• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.416-429
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• 2018

Typhoons and floods are natural disasters that occur frequently, and the damage resulting from these disasters must be in advance predicted to establish appropriate responses. Direct damages such as building collapse, human casualties, and loss of farms and fields have more attention from people than indirect damages such as increase of consumer prices. But indirect damages also need to be considered for living. The agricultural products are typical consumer items affected by typhoons and floods. Sudden, powerful typhoons are mostly accompanied by heavy rains and damage agricultural products; this increases the retail price of such products. This study analyzes the influence of natural disasters on the price of agricultural products by using a deep learning algorithm. We decided rice, onion, green onion, spinach, and zucchini as target agricultural products, and used data on variables that influence the price of agricultural products to create a model that predicts the price of agricultural products. The result shows that the model's accuracy was about 0.069 measured by RMSE, which means that it could explain the changes in agricultural product prices. The accurate prediction on the price of agricultural products can be utilized by the government to respond natural disasters by controling amount of supplying agricultural products.

• The Journal of Bigdata
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• v.8 no.1
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• pp.49-59
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• 2023

There are various items in the safety and health standards of the manufacturing industry, but they can be divided into work-related diseases and musculoskeletal diseases according to the standards for sickness and accident victims. Musculoskeletal diseases occur frequently in manufacturing and can lead to a decrease in labor productivity and a weakening of competitiveness in manufacturing. In this paper, to detect the musculoskeletal harmful factors of manufacturing workers, we defined the musculoskeletal load work factor analysis, harmful load working postures, and key points matching, and constructed data for Artificial Intelligence(AI) learning. To check the effectiveness of the suggested dataset, AI algorithms such as YOLO, Lite-HRNet, and EfficientNet were used to train and verify. Our experimental results the human detection accuracy is 99%, the key points matching accuracy of the detected person is @AP0.5 88%, and the accuracy of working postures evaluation by integrating the inferred matching positions is LEGS 72.2%, NECT 85.7%, TRUNK 81.9%, UPPERARM 79.8%, and LOWERARM 92.7%, and considered the necessity for research that can prevent deep learning-based musculoskeletal diseases.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.53-62
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• 2021

Deep learning models such as convolutional neural networks and recurrent neual networks process a huge amounts of data, so they require a lot of storage and consume a lot of time and power due to memory access. Recently, research is being conducted to reduce memory usage and access by compressing data using the feature that many of deep learning data are highly sparse and localized. In this paper, we propose a compression-decompression method of storing only the non-zero data and the location information of the non-zero data excluding zero data. In order to make the location information of non-zero data, the matrix data is divided into sections uniformly. And whether there is non-zero data in the corresponding section is indicated. In this case, section division is not executed only once, but repeatedly executed, and location information is stored in each step. Therefore, it can be properly compressed according to the ratio and distribution of zero data. In addition, we propose a hardware structure that enables compression and decompression without complex operations. It was designed and verified with Verilog, and it was confirmed that it can be used in hardware deep learning accelerators.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.111-121
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• 2020

This study analyzed the effects on system quality, learning commitment, and satisfaction in a mobile environment. A survey was conducted on 192 students enrolled in K University, and the research results are as follows. first, it was found that usefulness had an effect on learning commitment, but connectivity and reliability did not affect learning commitment. second, it was found that the usefulness and connectivity of the system quality had a significant effect on the satisfaction of use. third, the intermediary effect of learning immersion was verified as the connectivity, reliability, and usefulness of the system quality influence the satisfaction of use. connectivity and reliability had no mediating effect of learning commitment, and usefulness was found to play a partial mediating role in affecting user satisfaction. This study is meaningful in that it can provide a plan for improving the quality management of mobile learning and improving the learning effect by analyzing the effects on mobile learning from multiple perspectives.

• Journal of the Korean Geotechnical Society
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• v.35 no.9
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• pp.37-45
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• 2019

Quick identification of the condition of tunnel face and optimized determination of support patterns during tunnel excavation in underground construction projects help engineers prevent tunnel collapse and safely excavate tunnels. This study investigates a CNN technique for quick determination of rock quality classification depending on the condition of tunnel face, and presents the procedure for rock quality classification using a deep learning technique and the improved method for accurate prediction. The VGG16 model developed by tens of thousands prestudied images was used for deep learning, and 1,469 tunnel face images were used to classify the five types of rock quality condition. In this study, the prediction accuracy using this technique was up to 83.9%. It is expected that this technique can be used for an error-minimizing rock quality classification system not depending on experienced professionals in rock quality rating.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1653-1661
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• 2022

In this study, the classification of cold water and normal water based on Geo-Kompsat 2A images was performed. Daily mean surface temperature products provided by the National Meteorological Satellite Center (NMSC) were used, and convolution neural network (CNN) deep learning technique was applied as a classification algorithm. From 2019 to 2022, the cold water occurrence data provided by the National Institute of Fisheries Science (NIFS) were used as the cold water class. As a result of learning, the probability of detection was 82.5% and the false alarm ratio was 54.4%. Through misclassification analysis, it was confirmed that cloud area should be considered and accurate learning data should be considered in the future.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1325-1328
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• 2022

언제 어디서든 한 손으로 미디어 콘텐츠를 소비할 수 있게 해주는 모바일 기기들이 기존 전통적 미디어 콘텐츠 단말기였던 TV나 데스크톱 PC들을 대체하게 되면서 세로형 영상 콘텐츠에 대한 수요가 나날이 높아져 가고 있다. 이와 더불어 모바일 단말기 제조사들은 서로 간의 경쟁에서 앞서기 위해 제품 차별화 전략을 수립하고 모바일 사용자들의 요구 사항을 세세하게 맞추기 위한 결과, 저마다 다른 디스플레이 해상도 규격을 가진 모바일 기기들이 생산되고 있는 상황이다. 이에 미디어 콘텐츠 제작자들은 기존 가로형 영상 콘텐츠와 더불어 새롭게 요구되는 세로형 영상 콘텐츠들을 저마다 다른 해상도 규격에 맞추는데 많은 시간과 비용을 투자하고 있다. 더 나아가 모바일 단말기 해상도 규격과 맞지 않는 영상 콘텐츠를 시청하게 될 경우, 모바일 사용자 입장에서는 디스플레이 전체 영역을 뷰포트로 잡을 수 없어 시청 만족도가 떨어질 수 있다. 이에 본 논문은 한 번의 콘텐츠 제작을 통해서도 추가 비용 없이 다양한 디스플레이 규격을 가진 단말기들에 대해 맞춤형 콘텐츠 서비스 제공을 가능하게 하여 미디어 콘텐츠 소비자들에게 충분한 시청 몰입감을 제공해줄 수 있는 단말 적응적 미디어 화면비 변환 시스템을 제안한다. 단말 적응적 미디어 화면비 변환 시스템은 딥러닝 네트워크 모델과 이미지 관련 라이브러리를 기반으로 하여 설계한 시스템이며, 사용자가 시청하기 원하는 영역을 판단하고, 사용자가 원하는 뷰포트 종횡비에 따라 해당 영역을 잘라내어 사용자가 원하는 세로형 영상 콘텐츠를 제공해준다.

• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.505-511
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• 2023

In the present study, a deep learning model was established to predict the motion response of small fishing vessels. Hydrodynamic performances were evaluated for two small fishing vessels for the dataset of deep learning model. The deep learning model of the Long Short-Term Memory (LSTM) which is one of the recurrent neural network was utilized. The input data of LSTM model consisted of time series of six(6) degrees of freedom motions and wave height and the output label was selected as the time series data of six(6) degrees of freedom motions. The hyperparameter and input window length studies were performed to optimize LSTM model. The time series motion response according to different wave direction was predicted by establised LSTM. The predicted time series motion response showed good overall agreement with the analysis results. As the length of the time series increased, differences between the predicted values and analysis results were increased, which is due to the reduced influence of long-term data in the training process. The overall error of the predicted data indicated that more than 85% of the data showed an error within 10%. The established LSTM model is expected to be utilized in monitoring and alarm systems for small fishing vessels.

• Digital Contents
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• no.9 s.148
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• pp.112-115
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• 2005

한국데이터베이스진흥센터가 운영하고 있는 온엑스포(www.onexpo.or.kr)에서는 9월과 10월 두 달 동안‘2005 캐릭터·애니메이션 온엑스포’를 개최한다. 온엑스포는 국내 디지털콘텐츠 기업의 제품을 온라인상에서 상설 전시하고 해당 기업의 홍보와 마케팅을 지원하는 국내 최대 규모의 사이버 전시장으로, 전시 기업의 홍보를 효과적으로 지원하기 위해 분기별로 주제를 정해 테마전시회를 개최하고 있다. 지난 3월과 4월에는‘이러닝 온엑스포’를, 5월과 6월에는‘게임 온엑스포’를 개최한 바 있으며, 이번에는 국내의 캐릭터·애니메이션의 트랜드를 알아보고 대표적인 기업을 소개하기 위해 ‘2005 캐릭터·애니메이션 온엑스포’를 개최한다. 이번 호에서는‘2005 캐릭터·애니메이션 온엑스포’참가 기업 중 우리나라의 캐릭터·애니메이션 사업을 이끌어 갈 피콤엔터테인 먼트·은아트·스튜디오 짜박을 소개한다.