• Journal of Marine Life Science
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• v.6 no.1
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• pp.38-46
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• 2021

The blood clam, Tegillarca granosa, is economically important in marine bivalve and is used in fisheries industry among western Pacific Ocean Coasts especially in Korea, China, and Japan. The number of chromosomes in the blood clam is known as 2n=38, but the genome size and genetic information of the genome are not still clear. In order to predict the genomic size of the T. granosa, the in-silico analysis analysed the genomic size using short DNA sequence information obtained using the NGS Illumina HiSeq platform. As a result, the genomic size of T. granosa was estimated to be 770.61 Mb. Subsequently, a draft genome assembly was performed through the MaSuRCA assembler, and a simple sequence repeat (SSR) analysis was done by using the QDD pipeline. 43,944 SSRs were detected from the genome of T. granosa and 69.51% di-nucleotide, 16.68% trinucleotide, 12.96% tetra-nucleotide, 0.82% penta-nucleotide, and 0.03% hexa-nucleotide were consisted. 100 primer sets that could be used for genetic diversity studies were selected. In the future, this study will help identify the genetic diversity of T. granosa and population genetic studies, and further identify the classification of origin between homogenous groups.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.340-350
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• 2021

This study was conducted by utilizing the Q research method, which is one of the qualitative analysis methods that can approach the in-depth and essential meaning of consumers' restaurant O2O service operation behavior. The purpose of this study is to classify the behavior of restaurant O2O services by type, to find out the characteristics of variables, and to suggest future improvement directions. An exploratory study was conducted using the Q-methodology to analyze the subjective perception of the restaurant O2O service behavior. To this end, positive and negative statement cards were prepared, P samples were selected, and Q-sort, which was subjected to classification, was analyzed using the PC QUANL program and Q factor analysis. As a result of the analysis, it was classified into three single types. Type 1 【(N= 7: Restaurant O2O Service Convenience Syndrome Type】, Type 2 【(N= 7): Restaurant O2O Service Benefit Pursuit Type】, Type 3 【(N= 6): Restaurant O2O Service Convenience Type】 The name of the factor was set as [Type], and it was found that each type has different characteristics. Through this analysis, the marketing strategy according to each factor detected is presented, and the point of supplementing the restaurant's O2O service and the direction of future operation. services in future studies.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• 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.