• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.288-301
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• 2017

In this paper, we propose an optimized algorithm for texture classification by computing a completed modeling of the local binary pattern (CLBP) instead of the traditional LBP of a scalable block size in an image. First, we show that the CLBP descriptor is a better representative than LBP by extracting more information from an image. Second, the CLBP features of scalable block size of an image has an adaptive capability in representing both gross and detailed features of an image and thus it is suitable for image texture classification. This paper successfully implements a machine learning scheme by applying the CLBP features of a scalable size to the Support Vector Machine (SVM) classifier. The proposed scheme has been evaluated on Outex and CUReT databases, and the evaluation result shows that the proposed approach achieves an improved recognition rate compared to the previous research results.

• International Journal of Control, Automation, and Systems
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• 제5권6호
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• pp.674-680
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• 2007

The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

• 교육녹색환경연구
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• 제9권3호
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• pp.20-33
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• 2010

The purpose of this research is to investigate and analyze the recognition of students and teachers towards an influence of a subject classroom system to teacher's learning activities and student's learning attitude. The study was subjected to students and teachers at a school that has innovatively operated subject classroom system from before 2009. The results of the research are as follows. First, the result of investigation shows that the quality of class has been improved. The formats of managing class and class materials have become various. Second, there is an affirmative exchange in student's learning attitude, such as student's active participation, concentration, preparation and interest toward a class. Third, the fifty percent of teachers answered it that a block time system and intensive study system is required to manage an efficient subject classroom system. Lastly, the investigation shows that teachers and students are generally satisfied with running the subject classroom system. However, the satisfaction ratio of students is lower than the one of teachers.

• 정보교육학회논문지
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• 제21권5호
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• pp.583-593
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• 2017

초보자들을 위한 프로그래밍 교육의 어려움을 극복하기 위해 UMC(Use-Modify-Create) 학습, 디자인 기반학습, 발견학습, 놀이학습 등 다양한 학습전략을 적용한 연구들이 이루어지고 있다. 이에 본 연구에서는 학습자의 컴퓨팅 사고 향상을 위해 HVC(History-VR Coding-Collaboration) 학습전략 모델을 개발하였다. HVC 모델은 블록형태의 결합 모듈로 구성되어 있으며 이를 12차시로 구성된 스토리텔링 기반의 가상현실 블록 프로그래밍 교육과정을 개발하여 적용하였다. 연구 결과 HVC 모델 및 SW교육 프로그램이 학습자의 컴퓨팅 사고 향상에 유의미한 차이를 보였다.

• 지역사회간호학회지
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• 제18권1호
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• pp.156-164
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• 2007

Purpose: This study was to investigate adequate strategies of PBL in community health nursing for learning in the real community situation. Method: Data were collected in a PBL class of 14 third-year students who solved problems and assessed PBL in community health nursing related to visiting nursing and chronic diseases. Results: The students guessed situations diversely, chose learning issues widely and mapped the learning concepts specifically. In the assessment of the presentation, the peers of the same group gave the highest score $29.00{\pm}3.36$, the tutor lowest score $22.83{\pm}5.15$. In 5-point Likerts scale, the group dynamic was highest ($4.18{\pm}.61$) and the presentation was lowest ($3.59{\pm}.84$). Conclusion: The group needs to include students who have experiences in the practice at the health centers. The PBL class should be managed by the block system along with the conventional learning. Students needs to practice the self-directed learning and the presentation in a first semester and then PBL. The introduction of community health nursing begins with the conventional lecture and the programs on life circle and health centers through PBL in the comprehensive curriculums.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.675-681
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• 1989

As an adaptive control function generator, the CMAC (Cerebellar Model Arithmetic or Articulated Controller) based learning control has drawn a great attention to realize a rather robust real-time manipulator control under the various uncertainties. There remain, however, inherent problems to be solved in the CMAC application to robot motion control or perception of sensory information. To apply the CMAC to the various unmodeled or modeled systems more efficiently, It is necessary to analyze the effects of the CMAC control parameters an the trained net. Although the CMAC control parameters such as size of the quantizing block, learning gain, input offset, and ranges of input variables play a key role in the learning performance and system memory requirement, these have not been fully investigated yet. These parameters should be determined, of course, considering the shape of the desired function to be trained and learning algorithms applied. In this paper, the interrelation of these parameters with learning performance is investigated under the basic learning schemes presented by authors. Since an analytic approach only seems to be very difficult and even impossible for this purpose, various simulations have been performed with prespecified functions and their results were analyzed. A general step following design guide was set up according to the various simulation results.

• 한국인터넷방송통신학회논문지
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• 제22권4호
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• pp.195-201
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• 2022

학습자 주도의 지속적 원격교육 환경을 위하여 학습자의 정확한 학습 패턴을 고려한 올바른 문제 추천 가이드에 대한 필요성이 증대하고 있다. 본 논문에서는 원격교육환경에서 수집되는 학습자의 문제패턴에 대하여 상황별 가중치를 부여하여 해당 데이터를 기반의 개별 학습자의 최적 문제추천 경로를 제시하는 방법으로 블록체인 기반 스마트 컨트랙트 기술을 연구하였다. 본 연구의 성능평가를 위하여 기존 유사 학습 환경과의 학습만족도 및 문제추천가이드의 유용성과 학습자 데이터 처리속도를 분석하였으며 본 연구를 통하여 15% 이상 학습 만족도 향상과 기존 학습 환경 대비 20% 이상의 학습데이터 처리속도향상을 확인하였다.

• 정보교육학회논문지
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• 제22권6호
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• pp.613-628
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• 2018

본 연구의 목적은 초등 과학영재 학생들을 대상으로 코딩블록(MODI)을 활용한 피지컬 컴퓨팅 교수 학습을 진행하고 그 결과를 분석함으로써 영재 학생들의 컴퓨팅 사고력을 신장시킬 수 있는 교수 학습 방법에 대한 시사점을 얻는 데 있다. 이 연구를 위하여 국제 교육성취도 평가 협회(IEA)에서 개발한 컴퓨터 정보 소양 평가 기준으로부터 MODI를 활용한 수업의 학습목표를 설정하였고, 학습목표에 따라 MODI를 활용한 피지컬 컴퓨팅 교수 학습 프로그램을 개발하였다. 또한 연구 개발한 프로그램은 전문가를 대상으로 타당도를 확인하였다. 개발된 프로그램을 이용하여 S교육대학교 초등 과학영재교육원 4~6학년 15명을 대상으로 코딩블록(MODI)을 활용한 피지컬 컴퓨팅 수업을 32차시 실시하였으며, 교수 학습 과정을 담은 수업 동영상 및 수업관찰 일지, 교사와 학생 설문지 및 면담 등의 자료를 수집하여 질적으로 분석하였다. 연구 결과를 근거로 학교교육 현장에서 코딩블록(MODI)을 활용한 피지컬 컴퓨팅 교수 학습과정에 대한 시사점을 제시하였으며, 창의적인 아이디어를 구현하는 코딩 교육을 통하여 컴퓨팅 사고력의 확장을 모색하였다.

• 정보교육학회논문지
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• 제22권3호
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• pp.357-366
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• 2018

초등학생을 대상으로 하는 소프트웨어 교육의 도구는 블록형 교육용 프로그래밍 언어(EPL)가 주로 사용되고 있다. 블록형 EPL은 SW 교육의 입문 도구로써 장점이 많지만 확장성에서는 한계를 가지고 있다. 본 연구에서는 실제 산업 현장에서도 활발하게 사용하고 있는 텍스트 기반의 프로그래밍 언어인 파이썬을 활용한 SW 교육의 접근 방안을 모색하였다. 파이썬을 활용한 학습 프로그램과 모형을 개발하고 초등학교 6학년 학생을 대상으로 10차시를 적용하였다. 그 결과 로봇 활용 파이썬 학습 모형을 적용한 학생들의 컴퓨팅 사고력 향상에 유의미한 효과가 있었으며 초등학생을 대상으로 텍스트 기반 프로그래밍 언어의 적용 가능성을 확인할 수 있었다.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2341-2347
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• 2021

Because single-photon emission computed tomography (SPECT) is one of the widely used nuclear medicine imaging systems, it is extremely important to acquire high-quality images for diagnosis. In this study, we designed a super-resolution (SR) technique using dense block-based deep convolutional neural network (CNN) and evaluated the algorithm on real SPECT phantom images. To acquire the phantom images, a real SPECT system using a^99mTc source and two physical phantoms was used. To confirm the image quality, the noise properties and visual quality metric evaluation parameters were calculated. The results demonstrate that our proposed method delivers a more valid SR improvement by using dense block-based deep CNNs as compared to conventional reconstruction techniques. In particular, when the proposed method was used, the quantitative performance was improved from 1.2 to 5.0 times compared to the result of using the conventional iterative reconstruction. Here, we confirmed the effects on the image quality of the resulting SR image, and our proposed technique was shown to be effective for nuclear medicine imaging.