• 대한산업공학회지
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• 제41권6호
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• pp.540-550
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• 2015

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

• 디지털융복합연구
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• 제15권1호
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• pp.393-401
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• 2017

이 연구는 마음그램을 활용한 인성교육프로그램의 개발을 위한 기초적인 틀과 방향을 제시하는 데에 목적이 있다. '마음그램'이란 기존의 분석심리학 및 유식학의 마음구조를 기반으로 인간의 마음을 네 가지 마음자리(탐닉형, 분노형, 경쟁형, 이기심형)로 유형화하고, 이를 위한 인성교육의 단계적 구조를 알기 쉽게 도식화한 것을 말한다. 마음유형별 프로그램의 설계는 세 단계로 구성된다. 첫 번째 단계는 관찰을 통해 자아의식의 원인을 자각하는 '발견'의 단계이며, 두 번째는 부정적인 욕구를 긍정적인 욕구로 조절하는 학습과정인 '전환'단계, 세 번째는 공동체의식의 함양을 목표로 하는 타자와의 '관계'지향의 단계이다.

• 한국컴퓨터정보학회논문지
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• 제23권10호
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• pp.11-21
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• 2018

In this paper we propose a novel log template discovery algorithm which achieves high quality of discovered log templates through iterative log filtering technique. Log templates are the static string pattern of logs that are used to produce actual logs by inserting variable values during runtime. Identifying individual logs into their template category correctly enables us to conduct automated analysis using state-of-the-art machine learning techniques. Our technique looks at the group of logs column-wise and filters the logs that have the value of the highest proportion. We repeat this process per each column until we are left with highly homogeneous set of logs that most likely belong to the same log template category. Then, we determine which column is the static part and which is the variable part by vertically comparing all the logs in the group. This process repeats until we have discovered all the templates from given logs. Also, during this process we discover the custom patterns such as ID formats that are unique to the application. This information helps us quickly identify such strings in the logs as variable parts thereby further increasing the accuracy of the discovered log templates. Existing solutions suffer from log templates being too general or too specific because of the inability to detect custom patterns. Through extensive evaluations we have learned that our proposed method achieves 2 to 20 times better accuracy.

• 한국수학교육학회지시리즈A:수학교육
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• 제43권4호
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• pp.419-440
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• 2004

The purpose of this paper is to know the effects on number and operation abilities of the 1st grade children of elementary school by applying teaching and learning activity throught communication. For this purpose, we have studied according to the following procedure. 1. We divised teaching and learning model through communication and applied in the actual teaching and learning activity. 2. We investigated the effects of number and operations abilities of the 1st grade children by applying teaching and learning activity through communication. To accomplish this purpose, we applied learning activity through communication to the 1st grade of 40 elementary school children for about six months(September 1, 1999 ～ February 20, 2000). In process of applying this model, we collected all sorts of cases in the children's learning activity and investigated children's response on learning activity through communication, interview with children and researcher's observation. We applied the model through communication in class and compared with the traditional learning. 1. In learning through communication, children could solve the problem in themselves with a sense of responsibility. 2. It was impossible to find out the degree of children's comprehension in the explanatory learning. But in the learning through communication, it was a great help to individualize and plan the learning because children express their ideas clearly. It has conclusion as follows. The learning activity through communication has effected on forming number and operations abilities of the 1st grade of elementary school children importantly. 1. Children have improved in the abilities through communication to express their own ideas. 2. Children have studied with a sense of responsibility not in the teacher-oriented learning but in the self-directed learning 3. Children could find out the mathematical concepts in themselves - correcting false concepts, reguiding concepts by errors, finding invisible errors, solving problems variously and knowing the easy method. 4. The activity through communication in mathematics was a base of children's individual learning and important data of next learning plan. 5. The mathematical concepts formed through communication had a high transfer of learning. 6. Children have taken pleasure of discovery and had affirmative attitude about mathematics learning. We can make sure that number and operations abilities of the 1st grade children are formed by applying teaching and learning activity through communication. However, help and control of teacher have to be with it.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.175-178
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• 2005

스마트 홈과 같은 유비쿼터스 환경은 다양한 센서 및 제어 네트워크가 밀집되어 있는 복잡한 시스템이다. 본 논문에서는 이러한 환경하에서 복잡한 인터페이스의 사용에 대한 사용자의 인지 부담(cognitive load)를 줄이고 개인화된(personalized) 서비스를 자율적으로 제공하기 위한 사용자 행동 패턴 선호도 학습 기법을 제안한다. 이를 위해 지식 발견(Knowledge Discovery)을 위한 평생 학습(life-long learning)의 관점에서 퍼지 귀납(Fuzzy Inductive)학습 방법론을 제안하며, 이것은 수치 데이터로부터 입력 공간에 대한 효율적인 퍼지 분할(fuzzy partition)을 얻어내고 일관성있는(consisitent) 퍼지 상관 룰(fuzzy association rule)을 얻어내도록 한다.

• Journal of Computing Science and Engineering
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• 제5권4호
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• pp.305-313
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• 2011

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.

• 천문학회지
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• 제55권4호
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• pp.131-138
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• 2022

We report the discovery of four quasars with M₁₄₅₀ ≳ -25.0 mag at z ~ 5 and supermassive black hole mass measurement for one of the quasars. They were selected as promising high-redshift quasar candidates via deep learning and Bayesian information criterion, which are expected to be effective in discriminating quasars from the late-type stars and high-redshift galaxies. The candidates were observed by the Double Spectrograph on the Palomar 200-inch Hale Telescope. They show clear Lyα breaks at about 7000-8000 Å, indicating they are quasars at 4.7 < z < 5.6. For HSC J233107-001014, we measure the mass of its supermassive black hole (SMBH) using its C _IV λ1549 emission line. The SMBH mass and Eddington ratio of the quasar are found to be ~10⁸ M_⊙ and ~0.6, respectively. This suggests that this quasar possibly harbors a fast growing SMBH near the Eddington limit despite its faintness (L_Bol < 10⁴⁶ erg s^-1). Our 100% quasar identification rate supports high efficiency of our deep learning and Bayesian information criterion selection method, which can be applied to future surveys to increase high-redshift quasar sample.

• 한국재료학회지
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• 제33권4호
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• pp.164-174
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• 2023

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material's compositional features. The compositional features were generated using the python module of 'Pymatgen' and 'Matminer'. Pearson's correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R² score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R² score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.

• 한국과학교육학회지
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• 제21권2호
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• pp.357-384
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• 2001

본 논문에서는 통합과학교육에 관한 이론적 방향과 실제적 교수 학습방안을 연계성 있게 제시하여 과학교사들의 통합과학교육에 관한 이해를 도움과 동시에 통합과학 수업 보조자료를 개발하여 실제수업에 활용할 수 있도록 하였다. 이를 위해 공통과학 교과내용 중 통합 과학적 성격이 특히 강한 에너지 단원을 대상으로 통합과학 교수 학습 방안을 모색하였는데, 이것은 이전 논문에서 구성한 '통합과학교육의 방향별 에너지 교수 학습 전략' 을 바탕으로 하고 여기에 각 방향별 특징에 적합한 수업 모형을 적용한 것이다. 즉, 지식내용중심의 통합은 물리, 화학, 생물, 지구과학의 지식을 통합하기 위해서 '에너지의 여행' 을 주제로 선정하고 ' 개방된 발견학습' 수업모형을 적용하여 개념과 탐구관련 중심으로 모색하였다. 사회문제중심의 통합은 과학관련 사회문제를 해결하기 위하여 '에너지의 미래'를 주제로 선정하고 '발생학습' 수업모형을 적용하여 학습자의 인지과정을 중심으로 모색하였다. 개인흥미중심의 통합은 과학과 개인흥미의 통합을 위하여 '에너지의 변신' 을 주제로 선정하고 '프로젝트' 수업모형을 적용하여 학습자의 흥미나 관심분야를 중심으로 모색하였다. 이상과 같은 방향에 따른 통합과학 교수 학습 방안은 다음과 같은 순서에 의해 모색되었다. 먼저, 각 주제별로 다루어야할 통합과학적 교수 학습 내용을 구성하고, 이를 바탕으로 각각의 주제를 통합적으로 수업하기 위한 통합과학적 수업 절차를 설계하였다. 그리고 작성한 수업 절차에 따라 실제 통합과학 수업에서 적용할 수 있는 통합과학적 수업 지도안을 작성하였다. 이상의 연구는 21세기를 대비한 통합과학교육의 방향정립과 교재, 교사, 학생을 고려한 종합적인 통합과학교육 프로그램 개발에 활용될 수 있을 것으로 생각된다.

• 정보교육학회논문지
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• 제8권4호
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• pp.537-545
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• 2004

프로젝트기반 학습은 학생들의 탐구력 향상과 창의성 신장에 많은 효과가 있음에도 불구하고 학교 정규수업 시간에 이를 수행하기에는 여러 가지로 제약이 따랐다. 그러나 최근에 인터넷이 널리 보급되면서 기존의 학교 수업에서의 시공간의 제약을 해결하게 되었다. 본 논문에서는 프로젝트 주제와 관련된 멀티미디어 자료를 사용하고 인터넷 서비스를 활용하여 학생들이 지속적으로 프로젝트에 참여할 수 있도록 하고 프로젝트 진행과정을 모두 볼 수 있는 프로젝트 기반 웹사이트를 개발하고 구현하였다. 개발한 웹사이트의 타당성을 검증하기 위해서 초등학교 4학년 한반을 대상으로 30일에 걸쳐 프로젝트를 진행한 결과 학생 중심의 자기주도적 학습 능력과 새로운 주제 발굴에 대한 탐구력이 향상되었다.