• Journal of Food Hygiene and Safety
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• v.37 no.1
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• pp.1-8
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• 2022

In this study, the pesticide residues in 106 commercial cereal grains were monitored from February to July 2021. For the investigation, 40 domestic and 66 imported products from large, small-to-medium sized offline and online distribution channels, were collected and analyzed by using the multiresidue method for 341 pesticides on GC/ECD, GC/NPD, GC/MSMS, UPLC/PDA, HPLC/FLD, LC/MSMS. Pesticides were detected in total of 8 samples (7.5%), of which one was from big box retailers, two from small and medium-sized distribution stores, and five from online shopping mall. Five (4.7%) samples were found to have pesticide residues greater than the maximum residue limits (MRLs). The detected pesticides in kidney beans (1 case), mung beans (6 cases), and sorghum (1 case), were MGK-264, chlorpyrifos, thiamethoxam, malathion, piperonyl butoxide, and pirimiphos-methyl. Specifically, an excessive amount of thiamethoxam was found from the imported mung bean (5 cases).

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.455-465
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• 2020

The purpose of this study was to investigate the relationship between physical activity and health-related quality of life of adults using the 7th National Health and Nutrition Survey. The study was conducted with 11,211 adults, and the health-related quality of life was defined using the EuroQol group's EQ-5D and physical activity using GPAQ. Data analysis was performed using the SAS 9.4 program, the general characteristics and degree of physical activity of the subject, Chi-square for KEQ-5D index, and Logistic Regression Analysis for the relationship between physical activity and quality of life. As a result of the study, the general characteristics of the subjects were marital status, educational status, occupation, smoking, alcohol consumption, economic status, stress, chronic disease, chronic disease treatment, physical activity due to leisure and physical activity due to occupation, depending on gender. There was a difference (p<0.05). As for the quality of life related to physical activity and health, the quality of life was significantly lower by 37% in the 'minimum physical activity group' of occupational physical activity (p<0.05). The results of this study are expected to be provided as basic data for physical activity-related health policy establishment and physical activity programs.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.191-209
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• 2022

This paper is a review of studies that focus on the prediction of a won/dollar exchange rate before and after the covid 19 pandemic. The Korea economy has an unprecedent situation starting from 2021 up till 2022 where the won/dollar exchange rate has exceeded 1,400 KRW, a first time since the global financial crisis in 2008. The US Federal Reserve has raised the interest rate up to 2.5% (2022.7) called a 'Big Step' and the Korea central bank has also raised the interested rate up to 2.5% (2022.8) accordingly. In the unpredictable economic situation, the prediction of the won/dollar exchange rate has become more important than ever. The authors separated the period from 2015.Jan to 2022.Aug into three periods and built a best fitted ARIMA/ARDL prediction model using the period 1. Finally using the best the fitted prediction model, we predicted the won/dollar exchange rate for each period. The conclusions of the study were that during Period 3, when the usual relationship between exchange rates and economic factors appears, the ARDL model reflecting the variable relationship is a better predictive model, and in Period 2 of the transitional period, which deviates from the typical pattern of exchange rate and economic factors, the SARIMA model, which reflects only historical exchange rate trends, was validated as a model with a better predictive performance.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.801-806
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• 2022

In the modern era, the necessity of color capability in the digital era is the demand of the era, and research on improving color practice on the subdivided digital four areas that are not in the existing practice is needed. For digital majors who are difficult to solve in existing paint color practice, classes in digital color practice in four more specialized areas are needed, and the use of efficient artificial intelligence was studied for classes in digitized color and color sense. In this paper, we tried to show the expansion of the color practice area by suggesting digital color practice and color matching method based on Photoshop artificial intelligence and big data technology that existing color and color matching were practice that only CMYK could do. In addition, based on the color quantification data of individual users provided by the latest Adobe Sceney program artificial intelligence, the purpose of the practice was to improve learners' predictions of actual color combinations and random colors using filter effects. In conclusion, it is a study on the use of programs that eliminate ambiguity in the mixing process of existing paint practice, secure digital color details, and propose a practical method that can provide effective learning methods for beginners and intermediates to develop their senses through artificial intelligence support. The Adobe program practice method necessary for coloration and main color through theoretical consideration and improvement of teaching skills that are better than existing paint practice were presented.

• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.435-449
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• 2022

Forest canopy height is an indispensable vertical structure parameter that can be used for understanding forest biomass and carbon storage as well as for managing a sustainable forest ecosystem. Plot-based field surveys, such as the national forest inventory, have been conducted to provide estimates of the forest canopy height. However, the comprehensive nationwide field monitoring of forest canopy height has been limited by its cost, lack of spatial coverage, and the inaccessibility of some forested areas. These issues can be addressed by remote sensing technology, which has gained popularity as a means to obtain detailed 2- and 3-dimensional measurements of the structure of the canopy at multiple scales. Here, we reviewed both international and domestic studies that have used remote sensing technology approaches to estimate the forest canopy height. We categorized and examined previous approaches as: 1) LiDAR approach, 2) Stereo or SAR image-based point clouds approach, and 3) combination approach of remote sensing data. We also reviewed upscaling approaches of utilizing remote sensing data to generate a continuous map of canopy height across large areas. Finally, we provided suggestions for further advancement of the Korean forest canopy height estimation system through the use of various remote sensing technologies.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.515-524
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• 2022

To effectively collect, manage, and share the maritime traffic information, it is necessary to identify the technology trends concerning this particular information and analyze its current status and problems. Therefore, this study observes the domestic and foreign technology trends involving maritime traffic information while analyzing and summarizing the current status and problems in collecting, managing, and sharing it. According to the data analysis, the problems in the collecting stage are difficulties in collecting visual information from long-distance radars, CCTVs, and cameras in areas outside the LTE network coverage. Notably, this explains the challenges in detecting smuggling ships entering the territorial waters through the exclusive economic zone (EEZ) in the early stage. The problems in the management stage include difficult reductions and expansions of maritime traffic information caused by the lack of flexibility in storage spaces mostly constructed by the maritime transportation system. Additionally, it is challenging to deal with system failure with system redundancy and backup as a countermeasure. Furthermore, the problems in the sharing stage show that it is difficult to share information with external operating organizations since the internal network is mainly used to share maritime transportation information. If at all through the government cloud via platforms such as LRIT and SASS, it often fails to effectively provide various S/W applications that help use maritime big data. Therefore, it is suggested that collecting equipment such as unmanned aerial vehicles and satellites should be constructed to expand collecting areas in the collecting stage. In the management and sharing stages, the introduction and construction of private clouds are suggested, considering the operational administration and information disclosure of each maritime transportation system. Through these efforts, an enhancement of the expertise and security of clouds is expected.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.77-84
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• 2022

This study aims to present a method for implementing a decision support system that can be used for selecting emerging technologies by applying a machine learning-based automatic classification technique. To conduct the research, the architecture of the entire system was built and detailed research steps were conducted. First, emerging technology candidate items were selected and trend data was automatically generated using a big data system. After defining the conceptual model and pattern classification structure of technological development, an efficient machine learning method was presented through an automatic classification experiment. Finally, the analysis results of the system were interpreted and methods for utilization were derived. In a DTW-kNN-based classification experiment that combines the Dynamic Time Warping(DTW) method and the k-Nearest Neighbors(kNN) classification model proposed in this study, the identification performance was up to 87.7%, and particularly in the 'eventual' section where the trend highly fluctuates, the maximum performance difference was 39.4% points compared to the Euclidean Distance(ED) algorithm. In addition, through the analysis results presented by the system, it was confirmed that this decision support system can be effectively utilized in the process of automatically classifying and filtering by type with a large amount of trend data.

• Journal of Service Research and Studies
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• v.10 no.4
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• pp.43-55
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• 2020

Recently, as the importance of experience data increases, there are many attempts to deal with experience data from a data science perspective. In the case of approaching as a collection method of a quantitative survey method that seeks to quantify numerically such as big data, it is difficult to interpret the value of experience in a wide range, and it is relatively expensive and time consuming, and personal information infringement There is a limit to the analysis due to the risk of However, since ethnography, a procedure for collecting experience data based on qualitative research, is mainly carried out in the natural real environment of future customers from the perspective of users, it is possible to confirm the nature that customers face with a small sample. In addition, it is also easy to interpret the relational dimension of the empirical data. Although the ethnography method of collecting experiential data is economical and efficient, it is important to reduce errors in the collection process because the lack of scientific procedures for the data collection process can be a problem. It is important to secure the validity of whether the correct measurement tool is used for ethnography-based experiential data collection and to secure the reliability of the use of a valid measurement tool and method by accurately selecting the measurement target. From this point of view, it is necessary to verify the reliability of the research method that clearly selects the measurement target and secures the validity for the development of the correct measurement method and tool for the collection of ethnography experience data. Therefore, in this study, a verification study was conducted on the data and methodology cases of the'I know you_AI' service that analyzes the customer experience of self-employed based on the ethnography method of collecting experience data..

• Korean Journal of Culture and Social Issue
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• v.23 no.4
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• pp.557-585
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• 2017

In this research, it was examined whether predicting the ripple effects of events influences decision-making difficulty. In addition, it was examined whether perceived accountability for decision-making results mediates the relation above. In Study 1, participants were presented with policy decision-making vignettes and were asked to report on the ripple effects of their policy decisions as well as on the difficulty of making the decision. Consistent with the hypothesis, the bigger the expected ripple effects, the greater difficulty participants felt in making policy decisions. In Study 2, ripple effect magnitudes were experimentally manipulated such that participants were led to predict big ripple effects in one condition and relatively small ripple effects in another condition. It was investigated whether participants predicting bigger ripple effects would perceive decision-making to be more difficult than participants predicting smaller ripple effects. Whether this relation would be mediated by perceived personal accountability for the results of decision-making was also examined. Consistent with expectations, it was found that in the moral domains of Harm/care, Fairness/reciprocity, and Ingroup/loyalty, participants predicting bigger ripple effects reported more difficult decision-making than their counterparts. The relation above was mediated by perceived personal accountability for decision-making results only in the domain of Ingroup/loyalty. In combination, these results showed that bigger predicted ripple effects contributed to greater decision-making difficulty. In addition, participants felt more responsible for the results of their decisions when predicting bigger ripple effects, which led them to feel greater decision-making difficulty in the domain of Ingroup/loyalty. The implications of these results and future directions for research are discussed.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.10-10
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• 2023

본질적으로 복잡하고 다양한 특성을 가지는 우리나라(도시, 농어촌, 도서산간, 섬 등)의 물 공급 시스템은 생활수준의 향상, 기후변화 및 가뭄위기, 소비환경 중심의 요구와 한정된 수자원을 잘 활용하기 위한 운영 및 관리가 매우 복잡하다. 이로 인한 수자원 고갈과 가뭄위기 등에 관련한 대책 및 방안으로 대체수자원인 지하수 활용방안들이 제시되고 있다. 따라서, 물 관리 시스템과 관련한 디지털 기술은 오늘날 플랫폼과 디지털 트윈의 도입을 통해 네트워크와 가상현실 세계의 연결이 통합되어진 4차 산업혁명 사업이 현실화되고 있다. 물 관리 시스템에 사용된 새로운 디지털 기술 "BDA(Big Data Analytics), CPS(Cyber Physical System), IoT(Internet of Things), CC(Cloud Computing), AI(Artificial Intelligence)" 등의 성장이 증가함에 따라 가뭄대응 위기와 도시 지하수 물 순환 시스템 운영이 증가하는 소비자 중심의 수요를 충족시키기 위해서는 지속가능한 지하수 공급을 효과적으로 관리되어야 한다. 4차 산업혁명과 관련한 기술성장이 증가함으로 인한 물 부문은 시스템의 지속가능성을 향상시키기 위해 전체 디지털화 단계로 이동하고 있다. 이러한 디지털 전환의 핵심은 데이터에 관한 것이며, 이를 활용하여 가치 창출을 위해서 "Digital Groundwater Technology/Twin(DGT)"를 극대화하는 방식으로 제고해야 한다. 현재 당면하고 있는 기후위기에 따른 가뭄, 홍수, 녹조, 탁수, 대체수자원 등의 수자원 재해에 대한 다양한 대응 방안과 수자원 확보 기술이 논의되고 있다. 이에 따른 "물 순환 시스템"의 이해와 함께 문제해결 방안도출을 위하여 이번 "기획 세션"에서는 지하수 수량 및 수질, 정수, 모니터링, 모델링, 운영/관리 등의 수자원 데이터의 플랫폼 동시성 구축으로부터 역동적인 "DGT"을 통한 디지털 트윈화하여, 지표수-토양-지하수 분야의 특화된 연직 프로파일링 관측기술을 다각도로 모색하고자 한다. "Digital Groundwater(DG)"는 지하수의 물 순환, 수량 및 수질 관리, 지표수-지하수 순환 및 모니터링, 지하수 예측 모델링 통합연계를 위해 지하수 플랫폼 동시성, ChatGPT, CPS 및 DT 등의 복합 디지털화 단계로 나가고 있다. 복잡한 지하환경의 이해와 관리 및 보존을 위한 지하수 네트워크에서 수량과 수질 데이터를 수집하기 위한 스마트 지하수 관측기술 개발은 큰 도전이다. 스마트 지하수 관측기술은 BD분석, AI 및 클라우드 컴퓨팅 등의 디지털 기술에 필요한 획득된 데이터 분석에 사용되는 알고리즘의 복잡성과 데이터 품질에 따라 영향을 미칠 수 있기 때문이다. "DG"는 지하수의 정보화 및 네트워크 운영관리 자동화, 지능화 등을 위한 디지털 도구를 활용함으로써 지표수-토양층-지하수 네트워크 통합관리에 대한 비전을 만들 수 있다. 또한, DGT는 지하수 관측센서의 1차원 데이터 융합을 이용한 지하수 플랫폼 동시성과 디지털 트윈을 연계할 수 있다.