scholarly journals Bearing assessment tool for longitudinal bridge performance

2020 ◽  
Vol 10 (5) ◽  
pp. 1023-1036
Author(s):  
David Garcia-Sanchez ◽  
Ana Fernandez-Navamuel ◽  
Diego Zamora Sánchez ◽  
Daniel Alvear ◽  
David Pardo
Keyword(s):  
Unsupervised Learning ◽  
Prestressed Concrete ◽  
Assessment Tool ◽  
Performance Indicator ◽  
Principal Component ◽  
Horizontal Displacement ◽  
Bridge Management ◽  
Strong Correlations ◽  
Bridge Performance ◽  
Critical Components

Abstract This work provides an unsupervised learning approach based on a single-valued performance indicator to monitor the global behavior of critical components in a viaduct, such as bearings. We propose an outlier detection method for longitudinal displacements to assess the behavior of a singular asymmetric prestressed concrete structure with a 120 m high central pier acting as a fixed point. We first show that the available long-term horizontal displacement measurements recorded during the undamaged state exhibit strong correlations at the different locations of the bearings. Thus, we combine measurements from four sensors to design a robust performance indicator that is only weakly affected by temperature variations after the application of principal component analysis. We validate the method and show its efficiency against false positives and negatives using several metrics: accuracy, precision, recall, and F1 score. Due to its unsupervised learning scope, the proposed technique is intended to serve as a real-time supervision tool that complements maintenance inspections. It aims to provide support for the prioritization and postponement of maintenance actions in bridge management.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

scholarly journals The PUFFinCA scale: development of an instrument for evaluating the primary care functions of family physicians in childhood asthma

2018 ◽  
Vol 20 ◽  
Author(s):  
Ayşe Nur Usturali Mut ◽  
Zeliha Aslı Öcek ◽  
Meltem Çiçeklioğlu ◽  
Şafak Taner ◽  
Esen Demir
Keyword(s):  
Primary Care ◽  
Childhood Asthma ◽  
Assessment Tool ◽  
Asthma Management ◽  
Correlation Coefficients ◽  
Family Physicians ◽  
Principal Component ◽  
Care Assessment ◽  
Provision Of Services ◽  
Children With Asthma

AbstractAimTo develop the Primary care fUnctions oF Family physicians in Childhood Asthma (PUFFinCA) scale for evaluating the cardinal process functions of primary care services (accessibility, comprehensiveness, continuity and coordination) provided by family physicians (FPs) in the management of childhood asthma.BackgroundIn the literature on the functions of primary care, there is no assessment tool focusing on children with asthma. Primary care assessment scales adapted to various languages are not suitable to adequately address the needs of special patient groups, such as children with asthma.MethodsIn this methodological study, the instrument development process was completed in four stages: establishing the pool of items, evaluating the content validity, applying the scale and statistical analysis. The scale was applied to 320 children who had asthma and received care in the clinic of the Department of Pediatrics, Division of Allergy and Pulmonology at Ege University School of Medicine, Turkey. The Cronbach’s α and Spearman–Brown coefficient were calculated to determine the reliability of the scale. Principal component analysis was used to determine the construct validity of the scale.FindingsThe PUFFinCA scale was found to have four-factor structure and 25 items. Cronbach’s α coefficient was 0.93. It has been determined that the reliability was excellent and the item-total correlation coefficients were >0.30 each. The factors were titled FP’s ‘functions of accessibility, first contact and continuity’, ‘functions of coordination and comprehensiveness of health services related to asthma management’, ‘provision of preventive care related to asthma’ and ‘provision of services for paid vaccinations’.

scholarly journals Use of Time- and Frequency-Domain Approaches for Damage Detection in Civil Engineering Structures

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
V. H. Nguyen ◽  
J. Mahowald ◽  
S. Maas ◽  
J.-C. Golinval
Keyword(s):  
Damage Detection ◽  
Frequency Domain ◽  
Prestressed Concrete ◽  
Civil Engineering ◽  
Damage Identification ◽  
Real Life ◽  
Hankel Matrix ◽  
Principal Component ◽  
Engineering Structures ◽  
Civil Engineering Structures

The aim of this paper is to apply both time- and frequency-domain-based approaches on real-life civil engineering structures and to assess their capability for damage detection. The methodology is based on Principal Component Analysis of the Hankel matrix built from output-only measurements and of Frequency Response Functions. Damage detection is performed using the concept of subspace angles between a current (possibly damaged state) and a reference (undamaged) state. The first structure is the Champangshiehl Bridge located in Luxembourg. Several damage levels were intentionally created by cutting a growing number of prestressed tendons and vibration data were acquired by the University of Luxembourg for each damaged state. The second example consists in reinforced and prestressed concrete panels. Successive damages were introduced in the panels by loading heavy weights and by cutting steel wires. The illustrations show different consequences in damage identification by the considered techniques.

scholarly journals Differences in acid phosphomonoesterase activity in forest soils of the Smrk Massive in the Moravskoslezské Beskydy Mts. (Czech Republic)

Beskydy ◽  
2012 ◽  
Vol 5 (2) ◽  
pp. 135-152
Author(s):  
A. Bajer ◽  
P. Samec ◽  
M. Žárník
Keyword(s):  
Relative Size ◽  
Chemical Properties ◽  
Principal Component ◽  
European Beech ◽  
Soil Characteristics ◽  
Strong Correlations ◽  
Spruce Stands ◽  
The Individual ◽  
A Site

The purpose of this paper is to determine the individual relations between APEA and specific soils and environmental factors. To disclose these relations, analysis of component vectors and principal component analysis (PCA) were performed. Vectors of soil characteristics with participation of APEA (aAKFE) and vectors of pedochemical variables (aCHEM) were also calculated. Their ratio (ia) indicated the relative size of the APEA impact on the relations between pedochemical characteristics. Based on the statistical analyses, different role of APEA in Norway spruce and in European beech stands was detected. While APEA in spruce stands did not show significant correlations with the other examined soil chemical properties, soils under beech stands displayed strong correlations with some of the pedochemical variables. The idea of this research is to find out whether APEA could be used as an indicator of forest vegetation status and of the anthropogenic load on a site.

scholarly journals Component-level Performance-based Seismic Assessment and Design Approach for Concrete Moment Frames

2016 ◽  
Vol 10 (1) ◽  
pp. 25-39
Author(s):  
Zhou Xinxian ◽  
Han Xiaolei ◽  
Ji Jing ◽  
Qi Yongle ◽  
Hang Chao
Keyword(s):  
Performance Indicator ◽  
Seismic Assessment ◽  
Performance Level ◽  
Statistical Distribution ◽  
Structural Performance ◽  
Structural Components ◽  
Moment Frames ◽  
Acceptance Criteria ◽  
Performance Levels ◽  
Critical Components

To solve the conservatism of acceptance criteria in ASCE/SEI 41 provisions, a new concept of component performance is put forward and an alternative method based on the statistical distribution of component performance levels to evaluate structural performance level is proposed. Independent component performance levels are redefined in detail and component performance indicator limits are developed, which are different from acceptance criteria for integral target performance level of entire building proposed by ASCE standards. Structural components are classified into critical components and general components. The relationship between structural performance levels and the statistical distribution of component performance levels, including performance levels of critical components, general components and non-structural components, is proposed. A framework for applying this method will be discussed in detailand implemented to a seven-story moment frame. It is concluded that this new evaluation method is simple and meaningful for performance-based seismic assessment and design.

scholarly journals Pulse Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Nanyue ◽  
Yu Youhua ◽  
Huang Dawei ◽  
Xu Bin ◽  
Liu Jia ◽  
...  
Keyword(s):  
Liver Disease ◽  
Fatty Liver ◽  
Healthy Volunteers ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Fatty Liver Disease ◽  
Principal Component ◽  
Identification Accuracy ◽  
Pulse Diagnosis ◽  
Significant Difference

Objective. To compare the signals of pulse diagnosis of fatty liver disease (FLD) patients and cirrhosis patients.Methods. After collecting the pulse waves of patients with fatty liver disease, cirrhosis patients, and healthy volunteers, we do pretreatment and parameters extracting based on harmonic fitting, modeling, and identification by unsupervised learning Principal Component Analysis (PCA) and supervised learning Least squares Regression (LS) and Least Absolute Shrinkage and Selection Operator (LASSO) with cross-validation step by step for analysis.Results. There is significant difference between the pulse diagnosis signals of healthy volunteers and patients with FLD and cirrhosis, and the result was confirmed by 3 analysis methods. The identification accuracy of the 1st principal component is about 75% without any classification formation by PCA, and supervised learning’s accuracy (LS and LASSO) was even more than 93% when 7 parameters were used and was 84% when only 2 parameters were used.Conclusion. The method we built in this study based on the combination of unsupervised learning PCA and supervised learning LS and LASSO might offer some confidence for the realization of computer-aided diagnosis by pulse diagnosis in TCM. In addition, this study might offer some important evidence for the science of pulse diagnosis in TCM clinical diagnosis.

A Study on Estimation Model of Incidence Factor of the Thermal Bridge Using In-Situ Measurement Infrared Thermography

2021 ◽  
Author(s):  
Eunho Kang ◽  
Hyomoon Lee ◽  
Dongsu Kim ◽  
Jongho Yoon
Keyword(s):  
Infrared Thermography ◽  
Coefficient Of Variation ◽  
Performance Indicator ◽  
Likelihood Function ◽  
Estimation Method ◽  
Estimation Model ◽  
Thermal Bridge ◽  
Convergence Results ◽  
Bridge Performance

Abstract Practical thermal bridge performance indicators (ITBs) of existing buildings may differ from calculated thermal bridge performance derived theoretically due to actual construction conditions, such as effect of irregular shapes and aging. To fill this gap in a practical manner, more realistic quantitative evaluation of thermal bridge at on-site needs to be considered to identify thermal behaviors throughout exterior walls and thus improve overall insulation performance of buildings. In this paper, the model of a thermal bridge performance indicator is developed based on an in-situ Infrared thermography method, and a case study is then carried out to evaluate thermal performance of an existing exterior wall using the developed model. For the estimation method in this study, the form of the likelihood function is used with the Bayesian method to constantly reflect the measured data. Subsequently, the coefficient of variation is applied to analyze required times for the assumed convergence. Results from the measurement for three days show that thermal bridge under the measurement has more heat losses, including 1.14 times, when compared to the non-thermal bridge. In addition, the results present that it takes about 40 hours to reach 1% of the variation coefficient. Comparison of the ITB estimated at coefficient of variation 1% (40 hours point) with the ITB estimated at end-of-experiment (72 hours point) results in 0.9% of a relative error.

COVID-19 vs. Healthcare Information System Self-Efficacy Perception (HISSEP)

2021 ◽  
pp. 271-292
Author(s):  
B. J. Weathersby-Holman
Keyword(s):  
Information System ◽  
Patient Care ◽  
Older Workers ◽  
Self Efficacy ◽  
Assessment Tool ◽  
Principal Component ◽  
Face Validity ◽  
Healthcare Information ◽  
Survey Tool ◽  
Healthcare Information System

Coronavirus has emphasized the importance of nursing contributions and their integral participation in interdisciplinary leadership teams providing patient care in healthcare organizations. Workforce shortages of qualified nurses in healthcare with technology skills are necessary to maintain a high level of patient care and healthcare operations. A validated instrument, Healthcare Information System Self-Efficacy Perception, was created providing a self-assessment tool for measuring an older working nurse's perception of self-efficacy of healthcare information system training within a healthcare environment. The study was the first of its kind to recognize the salient training differences that existed for older workers in a healthcare setting. The instrument was developed using a focus group, pilot study, and validated with registered nurses (RN) in a single healthcare organization. The sample (N=162) was assessed using an online survey tool. After face validity was established for HISSEP, a principal component factor analysis was conducted to determine content validity.

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