• Journal of Electrical Engineering and Technology
• /
• 제9권1호
• /
• pp.80-89
• /
• 2014

A stochastic optimal power flow (S-OPF) model considering uncertainties of load and wind power is developed based on chance constrained programming (CCP). The difficulties in solving the model are the nonlinearity and probabilistic constraints. In this paper, a limit relaxation approach and an iterative learning control (ILC) method are implemented to solve the S-OPF model indirectly. The limit relaxation approach narrows the solution space by introducing regulatory factors, according to the relationship between the constraint equations and the optimization variables. The regulatory factors are designed by ILC method to ensure the optimality of final solution under a predefined confidence level. The optimization algorithm for S-OPF is completed based on the combination of limit relaxation and ILC and tested on the IEEE 14-bus system.

• International journal of advanced smart convergence
• /
• 제12권2호
• /
• pp.17-24
• /
• 2023

Given the surge in the elderly population, and increasing in dementia cases, there is a growing interest in digital therapies that facilitate steady remote treatment. However, in the cognitive assessment of digital therapies through clinical trials, the absence of log data as an essential evaluation factor is a significant issue. To address this, we propose a solution of utilizing weighted derived variables based on high-importance variables' accuracy in log data utilization as an indirect cognitive assessment factor for digital therapies. We have validated the effectiveness of this approach using machine learning techniques such as XGBoost, LGBM, and CatBoost. Thus, we suggest the use of log data as a rapid and indirect cognitive evaluation factor for digital therapy users.

• ETRI Journal
• /
• 제46권2호
• /
• pp.205-217
• /
• 2024

We propose a bandwidth prediction approach based on deep learning. The approach is intended to accurately predict the bandwidth of various types of mobile networks. We first use a machine learning technique, namely, the gradient boosting algorithm, to recognize the connected mobile network. Second, we apply a handover detection algorithm based on network recognition to account for vertical handover that causes the bandwidth variance. Third, as the communication performance offered by 3G, 4G, and 5G networks varies, we suggest a bidirectional long short-term memory model with time distribution for bandwidth prediction per network. To increase the prediction accuracy, pretraining and fine-tuning are applied for each type of network. We use a dataset collected at University College Cork for network recognition, handover detection, and bandwidth prediction. The performance evaluation indicates that the handover detection algorithm achieves 88.5% accuracy, and the bandwidth prediction model achieves a high accuracy, with a root-mean-square error of only 2.12%.

• International Journal of Computer Science & Network Security
• /
• 제24권5호
• /
• pp.129-134
• /
• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
• /
• pp.310-315
• /
• 2001

We are focusing on an approach which handle a general Web as a resource in order to support self-directed learning for a student. Then, we are developing a Web based learning environment "Web-Retracer"for utilizing Web as teaching materials by a user′s Annotation. Although the learner can share the Web resource that the others utilized in this environment, Web resources unsuitable for a student′s needs becomes hindrance about her/his self-directed learning. In this paper, we propose a recommending method of the resource united with a student′s needs on the basis of a student′s learning and Web browsing history. This method analyzed the feature peculiar to a resource, and extracts the resource with which the needs of the feature and a student agreed.

• 인지과학
• /
• 제14권3호
• /
• pp.23-36
• /
• 2003

학습에 대한 이해-기반 접근에 따르면 새로운 정보는 기존의 배경지식과 통합되어 정신표상을 형성하며 이는 다른 새로운 정보를 결합하는데 사용된다고 가정한다. 지시문을 통한 학습상황에서 인간과 계산적 모형의 수행비교를 통해 이 접근법이 타당하다는 것을 보여주었다. 구성-통합 이론 (Kintsch, 1988; 1998)에 근거한 계산적 모형 (ADAPT-UNIX)은 사용자들이 UNIX 복합 명령문을 생성하는데 도움을 주기위해 제시된 지시문 학습에 높은 예측력을 보였다. 더불어, 제시된 지시문을 사용하여 올바른 복합명령문을 생성하는 과제수행도 실제 인간수행과 높은 유사성 보였다. 배경지식의 수준에 따라 지시문이 학습과 적용에 차별적인 영향을 미친다는 교육적 함의와 이해-기반 인지모델의 이론적 함의가 논의되었다.

• 공학교육연구
• /
• 제23권2호
• /
• pp.24-30
• /
• 2020

This paper proposes the approaches to the evaluation of learning using concepts of artificial intelligence. Among various techniques, deep learning algorithm is employed to achieve quantitative results of evaluation. In particular, this paper focuses on the process-based evaluation instead of the result-based one using face expression. The expression is simply acquired by digital camera that records face expression when students solve sample test problems. Face expressions are trained using convolutional neural network (CNN) model followed by classification of expression data into three categories, i.e., easy, neutral, difficult. To substantiate the proposed approach, the simulation results show promising results, and this work is expected to open opportunities for intelligent evaluation system in the future.

• 융합신호처리학회논문지
• /
• 제2권1호
• /
• pp.1-11
• /
• 2001

자동차 번호판은 조명과 카메라에 따라 영상에서 다양한 형태로 나타나고 영상내의 잡음으로 인해 알고리즘 방식으로 자동차 번호판을 인식하기가 쉽지 않다. 이러한 문제에 적합한 해결 방법으로 본 논문에서는 학습 기반의 자동차 번호판 인식 시스템을 제안한다. 제안한 시스템은 자동차 검출 모듈, 번호판 추출 모듈, 번호판 문자 인식 모듈로 구성된다 본 논문에서는 자동차 번호판 추출을 위해서 입력 영상의 잡음에 상대적인 영향이 적은 시간-지연 신경망(Time-Delay Neural Networks : TDNN)과 번호판 인식을 위해서 일반적인 신경망보다 일반화 성능이 뛰어난 서포트 벡터 머신(Support Vector Machines : SVMs)을 시스템에 적용한다. 주차장과 톨게이트에서 여러 시간대의 움직이는 자동차 영상들을 실험한 결과, 번호판 추출율은 97.5%, 번호판 문자 인식률은 97.2%의 성능을 내었고, 전체 시스템 성능은 947%이며 처리 시간은 약 1조 미만이다. 따라서 본 논문에서 제안한 시스템은 실세계에서 유용하게 적용될 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권6호
• /
• pp.1743-1758
• /
• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

• 한국엔터테인먼트산업학회논문지
• /
• 제13권8호
• /
• pp.457-467
• /
• 2019

본 연구에서는 예비유아교사가 학습자중심 교사교육을 통해 배워가는 학습경험과정과 그 학습경험이 그들에게 주는 의미를 통해 그 가치를 파악하는데 목적이 있다. 특히 유아교육 접근법 중 하나인 프로젝트 접근법 이론을 학습하고 직접 실행해 보는 학습경험을 살펴보기 위해 완전한 참여자로서 참여관찰, 개인면담 및 개인성찰일지, 활동 보고서 등의 정보를 수집하여 분석하였다. 예비유아교사들의 프로젝트 학습경험과정의 의미는 본인 관심으로 시작된 문제를 해결해 가는 중 유목적적인 실행을 통해 학습에 몰입하였으며, 자신이 경험한 학습의 기쁨을 타인과 공유하고 싶어 했다. 그리고 행위와 사고의 주체자로서 자신을 인식하며 이러한 학습경험을 토대로 자신의 학습활동에 집중할 수 있는 것으로 나타났다. 또한, 프로젝트 이론과 실행이 동시적으로 진행되어 프로젝트를 내면화하는 데 도움이 되었으며, 실제 상황의 경험을 통해 프로젝트의 변형 가능성을 체득하게 되었다. 종합해보면, 프로젝트 접근법은 예비교사에게 이론적 지식을 습득하게 하는 것뿐만 아니라, 학습의 주체로서의 경험을 통해 반성적 사고를 하고 실천적 지식을 형성할 수 있도록 한다는 가치를 확인할 수 있었다는 것에 의의가 있다. 따라서 예비교사를 교육하는 한 방안으로써 프로젝트 접근법에 대한 논의가 중점적으로 이루어질 필요가 있음을 시사한다.