• 한국정보과학회논문지:소프트웨어및응용
• /
• 제31권5호
• /
• pp.537-542
• /
• 2004

다층 퍼셉트론(Multi-Layer Perceptron, MLP) 구조는 그의 비선형 적합능력으로 인하여 매우 다양한 실제 문제에 적용되고 있다. 그러나 일반화된 MLP 구조의 적합능력은 은닉노드의 개수. 초기 가중 값 그리고 학습 회수 또는 학습 오차와 같은 구조인자(factor)들에 크게 영향을 받는다. 만약 이들 구조인자가 부적절하게 선택되면 일반화된 MLP 구조의 적합능력이 매우 왜곡될 수 있다. 따라서 MLP구조에 영향을 주는 인자들의 결합 영향을 살펴보는 것은 중요한 문제이다. 이 논문에서는 제어상자(controller box)를 통한 학습결과와 더불어 MLP구조를 일반화할 때 영향을 줄 수 있는 신경망의 일반적인 구조인자 들을 실증적으로 살펴보고 이들의 상대효과를 비교한다.

• 한국멀티미디어학회논문지
• /
• 제24권12호
• /
• pp.1624-1631
• /
• 2021

With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.

• Journal of Information Processing Systems
• /
• 제15권2호
• /
• pp.422-432
• /
• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• Journal of Advanced Marine Engineering and Technology
• /
• 제21권1호
• /
• pp.66-70
• /
• 1997

In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.

• 한국수학교육학회지시리즈D:수학교육연구
• /
• 제9권4호
• /
• pp.329-333
• /
• 2005

Based on author's practice of instructing Chinese gifted students to join the Chinese Mathematics Olympic (CMO), the paper adopted test analysis model of the Scholastic Aptitude Test of Mathematics (SAT-M), tested mathematics ability of 212 mathematical gifted students to join the CMO, applied correlation analysis and factor analysis and proposed the mathematics ability structure in Chinese gifted students including comprehensive operation ability, logic thinking ability, abstract generalization ability, spatial imagination ability, memory ability, transfer ability and intuition thinking ability. And it analyzed the expression form of these abilities respectively and gave some suggestion on mathematics teaching about gifted Chinese students.

• International Journal of Advanced Culture Technology
• /
• 제6권4호
• /
• pp.80-86
• /
• 2018

The intervention of Autism Spectrum Disorder (ASD) is limited research focus on the effect of occupational-based intervention. This study sought to determine the effect of occupational-based intervention of chopstick skills for children with ASD. This study included a total of 3 children with ASD.Using single-subject study design, a changing criterion design and ABC design were implemented. The participants' behavior was observed and recorded throughout each session. In this study, the results were analyzed through visual graphs. The amount of food that was moved using the chopsticks was gradually increased. The results show that all participants significantly improved in their ability to use chopsticks in each intervention session. In addition, Assessment of Motor and Process Skills (AMPS) improved the generalization. According to the AMPS, both the overall motor and process skills increased from baseline an average of 0.7 logit. The results of this study showed occupational-based intervention on chopsticks skill to be effective in acquisition and generalization of chopstick skill in children with ASD.

• Journal of Power Electronics
• /
• 제17권5호
• /
• pp.1288-1297
• /
• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• 한국학교수학회논문집
• /
• 제16권4호
• /
• pp.927-941
• /
• 2013

본 연구에서는 고등학교 학생들이 삼각형에 대한 코사인 법칙으로부터 사각형과 n각형에 대한 코사인 법칙을 유추적 사고를 통하여 발견하는 과정을 조사하였으며 삼각형에 대한 코사인 법칙에 대한 충분한 이해가 일반화된 법칙을 발견하고 증명하는데 어느 정도 영향을 미치는지를 분석하였다. 이와 같이 귀납적 추론이나 유추적 사고 활동을 통해 학생 스스로 지식을 발견하고, 스스로 발견한 수학적 지식을 논리적 추론이나 연역적 증명을 통해 정당화하는 경험을 쌓을 수 있을 때, 학생들은 이 지식을 자신의 것으로 내면화할 수 있게 되고, 다양한 상황에 자유롭게 활용할 수 있는 능력을 가질 수 있을 것이다.

• 한국정보과학회논문지:소프트웨어및응용
• /
• 제31권2호
• /
• pp.101-110
• /
• 2004

사회시스템이나 경제시스템 같이 동적으로 변하는 시스템에서는 그 구성원들 간에 복잡한 상호작용(행동)이 나타나게 되는데 구성원들의 행동은 변화하는 환경에 따라 적응하는 경향을 보인다. 그리고 이들의 행동양상은 흔히 생물학 분야의 조건반사에 비유되기도 한다. 본 논문에서는 복잡한 사회 현상을 모델링하고 분석하기 위하여 반복적 죄수의 딜레마 게임 상에서 에이전트들의 전략적 연합을 통하여 변화하는 환경에 잘 적응하는 일반화 능력이 우수한 에이전트들을 자동 생성하는 방법을 제안한다. 또한 에이전트에 신뢰도를 부여하여 연하의 의사결정에 참가하게 함으로써 일반화 성능을 향상시키는 방법을 소개한다 실험결과, 전략적 연합을 이용하여 진화된 에이전트들은 테스트 에이전트들에 비하여 일반화 성능이 우수함을 확인할 수 있었다.

• 한국수학교육학회지시리즈C:초등수학교육
• /
• 제17권2호
• /
• pp.77-93
• /
• 2014

본 연구의 목적은 영재의 사고양식 및 수학적 능력의 특성을 밝혀 영재의 특성을 고려한 프로그램 개발에 이바지하고자 하는 데 있다. 이를 위해 초등학교 수학영재교육 대상자와 일반학생을 대상으로 사고양식과 수학적 능력의 구성 요소를 분석하고, 두 변인간의 상호관련성을 탐색하였다. 연구 결과에 따르면 수학영재교육대상자가 일반학생에 비해 입법형, 사법형, 위계형, 전체형, 부분형 내부지향형, 자유형의 사고양식이 높을 뿐만 아니라 계산력, 추론 능력, 가역성, 일반화, 공간, 기억력의 수학적 능력 또한 수학영재교육대상자가 일반학생보다 높은 것으로 나타났다. 그리고 회기분석 결과, 사고양식과 수학적 능력 간에는 어느 정도 상관관계가 있음을 알 수 있었다.