• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.183-186
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• 2018

In this paper, we propose the database design for skill learning service through the internet from the viewpoint of service engineering. This paper we describe the outlines for a design theory for skill learning service, which can lead to the satisfaction of both learner and instructor. Compared to other services, motion control learning takes a considerable amount of time, and this leads to a difficulty for learners to rate the quality of the service as well as for the instructors to provide consistent quality and standard of teaching. To solve these problems, we use a relational database with MongoDB which is an unstructured database allowing to flexibly incorporate the demands of both learner and instructor into the database itself.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.350-351
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• 2018

최근 의료 현장에 인공지능 기술의 도입이 가속화 되고 있다. 특히, 의료영상 분석 분야의 관련된 기 시스템 및 소프트웨어의 패러다임을 변화시키고 있다. 본 연구는 인공지능 기술을 적용하기 위한 학습의료영상 구성을 제안하고 이를 기반으로 X-ray 영상 중 손부위에 적용하여 오른손과 왼손을 판별하는 응용에 적용하였다. 그리고 Deep Learning Algorithm의 CNN을 개선하여 개발한 Advanced GoogLeNet를 적용하여 97%이상의 정확도를 보였다. 본 연구를 통해 얻어진 인공지능에 적용하기 위한 학습데이터 셋 구성과 개선된 알고리즘은 다양한 의료영상분석에 적용하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1639-1641
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• 2012

본 연구에서는 기업에서의 e-Learning학습을 지원하기 위한 SNS기반의 소통 형 학습 및 협업 학습 지원 시스템을 개발하였다. 본 시스템은 e-Learning시스템에서 학습하고 평가하는 것을 넘어 SNS상에서 학습자 간 토론과 소통으로 학습자에게 보다 효과적인 학습환경을 제공하고 있다. 또한 학습자는 학습이 업무에 반영될 수 있도록 실제 학습자 업무 조직 별 토론과 팀 과제를 통해 실행 방법과 계획을 수립하게 함으로써 보다 효과적으로 학습이 업무에 활용될 수 있도록 지원하고 있다. SNS기반의 소통 형 학습시스템은 학습자에게 학습동기부여를 제공하여 보다 능동적인 학습참여를 유도하여 학습을 지원하고 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1539-1542
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• 2012

고정된 컴퓨터에서 학습하는 e-learning에서 탈피하여 이동 중에도 학습이 가능한 u-learning이 필요하여 u-learning의 한부분인 스마트러닝은 급변하는 정보화시대의 교육경향이 매우 빠르게 변화하고 있는 상황을 그대로 반영해주는 결과물이라고 할 수 있다. 스마트 러닝이 학습향상에 얼마나 영향을 미치는가를 분석하고 스마트 러닝 기능을 최대한 활용하여 최대의 학습 효과를 얻을 수 있는 방법을 제시하며 스마트기기를 이용해 실제 학습하는 사례를 적용한 동영상 강의 애플리케이션의 효율적인 관리 시스템을 분석 설계한다. 각종 콘텐츠를 비롯하여 동영상강의 어플리케이션을 통한 여러 학습수단을 배경으로 전체적인 면에서 학습 환경을 살펴봄으로써 학습효과에 보다 나은 방안을 제시하고자 한다.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.385-398
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• 2021

The complexity of deep learning models affects the real-time performance of gesture recognition, thereby limiting the application of gesture recognition algorithms in actual scenarios. Hence, a residual learning neural network based on a deep convolutional neural network is proposed. First, small convolution kernels are used to extract the local details of gesture images. Subsequently, a shallow residual structure is built to share weights, thereby avoiding gradient disappearance or gradient explosion as the network layer deepens; consequently, the difficulty of model optimisation is simplified. Additional convolutional neural networks are used to accelerate the refinement of deep abstract features based on the spatial importance of the gesture feature distribution. Finally, a fully connected cascade softmax classifier is used to complete the gesture recognition. Compared with the dense connection multiplexing feature information network, the proposed algorithm is optimised in feature multiplexing to avoid performance fluctuations caused by feature redundancy. Experimental results from the ISOGD gesture dataset and Gesture dataset prove that the proposed algorithm affords a fast convergence speed and high accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1681-1684
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• 2004

90 년대 후반부터 웹 기반 수업(Web-based instruction)이라 하여 인터넷을 이용한 새로운 교육방법이 시도되었다. WBI에 필요한 각종 프로그래밍을 수작업으로 진행하여야 한다는 문제점을 극복하기 위해 개발된 학습운영체제(Learning Management System)가 개발되면서 인터넷을 통한 교육은 급속히 확산되고 있다. 무선 인터넷 기술까지 수용하는 개념인 소위 e-Learning 체제로 발전되면서 e-Learning의 수요는 급속히 증가하게 되었다. e-Learning 기술 표준 개발을 실질적으로 주도하는 기관들인 IEEE, AICC, IMS가 제안하는 개별 표준안들을 ADL에서 SCORM(Sharable Content Object Reference Model)이라는 종합적인 표준안으로 수렴하게 되면서 SCORM을 기준으로 만든 다양한 컨텐츠가 개발되고있다.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.505-517
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• 2021

This research presented an implementation of autostereoscopic virtual three-dimensional (3D) button device as non-contact style. The proposed device has several characteristics about visible feature, non-contact use and artificial intelligence (AI) engine. The device was designed to be contactless to prevent virus contamination and consists of 3D buttons in a virtual stereoscopic view. To specify the button pressed virtually by fingertip pointing, a simple deep learning network having two stages without convolution filters was designed. As confirmed in the experiment, if the input data composition is clearly designed, the deep learning network does not need to be configured so complexly. As the results of testing and evaluation by the certification institute, the proposed button device shows high reliability and stability.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.197-207
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• 2022

The applications and user interfaces (UIs) of smart mobile devices are constantly diversifying. For example, deep learning can be an innovative solution to classify widgets in screen images for increasing convenience. To this end, the present research leverages captured images and the ReDraw dataset to write deep learning datasets for image classification purposes. First, as the validation for datasets using ResNet50 and EfficientNet, the experiments show that the dataset composed in this study is helpful for classification according to a widget's functionality. An implementation for widget detection and classification on RetinaNet and EfficientNet is then executed. Finally, the research suggests the Widg-C and Widg-D datasets-a deep learning dataset for identifying the widgets of smart devices-and implementing them for use with representative convolutional neural network models.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.264-267
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• 2022

Recently, federated learning (FL) has increased prominence as a viable approach for enhancing user privacy and data security by allowing collaborative multi-party model learning without exchanging sensitive data. Despite this, most present FL systems still depend on a centralized aggregator to generate a global model by gathering all submitted models from users, which could expose user privacy and the risk of various threats from malicious users. To solve these issues, we suggested a safe FL framework that employs differential privacy to counter membership inference attacks during the collaborative FL model training process and empowers blockchain to replace the centralized aggregator server.