• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.309-322
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• 2024

Identifying algae in water is important for managing algal blooms which have great impact on drinking water supply systems. There have been various microscopic approaches developed for algae classification. Many of them are based on the morphological features of algae. However, there have seldom been mathematical frameworks for comparing the shape of algae, represented as a planar continuous curve obtained from an image. In this work, we describe a recent framework for computing shape distance between two different algae based on the elastic metric and a novel functional representation called the square root velocity function (SRVF). We further introduce statistical procedures for multiple shapes of algae including computing the sample mean, the sample covariance, and performing the principal component analysis (PCA). Based on the shape distance, we classify six algal species in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis, Oscillatoria, and Anabaena), two diatoms (Fragilaria and Synedra), and one green algae (Pediastrum). We provide and compare the classification performance of various distance-based and model-based methods. We additionally compare elastic shape distance to non-elastic distance using the nearest neighbor classifiers.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.71-76
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• 2007

Object detection process which makes decision on the existence of meaningful objects in a given image is a crucial part of image recognition in computer vision system. Hausdorff distance metric has been used in object detection and shows good results in applications such as face recognition. It defines the dissimilarity between two sets of points and is used to find the object that is most similar to the given model. This paper proposes a Hausdorff distance based detection method that uses directional information of points to improve detection accuracy when the sets of points are derived from edge extraction as is in usual cases. In this method, elastic energy needed to make two directional points coincident is used as a measure of similarity.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.3 no.1
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• pp.29-42
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• 1997

The theory of plyometric exercise was introduced by Soviet Jump Coach Yuri Verhoshanski in 1967. Plyo comes from the Greek word pleythein, which means to increase. Plyo is the Greek word for "more", while metric means "to measure". The practical definition of plyometrics is a quick powerful movement involving a prestretching or countermovement that activates the stretch-shortening cycle. The purpose of plyometric training is to heighten the excitability of the nervous system for improved reactive ability of the neuromuscular system. The success of plyometric exercise is based on the utilization of the serial elastic properties and stretch-reflex properties of the muscle.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.601-612
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• 2024

A functional clustering analysis is a crucial machine learning technique in functional data analysis. Many functional clustering methods have been developed to enhance clustering performance. Moreover, due to the phase variability between functions, elastic functional clustering methods, such as applying the Fisher-Rao metric, which can manage phase variation during clustering, have been developed to improve model performance. However, aligning functions without considering the phase variation can distort functional information because phase variation can be a natural characteristic of functions. Hence, we propose a state-of-the-art functional hierarchical clustering that can manage phase and amplitude variations of functional data. This approach is based on the phase and amplitude separation method using the norm-preserving time warping of functions. Due to its invariance property, this representation provides robust variability for phase and amplitude components of functions and improves clustering performance compared to conventional functional hierarchical clustering models. We demonstrate this framework using simulated and real data.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• Coupled systems mechanics
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• v.4 no.4
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• pp.279-295
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• 2015

The article presents a theoretical-experimental approach developed for modeling the coefficient of sliding friction in the dynamic system tool-workpiece in slide diamond burnishing of low-alloy unhardened steels. The experimental setup, implemented on conventional lathe, includes a specially designed device, with a straight cantilever beam as body. The beam is simultaneously loaded by bending (from transverse slide friction force) and compression (from longitudinal burnishing force), which is a reason for geometrical nonlinearity. A method, based on the idea of separation of the variables (time and metric) before establishing the differential equation of motion, has been applied for dynamic modeling of the beam elastic curve. Between the longitudinal (burnishing force) and transverse (slide friction force) forces exists a correlation defined by Coulomb's law of sliding friction. On this basis, an analytical relationship between the beam deflection and the sought friction coefficient has been obtained. In order to measure the deflection of the beam, strain gauges connected in a "full bridge" type of circuit are used. A flexible adhesive is selected, which provides an opportunity for dynamic measurements through the constructed measuring system. The signal is proportional to the beam deflection and is fed to the analog input of USB DAQ board, from where the signal enters in a purposely created virtual instrument which is developed by means of Labview. The basic characteristic of the virtual instrument is the ability to record and visualize in a real time the measured deflection. The signal sampling frequency is chosen in accordance with Nyquist-Shannon sampling theorem. In order to obtain a regression model of the friction coefficient with the participation of the diamond burnishing process parameters, an experimental design with 55 experimental points is synthesized. A regression analysis and analysis of variance have been carried out. The influence of the factors on the friction coefficient is established using sections of the hyper-surface of the friction coefficient model with the hyper-planes.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.4
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• pp.409-415
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• 2015

For the design of Concrete-Filled Steel Tube(CFST) columns, the outside diameter D to the steel tube thickness t ratio(D/t ratio) is limited to prevent the local buckling of steel tubes. Each design code proposes the respective model to compute the maximum D/t ratio using the yield strength of steel $f_y$ or $f_y$ and the elastic modulus of steel E. Considering the uncertainty in $f_y$ and E, the reliability index ${beta}$ for the local buckling of a CFST section can be calculated by formulating the limit state function including the maximum D/t models. The resulted ${beta}$ depends on the maximum D/t model used for the reliability analysis. This variability in reliability analysis is due to ambiguity in choosing computational models and it is called as "modelling uncertainty." This uncertainty can be considered as "non-specificity" of an epistemic uncertainty and modelled by constructing possibility distribution functions. In this study, three different computation models for the maximum D/t ratio are used to conduct reliability analyses for the local buckling of a CFST section and the reliability index ${beta}$ will be computed respectively. The "non-specific ${beta}s$" will be modelled by possibility distribution function and a metric, degree of confirmation, is measured from the possibility distribution function. It is shown that the degree of confirmation increases when ${beta}$ decreases. Conclusively, a new set of reliability indices associated with a degree of confirmation is determined and it is allowed to decide reliability index for the local buckling of a CFST section with an acceptable confirmation level.