• Journal of Digital Convergence
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• v.16 no.9
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• pp.229-237
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• 2018

As IoT and AI have recently developed, interest in digital healthcare is increasing. Therefore, this study aims to identify technology trends through a patent analysis on digital healthcare and present future promising areas by analyzing domestic and foreign technology competitiveness and keywords. The detailed technologies to be analyzed were designated as Health Information Measurement Technology, Healthcare Platform Technology and Healthcare Remote Service Technology, and 61,166 patents were analyzed to identify the patent trends of the world's major patent offices and major patent applications. In addition, the analysis of the technological competitiveness of each detailed technology and Korea's technological competitiveness based on its patent activity, the rate of major market securing, and the uses of the patents showed that Korea's technological competitiveness was lower than global technology. In addition, the key keyword analysis showed that the core promising areas of digital healthcare were expected to require a focused strategy for fostering health care platform technologies in Korea.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.597-603
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• 2023

The paradigm of military operations has evolved from platform-centric warfare to network-centric warfare and further to information-centric warfare, driven by advancements in information technology. In recent years, with the development of cutting-edge technologies such as big data, artificial intelligence, and the Internet of Things (IoT), military operations are transitioning towards knowledge-centric warfare (KCW), based on artificial intelligence. Consequently, the military places significant emphasis on integrating advanced information and communication technologies (ICT) to establish reliable C4I (Command, Control, Communication, Computer, Intelligence) systems. This research emphasizes the need to apply data mining techniques to analyze and evaluate various aspects of C4I systems, including enhancing combat capabilities, optimizing utilization in network-based environments, efficiently distributing information flow, facilitating smooth communication, and effectively implementing knowledge sharing. Data mining serves as a fundamental technology in modern big data analysis, and this study utilizes it to analyze real-world cases and propose practical strategies to maximize the efficiency of military command and control systems. The research outcomes are expected to provide valuable insights into the performance of C4I systems and reinforce knowledge-centric warfare in contemporary military operations.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.1-13
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• 2021

This study tried to suggest ways to improve the indicator system to strengthen the personal information protection. For this purpose, the components of indicator system are derived through domestic and foreign literature, and it was selected as main the diagnostic indicators through FGI/Delphi analysis for personal information protection experts and a survey for personal information protection officers of public institutions. As like this, this study was intended to derive an inspection standard that can be reflected as a separate index system for personal information protection, by classifying the specific IT technologies of the 4th industrial revolution, such as big data, cloud, Internet of Things, and artificial intelligence. As a result, from the planning and design stage of specific technologies, the check items for applying the PbD principle, pseudonymous information processing and de-identification measures were selected as 2 common indicators. And the checklists were consisted 2 items related Big data, 5 items related Cloud service, 5 items related IoT, and 4 items related AI. Accordingly, this study expects to be an institutional device to respond to new technological changes for the continuous development of the personal information management level diagnosis system in the future.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.23-29
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• 2024

In this scholarly investigation, the focus is placed on the transformative potential of edge computing in enhancing Intelligent Transportation Systems (ITS) for the facilitation of autonomous driving. The intrinsic capability of edge computing to process voluminous datasets locally and in a real-time manner is identified as paramount in meeting the exigent requirements of autonomous vehicles, encompassing expedited decision-making processes and the bolstering of safety protocols. This inquiry delves into the synergy between edge computing and extant ITS infrastructures, elucidating the manner in which localized data processing can substantially diminish latency, thereby augmenting the responsiveness of autonomous vehicles. Further, the study scrutinizes the deployment of edge servers, an array of sensors, and Vehicle-to-Everything (V2X) communication technologies, positing these elements as constituents of a robust framework designed to support instantaneous traffic management, collision avoidance mechanisms, and the dynamic optimization of vehicular routes. Moreover, this research addresses the principal challenges encountered in the incorporation of edge computing within ITS, including issues related to security, the integration of data, and the scalability of systems. It proffers insights into viable solutions and delineates directions for future scholarly inquiry.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.22-28
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• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.

• Journal of Legislation Research
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• no.53
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• pp.269-310
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• 2017

This article aims at the study on Improvement of legal System which is related to automated vehicles in the era of the 4th industrial revolution. Legal aspects of driving automation have two view points. One is to permit a automated vehicle, the other is to regulate the behavior of driver on the road. Signifying elements of the 4th industrial revolution are IoT, AI, big data, cloud computing etc. Automated vehicles are the imbodiment of those new ICT technologies. The vehicle management act(VMA) rules about vehicle registration and approval of vehicle types. VMA defines a automated vehicle as a vehicle which can be self driven without handling of driver or passenger. Vehicle makers can take temporary driving permission for testing and research the driving automation. Current definition of automated vehicle of VMA is not enough for including all levels of SAE driving automation. In the VMA must be made also a new vehicle safty standard for automated vehicle. In the national assembly is curruntly pending three draft bills about legislation of artificial intelligence. Driving automation and AI technologies must be parallel developed. It is highly expected that more proceeding research of driving automation can be realized as soon as possible.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.176-183
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• 2021

Purpose: The introduction of smart factories that reflect the 4th industrial revolution technologies such as AI, IoT, and VR, has been actively promoted in Korea. However, in order to solve various problems arising from existing file-based operating systems, this research will focus on identifying and verifying non-file system-based data protection technology. Method: The research will measure security storage that cannot be identified or controlled by the operating system. How to activate secure storage based on the input of digital key values. Establish a control unit that provides input and output information based on BIOS activation. Observe non-file-type structure so that mapping behavior using second meta-data can be performed according to the activation of the secure storage. Result: First, the creation of non-file system-based secure storage's data input/output were found to match the hash function value of the sample data with the hash function value of the normal storage and data. Second, the data protection performance experiments in secure storage were compared to the hash function value of the original file with the hash function value of the secure storage after ransomware activity to verify data protection performance against malicious ransomware. Conclusion: Smart factory technology is a nationally promoted technology that is being introduced to the public and this research implemented and experimented on a new concept of data protection technology to protect crucial data within the information system. In order to protect sensitive data, implementation of non-file-type secure storage technology that is non-dependent on file system is highly recommended. This research has proven the security and safety of such technology and verified its purpose.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Food Science and Industry
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• v.52 no.4
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• pp.401-409
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• 2019

The need for win-win cooperation between small and medium-sized enterprises and large corporations is emphasized. For the sustainable growth of a healthy industry, the attractive parts of large companies and the capabilities of small and medium-sized enterprises must be harmonized and shared. It was also examined what items large companies should consider in selecting small and medium-sized enterprises as a partner. In the case of food producers, when considering safety issues, a comprehensive safety system should be established, not just a single factor. And cooperation is expected between small and medium-sized enterprises and other partners with new technologies such as AI, IoT, 3D printers and 5G technology. It is suggested that corporate development can be achieved by various models through win-win cooperation between large companies and small and medium-sized enterprises.