• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.7 s.94
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• pp.1783-1793
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• 1993

Pattern recognition technique using fuzzy c-means algorithm and multilayer perceptron was applied to classify tool wear states in turning. The tool wear states were categorized into the three regions 'Initial', 'Normal', 'Severe' wear. The root mean square(RMS) value of acoustic emission(AE) and current signal was used for the classification of tool wear states. The simulation results showed that a fuzzy c-means algorithm was better than the conventional pattern recognition techniques for classifying ambiguous informations. And normalized RMS signal can provide good results for classifying tool wear. In addition, a fuzzy c-means algorithm(success rate for tool wear classification : 87%) is more efficient than the multilayer perceptron(success rate for tool wear classification : 70%).

• Journal of Korean Academy of Nursing
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• v.30 no.5
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• pp.1243-1253
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• 2000

The purpose of this study was to verify the validity of the Patient Severity Classification Tool by examining the correlations between the APACHE Ⅲ and the Patient Severity Classification Tool and to propose admission criteria to the ICU. The instruments used for this study were the APACHE Ⅲ developed by Knaus and the Patient Severity Classification Tool developed by Korean Clinical Nurses Association. Data was collected from the 156 Medical ICU patients during their first 24 hours of admission at the Seoul National University Hospital by three trained Medical ICU nurses from April 20 to August 31 1999. Data were analyzed using the frequency, $x^2$, Wilcoxon rank sum test, and Spearman rho. There was statistically significant correlations between the scores of the APACHE III and the Patient Severity Classification Tool. Mortality rate was increased as patients classification of severity in both the APACHE III and the Patient Severity Classification Tool scored higher. The Patient Severity Classification Tool was proved to be a valid and reliable tool, and a useful tool as one of the severity predicting factors, ICU admission criteria, information sharing between ICUs, quality evaluations of ICUs, and ICU nurse staffing.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.376-379
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• 2003

In this paper we describe DCClass, a tool for fuzzy information granulation with transparency constraints. The tool is particularly suited to solve fuzzy classification problems, since it is able to automatically extract information granules with class labels. For transparency pursuits, the resulting information granules are represented in form of fuzzy Cartesian product of one-dimensional fuzzy sets. As a key feature, the proposed tool is capable to self-determining the optimal granularity level of each one-dimensional fuzzy set by exploiting class information. The resulting fun information granules can be directly translated in human-comprehensible fuzzy rules to be used for class inference. The paper reports preliminary experimental results on a medical diagnosis problem that shows the utility of the proposed tool.

• Journal of Sasang Constitutional Medicine
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• v.30 no.3
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• pp.40-47
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• 2018

Objectives The purpose of this study is to analyze the factors contributing to the classification of Sasang Constitution using Sasang Constitutional Analysis Tool 2. Methods A total of 99 subjects were assessed for the classification of Sasang Constitution using four measurement factors (face, voice, body shape, and questionnaire information) of Sasang Constitutional Analysis Tool 2. Results Taeeumin had significantly higher body weight and BMI. In the result of the agreement between the judgment of the four measurement factors and the final judgment of Sasang Constitution, the agreement degree of Soeumin was the highest value of 2.6. Taeeumin, Soeumin, and Soyangin showed the highest agreement with the individual judgment of face, body shape and questionnaire, and body shape, respectively. Conclusions It is difficult to conclude that any individual factor contributes significantly to the classification of Sasang Constitution. Further study on Sasang Constitutional Analysis Tool 2 involving more peoples is needed in order to determine the factors contributing to the classification of Sasang Constitution.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.263-277
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• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

• Journal of KIISE:Software and Applications
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• v.26 no.1
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• pp.167-167
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• 1999

The majority of text categorization systems use the term-based classification method. However, because of too many terms, this method is not effective to classify the documents in areal-time environment. This paper presents a real-time concept-based text categorization system,which classifies texts using thesaurus. The system consists of a Korean morphological analyzer, athesaurus tool, and a probability-vector similarity measurer. The thesaurus tool acquires the meaningsof input terms and represents the text with not the term-vector but the concept-vector. Because theconcept-vector consists of semantic units with the small size, it makes the system enable to analyzethe text with real-time. As representing the meanings of the text, the vector supports theconcept-based classification. The probability-vector similarity measurer decides the subject of the textby calculating the vector similarity between the input text and each subject. In the experimentalresults, we show that the proposed system can effectively analyze texts with real-time and do aconcept-based classification. Moreover, the experiment informs that we must expand the thesaurustool for the better system.

• Journal of Physiology & Pathology in Korean Medicine
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• v.30 no.6
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• pp.376-384
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• 2016

Systems biology is an emerging field aiming at a systems level understanding of living organisms and focusing on the characteristics of the whole network of them. The emergence of systems biology is partly because of the availability of huge amounts of data on organisms and the extensive support of computational technologies as the tools for understanding complex biological systems. The scientific understanding of Korean medicine has been obstructed because of the lack of proper methods examining the complex nature and the unique property of it. However, systems biology could give a chance understanding Korean medicine objectively and scientifically. Pattern classification is a unique tool of Korean medicine to diagnose and treat patients and systems biology would give a useful tool to interpret pattern classification. Various omics technologies has been used to explain the relations between pattern classification and biological factors and then many characteristics of pattern classification in various diseases have been discovered. Therefore, pattern classification could be a bridge to understand the features and differences of western medicine and Korean medicine and it could be a basis to develop pattern-based personalized medicine.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.133-140
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• 2007

The support vector machine has been well developed as a powerful tool for solving classification problems. In many real world applications, each training point has a different effect on constructing classification rule. Lin and Wang (2002) proposed fuzzy support vector machines for this kind of classification problems, which assign fuzzy memberships to the input data and reformulate the support vector classification. In this paper another intuitive approach is proposed by using the fuzzy ${\alpha}-cut$ set. It will show us the trend of classification functions as ${\alpha}$ changes.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.23-41
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• 1995

Business researchers have traditionally used statistical techniques for classification. In late 1980's, inductive learning started to be used for business classification. Recently, neural network began to be a, pp.ied for business classification. This study reviews the business classification studies, identifies a neural network a, pp.oach as the most powerful classification tool, and discusses the problems and issues in neural network a, pp.ications.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.145-153
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• 2009

Trajectory data are ubiquitous in the real world. Recent progress on satellite, sensor, RFID, video, and wireless technologies has made it possible to systematically track object movements and collect huge amounts of trajectory data. Accordingly, there is an ever-increasing interest in performing data analysis over trajectory data. In this paper, we develop a data mining tool for massive trajectory data. This mining tool supports three operations, clustering, classification, and outlier detection, which are the most widely used ones. Trajectory clustering discovers common movement patterns, trajectory classification predicts the class labels of moving objects based on their trajectories, and trajectory outlier detection finds trajectories that are grossly different from or inconsistent with the remaining set of trajectories. The primary advantage of the mining tool is to take advantage of the information of partial trajectories in the process of data mining. The effectiveness of the mining tool is shown using various real trajectory data sets. We believe that we have provided practical software for trajectory data mining which can be used in many real applications.