• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.385-395
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• 2018

With the development of Internet, cyber attack has become a major threat. To detect cyber attacks, intrusion detection system(IDS) has been widely deployed. But IDS has a critical weakness which is that it generates a large number of false alarms. One of the promising techniques that reduce the false alarms in real time is machine learning. However, there are problems that must be solved to use machine learning. So, many machine learning approaches have been applied to this field. But so far, researchers have not focused on features. Despite the features of IDS alerts are important for performance of model, the approach to feature is ignored. In this paper, we propose new feature set which can improve the performance of model and can be extracted from a single alarm. New features are motivated from security analyst's know-how. We trained and tested the proposed model applied new feature set with real IDS alerts. Experimental results indicate the proposed model can achieve better accuracy and false positive rate than SVM model with ordinary features.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.185-198
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• 2023

In marine seismic surveys, the inevitable occurrence of trace gaps in the near offset resulting from geometrical differences between sources and receivers adversely affects subsequent seismic data processing and imaging. The absence of data in the near-offset region hinders accurate seismic imaging. Therefore, reconstructing the missing near-offset information is crucial for mitigating the influence of seismic multiples, particularly in the case of offshore surveys where the impact of multiple reflections is relatively more pronounced. Conventionally, various interpolation methods based on the Radon transform have been proposed to address the issue of the nearoffset data gap. However, these methods have several limitations, leading to the recent emergence of deep-learning (DL)-based approaches as alternatives. In this study, we conducted an in-depth analysis of two representative DL-based studies to scrutinize the challenges that future studies on near-offset interpolation must address. Furthermore, through field data experiments, we precisely analyze the limitations encountered when applying previous DL-based trace interpolation techniques to near-offset situations. Consequently, we suggest that near-offset data gaps must be approached by extrapolation rather than interpolation.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.251-257
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• 2022

Nowadays, the issues of comprehensive formation of a person capable of self-education, self-development and creative self-realization in the conditions of distance education are relevant. There is a need to solve this problem, which is due to social, cultural, and pedagogical factors. This makes it necessary to find effective means of personality formation. In this matter, great importance is attached to the modern method of forming a creative personality - art therapy. Various approaches to the definition of art therapy have been clarified. They consider various forms of art therapy when working with children, adolescents and adults in the context of distance education. The most relevant are the two main forms of work - individual and group art therapy. Art therapy develops the individual's creativity. Therefore, during art therapy, attention is focused on the inner world, experiences, and feelings. Therefore, we believe that in the context of distance education, art therapy has everything for the powerful potential of personality formation. Scientists consider this therapy as therapy by means of art, which is based on experiences, conflicts that can be expressed in the visual arts and music. Art therapy helps to get rid of conflicts and experiences. This happens in the context of distance education through the development of attention to feelings, strengthening one's own personal value and increasing artistic competence. The article describes the signs that characterize art therapy. Art-therapeutic technologies in the context of distance education, which are now actively used by psychologists, teachers and art therapists themselves, are highlighted. The advantages of distance learning are considered. The characteristic features of distance learning and features of the use of art therapy by means of distance education in the process of professional training of specialists are determined.

• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.846-859
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• 2005

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering. However, their applications in fault diagnosis of rotating machinery are rather limited. Most of the published papers focus on some special fault diagnoses. This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies. The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.5-10
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• 2004

In this paper, we propose a service control framework and user interface in a class automation of ubiquitous computing environment. We propose UPnP-based service control architecture and introduce an example service scenarios for automatic classroom. We present context-aware design approaches to argument user interface. We also present intelligent content authoring that facilitates producing e-learning content using activity context.

• Proceedings of the KSRS Conference
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• v.2
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• pp.812-815
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• 2006

Proteins are essential agents for controlling, effecting and modulating cellular functions, and proteins with similar sequences have diverged from a common ancestral gene, and have similar structures and functions. Function prediction of unknown proteins remains one of the most challenging problems in bioinformatics. Recently, various computational approaches have been developed for identification of short sequences that are conserved within a family of closely related protein sequence. Protein function is often correlated with highly conserved motifs. Motif is the smallest unit of protein structure and function, and intends to make core part among protein structural and functional components. Therefore, prediction methods using data mining or machine learning have been developed. In this paper, we describe an approach for protein function prediction of motif-based models using data mining. Our work consists of three phrases. We make training and test data set and construct classifier using a training set. Also, through experiments, we evaluate our classifier with other classifiers in point of the accuracy of resulting classification.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.227-232
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• 2003

Recently, support vector learning attracts an enormous amount of interest in the areas of function approximation, pattern classification, and novelty detection. One of the main reasons for the success of the support vector machines(SVMs) seems to be the availability of global and sparse solutions. Among the approaches sharing the same reasons for success and exhibiting a similarly good performance, we have KFD(kernel Fisher discriminant) approach. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and the KFD approach for regression. After reviewing support vector regression, semi-parametric approach for including predetermined basis functions, and the KFD regression, this paper presents an extension of the conventional KFD approach for regression toward the direction that can utilize predetermined basis functions. The applicability of the presented method is illustrated via a regression example.

• Research in Mathematical Education
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• v.15 no.2
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• pp.115-126
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• 2011

The aim of this study was to explore teachers' values about teaching mathematics in the classrooms which implemented Lesson Study and Open Approach as a teaching approach. The targeted group was 83 school teachers from 4 schools participating in a teacher professional development project. The data was gathered through teacher questionnaires, lesson observations and interviews. Data analysis is based on Bishop's (1988; 2003; 2007) and Komin's (1990) frameworks. The results from the implementation of Lesson Study and Open Approach in Thai classroom found the different of the roles and behaviors of teachers and students in classroom. The results revealed 3 kinds of values about teaching: Mathematical values, General educational values, Mathematics educational values and also found that most of the teachers valued problem solving as an innovative teaching approach as against traditional approaches they were familiar with.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.401-403
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• 1998

This paper describes the neural network control method for the identification and control of chaotic nonlinear dynamical systems effectively. In our control method, the controlled system is modeled by an unknown NARMA model, and a feedforward neural network is used for identifying the chaotic system. The control signals are directly obtained by minimizing the difference between a setpoint and the output of the neural network model. Since learning algorithm guarantees that the output of the neural network model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the setpoint.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1187-1190
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• 1993

The problem of the optimization of fuzzy relational models for dealing with (non-fuzzy) numerical data is investigated. In this context, interfaces optimization assumes particular importance, becoming a determinant factor in what concerns the overall model performance. Considering this, several scenarios for building fuzzy relational models are presented. These are: (i) optimizing I/O interfaces in advance (independently from the linguistic part of the model); (ii) optimizing I/O interfaces in advance and allowing that their optimized parameters may change during the learning of the linguistic part of the model; (iii) build simultaneously both interfaces and the linguistic subsystem; and (iv) build simultaneously both linguistic subsystem and interfaces, now subject to semantic integrity constraints. As linguistic subsystems, both a basic type and an extended versions of fuzzy relation equations are exploited in each one of these scenarios. A comparative analysis of the differ nt approaches is summarized.