• Geomechanics and Engineering
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• 제37권5호
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• 한국해양공학회지
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• 제13권3호통권33호
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• pp.29-35
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• 1999

At the design of Mark III membrane type LNG tank, an analytical and experimental approach on the fatigue strengths of membrane and its welds are very important in order to assist designers and surveyors. In this study, fatigue tests of lap weld of Mark III membrane type LNG tank were carried out and cumulative damage factor was calculated in order to estimate the fatigue life by probability density function and rule methods. It contained the following tests and reviews : 1) The fatigue tests of lap weld of stainless steel according to statistical testing method recommended by JSME, 2)Preparation of S-N curve for lap welds considering the statistical properties of the results of fatigue tests. 3) Procedure for estimating the initiation life of fatigue crack of lap welds under variable loads by the rule lf classification society and probability density function, 4) Guideline for inspection of lap welds fo membrane type LNG tank.

• 전기학회논문지
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• 제57권6호
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• 대한지구과학교육학회지
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• 제4권2호
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• pp.102-107
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• 2011

In this study, elementary school science program, this category did not affect any troubleshooting analyzed. Thinking Science Program to buy for them in group activities by using one of the elements of a program of treatment and cognitive level effects were two kinds of research questions. 102, 5th grade four classes were involved, these two classes of the experimental group and the remaining two classes were divided into a control group. Pre-test between the two groups is compared to the level and classification problem-solving skills but the skills did not show a statistically significant difference. Thinking Science activity after application of classification and posttest the experimental group than in the control group problem solving abilities of students classified at the level of statistical significance was higher. Thinking Science program is a treatment effect for each level of analysis, tests, regardless of cognitive level was more effective. Through theses findings, Thinking Science activities 5th grade category classification problem-solving skills of students found to be effective in improving and these types of programs actively introduced in the field suggests that we need to see.

• 한국임상약학회지
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• 제10권3호
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• pp.130-139
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• 2000

This study is intended to set the criteria for the classification of prescription and non-prescription drugs, and classify hospital pharmacy formulations according to the criteria. 717 hospital pharmacy formulations were collected ken the Center for review and evaluation of health insurance, and national provincial offices. Hospital pharmacy formulations were evaluated based on the 'Guidelines on the Hospital Pharmacy Formulations (Notification No. 2000-46)'by the Ministry of Health and Welfare. Drug classification advisory committee was composed of twelve medical and pharmaceutical specialists, and suggested opinions on the drug classification. Among 717 formulations, 651 drugs $(90.8\%)$ satisfied the basic conditions for the hospital pharmacy formulations. 312 formulations $(43.5\%)$ were classified as drugs for the disinfection and tests. For the rest of them, 231 formulations were classified as prescription drugs whereas 108 drugs were as non-prescription drugs. 56 non-prescription drugs were included as hospital formulations, because there were no therapeutic alternatives. Iu sum 599 drugs $(83.5\%)$ were suggested as hospital pharmacy formulations. The study also recommends pharmaceutical companies to produce drugs of limited commercial value, and doctors to change their unique prescribing behavior in order to prevent the abuse of hospital pharmacy formulations.

• 대한인간공학회지
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• 제31권4호
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• pp.533-540
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• 2012

Objective: This paper targets a framework of a hand gesture based interface design. Background: While a modeling of contact-based interfaces has focused on users' ergonomic interface designs and real-time technologies, an implementation of a contactless interface needs error-free classifications as an essential prior condition. These trends made many research studies concentrate on the designs of feature vectors, learning models and their tests. Even though there have been remarkable advances in this field, the ignorance of ergonomics and users' cognitions result in several problems including a user's uneasy behaviors. Method: In order to incorporate compatibilities considering users' comfortable behaviors and device's classification abilities simultaneously, classification-oriented gestures are extracted using the suggested human-hand model and closed-loop classification procedures. Out of the extracted gestures, the compatibility-oriented gestures are acquired though human's ergonomic and cognitive experiments. Then, the obtained hand gestures are converted into a series of hand behaviors - Handycon - which is mapped into several functions in a mobile device. Results: This Handycon model guarantees users' easy behavior and helps fast understandings as well as the high classification rate. Conclusion and Application: The suggested framework contributes to develop a hand gesture-based contactless interface model considering compatibilities between human and device. The suggested procedures can be applied effectively into other contactless interface designs.

• 아동학회지
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• 제4권
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• pp.37-51
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• 1983

The purpose of this study was the investigation of theoretical and empirical relationships between Piagetian and psychometric assessments of intelligence. Specifically, the factor structure of Piagetian-type scales, the relationship between Piagetian scales and psychometric intelligence tests, and differences in the factor structure of Piagetian and psychometric assessments of intelligence were studied. The subjects of this stuby were 70 children (35 boys and 35 girls) in the 1st grade of an elementary school in Seoul The Piagetian-type scales and the K-WISC were administered individually, and the General Intelligence Test was administered to groups of children. Statistical analysis of the obtained data consisted of the SPSS Computer program including factor analysis and Pearson's product moment correlation coefficient. The Piagetian-type scales were found to consist of three factors, which accounted for 55 percent of the total common-factor variance. Factor-I was a factor indicating "conservation". Factor-II was a factor indicating "moral judgements". Factor-III was a factor indicating "classification and identity". Correlations between subtests of psychometric tests and Piagetian scales were relatively low or moderate. Relations between IQs assessed by the psychometric tests and Piagetian scales were also relativeyly low or moderate. Eight factors were extracted from the joint factor analysis of psychometric intelligence tests and Piagetian scales, and they accounted for 67 percent of the total common-factor variance. Factors-I, II, III, and V consisted of subtests of psychometric assessments, and Factors-IV, VI, VII and VIII were composed of Piagetian scales. Factor-I was a factor for "reasoning ability based upon language". Factor-II was a factor for "performance ability". Factor-III was a factor for "grouping ability". Factor-IV was a factor for "conservation". Factor-V was a factor indicating "symbol and language usage ability". Factor- VI was a factor indicating "moral judgments". Factor-VII was a factor indicating "length consevation". Factor-VIII was a factor indicating "classification and identity".

• 한국화재소방학회:학술대회논문집
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• 한국화재소방학회 2008년도 추계학술논문발표회 논문집
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• pp.99-104
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• 2008

The combustion properties of sandwich panels were tested and analyzed according to ISO 13784-1(Room Corner Test for Sandwich panel building systems) test method for the purpose of establishing the classification of reaction to fire performance. Several variables including heat release rate, smoke production rate, FIGRA, SMOGRA, and so on, were analyzed for specific four materials about sandwich panel systems on each 5 times, totally 20 times. Finally, elements for Classification system were suggested and evaluations for those elements were made.

• The Journal of the Acoustical Society of Korea
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• 제17권3E호
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• pp.12-21
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• 1998

In this paper, we describe the one channel five-way, V/U/M/N/S (Voice/Unvoice/Nasal/Silent), classification algorithm for automatically classifying speech. The decision making process is viewed as a pattern viewed as a pattern recognition problem. Two aspects of the algorithm are developed: feature selection and classifier type. The feature selection procedure is studied for identifying a set of features to make V/U/M/N/S classification. The classifiers used are a vector quantization (VQ), a neural network(NN), and a decision tree method. Actual five sentences spoken by six speakers, three male and three female, are tested with proposed classifiers. From a set of measurement tests, the proposed classifiers show fairly good accuracy for V/U/M/N/S decision.

• 한국멀티미디어학회논문지
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• 제21권5호
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• pp.617-625
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.