• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.1
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• pp.78-88
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• 2022

Objectives: To develop the smart sensor to protect worker's health from chemical exposure by adopting ICT (Information and Communications Technology) technologies. Methods: To develope real-time chemical exposure monitoring system, IoT (Internet of Things) sensor technology and regulations were reviewed. We developed and produced smart sensor. A smart sensor is a system consisting of a sensor unit, a communication unit, and a platform. To verify the performance of smart sensors, each sensor has been certified by the Korea Laboratory Accreditation Scheme (KOLAS). Results: Chemicals (TVOC; Total Volatile Organic Compounds, Cl₂: Chlorine, HF: Hydrogen fluoride and HCN: Hydrogen cyanide) were selected according to a priority logic (KOSHA Alert, acute poisoning statistics, literature review). Notifications were set according to OEL (occupational exposure limit). Sensors were selected based on OEL and the capabilities of the sensors. Communication is designed to use LTE (Long Term Evolution) and Wi-Fi at the same time for convenience. Electronic platform were applied to build this monitoring system. Conclusions: Real-time monitoring system for OEL of hazardous chemicals in workplace was developed. Smart sensor can detect chemicals to complement monitoring of traditional workplace environmental monitoring such as short term and peak exposure. Further research is needed to expand the scope of application, improve reliability, and systematically application.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.10
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• pp.373-382
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• 2016

Under the Agricultural technology is influenced from climate that is requisite of seasonal. So this system will cover the problems and develop the agricultural industry as well. So far, the agricultural industry is developing however, it has the points of the weakness because of natural disasters such as wind risk and heavy snow. This paper designs system to change vinyl on the greenhouse. This is a preliminary study for the real-time feedback control of greenhouse. The study developed a wireless IoT sensor system based on authentic technology capacities, to integrate with the protected horticulture Management System. These system was used to evaluate the levels of the snow cover and wind through IoT devices. The existing greenhouse uses the warm water to clear snow or to change methods. This system will recover by changing the vinyl which is covered outside of the greenhouse. The points of the system is changing vinyl to spin pipe. It is contained extra vinyl. The effects of this system are minimized labor protected crops from natural disasters. For this purpose, the study first developed a wireless IoT sensor unit that integrates an MEMS device and wireless communication module. Also, the study developed an operating program that enables real-time response measurement. It will help operational and maintenance greenhouse as a result.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• Journal of Legislation Research
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• no.53
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• pp.177-211
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• 2017

In the course of the emergence and development of new ICT technologies and services such as Big Data, Internet of Things and Artificial Intelligence, the future will change by these new innovations in the Fourth Industrial Revolution. The future of this fourth industrial revolution will change and our future will be data-based society or economy. Since there is personal information at the center of it, the development of the economy through the utilization of personal information will depend on how to make the personal information protection laws. In Korea, which is trying to lead the 4th industrial revolution, it is a legal interest that can not give up the use of personal information, and also it is an important legal benefit that can not give up the personal interests of individuals who want to protect from personal information. Therefore, it is necessary to change the law on personal information protection in a rational way to harmonize the two. In this regard, this article discusses the problems of duplication and incompatibility of the personal information protection law, the scope of application of the personal information protection law and the uncertainty of the judgment standard, the lack of flexibility responding to the demand for the use of reasonable personal information, And there is a problem of reverse discrimination against domestic area compared to the regulated blind spot in foreign countries. In order to solve these problems and to improve the legislation of personal information protection in the era of the fourth industrial revolution, we proposed to consider both personal information protection and safe use by improving the purpose and regulation direction of the personal information protection law. The balance and harmony between the systematical maintenance of the personal information protection legislation and laws and regulations were also set as important directions. It is pointed out that the establishment of rational judgment criteria and the legislative review to clarify it are necessary for the constantly controversial personal information definition regulation and the method of allowing anonymization information as the intermediate domain. In addition to the legislative review for the legitimate and non-invasive use of personal information, there is a need to improve the collective consent system for collecting personal information to differentiate the subject and to improve the legislation to ensure the effectiveness of the regulation on the movement of personal information between countries. In addition to the issues discussed in this article, there may be a number of challenges, but overall, the protection and use of personal information should be harmonized while maintaining the direction indicated above.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.301-314
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• 2013

The existing computer environment, numerous mobile environments and the internet environment make human life easier through the development of IT technology. With the emergence of the mobile and internet environment, data is getting bigger rapidly. From this environment, we can take advantage of using those data as economic assets for organizations which make dreams come true for the emerging Big Data environment and Big Data security services. Nowadays, Big Data services are increasing. However, these Big Data services about Big Data security is insufficient at present. In terms of Big Data security the number of security by Big Data studies are increasing which creates value for Security by Big Data not Security for Big Data. Accordingly in this paper our research will show how security for Big Data can vitalize Big Data service for organizations. In details, this paper derives the priorities of the components of Big Data Information Security Service by AHP.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• Proceedings of Korean Society of Archives and Records Management
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• 2019.05a
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• pp.17-23
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• 2019

People who usually receive weather information are now becoming agents providing such information through crowdsourcing based on the Internet. As the archival academia recognizes the significance of information management including data, it is necessary to focus on the change and the current state of the meteorological information. Therefore, this dissertation has confirmed the current state and the problem of the meteorological network built by the information provided by the agent. In addition, it has analyzed the collection, use, and possibility of meteorological information by participating in the forecast process through crowdsourcing to identify how to gather information in the field of meteorology. Furthermore, it suggests a future development prospect of meteorological application through crowdsourcing.