• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.36-42
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• 2021

With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.25-40
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• 2022

This study is about how the quality of M2M big data that provides real-time information affects users. Recently, there are many difficulties in acquiring and managing data because data types such as variety, data volume, and data velocity are changing rapidly and diversified. This not only leads to a decrease in data quality but also it can give a negative impact when making decisions using data. Generally, the quality of data is defined as 'suitability for use', which means that data quality must meet the expectations of user needs. Therefore, data providers need activities to improve data quality for this purpose, and the key is to identify data quality dimensions in each field where data is used and provide data suitable for the level of user needs. In this study, the relationship between the quality area of real-time M2M data used in the traffic information system and user satisfaction was analyzed. Research models and hypotheses were established to analyze the effects between variables related to M2M big data. In order to test the hypothesis, a causal relationship between the major factors was identified by conducting a survey and analyzing the data users.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.4
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• pp.76-89
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• 2023

One of the technologies in urban agriculture, hydroponics cultivation, has primarily focused on technological development, resulting in a lack of research on urban agriculture's cultural utilization aspects, encompassing cultural values associated with urban residents' leisure activities. Therefore, this study aimed to identify the participation constraints perceived by school community members when implementing urban farming activities using hydroponics and understand the structural relationships between the variables that influence decision-making from the perspective of leisure activities in urban farming. As a result, participation constraints in urban farming activities utilizing hydroponics were first categorized into intrinsic, interpersonal, and structural factors. Second, the results of hypothesis model verification showed that interpersonal constraints significantly influenced the participants' willingness to participate and their expected behavior. This study found the multidimensional perceptions of school community members regarding hydroponic urban farming conducted in urban spaces, particularly rooftops, and revealed the influence of decision-making factors on participation when conducting urban farming activities using hydroponic cultivation.

• Smart Media Journal
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• v.11 no.11
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• pp.49-62
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• 2022

Recently, with the development of Smart City Solutions such as Big data, AI, IoT, Autonomous driving, and Digital twins around the world, the proliferation of various smart devices and social media, and the record of the deeds that people have left everywhere, the construction of Smart Cities using the "Big Data" environment in which so much information and data is produced that it is impossible to gauge the scale is actively underway. The Purpose of this study is to construct an objective and systematic analysis Model based on Big Data to improve the transportation convenience of citizens and formulate efficient policies in Urban Information and Public Transportation accessibility in sustainable Smart Cities following the 4th Industrial Revolution. It is also to derive the methodology of developing a Big Data-Based public transport accessibility and policy management Platform using a sustainable Urban Public DB and a Private DB. To this end, Detailed Living Areas made a division and the accessibility of basic living amenities of Gwangju Metropolitan City, and the Public Transportation system based on Big Data were analyzed. As a result, it was Proposed to construct a Big Data-based Urban Information and Public Transportation accessibility Platform, such as 1) Using Big Data for public transportation network evaluation, 2) Supporting Transportation means/service decision-making based on Big Data, 3) Providing urban traffic network monitoring services, and 4) Analyzing parking demand sources and providing improvement measures.

• Knowledge Management Research
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• v.23 no.4
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• pp.275-294
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• 2022

In recent years, with the continuous integrative development of e-commerce and social media, social commerce, as a trust-centered social transaction mode, has become an important performance form of e-commerce. The good experience of online community and abundant user-generated content (UGC) attract more and more users and businesses to participate in the community contribution. In this context, the cost of accessing information is continuously decreasing, which not only makes the purchase process more concise and efficient, but also greatly increases the possibility of consumers' impulsive consumption. However, there are very few empirical studies on the internal influencing mechanism of consumers' impulsive consumption based on the characteristics of UGC for social commerce. In view of this, based on S-O-R model, this study constructs a model of consumers' impulsive consumption in the context of social commerce from the characteristics of UGC, with perceived risk as the mediating variable and tie strength as the moderating variable. The results show that content authenticity, content usefulness, and content valence of UGC have significant negative impacts on consumers' risk perception in the process of purchase decision-making, and consumers' perceived risk has a significant negative impact on consumers' impulsive consumption. Meanwhile, the tie strength between UGC producer and UGC receiver plays a moderating role between content usefulness and perceived risk, as well as between perceived risk and impulsive consumption. Finally, combined with the above findings, this study provides effective suggestions for relevant participants in social commerce in terms of business management.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.43-43
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• 2021

Sesan강과 Srepok강은 베트남, 캄보디아, 라오스가 공유하는 3S강 유역 (Sesan강, Srepok강, Sekong강)의 일부로 국제 공유하천으로 관리되고 있다. 3S강 유역은 Mekong강의 중요한 지류이며 Mekong강 유역의 상당 부분을 구성한다(Mekong강 유역 면적의 10%, 연간 총 유출량의 20%). 베트남에 속해 있는 Sesan강 유역면적은 11,255km², Srepok강 유역면적은 18,162km²이다. Sesan강과 Srepok강의 상류는 베트남 중부 고원의 긴 산맥에 위치하고 있으며, 하류는 캄보디아에 위치해 있어 상·하류간 긴밀한 협력이 필요하다. Sesan강과 Srepok강 유역은 기후변화에 따른 홍수, 가뭄, 수력발전소 건설로 인한 유출량 변동에 따른 상·하류 분쟁, 사면침식 및 퇴적 등 많은 문제와 도전에 직면할 것으로 예측되고 있다. 본 연구에서는 World Bank의 "Viet Nam Mekong Integrated Water Resources Management (M-IWRM) Project의 일환으로 베트남 정부 차원에서 처음으로 구축한 수자원관리 의사결정지원시스템인 "DSS-2S"를 활용하여, Sesan-Srepok강 유역의 수자원 계획을 수립하였다. DSS-2S는 MIKE Hydro Basin을 기반으로 SWAT모델 등과 연계 하여 구축되었다. DSS-2S는 2S 유역의 모든 주요 하천과 지류를 반영하였으며. 여기에는 17개의 수력발전 댐과 주요 지류에서 용량이 3백만 m³ 이상인 기타 저수지가 포함되었다. 이 보다 작은 용량의 저수지는 대표적인 저수지로 그룹화 되어 반영되었다. 기후변화 및 사회-경제적 발전계획 등을 반영하여, 2030년과 2050년을 목표연도로 생활, 공업, 농업, 관광, 유지용수 등 용수 수요를 추정하였다. 50% 및 85% 빈도의 공급 가능성을 고려하여 물 배분은 물 수요를 충족하고 지하수 개발 최소화를 기준으로 고려되었다. 분석 결과에 의하면 2S강 유역의 총 수자원은 32.2억 m³으로 그중 지표수자원은 29.2억 m³, 안정적으로 이용 가능한 지하수자원은 2.97억 m³으로 분석 되었으며, 지표수와 지하수 연계를 고려하면 전체 2S 강 유역에 물 부족하지는 않으나, 개별 공급 지점을 고려할 때 4월과 5월에 일부 지역에서 물 부족이 나타날 것으로 예측 된다. 장래 물 부족 해결을 위한 대안들을 제시하였으며, 본 성과는 베트남 중앙 정부의 장기수자원 종합계획 수립의 기본 자료로 활용 될 예정이다.

• Information Systems Review
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• v.22 no.2
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• pp.101-120
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• 2020

This study investigates the effects of privacy psychological characteristics of B2C logistics services users on their willingness to comply with their logistics companies' information protection policy. Using cognitive valence theory as a theoretical framework, this study proposes a research model to examine the relationships between users' logistics security knowledge, privacy trust, privacy risk, privacy concern, and their willingness of information protection policy compliance. To test the proposed model, we conducted a survey from actual users of logistics services and collected valid 151 samples. We analyzed the data using a structural equation modeling software. The empirical results show that logistics security knowledge positively affects privacy trust; privacy concern positively influences privacy risk; privacy trust, privacy risk, and privacy concern positively influence behavioral willingness of compliance. However, logistics security knowledge does not affect behavioral willingness of compliance. The results of the study provide several contributions to the literature of B2C logistics services domain and managerial implications to logistics services companies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.77-85
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• 2024

In this dissertation, a we designed and implemented a patrol robot that integrates a thermal imaging camera, speed dome camera, PTZ camera, radar, lidar sensor, and smartphone. This robot has the ability to monitor and respond efficiently even in complex environments, and is especially designed to demonstrate high performance even at night or in low visibility conditions. An orbital movement system was selected for the robot's mobility, and a smartphone-based control system was developed for real-time data processing and decision-making. The combination of various sensors allows the robot to comprehensively perceive the environment and quickly detect hazards. Thermal imaging cameras are used for night surveillance, speed domes and PTZ cameras are used for wide-area monitoring, and radar and LIDAR are used for obstacle detection and avoidance. The smartphone-based control system provides a user-friendly interface. The proposed robot system can be used in various fields such as security, surveillance, and disaster response. Future research should include improving the robot's autonomous patrol algorithm, developing a multi-robot collaboration system, and long-term testing in a real environment. This study is expected to contribute to the development of the field of intelligent surveillance robots.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.