Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

Risk Factor Identification, Classification and Prediction Summary of Chronic Kidney Disease

2020 ◽  
Vol 13 ◽  
Author(s):  
Pramila Arulanthu ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Disease Diagnosis ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Medical Field ◽  
Data Set ◽  
Medical Issues ◽  
Health Organization

: The medical data has an enormous quantity of information. This data set requires effective classification for accurate prediction. Predicting medical issues is an extremely difficult task in which Chronic Kidney Disease (CKD) is one of the major unpredictable diseases in medical field. Perhaps certain medical experts do not have identical awareness and skill to solve the issues of their patients. Most of the medical experts may have underprivileged results on disease diagnosis of their patients. Sometimes patients may lose their life in nature. As per the Global Burden of Disease (GBD-2015) study, death by CKD was ranked 17th place and GBD-2010 report 27th among the causes of death globally. Death by CKD is constituted 2·9% of all death between the year 2010 and 2013 among people from 15 to 69 age. As per World Health Organization (WHO-2005) report, 58 million people expired by CKD. Hence, this article presents the state of art review on Chronic Kidney Disease (CKD) classification and prediction. Normally, advanced data mining techniques, fuzzy and machine learning algorithms are used to classify medical data and disease diagnosis. This study reviews and summarizes many classification techniques and disease diagnosis methods presented earlier. The main intention of this review is to point out and address some of the issues and complications of the existing methods. It is also attempts to discuss the limitations and accuracy level of the existing CKD classification and disease diagnosis methods.

scholarly journals Experimental Comparison of Radon Domain Approaches for Resident Space Object’s Parameter Estimation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1298
Author(s):  
Selenia Ghio ◽  
Marco Martorella ◽  
Daniele Staglianò ◽  
Dario Petri ◽  
Stefano Lischi ◽  
...  
Keyword(s):  
Experimental Data ◽  
Rotation Period ◽  
Research Topic ◽  
Experimental Comparison ◽  
Rotational Period ◽  
Space Objects ◽  
Resident Space Objects ◽  
The Moment ◽  
Inverse Radon Transform ◽  
Inverse Radon

The fast and uncontrolled rise of the space objects population is threatening the safety of space assets. At the moment, space awareness solutions are among the most calling research topic. In fact, it is vital to persistently observe and characterize resident space objects. Instrumental highlights for their characterization are doubtlessly their size and rotational period. The Inverse Radon Transform (IRT) has been demonstrated to be an effective method for this task. The analysis presented in this paper has the aim to compare various approaches relying on IRT for the estimation of the object’s rotation period. Specifically, the comparison is made on the basis of simulated and experimental data.

Heat Disease Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 846-852
Author(s):  
Prof. Dr. R. Sandhiya
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Assessment Process ◽  
Disease Prediction ◽  
Test Results ◽  
Medical Field ◽  
Modern Age ◽  
Linear Svm

In recent times, the diagnosis of heart disease has become a very critical task in the medical field. In the modern age, one person dies every minute due to heart disease. Data science has an important role in processing big amounts of data in the field of health sciences. Since the diagnosis of heart disease is a complex task, the assessment process should be automated to avoid the risks associated with it and alert the patient in advance. This paper uses the heart disease dataset available in the UCI Machine Learning Repository. The proposed work assesses the risk of heart disease in a patient by applying various data mining methods such as Naive Bayes, Decision Tree, KNN, Linear SVM, RBF SVM, Gaussian Process, Neural Network, Adabost, QDA and Random Forest. This paper provides a comparative study by analyzing the performance of various machine learning algorithms. Test results confirm that the KNN algorithm achieved the highest 97% accuracy compared to other implemented ML algorithms.

scholarly journals Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 557 ◽  
Author(s):  
Cristiano Fragassa ◽  
Matej Babic ◽  
Carlos Perez Bergmann ◽  
Giangiacomo Minak
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Mechanical Properties ◽  
Pattern Recognition ◽  
Cast Iron ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tensile Behaviour ◽  
Cast Alloys ◽  
Pattern Recognition Analysis

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

Computational and Experimental Comparison of Different Volute Geometries in a Radial Compressor

2000 ◽  
Author(s):  
Arttu Reunanen ◽  
Harri Pitkänen ◽  
Timo Siikonen ◽  
Harri Heiska ◽  
Jaakko Larjola ◽  
...  
Keyword(s):  
Experimental Data ◽  
Time Dependent ◽  
Experimental Comparison ◽  
Cfd Analysis ◽  
Laboratory Measurements ◽  
Radial Compressor ◽  
Sliding Mesh ◽  
Overall Performance ◽  
Operating Points ◽  
Mesh Technique

Two different volute geometries of a radial compressor at three different operating points have been analyzed using Computational Fluid Dynamics and detailed laboratory measurements. The performance of the volutes were compared using steady-state CFD-analysis, where the volute and the impeller with diffuser were modeled separately. In addition, a time dependent simulation of the complete compressor using the sliding mesh technique was performed for one operation point. Both volutes were manufactured and the overall performance of the compressor, the pressure distribution in the volute and the flow field in the volute inlet were measured with the respective volute geometries. The results obtained from steady, quasi-steady and time-accurate simulations are compared with experimental data.

Towards a user preference model for interior lighting, Part 3: An alternative model

2019 ◽  
Vol 52 (2) ◽  
pp. 189-201
Author(s):  
TQ Khanh ◽  
P Bodrogi ◽  
X Guo
Keyword(s):  
Alternative Model ◽  
User Preference ◽  
Colour Preference ◽  
Type Representation ◽  
Contour Map ◽  
Preference Model ◽  
Colour Temperature ◽  
Independent Variables ◽  
Interior Lighting ◽  
Present Part

In Parts 1 and 2 of this work, an experiment was described in which subjects assessed their visual impressions of scene brightness (B), visual clarity (VC), colour preference (CP) and scene preference (SP) in a real room. In this room, the horizontal illuminance ( Ev), the correlated colour temperature (CCT) and the level of chroma enhancement caused by the spectrum of the light source (Δ C*) were changed systematically. In the present Part 3, these mean subjective B, VC, CP and SP scale values are re-analysed in terms of an alternative model based on a different set of independent variables: CCT, Δ C* and the circadian stimulus (CS). Contour map diagrams resulting from the new modelling equations are shown and compared with the conventional Kruithof-type representation.

A Measurement and Analyze System for the KY231 Material Strain Tester Base on STM8S207 and Virtual Instrument

2013 ◽  
Vol 785-786 ◽  
pp. 1341-1347
Author(s):  
Tie Wang Liang ◽  
Yan Ren
Keyword(s):  
Experimental Data ◽  
Circuit Design ◽  
Measurement System ◽  
Virtual Instrument ◽  
Historical Data ◽  
Usb Interface ◽  
Measurement And Analysis ◽  
Analysis System ◽  
As Stress ◽  
Physical Quantities

A measurement and analysis system is designed base on the requirement of digital upgrade to the KY231 Material strain tester. This system is made up of two parts :The analyze system based on the LABWINDOWS and the measurement system based on STM8S207. The physical quantities (such as stress, pressure, deformation, etc.) of the metal specimen are acquired, displayed and analyzed digitally in this system, meanwhile the experimental data and curves can be stored in the systerm and compared to the historical data saved in the previous files. Because the USB interface is too complex to the MCU systerm form the aspart of the programming and circuit design, the PL2303 is used to simulate USB interface as virtual RS232 for the communication between the measurement system and the analyze system.

Collinear-Type Statically Balanced Compliant Micro Mechanism (SB-CMM): Experimental Comparison Between Pre-Curved and Straight Beams

2011 ◽  
Author(s):  
Nima Tolou ◽  
Pablo Estevez ◽  
Just L. Herder
Keyword(s):  
Experimental Data ◽  
Range Of Motion ◽  
Robust Design ◽  
Positive Constant ◽  
Experimental Comparison ◽  
Constant Force ◽  
Finite Range ◽  
Curved Beams ◽  
Actuation Force ◽  
Fabrication Errors

The feasibility of collinear-type statically balanced compliant micro mechanism (SB-CMM) with pre-curved beams has been studied experimentally and compares to those with initially straight beams. The collinear-type SB-CMM is a near zero stiffness micro mechanism with a near zero actuation force in a finite range of motion. However, from the experimental data, it was found that the collinear-type SB-CMM are sensitive to fabrication errors, as a results negative or positive constant force instead of zero actuation force maybe obtained. For the case of pre-curved beams, the curvature can be tuned for better zero stiffness behavior. Further improvements in the concept are considered towards a robust design and reliable prototypes, such as optimization and accurate dimensioning and fabrication.

Sign in / Sign up

Export Citation Format

Share Document