• 대한수학교육학회지:수학교육학연구
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• 제9권1호
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• pp.263-278
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• 1999

Computer with various powerful functions has profound potential for mathematics instruction and learning. As computer technology progress, its applicability to mathematics education become more comprehensive. Not only its functional development but various psychological positions also changed the way computer technology utilized in mathematics education. In behaviorist's perspective, computer viewed as a teaching machine and constructivist viewed computer as microworld where students could explore various mathematical contents. Both theoretical positions emphasized individual aspect of learning because behaviorist tried to individualize learning using computer and constructivist focused on the process of individual construction. But learning is not only a individual event but also a social event. Therefore we must take social aspect into account. This is especially important when it comes to computer based learning. So far, mathematics loaming with computer weighed individual aspect of loaming. Even in microworld environment, learning should be mediated by teacher and collaborative learning activities. In this aspect, the roles of teacher and peers are very important and socio-cultural perspective sheds light on the computer based learning. In socio-cultural perspective, the idea of scaffold is very important in learning and students gradually internalize the social dimension and scaffolding is gradually faded. And in the zone of proximal development, teacher and more competent peers guide students to formulate their own understanding. In sum, we must take following points into account. First of all, computer should not be viewed as a medium for individualized teaming. That is, interaction with computer should be catalyst for collaborative activities with peers. So, exploration in computer environment has to be followed by small group activities including small group discussion. Secondly, regardless of the role that computer would play, teacher should play a crucial role in computer based learning. This does not mean teacher should direct every steps in learning process. Teacher's intervention should help student construct actively. Thirdly, it is needed to conceptualize computer in learning situation as medium. This would affect learning situation and result in the change of pre-service and in-service teacher training. Computer to be used effectively in mathematics classroom, researches on assessment of computer based learning are needed.

• 한국융합학회논문지
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• 제9권4호
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• pp.271-283
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• 2018

본 연구는 부모양육태도, 친구관계, 학교환경 등 청소년의 발달환경이 비행행동에 미치는 영향을 파악하고, 컴퓨터, 휴대전화 등 매체환경이 그 관계에서 어떤 매개역할을 하는지를 규명하는 데 목적이 있다. 초기 청소년인 중학교 1학년 총 518명 대상으로 한국아동 청소년패널 6차년도 조사결과를 활요하였다. 분석결과, 부모양육태도가 애정형, 과잉간섭형, 학대형일수록, 친구와 소외관계가 깊을수록 비행행동은 더 증가하는 것으로 나타났다. 부모의 비일관성, 친구간 의사소통은 컴퓨터와 휴대폰을 더 자주 사용하게 하였다. 컴퓨터, 휴대전화를 자주 사용할수록 비행행동도 늘어났다. 따라서 청소년의 가정환경, 친구관계는 비행행동에 영향을 미치므로 부모교육프로그램, 친구간 소통프로그램 등 비행행동을 방지하는 발달환경의 조성 전략이 필요할 것이다. 비행행동을 완화하기 위해 최근 사회문제화 되고 있는 컴퓨터나 휴대전화 등에 대해 올바른 활용을 위한 교육프로그램 개발 및 시행이 요구된다.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• 한국컴퓨터정보학회논문지
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• 제15권11호
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• pp.195-204
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• 2010

본 논문에서는 유비쿼터스 컴퓨팅 환경에서 환경 관제 서비스를 제공하는 효과적인 실내 환경 관제를 위한 시스템을 제안한다. 환경 관제 시스템은 사용자 환경에 대한 정보를 수집하여 실내 공기질 유지 관리 규정에 따라 쾌적한 환경을 유지하고 실내 환경을 건강하게 유지하기 위한 시스템이다. 환경 관제 서비스를 제공하기 위해서 유비쿼터스 컴퓨팅 환경에서 서비스의 개발과 지원을 위한 액티브 모델 기반의 분산 프레임워크와 서비스 지향 아키텍처를 사용하였다. 본 논문에서 제안한 환경 관제 시스템의 수행성을 검증하기 위해 컴포넌트의 동작을 보이고 환경관제 서비스의 GUI화면을 통해 결과 화면을 보였다.

• 한국컴퓨터정보학회논문지
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• 제10권4호
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• pp.165-171
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• 2005

고속의 컴퓨터, 대용량 저장장치, 초고속 네트워크는 현재 우리가 쉽게 접근할 수 있는 컴퓨팅 인프라이다. 하지만 분자 시뮬레이션과 같은 자연과학 및 응용과학 분야에의 시뮬레이션에서는 여전히 더 많은 컴퓨팅 파워, 더 커다란 저장장치를 필요로 한다. 이러한 요구는 그리드 컴퓨팅(1)이라는 차세대 분산 컴퓨팅 환경을 우리에게 제시하였다. 하지만 현재까지 제안된 그리드 컴퓨팅 기술은 통신 인터페이스와 프로토콜 등의 글로버스 툴킷(2, 3)과 같은 미들웨어 수준에 대한 연구만이 중심이 되고 있다. 이러한 환경은 응용 플랫폼에 대한 연구의 부족과 어플리케이션의 부족을 가져왔으며, 그 결과 사용자는 그리드 컴퓨팅 기술에 대한 이용을 미비하게 만들었다. 따라서 본 연구에서는 분자 시뮬레이션 그리드 (MGrid: Molecular Simulation Grid System) 에서 적용을 목적으로 고효율(High Throughput)의 시뮬레이션 실험을 위한 사용자 환경(User Environment)을 정의하고, 사용자에게 친근한 추상화된 작업 모델을 제안함으로써 보다 효율적이고 안정적인 그리드 자원 이용을 가능하게 한다.(4, 5)

• Smart Structures and Systems
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• 제24권6호
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1431-1445
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• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

• 한국통신학회논문지
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• 제32권4C호
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• pp.454-461
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• 2007

차세대 컴퓨터는 초경량 이동 컴퓨터로서 작은 크기에 휴대하기 편리하고 사용자가 이동 중이라도 주변의 휴대장치들과 통신하여 동적으로 사용자 상황에 맞는 서비스를 제공한다. 사용자 상황에 맞는 서비스를 제공하기 위해서는 사용자나 컴퓨터의 정보를 보호할 수 있도록 보안의 문제점이 해결되어야 하며, 전원 제약적이고, 시스템 제한적인 초경량 이동 컴퓨팅 환경에 맞는 보안 기술이 필요하다. 본 논문에서는 초경량 이동 컴퓨팅 환경에서 효율적으로 운영 가능한 컴포넌트 기반 미들웨어를 소개하고 미들웨어에서 동적으로 적재 및 실행되는 보안 컴포넌트를 설계하고 구현한다. 구현된 보안 컴포넌트는 RC5 알고리즘을 이용한 암호화 기술과 SHA-1 알고리즘을 이용한 인증 기술을 포함한다.

• Journal of International Society for Simulation Surgery
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• 제3권2호
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• pp.39-45
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• 2016

The augmented reality is the environment which consists of real-world view and information drawn by computer. Since the image which user can see through augmented reality device is a synthetic image composed by real-view and virtual image, it is important to make the virtual image generated by computer well harmonized with real-view image. In this paper, we present reviews of several works about computer vision and graphics methods which give user realistic augmented reality experience. To generate visually harmonized synthetic image which consists of a real and a virtual image, 3D geometry and environmental information such as lighting or material surface reflectivity should be known by the computer. There are lots of computer vision methods which aim to estimate those. We introduce some of the approaches related to acquiring geometric information, lighting environment and material surface properties using monocular or multi-view images. We expect that this paper gives reader's intuition of the computer vision methods for providing a realistic augmented reality experience.

• 한국멀티미디어학회논문지
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• 제24권7호
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• pp.890-902
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• 2021

Although the object detection accuracy with still images has been significantly improved with the advance of deep learning techniques, the object detection problem with video data remains as a challenging problem due to the real-time requirement and accuracy drop with occlusion. In this research, we propose a method in pig detection for video monitoring environment. First, we determine a motion, from a video data obtained from a tilted-down-view camera, based on the average size of each pig at each location with the training data, and extract key frames based on the motion information. For each key frame, we then apply YOLO, which is known to have a superior trade-off between accuracy and execution speed among many deep learning-based object detectors, in order to get pig's bounding boxes. Finally, we merge the bounding boxes between consecutive key frames in order to reduce false positive and negative cases. Based on the experiment results with a video data set obtained from a pig farm, we confirmed that the pigs could be detected with an accuracy of 97% at a processing speed of 37fps.