• ETRI Journal
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• v.43 no.4
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• pp.694-701
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• 2021

Catastrophic events cause casualties, damage property, and lead to huge social impacts. To build common standards and facilitate international communications regarding disasters, the relevant authorities in social management rank them in subjectively imposed terms such as direct economic losses and loss of life. Terrorist attacks involving uncertain human factors, which are roughly graded based on the rule of property damage, are even more difficult to interpret and assess. In this paper, we collected 114 183 open-source records of terrorist attacks and used a machine learning method to grade them synthetically in an automatic and objective way. No subjective claims or personal preferences were involved in the grading, and each derived common factor contains the comprehensive and rich information of many variables. Our work presents a new automatic ranking approach and is suitable for a broad range of gradation problems. Furthermore, we can use this model to grade all such attacks globally and visualize them to provide new insights.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.12
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• pp.171-178
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• 2002

With increasing the needs for micro and precision parts, micro machining technology using micro tools has been studied to fabricate a small part with high density such as electronics, optics, communications, and medicine industry more than before. Though these micro tools have developed rapidly, it is difficult to apply them to micro fabrication technologies, because of the inherent manufacturing. In this study, micro tools (WC) to produce micro structures and parts were manufactured by cylindrical grinding machine employing ELID (Electrolytic In-process Dressing) technique and the micro tools are fabricated as square shape with the dimension less than 100${\mu}{\textrm}{m}$. With the micro tools on the same machine, characteristics of micro grooving and drilling are evaluated. Also we compare normal micro machining with ultrasonic micro machining on the vibration table. It is confirmed that the developed micro tools are fully applicable to micro grooving, micro drilling and free form cutting.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.279-289
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• 2024

Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is difficult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying meta-classifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.765-776
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• 2006

In this paper we propose an estimation method on the regression model with randomly censored observations of the training data set. The weighted least squares support vector machine regression is applied for the regression function estimation by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed estimation method.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.201-207
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• 2015

Support vector machine (SVM) is a well known kernel-trick supervised learning tool. This study proposes a working scheme for kernel-trick regression and classification (KtRC) as a SVM alternative. KtRC fits the model on a number of random subsamples and selects the best model. Empirical examples and a simulation study indicate that KtRC's performance is comparable to SVM.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.591-610
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• 2019

Deep Learning is one of the machine learning methods to find features from a huge data using non-linear transformation. It is now commonly used for supervised learning in many fields. In particular, Convolutional Neural Network (CNN) is the best technique for the image classification since 2012. For users who consider deep learning models for real-world applications, Keras is a popular API for neural networks written in Python and also can be used in R. We try examine the parameter estimation procedures of Deep Neural Network and structures of CNN models from basics to advanced techniques. We also try to figure out some crucial steps in CNN that can improve image classification performance in the CIFAR10 dataset using Keras. We found that several stacks of convolutional layers and batch normalization could improve prediction performance. We also compared image classification performances with other machine learning methods, including K-Nearest Neighbors (K-NN), Random Forest, and XGBoost, in both MNIST and CIFAR10 dataset.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.225-232
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• 2003

The support vector machine (SVM) is becoming increasingly popular in classification. The import vector machine (IVM) has been introduced for its advantages over SMV. This paper tries to improve the IVM. The proposed method, which is referred to as the polychotomous machine (PM), uses the Newton-Raphson method to find estimates of coefficients, and the Rao and Wald tests, respectively, for addition and deletion of import points. Because the PM basically follows the same addition step and adopts the deletion step, it uses, typically, less import vectors than the IVM without loosing accuracy. Simulated and real data sets are used to illustrate the performance of the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.325-328
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• 2013

Recently, the national carrier of the next-generation growth engine of M2M (machine to Machine) is attracting attention. Therefore, its uses and utilization of the various disciplines is getting wider, and the increasing number of terminals to be used compared to the existing communication of the information that is sent in every object may have increased. Each data transfer from the bulk terminal to a mobile network, if the traffic situation reaches this limit does not facilitate the processing of M2M communication services, this situation can occur. This study measures the mobile networks will be used in M2M communications when it reaches the breaking point for the smooth processing of M2M services are presented. Things grows increasingly smaller the further development of M2M technology, the explosion in mobile communication network, the data traffic will reach the limitations of these methods will be used in determining the ranking of the M2M communication services should be treated as a priority, to beM2M services to mobile networks will help to facilitate.

• Electronics and Telecommunications Trends
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• v.33 no.2
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• pp.39-47
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• 2018

The fundamental problem of communication is that of transmitting a message from a source to a destination over a channel through the use of a transmitter and receiver. To derive a theoretically optimal solution, the transmitter and receiver can be divided into several processing blocks, with each component analyzed and optimized. The idea of machine learning (or deep learning) communications systems goes back to the original definition of the communi-cation problem, and optimizes the transmitter and receiver jointly. Although today's systems have been optimized over the last decades, and it seems difficult to compete with their performance, deep learning based communication is attractive owing to its simplicity and the fact that it can learn to communicate over any type of channel without the need for mathematical modeling or analysis.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.873-878
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• 2003

LS-SVM(Least-Squares Support Vector Machine) has been used as a promising method for regression as well as classification. Suykens et al.(2000) used only the magnitude of residuals to obtain SVs(Support Vectors). Suykens' method behaves well for homogeneous model. But in a heteroscedastic model, the method shows a poor behavior. The present paper proposes a new method to get SVs. The proposed method uses the variance of noise as well as the magnitude of residuals to obtain support vectors. Through the simulation study we justified excellence of our proposed method.