• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

• Knowledge Management Research
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• v.23 no.2
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• pp.123-142
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• 2022

The non-face-to-face environment accelerated by COVID-19 has speeded up the dissemination of digital virtual ecosystems and metaverse. In order for the metaverse to be sustainable, digital twins that are compatible with the real world are key, and critical technology for that is AR (Augmented Reality). In this study, we examined research trends about AR, and will propose the directions for future AR research. We conducted LDA based topic modeling on 11,049 abstracts of published domestic and foreign AR related papers from 2009 to Mar 2022, and then looked into AR that was comprehensive research trends, comparison of domestic and foreign research trends, and research trends before and after the popularity of metaverse concepts. As a result, the topics of AR related research were deduced from 11 topics such as device, network communication, surgery, digital twin, education, serious game, camera/vision, color application, therapy, location accuracy, and interface design. After popularity of metaverse, 6 topics were deduced such as camera/vision, training, digital twin, surgical/surgical, interaction performance, and network communication. We will expect, through this study, to encourage active research on metaverse AR with convergent characteristics in multidisciplinary fields and contribute to giving useful implications to practitioners.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.780-790
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• 2022

Quantitative risk levels must be presented by analyzing the causes and consequences of accidents and predicting the occurrence patterns of the accidents. For the analysis of marine accidents related to vessel traffic, research on the traffic such as collision risk analysis and navigational path finding has been mainly conducted. The analysis of the occurrence pattern of marine accidents has been presented according to the traditional statistical analysis. This study intends to present a marine accident prediction model using the statistics on marine accidents related to vessel traffic. Statistical data from 1998 to 2021, which can be accumulated by month and hourly data among the Korean domestic marine accidents, were converted into structured time series data. The predictive model was built using a long short-term memory network, which is a representative artificial intelligence model. As a result of verifying the performance of the proposed model through the validation data, the RMSEs were noted to be 52.5471 and 126.5893 in the initial neural network model, and as a result of the updated model with observed datasets, the RMSEs were improved to 31.3680 and 36.3967, respectively. Based on the proposed model, the occurrence pattern of marine accidents could be predicted by learning the features of various marine accidents. In further research, a quantitative presentation of the risk of marine accidents and the development of region-based hazard maps are required.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.33 no.1
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• pp.22-30
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• 2023

Currently, almost all product development in the jewelry industry utilizes 3D CAD and 3D printing. In this situation, 3D CAD modeling and 3D printing ability units in colleges, Tomorrow Learning Card Education, and Course Evaluation-type jewelry design related education are conducted with developed curriculum based on the standards for training standards, training hours, training equipment, and practice materials presented by NCS. Accordingly, this study analyzes 3D CAD modeling and 3D printing training facilities, training hours, training equipment, etc into three categories of NCS precious metal processing and jewelry design, and studies the development of educational systems such as 3D CAD/3D printing curriculum and various environments that meet these standards. Education using this 3D CAD/3D printing education system will enable us to continuously supply professional talent with practical skills not only in the jewelry industry but also in the entire 3D CAD/3D printing manufacturing industry, which is called as one of the pillars of the 4th Industry. The quality of employment of trainees receiving education and the long-term retention rate after employed can also have a positive effect. In addition, excellent educational performance will help improve the recruitment rate of new students in jewelry jobs or manufacturing-related departments, which are difficult to recruit new students in recent years.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.469-490
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• 2023

Survey was carried out on the acoustic outputs from 12 shock wave fields produced by the 10 extracorporeal shock wave lithotriptors whose technical documents are available, among the 33 devices approved by the Ministry of Food & Drug Safety (MFDS).The results show that the acoustic outputs (P+, P-, efd, and E), critical to the therapeutic efficacy and the patient safety, are largely different between the devices. The maximum values of P+, P-, efd, and E vary up to 2.08, 3.72, 3.89, and 15.98 times, respectively. The acoustic output parameters are not thoroughly provided in the technical documents, and some of data (eg. efd) are suspected to be abnormal outside usual ranges. The large device to device differences in the shock wave outputs are likely to undermine equivalence between the ESWL devices approved for the same indication. To verify the reliability of the data in the technical documents of the approved devices and to confirm if the acoustic outputs from the devices in clinical use are the same as those in their technical documents, an authorized test laboratory should be available. A postapproval monitoring led by the regulatory agency is suggested to maintain the acoustic outputs from the ESWL devices that suffer from degrading in performance due to aging.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.3
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• pp.143-150
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• 2023

The global outbreak of COVID-19 has underscored the pressing need for robust infection control practices in pulmonary function laboratories (PFL). However, the existing guidelines and regulatory frameworks provided by relevant authorities in the country have revealed certain deficiencies in effectively addressing this significant public health crisis. This study surveyed the infection control regulations, disposable item usage, ventilation facilities, spatial separation, and the configuration of entrance doors in 51 domestic hospital facilities from Oct 1, 2021, to Nov 2, 2021. The survey findings revealed that while there was a relatively satisfactory adherence to airborne, droplet, and contact precautions with adequate awareness and utilization of personal protective equipment, the environmental disinfection practices exhibited a suboptimal performance rate of 39.22% per patient. Depending on the specific survey domains, substantial variations were observed in the utilization of disposable items (81.05%), ventilation systems (45.75%), dedicated testing spaces (80.39%), separation of administrative areas (15.69%), and the installation of automated doors (19.61%). This study not only highlights the paramount importance of infection control in PFLs within domestic medical institutions but also provides foundational data for developing and enhancing standardized guidelines that align with international benchmarks for infection control in these settings.

• Clean Technology
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• v.16 no.3
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• pp.213-219
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• 2010

Vortex tube is the device that can separate small particles from the compressed gas, as well as compressed gas into hot and cold gas. Due to energy and particle separation ability, a vortex tube can be used as the main component of the $CO_2$ absorption device. In this study, experimental approach has been performed to analyze the energy separation characteristics of the vortex tube. To obtain the preliminary design data, energy separation characteristics of the vortex tube has been tested for orifice diameter, nozzle area ratio, and tube length. As a result, the orifice diameter is the major factor of the vortex tube design. The nozzle area ratio and tube length have a minor effect on the energy separation performance. For Dc=0.6D, AR=0.14~0.16, and L=16D, maximum energy separation has been occurred. The result from this study can be used as the basic design data of the $100Nm^3$/hr class vortex tube applied to the $CO_2$ absorption device. Compared with the $CO_2$ absorption process containing an absorption tower, the process with a vortex tube is expected to have a huge advantage of saving the installation space and the operating cost.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.6
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• pp.42-57
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• 2022

This study aims to create a basic model for classifying the activity photos that urban park users shared on social media using Deep Learning through Artificial Intelligence. Regarding the social media data, photos related to urban parks were collected through a Naver search, were collected, and used for the classification model. Based on the indicators of Naturalness, Potential Attraction, and Activity, which can be used to evaluate the characteristics of urban parks, 21 classification categories were created. Urban park photos shared on Naver were collected by category, and annotated datasets were created. A custom CNN model and a transfer learning model utilizing a CNN pre-trained on the collected photo datasets were designed and subsequently analyzed. As a result of the study, the Xception transfer learning model, which demonstrated the best performance, was selected as the urban park user activity image classification model and evaluated through several evaluation indicators. This study is meaningful in that it has built AI as an index that can evaluate the characteristics of urban parks by using user-shared photos on social media. The classification model using Deep Learning mitigates the limitations of manual classification, and it can efficiently classify large amounts of urban park photos. So, it can be said to be a useful method that can be used for the monitoring and management of city parks in the future.

• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.45-54
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• 2023

The settlement prediction during the design phase is primarily conducted using theoretical methods. However, measurement-based settlement prediction methods that predict future settlements based on measured settlement data over time are primarily used during construction due to accuracy issues. Among these methods, the hyperbolic method is commonly used. However, the existing hyperbolic method has accuracy issues and statistical limitations. Therefore, a weighted nonlinear regression hyperbolic method has been proposed. In this study, two weighting methods were applied to the weighted nonlinear regression hyperbolic method to compare and analyze the accuracy of settlement prediction. Measured settlement plate data from two sites located in Busan New Port were used. The settlement of the remaining sections was predicted by setting the regression analysis section to 30%, 50%, and 70% of the total data. Thus, regardless of the weight assignment method, the settlement prediction based on the hyperbolic method demonstrated a remarkable increase in accuracy as the regression analysis section increased. The weighted nonlinear regression hyperbolic method predicted settlement more accurately than the existing linear regression hyperbolic method. In particular, despite a smaller regression analysis section, the weighted nonlinear regression hyperbolic method showed higher settlement prediction performance than the existing linear regression hyperbolic method. Thus, it was confirmed that the weighted nonlinear regression hyperbolic method could predict settlement much faster and more accurately.