• 정보교육학회논문지
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• 제11권3호
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• pp.299-306
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• 2007

정부교육기관의 조사에 따르면 학습부진아에 대한 학습 지원이 절실한 상황이다. 이에 본 논문에서는 학습 스타일 심리검사를 이용하여 부진아에게 맞는 학습방법으로 학습을 지원하는 부진아 학습 지원 시스템을 구축하였다. 제안한 시스템은 첫째, 부진아의 특성으로 구성된 의사결정트리와 사전평가가 점수를 통하여 부진아를 진단한다. 둘째, 부진아로 판단된 학생은 학습 스타일 심리검사를 실시하여 강의형 학습(청각형), 멀티미디어형 학습(시각형), 게임형 학습(촉각형) 방법 중에 한 형태의 학습방법으로 학습을 지원한다. 셋째, 사후평가를 통하여 학업성취도를 학인하고 학업성취도가 낮은 학생에 대해서는 교수자와의 일대일 개별지도를 지원한다. 제안한 시스템을 사용하여 학습부진아를 실험집단과 비교집단으로 나누어 학습을 검증한 결과 학습 스타일 심리검사를 실시하여 학습을 했을 경우의 학습성취도가 평균 10% 향상되었다.

• Journal of Information Processing Systems
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• 제15권6호
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• pp.1335-1349
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• 2019

Crowdsensing technologies can improve the efficiency of smart parking system in comparison with present sensor based smart parking system because of low install price and no restriction caused by sensor installation. A lot of sensing data is necessary to predict parking lot saturation in real-time. However in real world, it is hard to reach the required number of sensing data. In this paper, we model a saturation predication combining a time-based prediction model and a sensing data-based prediction model. The time-based model predicts saturation in aspects of parking lot location and time. The sensing data-based model predicts the degree of saturation of the parking lot with high accuracy based on the degree of saturation predicted from the first model, the saturation information in the sensing data, and the number of parking spaces in the sensing data. We perform prediction model learning with real sensing data gathered from a specific parking lot. We also evaluate the performance of the predictive model and show its efficiency and feasibility.

• 한국컴퓨터정보학회지
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• 제14권1호
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• pp.185-196
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• 2006

이 논문에서는 국제 학습 콘텐츠 표준인 SCORM을 지원하는 공개 소프트웨어 기반의 학습관리 시스템을 구축하기 위해, 기존 학습 관리 시스템을 비교 분석하여 무들(Moodle)을 선정하였고 학사관리시스템, 스트리밍서비스 등의 기존 시스템들과 연동 운용함으로써, 공개 소프트웨어 기반 학습관리 시스템의 현실적 가능성을 보였다. 공개 학습 관리 시스템인 무들은 모듈화 구조를 수용해 사용의 편의성과 확장성을 충분히 제공하고 있으며, SCORM지원을 원활하게 하고 있다. 또한, 사회적 구성주의 학습 이론을 기반으로 설계, 구현되어 있다는 중요한 장점을 갖고 있다. 이 연구는 학습 관리 시스템을 포함한 공개S/W 기반 e-Learning시스템 구축이 안정적으로 가능하다는 것을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권3호
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• pp.560-574
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• 2011

In this paper, we propose a hybrid method of Machine Learning (ML) algorithm and a rule-based algorithm to implement a lightweight Named Entity (NE) extraction system for Korean SMS text. NE extraction from Korean SMS text is a challenging theme due to the resource limitation on a mobile phone, corruptions in input text, need for extension to include personal information stored in a mobile phone, and sparsity of training data. The proposed hybrid method retaining the advantages of statistical ML and rule-based algorithms provides fully-automated procedures for the combination of ML approaches and their correction rules using a threshold-based soft decision function. The proposed method is applied to Korean SMS texts to extract person's names as well as location names which are key information in personal appointment management system. Our proposed system achieved 80.53% in F-measure in this domain, superior to those of the conventional ML approaches.

• 한국산학기술학회논문지
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• 제11권9호
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• pp.3417-3421
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• 2010

USN은 인간의 생활공간, 생활기기, 기계 등 모든 사물에 컴퓨팅 및 네트워킹 기능을 부여하여 환경과 상황의 자동인지를 통해 사용자에게 최적의 서비스를 가능하게 함으로써 인간생활의 편리성과 안정성을 고도화 하는 기술이라 할 수 있다. 본 논문에서는 이와 같은 USN기술을 이용하여 학습자의 학습환경을 실시간으로 파악하여 이를 기초로 최적의 학습환경으로 만들어주기 위한 학습지원시스템을 임베디드 시스템 기반으로 개발한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.993-996
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• 1993

This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.

• 디지털산업정보학회논문지
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• 제6권2호
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• pp.169-182
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• 2010

The purpose of the study is to prospect the application of knowledge awareness system in the use of the digital textbook which is one of the main educational political projects based on the exploration of the awareness theory for the computer-supported collaborative learning. To do this, first, knowledge awareness theory for computer-supported collaborative learning (CSCL) as a rationale for digital textbook which supports the collaborative learning was introduced. Second, three functionalities of knowledge awareness systems were extracted by analyzing the representative knowledge awareness tools of CSCL environment. Third, application prospects of knowledge awareness system toward the development and utilization of digital textbook were presented. The paper suggested the need of more researches such as the prototype development of digital textbook which applies the knowledge awareness system's functionalities and empirical researches which examine their effectiveness and efficiency.

• 한국융합학회논문지
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• 제5권3호
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• pp.45-51
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• 2014

Computer science and computing technologies are applied into mathematical, science, medical, engineering and educational applications. The models are used to solve the issues in all the domains. Educational systems are used top down, bottom up, Gap Analysis model in the educational learning system. Educational learning process integrated with Lerner, content and the methodology. The Learners and content are same in the educational system or similar courses but the teaching methodologies are differing one with another. The determinations of teaching methodologies are based on the factors related to that particular model or subject. The learning model influencing determinations are made by the surveys, analysis and observation of data to maximize the learning outcome. This paper attempted to evaluate the SNE learners cognitive using NASA Scaling.

• 한국멀티미디어학회논문지
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• 제21권5호
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• pp.626-637
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• 2018

Human exchanges information not only through words, but also through body gesture or hand gesture. And they can be used to build effective interfaces in mobile, virtual reality, and augmented reality. The past 2D gesture recognition research had information loss caused by projecting 3D information in 2D. Since the recognition of the gesture in 3D is higher than 2D space in terms of recognition range, the complexity of gesture recognition increases. In this paper, we proposed a real-time gesture recognition deep learning model and application in 3D space using deep learning technique. First, in order to recognize the gesture in the 3D space, the data collection is performed using the unity game engine to construct and acquire data. Second, input vector normalization for learning 3D gesture recognition model is processed based on deep learning. Thirdly, the SELU(Scaled Exponential Linear Unit) function is applied to the neural network's active function for faster learning and better recognition performance. The proposed system is expected to be applicable to various fields such as rehabilitation cares, game applications, and virtual reality.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.135-147
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• 2023

Internet of things has revolutionaries every field of life due to the use of artificial intelligence within Machine Learning. It is successfully being used for the study of Radiation monitoring, prediction of Ultraviolet and Electromagnetic rays. However, there is no particular system available that can monitor and detect waves. Therefore, the present study designed in which IOT enables intelligence system based on machine learning was developed for the prediction of the radiation and their effects of human beings. Moreover, a sensor based system was installed in order to detect harmful radiation present in the environment and this system has the ability to alert the humans within the range of danger zone with a buzz, so that humans can move to a safer place. Along with this automatic sensor system; a self-created dataset was also created in which sensor values were recorded. Furthermore, in order to study the outcomes of the effect of these rays researchers used Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Extra Trees, Bagging Classifier, Random Forests, Logistic Regression and Adaptive Boosting Classifier were used. To sum up the whole discussion it is stated the results give high accuracy and prove that the proposed system is reliable and accurate for the detection and monitoring of waves. Furthermore, for the prediction of outcome, Adaptive Boosting Classifier has shown the best accuracy of 81.77% as compared with other classifiers.