• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.05b
• /
• pp.889-893
• /
• 2005

본 연구에서는 기후변화에 따른 신뢰성 높은 수자원 영향평가를 수행하기 위한 방안으로 유출모형에 따른 불확실성을 최소화할 수 있는 슈퍼앙상블 기법을 제안하였다. 유출모형들은 자연현상을 개념화하는 과정에서 목적에 따라 알고리즘이나 구조가 다르게 개발된다. 따라서 동일한 유역에 동일한 입력자료를 사용하더라도 유출모의 결과는 상이하며 이는 곧 불확실성으로 작용한다. 이러한 불확실성을 최소화하기 위한 방법으로 본 연구에서는 통계적기법인 인공신경망 모형을 이용하여 모형별 유출결과를 향상시킬 수 있는 슈퍼앙상블 기법을 개발하고 적용성을 분석하였다. 적용 대상유역으로는 한강수계에 위치한 괴산댐유역을 선정하였으며, 적용 모형으로는 일체형 모형인 Tank 모형과 준분포형 모형인 PRMS 모형을 이용하여 슈퍼앙상블을 구축하고 검정하였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.10b
• /
• pp.307-309
• /
• 2001

분류문제에서 유용한 학습패턴은 클래스들간의 분류경계에 근접한 정상패턴들을 말한다. 본 연구에서는 다양한 구조와 학습 파라미터를 가진 신경망 앙상블을 구성하고 그 출력값의 편기와 분산에 기초한 패턴절수를 정의한다. 전체 학습패턴 중 일정한 임계값 이상의 패턴점수를 가진 패턴들만이 학습패턴으로 선정된다. 제안한 방법은 두 개의 인공문제와 두 개의 실제문제 (UCI Repository)에 적응, 검증되었다. 그 결과 선택된 패턴만으로 학습한 경우, 메모리 공간 절약 및 계산시간 단축의 효과뿐만 아니라 복잡도가 큰 모델이라도 과적합을 하지 않았고 실험적으로 안정된 결과를 산출했으며, 적은 수의 학습패턴만으로도 일반화 성능을 향상시키거나 적어도 저하시키지 않았다는 것을 보였다.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.4
• /
• pp.152-159
• /
• 2002

Fitness sharing that shares fitness if calculated distance between individuals is smaller than sharing radius is one of the representative speciation methods and can complement evolutionary algorithm which converges one solution. Recently, there are many researches on designing neural network architecture using evolutionary algorithm but most of them use only the fittest solution in the last generation. In this paper, we elaborate generating diverse neural networks using fitness sharing and combing them to compute outputs then, propose calculating distance between individuals using modified Kullback-Leibler entropy for improvement of fitness sharing performance. In the experiment of Australian credit card assessment, breast cancer, and diabetes in UCI database, proposed method performs better than not only simple average output or Pearson Correlation but also previous published methods.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
• /
• 2002.11a
• /
• pp.273-277
• /
• 2002

본 논문에서는 원형편파를 송신하고 수직 및 수평편파를 합성하는 방식의 원형편파 다이버시티시스템을 설계 및 제작하였나. 중심주과수 2.4GHz의 수직 및 수평편파안테나 그리고 90도 Hybrid Combiner를 설계 제작하여 원형편파 다이버시티시스템의 송신계를 구성하였고, 수직 및 수평편파 안테나와 편파 다이버시티 합성기를 설계 제작하여 원형편파 다이버시티시스템의 수신계를 구성하였다. 선계에 사용된 프로그램은 Ensemble 5.0이고 제작된 안테나, 위상기 및 합성기는 Network Analyzer(8753ES)를 이용하여 측정했으며 시뮬레이션 결과와 측정결과가 상당히 일치함을 확인했다.

• International Journal of Contents
• /
• v.7 no.3
• /
• pp.1-5
• /
• 2011

It has been an interesting challenge to find a good classifier for imbalanced data, since it is pervasive but a difficult problem to solve. However, classifiers developed with the assumption of well-balanced class distributions show poor classification performance for the imbalanced data. Among many approaches to the imbalanced data problems, the algorithmic level approach is attractive because it can be applied to the other approaches such as data level or ensemble approaches. Especially, the error back-propagation algorithm using the target node method, which can change the amount of weight-updating with regards to the target node of each class, attains good performances in the imbalanced data problems. In this paper, we analyze the relationship between two optimal outputs of neural network classifier trained with the target node method. Also, the optimal relationship is compared with those of the other error function methods such as mean-squared error and the n-th order extension of cross-entropy error. The analyses are verified through simulations on a thyroid data set.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.456-457
• /
• 2023

ADHD(과활동성 주의력 결핍 장애) 환자 수가 증가하며 주의력 집중이 사회적 문제로 대두되고 있다. 그러나 ADHD에 대한 이해나 요인에 대한 연구는 미흡하다. 본 연구에서는 아동기 전신마취가 ADHD 발생에 영향이 있다는 연구를 기반으로, 상관관계 분석과 선형회귀분석, Lasso Regression, Support Vector Regression, Deep Neural Network, Ensemble, Random Forest Regression을 활용하여 ADHD 증가 요인에 대해 탐구했다. 분석 결과는 전신 마취에 노출될 가능성이 높은 아동의 경우 ADHD에 노출될 가능성 역시 높을 수 있음을 시사한다.

• Convergence Security Journal
• /
• v.21 no.2
• /
• pp.57-66
• /
• 2021

Deep neural networks (DNNs) provide excellent performance for image, speech, and pattern recognition. However, DNNs sometimes misrecognize certain adversarial examples. An adversarial example is a sample that adds optimized noise to the original data, which makes the DNN erroneously misclassified, although there is nothing wrong with the human eye. Therefore studies on defense against adversarial example attacks are required. In this paper, we have experimentally analyzed the success rate of detection for adversarial examples by adjusting various parameters. The performance of the ensemble defense method was analyzed using fast gradient sign method, DeepFool method, Carlini & Wanger method, which are adversarial example attack methods. Moreover, we used MNIST as experimental data and Tensorflow as a machine learning library. As an experimental method, we carried out performance analysis based on three adversarial example attack methods, threshold, number of models, and random noise. As a result, when there were 7 models and a threshold of 1, the detection rate for adversarial example is 98.3%, and the accuracy of 99.2% of the original sample is maintained.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.214-222
• /
• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.06a
• /
• pp.275-276
• /
• 2022

VTS operators instruct ships to wait for entry and departure to sail in one-way to prevent ship collision accidents in ports with narrow routes. Currently, the instructions are not based on scientific and statistical data. As a result, there is a significant deviation depending on the individual capability of the VTS operators. Accordingly, this study built a 1d-convolutional neural network model by collecting ship and weather data to predict the exact travel time for ship entry/departure waiting for instructions in the port. It was confirmed that the proposed model was improved by more than 4.5% compared to other ensemble machine learning models. Through this study, it is possible to predict the time required to enter and depart a vessel in various situations, so it is expected that the VTS operators will help provide accurate information to the vessel and determine the waiting order.

• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.135-146
• /
• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.