• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1569-1577
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• 2015

Recently, the development of IT technology, utilization of smart devices 3-5-year-old infants Upon smart device penetration rate per household increases has also sharply increased. In this paper, the educational contents for kid are considered and implemented by reflecting this trend. Content that has been produced in this paper are based on learning theory of constructivism, was to be performed naturally learn the content of the table-top display of applying the NUI technology. Also, the content creation that can help you learn infants through experiments crafted tabletop display interface content, it is believed to be the basis for the improvement of usability.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.120-123
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• 2020

The surveillance system to prevent crime and accidents in advance has become a necessity, not an option in real life. Not only public institutions but also individuals are installing surveillance cameras to protect their property and privacy. However, since the installed surveillance camera cannot be monitored for 24 hours, the focus is on the technology that tracks the video after an accident occurs rather than prevention. In this paper, we propose a system model that monitors abnormal behaviors that may cause crimes through real-time video, and when a specific behavior occurs, the surveillance system automatically detects it and responds immediately through an alarm. We are a model that analyzes real-time images from surveillance cameras and uses I3D models from analysis servers to analyze abnormal behavior and deliver notifications to web servers and then to clients. If the system is implemented with the proposed model, immediate response can be expected when a crime occurs.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1055-1058
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• 2021

2019년 산업재해 현황 분석 결과 복장, 보호구의 잘못 사용으로 사고가 발생한 비율이 20%로 높은 비율을 차지했고, 전체 사고자 중 두부 손상을 입은 비율이 41%로 가장 높은 비율을 보였다[1]. 고용 노동부가 발표한 '건설현장 추락위험 일제점검 결과(2021.7)'에서는 안전모 미착용 근로자가 32.6%를 차지하였다[2]. 우리는 ICT기술을 활용해 안전모의 기능개선 가능성을 확인하였고, 안전사고를 예방하고, 빠르게 감지할 수 있는 스마트 안전모를 개발하고자 하였다. 그리고 본 연구를 통해 IoT 센서들과 딥러닝 영상분석을 이용한 스마트 안전모 서비스는 작업 전 부정착용 방지, 작업 중 위험감지, 사고 발생 시 빠른 감지를 통한 신속한 대처를 목표로 하여, 안전한 작업환경을 만들 수 있는 가능성을 제시하고자 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.419-427
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• 2021

Due to the effects of climate change and the reduction of the labor force due to COVID-19, the crop yield, harvest time, and cultivated area are rapidly changing every year. In order to respond flexibly to this situation, attempts to apply smart farm technology based on ICT (Information and Communication Technology) to individual farms are increasing. On the other hand, various stakeholders are trying to predict the yield of crops using artificial intelligence and IoT technology, but accurate prediction is difficult due to the lack of learning data. In this study, in order to overcome the data collection problem limited to a specific institution, a smart farmer service technology based on community mapping was developed in which farmers directly participate, input and share accurate data to predict production. In the process, analysis was performed on napa cabbage, which is a vegetable with a large price change compared to production.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.85-93
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• 2021

The government is pursuing plans to create new e-government services. In terms of improving business procedures, dBrain (finance), e-people (personnel), and Onnara (electronic payment and business management) have achieved considerable results, and are currently making efforts to improve existing administrative services using newly emerged ICT. Among them, this paper attempted to study whether self-learning-based intelligent administrative services are efficient in the work process of public officials promoting actual work and affect their continued use. Based on individual perceptions and attitudes toward advanced ICTs such as AI, big data, and blockchain, public officials' influences on administrative services were identified and verified using UTAUT variables. They believe that the establishment and introduction of innovative administrative services can be used more efficiently, and they have high expectations for the use and provision of services as ICT develops. In the future, a model will be also applied to citizens

• The Journal of Bigdata
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• v.7 no.1
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• pp.63-73
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• 2022

COVID-19 has continued from 2020 to the present, and many social changes have occurred. Wearing a mask has become mandatory, and if you do not wear a mask, you cannot use public facilities or restaurants. For this reason, most public facility entrances are equipped with a mask recognition system to check whether a mask is worn. However, it is unclear whether people who cover their mouths with a scarf or who do not wear a mask properly can be identified. In this study, we proposed an embedded mask recognition system using YOLOv5. Unlike the existing mask recognition system, it was able to distinguish not only whether a mask was worn, but also whether a mask was worn in various exceptional situations, such as a person with a scarf or a person covering their mouth with their hands, and showed excellent performance when mounted on the Nvida Jetson Nano Board.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.156-162
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• 2022

This study estimates the relative position between body segments using segment orientation and segment-to-joint center (S2J) vectors. In many wearable motion tracking technologies, the S2J vector is treated as a constant based on the assumption that rigid body segments are connected by a mechanical ball joint. However, human body segments are deformable non-rigid bodies, and they are connected via ligaments and tendons; therefore, the S2J vector should be determined as a time-varying vector, instead of a constant. In this regard, our previous study (2021) proposed a method for determining the time-varying S2J vector from the learning dataset using a regression method. Because that method uses a deformation-related variable to consider the deformation of S2J vectors, the optimal variable must be determined in terms of estimation accuracy by motion and segment. In this study, we investigated the effects of deformation-related variables on the estimation accuracy of the relative position. The experimental results showed that the estimation accuracy was the highest when the flexion and adduction angles of the shoulder and the flexion angles of the shoulder and elbow were selected as deformation-related variables for the sternum-to-upper arm and upper arm-to-forearm, respectively. Furthermore, the case with multiple deformation-related variables was superior by an average of 2.19 mm compared to the case with a single variable.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1719-1729
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• 2021

Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ~ 0.97 and a difference of root-mean-square-error ~ 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea.

• Smart Media Journal
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• v.13 no.3
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• pp.9-17
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• 2024

Due to the recent pandemic and the development of ICT technology, the use of non-face-to-face and unmanned systems is expanding, and it is very important to understand emotions in communication in non-face-to-face situations. As emotion recognition methods for various facial expressions are required to understand emotions, artificial intelligence-based research is being conducted to improve facial expression emotion recognition in image data. However, existing research on facial expression emotion recognition requires high computing power and a lot of learning time because it utilizes a large amount of data to improve accuracy. To improve these limitations, this paper proposes a method of recognizing facial expressions using age and gender, which are image meta information, as a method of recognizing facial expressions with even a small amount of data. For facial expression emotion recognition, a face was detected using the Yolo Face model from the original image data, and age and gender were classified through the VGG model based on image meta information, and then seven emotions were recognized using the EfficientNet model. The accuracy of the proposed data classification learning model was higher as a result of comparing the meta-information-based data classification model with the model trained with all data.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.