principal Component ◽  
Sloan Digital Sky Survey ◽  
Machine Learning Techniques ◽  
Evolutionary System ◽  
Stellar Spectra ◽  
Learning Techniques ◽  
Machine Learning Model

AbstractThe heavenly bodies are objects that swim in the outer space. The classification of these objects is a challenging task for astronomers. This article presents a novel methodology that enables an efficient and accurate classification of cosmic objects (3 classes) based on evolutionary optimization of classifiers. This research collected the data from Sloan Digital Sky Survey database. In this work, we are proposing to develop a novel machine learning model to classify stellar spectra of stars, quasars and galaxies. First, the input data are normalized and then subjected to principal component analysis to reduce the dimensionality. Then, the genetic algorithm is implemented on the data which helps to find the optimal parameters for the classifiers. We have used 21 classifiers to develop an accurate and robust classification with fivefold cross-validation strategy. Our developed model has achieved an improvement in the accuracy using nineteen out of twenty-one models. We have obtained the highest classification accuracy of 99.16%, precision of 98.78%, recall of 98.08% and F1-score of 98.32% using evolutionary system based on voting classifier. The developed machine learning prototype can help the astronomers to make accurate classification of heavenly bodies in the sky. Proposed evolutionary system can be used in other areas where accurate classification of many classes is required.

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