• Korean Journal of Clinical Laboratory Science
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• v.51 no.3
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• pp.386-395
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• 2019

The aim of this study was to introduce clinical laboratory science techniques with the core technology of the 4th Industrial Revolution. Among the core technologies of the 4th Industrial Revolution, AI, IOT, block-chain, robotics, and nanotechnology were analyzed and linked by themes. The scope of the job of clinical laboratory technologists (also known as medical laboratory technologists and medical technologists) is laboratory medicine testing, pathology testing, and clinical physiology testing. Through a number of previous papers, 73 linkages in the laboratory medicine area, 27 linkages in the pathology area, and 47 linkages in the clinical physiology area were examined. In the 4th industrial revolution and clinical laboratory science techniques, AI (4), IOT (3), block-chain (4), robotics (3) and nanotechnology (15) sectors were surveyed. The limitation of this study was the limitation in collecting and analyzing all the data and non-clinical areas were not analyzed. In addition, there was no validity test and no similar study. In conclusion, the core technologies of the 4th industrial revolution and clinical laboratory science techniques are closely related. Therefore, further research on the future and social benefits of clinical laboratory science techniques is needed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.184-192
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• 2021

Domestic agriculture is facing real problems, such as a decrease in the population in rural areas, a shortage of labor due to an aging population, and increased risks due to the deepening of climate change. Smart farming technology is being developed to solve these problems. In the development of smart agricultural technology, irrigation control plays an important role in creating an optimal growth environment and is an important issue in terms of environmental protection. This paper is about the study of collecting and analyzing the rhizosphere environmental data of domestic paprika farms for the purpose of improving the quality of crops, reducing production costs, and increasing production. Irrigation control modeling presented in this paper Control modeling is to graphically present changes in a medium weight, feed, and drainage due to regional climatic features. To derive the graph, the parameters were determined through data collection and analysis, and the suggested irrigation control modeling method was applied to the collected rhizosphere environmental data to control irrigation in 6 regions (Gangwon-do, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, and Gyeongnam). The parameters were obtained and graphs were derived from them. After that, a study was conducted to analyze the derived parameters to verify the validity of the irrigation control modeling method and to correlate them with climatic features (average temperature and precipitation).

• Journal of Digital Convergence
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• v.18 no.12
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• pp.29-36
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• 2020

The background of this study is that machine learning administrative services are recently attracting attention as a major policy tool for non-face-to-face administrative services in the post-corona era. This study investigated the types of work expected to be effective when introducing machine learning administrative services for Seoul Metropolitan Government officials who are piloting machine learning administrative services. The research method is a machine that can be introduced by organizational unit by distributing and collecting questionnaires for Seoul administrative organizations that have performed machine learning-based administrative services for one month in July 2020 targeting Seoul public officials using machine learning-based administrative services. By analyzing the learning administration service and application service, the business characteristics of each machine learning administration service type such as supervised learning work type, unsupervised learning work type, and reinforced learning work type were analyzed. As a result of the research analysis, it was found that there were significant differences in the characteristics of administrative tasks by supervised and unsupervised learning areas. In particular, it was found that the reinforcement learning domain contains the most appropriate business characteristics for machine learning administrative services. Implications were drawn. The results of this study can be provided as a reference material to practitioners who want to introduce machine learning administration services, and can be used as basic data for research to researchers who want to study machine learning administration services in the future.

• Journal of Environmental Impact Assessment
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• v.30 no.1
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• pp.49-60
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• 2021

Environmental impact assessment (EIA) practitioners play a very pivotal role in establishing EIA policies, and when implementing EIA environmental conflicts can be prevented and resolved by sharing information with stakeholders and coordinating opinions. For this reason, grasping the perceptions of stakeholders including practitioners about the overall system, such as EIA policies and implementation, can be helpful in setting improvement directions and policy directions for EIA. However, there is an insufficient information on the perception and understanding of stakeholders about the EIA system and operation currently in effect in Korea. Therefore, this study diagnoses operational and procedural problems for the EIA system, which is a decision-making tool and a precautionary technique that can minimize adverse effects through the environmental information analysis method, and improvement points and systems of the EIA system in the future. We tried to find a complement of an online survey of 37 questions,responses from 95 responses from stakeholders of EIA were summarized. Stakeholders were aware of the problems of the operation of the current system and the preparation of the evaluation form, and this was reflected. Period and cost of preparation of EIS (49%), the introduction of a new method (26%) and the items of collecting opinions and conflict management (41%), which showed high negative response rates (dissatisfied and very dissatisfied), are considered to be areas that we need to supplement further in the future. As society develops rapidly, the system needs to be supplemented accordingly, and policy improvement efforts are needed for items with high negative responses as a result of the survey.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.199-213
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• 2022

The unsupervised domain adaptation can solve the impractical issue of repeatedly collecting high-quality training data every year for annual crop classification. This study evaluates the applicability of deep learning-based unsupervised domain adaptation models for crop classification. Three unsupervised domain adaptation models including a deep adaptation network (DAN), a deep reconstruction-classification network, and a domain adversarial neural network (DANN) are quantitatively compared via a crop classification experiment using unmanned aerial vehicle images in Hapcheon-gun and Changnyeong-gun, the major garlic and onion cultivation areas in Korea. As source baseline and target baseline models, convolutional neural networks (CNNs) are additionally applied to evaluate the classification performance of the unsupervised domain adaptation models. The three unsupervised domain adaptation models outperformed the source baseline CNN, but the different classification performances were observed depending on the degree of inconsistency between data distributions in source and target images. The classification accuracy of DAN was higher than that of the other two models when the inconsistency between source and target images was low, whereas DANN has the best classification performance when the inconsistency between source and target images was high. Therefore, the extent to which data distributions of the source and target images match should be considered to select the best unsupervised domain adaptation model to generate reliable classification results.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.309-321
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• 2022

As the spread of COVID19 has compelled activities in various fields to transform to adapt to the non-face-to-face environment, various activities have either already been transitioned into non-face-to-face methods or been searching for alternative methods to carry out activities in a non-face-to-face manner. However, there are apparent limits in handling this transition with the pre-existing digital technology. Ironically, said limitations are more apparent in the UX design field that has thus far emphasized resolutions based on digital technology. The reason for this stems from the nature of UX design which strongly emphasizes the importance of collaboration. Especially, in the field of UX design, problems are expected to surface under areas of communication and collaboration in workshops, which are productive means of collecting the ideas of interested parties and coming up with other new ideas. Based on the aforementioned rise of necessity, this study aims to assess the characteristics of workshops in the field of UX design and suggest an effective method of transitioning UX workshops into a non-face-to-face environment. Along the line of this process, this study has created a standard process in regards to design workshops with active creation, suggestion, and acceptance of ideas, among the various types of workshops defined by the Nielsen Norman Group. This study also developed a framework consisting of non-face-to-face workshops by combining with the standard process the methodologies of workshop activation and non-face-to-face services meant for communication and designing activities, and confirmed the adaptability and the effectiveness of said transition against various types of workshops. Application of the results of this study is expected to effectively lead the transition into the non-face-to-face environment and improve the collaborative efforts of the interested parties via workshops.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• Korean Journal of Construction Engineering and Management
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• v.23 no.6
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• pp.65-75
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• 2022

As performance measurement is important for systematic management, the key indicators for performance measurement have been consistently researched in the construction industry. However, there are only a few cases in which performance measurement is performed because it requires strenuous efforts to collect data for measurement. Unlike the public sector, which has been collecting project data through laws, the private sector has very little data to measure performance. In contrast, supervision work concerns important data necessary for the performance management on building construction sites in accordance with the Building Act. Therefore, in this study, we used the data from supervisory reports to measure the performance of private building projects. First, we derived 6 performance areas and 15 indicators through a few rounds of expert group discussions and 2 surveys. Then, we identified the performance indicators with high feasibility of data collection and computed their degree of significance via the analytic hierarchy process. It is expected that the performance indicators and their computational processes derived in this study can be used to systematically measure the performance and aid the speedy diagnosis of private building construction sites.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Smart Media Journal
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• v.12 no.10
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• pp.55-62
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• 2023

Innovation and change are occurring rapidly in the agriculture and livestock industry, and new technologies such as smart bams are being introduced, and data that can be used to control equipment is being collected by utilizing various sensors. However, there are various challenges in the operation of bams, and virtual sensor technology is needed to solve these challenges. In this paper, we define various data items and sensor data types used in livestock farms, study cases that utilize virtual sensors in other fields, and implement and design a virtual sensor system for the final smart livestock farm. MBE and EVRMSE were used to evaluate the finalized system and analyze performance indicators. As a result of collecting and managing data using virtual sensors, there was no obvious difference in data values from physical sensors, showing satisfactory results. By utilizing the virtual sensor system in smart livestock farms, innovation and efficiency improvement can be expected in various areas such as livestock operation and livestock health status monitoring. This paper proposes an innovative method of data collection and management by utilizing virtual sensor technology in the field of smart livestock, and has obtained important results in verifying its performance. As a future research task, we would like to explore the connection of digital livestock using virtual sensors.