• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.60-66
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• 2023

Currently, the maritime industry is focusing on developing technologies that promote autonomy and intelligence, such as smart ships, autonomous ships, and eco-friendly technologies, to enhance ship operational efficiency. Many countries are conducting research on different methods to ensure ship safety while increasing operational efficiency. This study aims to develop a real-time ship operational efficiency analysis model using data analysis methods to address the current limitations of the present technologies in the real-time evaluation of operational efficiency. The model selected ship operational efficiency factors and ship operational condition factors to compare the operational efficiency of the ship with present and classified factors to determine whether the present ship operational efficiency is appropriate. The study involved selecting a target ship, collecting data, preprocessing data, and developing classification models. The results of the research were obtained by determining the improved ship operational efficiency based on the ship operational condition factors to support ship operators.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• The Journal of Bigdata
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• v.7 no.2
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• pp.153-172
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• 2022

Online shopping has transformed and rapidly grown the entire market at the forefront of wholesale and retail services as an effective solution to issues such as digital transformation and social distancing policy (COVID-19 pandemic). Small business owners, who form the majority at the center of the online shopping industry, are constantly collecting policy changes and market trend information to overcome these problems and use them for marketing and other sales activities in order to overcome these problems and continue to grow. Objective and refined information that is more closely related to the business is also needed. Therefore, in this paper, through the collection and analysis of big data information, which is the core technology of digital transformation, key variables are set in product classification, sales trends, consumer preferences, and review information of online shopping malls, and a method of using them for competitor comparison analysis and business sustainability evaluation has been prepared and we would like to propose it as a service. If small and medium-sized businesses can benchmark competitors or excellent businesses based on big data and identify market trends and consumer tendencies, they will clearly recognize their level and position in business and voluntarily strive to secure higher competitiveness. In addition, if the sustainable growth of the online shopping mall operator can be confirmed as an indicator, more efficient policy establishment and risk management can be expected because it has an improved measurement method.

• The Journal of Bigdata
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• v.8 no.1
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• pp.49-59
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• 2023

There are various items in the safety and health standards of the manufacturing industry, but they can be divided into work-related diseases and musculoskeletal diseases according to the standards for sickness and accident victims. Musculoskeletal diseases occur frequently in manufacturing and can lead to a decrease in labor productivity and a weakening of competitiveness in manufacturing. In this paper, to detect the musculoskeletal harmful factors of manufacturing workers, we defined the musculoskeletal load work factor analysis, harmful load working postures, and key points matching, and constructed data for Artificial Intelligence(AI) learning. To check the effectiveness of the suggested dataset, AI algorithms such as YOLO, Lite-HRNet, and EfficientNet were used to train and verify. Our experimental results the human detection accuracy is 99%, the key points matching accuracy of the detected person is @AP0.5 88%, and the accuracy of working postures evaluation by integrating the inferred matching positions is LEGS 72.2%, NECT 85.7%, TRUNK 81.9%, UPPERARM 79.8%, and LOWERARM 92.7%, and considered the necessity for research that can prevent deep learning-based musculoskeletal diseases.

• Journal of Environmental Health Sciences
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• v.49 no.3
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• pp.134-148
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• 2023

Background: The conducting of health effect surveys (HESs) in environmentally contaminated vulnerable areas (ECVAs) by the central and local governments has been increasing apace with the increase in demand for HESs since the Environmental Health Act was enacted in South Korea in 2008. Objectives: This study aimed to review the HESs of residents in ECVAs conducted in South Korea. Methods: An analysis was performed on 125 reports obtained from the Environment Digital Library, PRISM, and local government websites after selecting from 803 projects obtained as ECVAs from the Korea ON-Line E-Procurement System (1997~2021), National Institute Environment Research (2000~2021), and Korea Environmental Industry and Technology Institute (2009~2021). The reports were classified by background (residents' demand, HES, and more), research design (cross-sectional study, cohort, ecological study, and panel), pollution source (abandoned metal mine (AMM), industrial complex (IC), and more), and assessment method of exposure and health effects. The survey area was converted into administrative district codes for geographical mapping. Results: There were 37, 34, 18, and 10 cases associated with AMM, IC, waste incinerators, and coal-fired power plants, respectively. Most of the studies conducted were cross-sectional studies and ecological studies. The proportion of epidemiological investigations by residents' demand showed an increase from 0.0% to 8.9% for the central government while decreasing from 16.7% to 14.3% for local governments after 2008 compared to before 2008. HESs increased at both the central and local government levels since 2014. For the evaluation method, 365 environmental hazards, 319 health outcomes, and 302 biological markers were investigated, with the most commonly investigated items being metals, cancer, and blood metals. Conclusions: HESs of residents in ECVAs in South Korea have been continuously developed both quantitatively and qualitatively. Future improvements are expected, and systematic review and classification of the HESs is warranted.

• Smart Media Journal
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• v.12 no.10
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• pp.71-84
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• 2023

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.66-73
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• 2023

The construction industry suffers losses due to failures in demand forecasting due to price fluctuations in construction raw materials, increased user costs due to project cost changes, and lack of forecasting system. Accordingly, it is necessary to improve the accuracy of construction raw material price forecasting. This study aims to predict the price of construction raw materials and verify applicability through the improvement of the Data Refactor technique. In order to improve the accuracy of price prediction of construction raw materials, the existing data refactor classification of low and high frequency and ARIMAX utilization method was improved to frequency-oriented and ARIMA method utilization, so that short-term (3 months in the future) six items such as construction raw materials lumber and cement were improved. ), mid-term (6 months in the future), and long-term (12 months in the future) price forecasts. As a result of the analysis, the predicted value based on the improved Data Refactor technique reduced the error and expanded the variability. Therefore, it is expected that the budget can be managed effectively by predicting the price of construction raw materials more accurately through the Data Refactor technique proposed in this study.

• Resources Recycling
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• v.32 no.6
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• pp.54-66
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• 2023

Titanium's importance as a mineral resource is increasing, but the Korean industry depends on imports. Ilmenite is the principal titanium ore. However, research and development from raw materials have not been investigated yet in detail. Hence, measures to secure a stable titanium supply chain are urgently needed. Accordingly, through beneficiation technology, we evaluated the possibility of technological application for the efficient recovery of valuable minerals. As a result of the experiments, we confirmed that mineral particles existed as fine particles due to weathering, making recovery through classification difficult. Consequently, applying beneficiation technologies, i.e., specific gravity separation, magnetic separation, and flotation, makes it possible to recover valuable minerals such as hematite and rutile. However, there are limitations in increasing the quality and yield of TiO₂ due to the mineralogical characteristic of the hematite and rutile contained in titanium ore. Hametite is combined with rutile even at fine particles. Therefore, it is essential to develop mineral processing routes, to recover iron, vanadium, and rare earth elements as resources. On that account, we used grinding technology that improves group separation between constituent minerals and magnetic separation technology that utilizes the difference in magnetic sensitivity between fine mineral particles. The development of beneficiation technology that can secure the economic feasibility of valuable materials after reforming iron oxide and titanium oxide components is necessary.

• Industry Promotion Research
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• v.8 no.4
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• pp.187-200
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• 2023

The purpose of this paper is to correspond to four-elements in astrology theory, an intellectual from ancient times, that show personality temperament among MBTI, a representative personality type test in modern times, furthermore, by examining 16 personality type cards in tarot, a play culture and fortune telling culture in which the four-element theory is integrated in symbols, it is a comparative consideration that connects the characteristics of the character types contained in them to the 16 personality types of MBTI. The four preferred types of MBTI are Extravesion(E) and Introversion(I), Sensing(S) and Intuition(N), Thinking(T) and Feeling(F), Judgment(J) and Perception(P). Among them, Western four-elements were able to respond to Fire, Water, Air, and Earth in the order of NF(iNtuitive Feeling Type), SF(Sensory Feeling Type), NT(iNtuitive Thinking Type), and ST(Sensory Thinking Type). This is a result that can be derived by comparing individual personality theory and MBTI temperament theory among the symbols contained in ancient astrological theories. And the classification of boys, knights, queens, and kings in the four classes of person cards could be divided according to the MBTI attitude index. The boy showed an adaptive introvert using I and P, the knight showed an adaptive extrovert using E and P, the queen showed a decisive introvert using I and J, and the king showed an adaptive extrovert using E and J.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.47-65
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• 2023

As stakeholder demands and sustainable finance grow, ESG management and ESG evaluation are becoming important. SMEs should also prepare for the trends of ESG rating practices that affects supply chain management and financial transactions. However, SMEs have no choice but to focus on survival first, so there are restrictions on putting into ESG management. In addition, there is a lack of research on the legitimacy of ESG management by SMEs, and volatility in ESG evaluation systems and rating grades is also increasing. Accordingly, it is necessary to review ESG evaluation trends and practical guidelines along with the review of previous studies. As a result of the exploratory study, SMEs need to implement ESG management and make efforts to specialize in ESG related new businesses under conditions in which their survival base is guaranteed in terms of implementation strategies. In addition, it is necessary to focus on the strategic use of various evaluation results along with accumulating information favorable for ESG evaluation through organizational learning and software management. The implications of this study are that various studies such as the classification criteria for SMEs and the relationship between ESG evaluation grades and long-term survival rates are needed in ESG evaluation of SMEs. At the government policy level, it is time to consider the ESG evaluation system exclusively for SMEs so that ESG management can be implemented and ESG evaluation at different levels by industry and size.