• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.

• Journal of Venture Innovation
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• v.7 no.3
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• pp.85-100
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• 2024

ESG management refers to corporate management that takes into account environmental, social, and governance factors, while digital transformation goes beyond the mere automation or digitization of existing tasks to drive an innovative change in the essence of work and the way value is created. Therefore, digital transformation can help companies achieve ESG goals and implement sustainable business practices, establishing a complementary relationship between digital transformation and ESG management for corporate sustainability and growth. This relationship maximizes the synergy of integrating digital transformation with ESG management, enabling companies to utilize resources efficiently and prevent redundant investments, ultimately enhancing sustainable management performance. In this study, we propose the simultaneous promotion of business process reengineering (BPR), in which both digital transformation and ESG management are integrated. This is because the collection, analysis, and decision-making processes related to various data for promoting ESG management must be organically integrated with digital transformation technologies. Therefore, we analyzed each ESG management objective presented in the K-ESG guidelines and identified the corresponding digital transformation technologies through expert interviews and a review of prior research. The K-ESG guidelines serve as a useful ESG diagnostic system that enables companies to identify improvement tasks and manage performance based on goals through self-assessment of ESG levels. By developing a model based on the K-ESG guidelines for the integrated promotion of digital transformation and ESG management, companies can simultaneously improve ESG performance and drive digital innovation, reducing redundant investments and trial-and-error while utilizing diverse resources efficiently. This study provides practical and academic implications by developing a concrete and actionable new research model for researchers and businesses.

• Journal of Food Hygiene and Safety
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• v.24 no.3
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• pp.194-199
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• 2009

The effects of part of plum, alcohol content and addition of t-resveratrol on the formation of ethyl carbamate during the fermentation for wine were investigated at a time interval (45 days) for 6 months. The concentration of the ethyl carbamate in plum wine was determined according to KFDA guideline for ethyl carbamate analysis. In the plum wine with 16% or 30% alcohol content, the concentrations of ethyl carbamate were increased with time of fermentation periods. The maximum concentrations of ethyl carbamate in 16% and 30% plum wines after the fermentation for 6 months were $0.071{\mu}g$/g and $0.188{\mu}g$/g, respectively. When t-resveratrol was added at the level of $10{\mu}g$/g in both 16% and 30% plum wine, the concentrations of ethyl carbamate at 6 months were 0.078 and $0.216{\mu}g$/g, respectively. The addition of t-resveratrol at the level of $300{\mu}g$/g in both 16% or 30% plum wine, the concentrations of ethyl carbamate at 6 months were 0.078 and $0.169{\mu}g$/g, respectively. The ethyl carbamate in the plum wine was not formed during fermentation for 6 month as using the flesh of plum, but $0.588{\mu}g$/g of ethyl carbamate was formed as using plum with plum seed. The addition of $300{\mu}g$/g of t-resveratrol actually increased the concentration of the ethyl carbamate by $0.088{\mu}g$/g as fermented for 6 months using plum with seed. These results suggest that the flesh of plum should be used to reduce the formation of ethylcarbamate for production of plum wine and that the addition of t-resveratrol during fermentation of plum wine can not reduce the concentration of the ethyl carbamate.

• Archives of design research
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• v.11 no.2
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• pp.129-140
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• 1998

This study aimed to grqJe for and present guideline roodel when the qJerator of domestic small manufacturing industry try to introch1ce the first industrial design by easier and more effective method. As the method of study, first of aiL examined the necessary of introducing industrial design throogh coosidering about the role and importance of small business. And next, analysed and examined the result of researching by enquete that is for qJerators of cbnestic small business. As a result, preconditioos for effective introducing industrial design were found. And, based 00 the preconditioos that were found through researching by enquete, examined the approachable introducing methods. Finally, set up the effectivable introducing methods of industrial design for doo1estic small manufacturing industry as a graphical model. As a result of study, First, the operator of small business who try to introduce industrial design needs to be well aware of these six cooditions as a prenise of effective awroach.1) coosciousness of role and versus a nation and a people of own industry Cereative 2) managing coosideratim and examinatim of a necessity of introducing industrial design as a cata1yst 3) A certain understanding aIntt essence and value of industrial design 4) Study and examinatim about a case of sucessful introducing industrial design arxl common introducing method of small business.5) Befarehand examinatim of introducing method making use of professional design organization and consultatim wicket 6) Prodent examination about the appointlrent puprpose, method of designer and infonmtion about designer. Second, as the position of small bnsiness that introduce industrial design fur the first time, it is confirmed that the aroroach going with introducing types - preliminary introducing, partitial introducing, regular introducing, whole industry level introducing - considered necessity rate of introducing industrial design and introducing range at the same time. This method is able to approach step by step, but it is confinmed that there is a characteristic in being able to select the method freely, and understanding easily for being coostructed visual form.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.3
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• pp.311-317
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• 2007

The purpose of this study is to evaluate the nutrient intake and selected blood parameters of adults with metabolic syndrome (MS) and to provide data in forming a dietary guideline for the prevention of chronic diseases. Subjects were recruited and divided into two groups according to the NCEP-ATP III criteria and WHO Asia-Pacific Area criteria for obesity. MS group was defined as subjects who have three or more risk factors and control group was defined as those with two or less of the risk factors. The average age, height, weight, body mass index (BMI) were 58.8 years, 158.0 cm, 66.3 kg, $26.5\;kg/m^{2}$, respectively, in the MS group; and 58.4 years, 158.9 cm, 59.6 kg, $23.5\;kg/m^{2}$, respectively, in the control group. The weight and BMI in the MS group were significantly higher than those in the control (p<0.001). There was no significant difference in the food and nutrient intake between the MS and control group. Male subjects in the MS group showed significantly higher intake of mushrooms than those in the control (p<0.05). Egg consumption in the MS group was significantly lower than those in the control (p<0.01). Consumption of vegetables and fiber was significantly lower for female subjects in the MS group than those in the control (p<0.05). Serum GPT, AI and WBC count in the MS group (27.8 IU/L, 3.7, $5964.2\;{\mu}/L$) were significantly higher than those in the control (22.6 IU/L, 3.2, $5250.0\;{\mu}/L$; p<0.01, p<0.001, p<0.01). In conclusion, consuming fiber and vegetables may prevent and reduce metabolic syndrome in adult men and women, and this study demonstrates the need for proper dietary management for them.

• Neonatal Medicine
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• v.16 no.2
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• pp.172-181
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• 2009

Purpose: To identify trends in causative bacterial organisms for neonatal sepsis and antimicrobial susceptibilities over 10 years in one neonatal intensive care unit. Methods: We retrospectively reviewed the cases of culture-proven neonatal sepsis between January 1998 and December 2007. The 10-year period was divided into two phases (phase I, 1998-2002; phase II, 2003-2007) to distinguish the differences during the entire period. Results: Total 350 episodes of neonatal sepsis were identified in 315 neonates. The common pathogens of early-onset sepsis were S. epidermidis, S. aureus, P. aeruginosa, and E. cloacae in phase I, and S. epidermidis and E. cloacae in phase II. In cases of late-onset sepsis, coagulase negative Staphylococcus, S. aureus, and K. pneumoniae were isolated frequently in both phases. The incidence of sepsis caused by multi-drug resistant organisms decreased with strict infection control. Gram positive organisms showed 0-20% susceptibility to penicillin, ampicillin, and cefotaxime in both phases. Sensitivity to amikacin for Enterobacter spp. increased, whereas P. aeruginosa showed decreased sensitivity in phase II. Between 50% and 60% of other gram negative bacteria, except P. aeruginosa, were susceptible to cefotaxime in phase II in contrast to phase I. Greater than 80% of gram negative bacteria were sensitive to imipenem except P. aeruginosa and ciprofloxacin in both phases. Conclusion: The trend in causative microorganisms and antimicrobial susceptibilities can be used as a guideline for selection of appropriate antibiotics. A particular attention should be paid to infection control, especially to reduce sepsis caused by multi-drug resistant organisms.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.199-230
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• 2021

Innovative new products and services are being launched through the convergence between heterogeneous industries, and social interest and investment in convergence industries such as AI, big data-based future cars, and robots are continuously increasing. However, in the process of commercialization of convergence new products and services, there are many cases where they do not conform to the existing regulatory and legal system, which causes many difficulties in companies launching their products and services into the market. In response to these industrial changes, the current government is promoting the improvement of existing regulatory mechanisms applied to the relevant industry along with the expansion of investment in new industries. This study, in these convergence industry trends, aimed to analysis the existing regulatory system that is an obstacle to market entry of innovative new products and services in order to preemptively predict regulatory issues that will arise in emerging industries. In addition, it was intended to establish a regulatory impact analysis system to evaluate adequacy and prepare improvement measures. The flow of this study is divided into three parts. In the first part, previous studies on regulatory impact analysis and evaluation systems are investigated. This was used as basic data for the development direction of the regulatory impact framework, indicators and items. In the second regulatory impact analysis framework development part, indicators and items are developed based on the previously investigated data, and these are applied to each stage of the framework. In the last part, a case study was presented to solve the regulatory issues faced by actual companies by applying the developed regulatory impact analysis framework. The case study included the autonomous/electric vehicle industry and the Internet of Things (IoT) industry, because it is one of the emerging industries that the Korean government is most interested in recently, and is judged to be most relevant to the realization of an intelligent information society. Specifically, the regulatory impact analysis framework proposed in this study consists of a total of five steps. The first step is to identify the industrial size of the target products and services, related policies, and regulatory issues. In the second stage, regulatory issues are discovered through review of regulatory improvement items for each stage of commercialization (planning, production, commercialization). In the next step, factors related to regulatory compliance costs are derived and costs incurred for existing regulatory compliance are calculated. In the fourth stage, an alternative is prepared by gathering opinions of the relevant industry and experts in the field, and the necessity, validity, and adequacy of the alternative are reviewed. Finally, in the final stage, the adopted alternatives are formulated so that they can be applied to the legislation, and the alternatives are reviewed by legal experts. The implications of this study are summarized as follows. From a theoretical point of view, it is meaningful in that it clearly presents a series of procedures for regulatory impact analysis as a framework. Although previous studies mainly discussed the importance and necessity of regulatory impact analysis, this study presented a systematic framework in consideration of the various factors required for regulatory impact analysis suggested by prior studies. From a practical point of view, this study has significance in that it was applied to actual regulatory issues based on the regulatory impact analysis framework proposed above. The results of this study show that proposals related to regulatory issues were submitted to government departments and finally the current law was revised, suggesting that the framework proposed in this study can be an effective way to resolve regulatory issues. It is expected that the regulatory impact analysis framework proposed in this study will be a meaningful guideline for technology policy researchers and policy makers in the future.