• 한국인터넷방송통신학회논문지
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• 제19권6호
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• pp.227-233
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• 2019

최근 딥러닝 기술의 발달과 함께 신경 네트워크는 컴퓨터 비전에서도 성공을 거두고 있다. 컨볼루션 신경망은 단순한 영상 분류 작업뿐만 아니라 객체 분할 및 검출 등 난이도가 높은 작업에서도 탁월한 성능을 보였다. 그러나 그러한 많은 심층 학습 모델은 지도학습에 기초하고 있으며, 이는 이미지 라벨보다 주석 라벨이 더 많이 필요하다. 특히 semantic segmentation 모델은 훈련을 위해 픽셀 수준의 주석을 필요로 하는데, 이는 매우 중요하다. 이 논문은 이러한 문제를 해결하기 위한 네트워크 훈련을 위해 영상 수준 라벨만 필요한 약지도 semantic segmentation 방법을 제안한다. 기존의 약지도학습 방법은 대상의 특정 영역만 탐지하는 데 한계가 있다. 반면에, 본 논문에서는 우리의 모델이 사물의 더 다른 부분을 인식하도 multi-classifier 심층 학습 아키텍처를 사용한다. 제안된 방법은 VOC 2012 검증 데이터 세트를 사용하여 평가한다.

• 한국게임학회 논문지
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• 제6권3호
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• pp.43-50
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• 2006

본 연구는 초등학교 수학과 수 연산 영역에서의 수준별 학습을 지원하기 위한 게임형 학습 프로그램을 개발하는 것이다. 수학과는 위계성이 강한 단계형 교과로 학습자 개개인의 수준을 고려한 수준별 학습이 꼭 필요한 교과이다. 그러나 일선 학교에서는 다인수 학급 및 보충지도 시간 부족 등으로 인하여 학습자 수준에 맞는 개별적인 수준별 학습이 잘 이루어지기 않고 있는 실정이다. 따라서, 본 연구는 수준별 개별화 학습을 지원하여 올바른 수와 연산개념의 형성을 위한 게임형 학습 프로그램을 개발한다. 본 학습 프로그램은 학습자의 학습 진척도와 성취도를 검사하여, 학습자에게 맞는 학습 단계를 자동으로 조절해 준다. 그리고 학습 중에도 학습자의 수준을 고려한 힌트와 구체물 조작을 통해 문제를 해결할 수 있도록 도움 기능을 제공한다. 본 학습 프로그램을 학습자들에게 적용한 결과 수학과 학습에 대한 흥미도를 높아지고 특히, 힌트 및 도우미 기능은 문제 해결에 도움이 되는 것으로 나타났다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.231-234
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• 2004

Ratio that robot occupies is low level worldwide fairly in suspension wire, electricity electron and neutralization learning industry and domestic industry of this is staring in average love. Can speak that grafting of robotic machine and neutralization learning industry is high in terms of side of creation of the added value or progress of technology rightly hereupon. This research raises or designed multi-function handling robot that can make welding, assembly conveniently catching large size work water

• Journal of Forest and Environmental Science
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• 제40권1호
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• pp.15-23
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• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.176-190
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• 2018

This paper presents a novel convolutional neural network based multi-feature fusion learning method for non-rigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.

• 한국정보시스템학회지:정보시스템연구
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• 제29권2호
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• pp.27-50
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• 2020

Purpose The research on the effective use of IS needs to embrace the alignment to organization learning process, which expands the limited focus on dynamic capability of IS use. In addition, it should be done in multi-level analysis with system, user, task, and organization. The current study suggests the inclusion of multi-level analysis of effective use of IS in the perspective of exploration and exploitation. Design/methodology/approach This review selected the representative studies in IS discipline which have investigated the effective use of IS, dynamic capability, operational capability, exploration, exploitation, or organizational learning. In the search of academic archives with those keywords, seventeen papers which have been most cited were chosen and validated whether the focus constructs are directly theorized or validated the suggested keywords. In addition, the level of analysis was verified whether it includes one or more levels of system, individual, task, or organization. Based on the initial analysis of dynamic capability, the further review of research on explorational and exploitational capabilities was implemented. Findings The present review study on previous literature on effective use of IS presented that it is largely implemented in the level of individual but few of them has included organization level. Similarly, the direct investigation of explorational and exploitational use of IS has not been done so much. The needs of study on effective use of IS in depth have been inquired for a decade. However, the review presented that it still lacks profound theories and empirical validations compared to those of adoption stage of IS. Based on the review, future research on the transition between explorational and exploitational use of IS is suggested.

• ETRI Journal
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• 제41권6호
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• pp.750-759
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• 2019

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.

• 한국시뮬레이션학회논문지
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• 제3권2호
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• pp.69-76
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• 1994

Traditionally, simulation-based learning games which are known as flight-simulators have been constructed as a black-box game. Within a black-box game, game-players can view and modify only a part of model parameters. Game-players cannot change the structure of a simulation model. In a black-box game, game-players cannot understand and learn the system structure which is responsible for the system behavior. In this paper, the multi-tasking at the level of operating systems is exploited to enhance the transparency of simulation-based learning game. The white-box game or transparent-box game allows game-players ot view and modify the model structure. The multi-tasking solution for white-box learning game is implemented with Smalltalk language on MS-/windows operating system.

• ETRI Journal
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• 제45권1호
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• pp.93-104
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• 2023

Coreference resolution is a task in discourse analysis that links several headwords used in any document object. We suggest pointer networks-based coreference resolution for Korean using multi-task learning (MTL) with an attention mechanism for a hierarchical structure. As Korean is a head-final language, the head can easily be found. Our model learns the distribution by referring to the same entity position and utilizes a pointer network to conduct coreference resolution depending on the input headword. As the input is a document, the input sequence is very long. Thus, the core idea is to learn the word- and sentence-level distributions in parallel with MTL, while using a shared representation to address the long sequence problem. The suggested technique is used to generate word representations for Korean based on contextual information using pre-trained language models for Korean. In the same experimental conditions, our model performed roughly 1.8% better on CoNLL F1 than previous research without hierarchical structure.

• Smart Structures and Systems
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• 제31권4호
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.