• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2895-2912
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• 2014

Content-based image retrieval has been the most important technique for managing huge amount of images. The fundamental yet highly challenging problem in this field is how to measure the content-level similarity based on the low-level image features. The primary difficulties lie in the great variance within images, e.g. background, illumination, viewpoint and pose. Intuitively, an ideal similarity measure should be able to adapt the data distribution, discover and highlight the content-level information, and be robust to those variances. Motivated by these observations, we in this paper propose a probabilistic similarity learning approach. We first model the distribution of low-level image features and derive the free energy kernel (FEK), i.e., similarity measure, based on the distribution. Then, we propose a learning approach for the derived kernel, under the criterion that the kernel outputs high similarity for those images sharing the same class labels and output low similarity for those without the same label. The advantages of the proposed approach, in comparison with previous approaches, are threefold. (1) With the ability inherited from probabilistic models, the similarity measure can well adapt to data distribution. (2) Benefitting from the content-level hidden variables within the probabilistic models, the similarity measure is able to capture content-level cues. (3) It fully exploits class label in the supervised learning procedure. The proposed approach is extensively evaluated on two well-known databases. It achieves highly competitive performance on most experiments, which validates its advantages.

• 영재교육연구
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• 제25권4호
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• pp.529-546
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• 2015

본 연구는 보다 나은 영재교육을 제공하기 위해 영재교육의 성과인 만족도와 성취도에 영향을 미치는 변인을 찾고 이들 사이의 구조적 관계를 살펴보고자 하였다. 영재교육의 성과에 영향 미치는 변인은 $Gagn{\acute{e}}$의 재능분화이론을 통해 과제집착력, 자기조절학습능력, 부모지원을 선정하였다. 본 연구는 경기도 영재교육원 학생 182명을 대상으로 진행하였다. 영재교육에서의 과제집착력, 자기조절학습능력, 부모지원, 만족도 및 성취도간의 관계를 살펴본 결과는 다음과 같다. 첫째, 영재교육에서의 과제집착력, 자기조절학습능력 및 부모지원은 만족도에 영향을 미쳤다. 둘째, 과제집착력과 부모지원은 성취도에 영향을 미치는 것이 확인되었다. 따라서 영재교육에서는 학생들의 과제집착력, 자기조절학습능력 및 부모지원을 높여 만족도와 성취도를 높여 나가야 할 것이다.

• 스마트미디어저널
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• 제11권11호
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• pp.92-98
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• 2022

섬유산업에서 생산된 직물의 결함을 식별하는 것은 품질관리를 위한 핵심적인 절차이다. 본 연구는 직물의 이미지를 분석하여 결함을 검출하는 모델을 만들고자 하였다. 연구에 사용된 모델은 딥러닝 기반의 VGGNet 과 ResNet이었고, 두 모델의 결함 검출 성능을 비교하여 평가하였다. 정확도는 VGGNet 모델이 0.859, ResNet 모델이 0.893으로 ResNet 모델의 정확도가 더 높은 결과를 보여주었다. 추가적으로 딥러닝 모델이 직물의 이미지 내에서 결함으로 인식한 부분의 위치를 알아보기 위하여 XAI(eXplainable Artificial Intelligence)기법인 Grad-CAM 알고리즘을 사용하여 모델의 관심영역을 도출하였다. 그 결과 딥러닝 모델이 직물의 결함으로 인식한 부분이 육안으로도 실제 결함이 있는 것으로 확인되었다. 본 연구의 결과는 직물의 결함 검출에 있어서 딥러닝 기반의 인공지능을 활용함으로써 섬유의 생산과정에서 발생하는 시간과 비용을 줄일 수 있을 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• 정보교육학회논문지
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• 제10권1호
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• pp.143-152
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• 2006

기업에서 실제로 수행된 블렌디드 학습(Blended Learning)의 효과성을 규명해보고 이를 토대로 온라인 학습과 오프라인 학습을 효과적으로 병행하기 위한 시사점을 도출하고자 본 연구가 수행되었다. 연구 목적을 달성하기 위해 블렌디드 학습의 효과성에 대한 설명적 혼합설계 방식으로 체제적 분석을 시도하였다. 블렌디드 학습의 효과에 대한 양적 분석 결과, 종합진단과 타인진단에서는 블렌디드 학습이 리더십 역량을 향상시키는데 유의미한 영향을 미치는 것으로 나타났고, 사후 설문조사 및 핵심 면담대상자집단과의 면담 결과 블렌디드 학습의 유용성을 확인할 수 있었다. 이러한 연구 결과를 바탕으로, 블렌디드 학습 방식으로 운영한 프로그램의 분석을 통해 블렌디드 학습 효과에 영향을 주는 특성을 분석하고, 블렌디드 학습의 효과성 검증에 대한 방향을 제시하고자 하였다.

• 한국수학교육학회지시리즈A:수학교육
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• 제61권2호
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• pp.257-271
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• 2022

본 연구는 학습분석학을 기반으로 블렌디드 수학 수업에서 발생하는 학습 데이터를 활용하여 수학 학업성취도를 예측하는 요인이 무엇인지 탐색하고, 그 결과를 활용하여 수학 학업성취도를 예측하는 인공지능 모델을 개발하고자 하였다. 초등학교 5~6학년 학생 205명의 수학 학습 성향, LMS 데이터, 평가 결과를 수집하여 랜덤포레스트 모델을 분석하였다. 수학 학습성향에는 수학학습 자신감, 수학불안, 수학교과 흥미, 수학학습 자기관리, 수학학습 전략이 포함되었다. LMS 데이터로 e학습터의 진도율, 학습 횟수, 학습 시간을 수집하였다. 평가는 진단평가와 각 단원의 단원평가 결과를 사용하였다. 분석 결과 수학 학습성향 중 수학 학습 전략이 저성취 학생을 예측에 가장 중요한 요인으로 나타났다. LMS 학습 데이터는 예측에 미미한 영향을 주었다. 본 연구는 인공지능 모델이 블렌디드 수학 수업에서 발생하는 학습 데이터로 저성취 학생을 예측할 수 있음을 시사한다. 또한 분석 결과를 통해 교사가 학생을 평가하고 피드백하는 데 구체적인 정보를 제공하여 교사의 평가 활동에 보조적인 역할을 할 수 있을 것으로 기대한다.

• 한국공간구조학회논문집
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• 제24권3호
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• pp.43-51
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• 2024

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson's ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

• 대한한방소아과학회지
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• 제19권1호
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• pp.67-82
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• 2005

Objectives : This study was conducted to find out the effect of Yukmijiwhangtanggamibang (YM) on growth, learning and memory of rats. Methods : It was divided SD rats into Sham group, 192 Saporin injection(SA+Saline) group and Injection of 192 Saporin with YM(SA+YM) group. Growth measure length of bone and tail. Memory performance was used aquisition test and learning retention of morris water maze. It was detected acetylcholinesterase(AChE), cholineacetyltransferase(ChAT) at medial septum and hippocampus by immunohistochemistry Results : Body Weight of the SA+YM Group increased effectively, as compared with SA Saline group. Growth of bone in the SA+YM Group increased effectively, as compared with SA+Saline group. Growth of Tail in the SA+YM Group increased effectively, as compared with SA_Saline group. The SA+YM Group in Aquisition Test improved effectively, as compared with SA+Saline group. The SA+YM Group in Learning Test improved effectively, as compared with SA+Saline group. The numbers of ChAT cells in Medial septum increased effectively, as compared with SA+Saline group. The numbers of ChAT cells in CA1 of Hippocampus increased, but was not effective. Conclusion : These results suggest that YM has an improving effect on the impaired learning through the effects on memory registration and retrieval.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3266-3285
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• 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1203-1212
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• 2017

Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting and stacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), and support vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally, we conduct two statistical significance tests to evaluate the performance differences among classifiers.