• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• Convergence Security Journal
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• v.17 no.5
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• pp.93-99
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• 2017

Recently IoT(Internet of Things) service industry has grown very rapidly. In this paper, we investigated the changes in IoT service industry as well as new direction of human life in future global society. Under these changing market conditions, competition has been also changed into global and ecological competition. But compared to the platform initiatives and ecological strategies of global companies, Korean companies' vision of building ecosystems is still unclear. In addition, there is a need of internetworking between mobile and IoT services. IoT security Protocol has weakness of leaking out information from Gateway which connected wire and wireless communication. As such, we investigate the structure of IoT and AI service ecosystem in order to gain strategic implications and insights for the security industry in this paper.

• Journal of Smart Tourism
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• v.3 no.1
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• pp.41-47
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• 2023

Tourism and hospitality have encountered significant changes in recent years as a result of the rapid development of information technology (IT). Customers now expect more expedient services and customized travel experiences, which has intensified competition among service providers. To meet these demands, businesses have adopted sophisticated IT applications such as ChatGPT, which enables real-time interaction with consumers and provides recommendations based on their preferences. This paper focuses on the AI support-prompt middleware system, which functions as a mediator between generative AI and human users, and discusses two operational rules associated with it. The first rule is the Information Processing Rule, which requires the middleware system to determine appropriate responses based on the context of the conversation using techniques for natural language processing. The second rule is the Information Presentation Rule, which requires the middleware system to choose an appropriate language style and conversational attitude based on the gravity of the topic or the conversational context. These rules are essential for guaranteeing that the middleware system can fathom user intent and respond appropriately in various conversational contexts. This study contributes to the planning and analysis of service design by deriving design rules for middleware systems to incorporate artificial intelligence into tourism services. By comprehending the operation of AI support-prompt middleware systems, service providers can design more effective and efficient AI-driven tourism services, thereby improving the customer experience and obtaining a market advantage.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.405-413
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• 2022

Since the COVID-19 outbreak, the active utilization of new health care service utilizing the ICT technology and data science such as telemedicine, smart hospital, AI dignosis has been increasingly found. In this study we examined the business model of Amazon healthcare which leads disruptive innovation in U.S. health care industry with the introduction of hybrid model of telemedicin, in-person care and customer-centric online drug delivery, home-use diagnostic kit, characterized by the integrated model combining medical care, drug delivery and the use of diagnostic kit. We showed using the multiproduct competition model that the synergy effect between the Amazon's original business areas and the healthcare business area causes the active market penetration and the increase in the customer value from utilization of the Amazon care. Using Hotelling's spatial competition model, we also showed that the competition in the health care market can be greater when consumer's choice of health care providers are available in telemedicine platform. In the long, run the issue of competition being weakened due to the exit of less competent healthcare providers may arise, to which the policymakers in the charge of fair competition in health care industry should pay attention.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.105-111
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• 2018

Last March, the world Go competition between AlphaGo, AI Go program developed by Google Deep Mind and professional Go player Lee Sedol has shown us that the 4th industrial revolution using AI has come close. Especially, there ar many system combined with AI hae been developing including program for researching legal information, system for expecting jurisdiction, and processing big data, there is saying that even AI legal person is ready for its appearance. As legal field is mostly based on text-based document, such characteristic makes it easier to adopt artificial intelligence technology. When a legal person receives a case, the first thing to do is searching for legal information and judical precedent, which is the one of the strength of AI. It is very difficult for a human being to utilize a flow of legal knowledge and figures by analyzing them but for AI, this is nothing but a simple job. The ability of AI searching for regulation, precedent, and literature related to legal issue is way over our expectation. AI is evaluated to be able to review 1 billion pages of legal document per second and many people agree that lot of legal job will be replaced by AI. Along with development of AI service, legal service is becoming more advanced and if it devotes to ethical solving of legal issues, which is the final goal, not only the legal field but also it will help to gain nation's trust. If nations start to trust the legal service, it would never be completely replaced by AI. What is more, if it keeps offering advanced, ethical, and quick legal service, value of law devoting to the society will increase and finally, will make contribution to the nation. In this time where we have to compete with AI, we should try hard to increase value of traditional legal service provided by human. In the future, priority of good legal person will be his/her ability to use AI. The only field left to human will be understanding and recovering emotion of human caused by legal problem, which cannot be done by AI's controlling function. Then, what would be the attitude of legal people in this period? It would be to learn the new technology and applying in the field rather than going against it, this will be the way to survive in this new AI period.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.19-29
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• 2020

As the AI speaker market is growing rapidly in recent years, the competition for the preoccupation of children who are the main users and the future prospective customers of the related companies is very intense. However, there is a lack of empirical research on how children interact with AI speakers. Therefore, this research examines the interactions between children and AI speakers, primarily through field studies, to extract what functions they use and what features they have. For this purpose, 799 conversations were collected and analyzed using the log data of the AI speaker recorded in real time. As a result, children were more likely to use children's songs, fairy tales, emotional conversations, and personification compared to adults. In addition, content analysis by specific types resulted in success/failure cases of interaction between children and AI speakers and proposed improvements by failure type. This study is meaningful in that it identifies children's AI speaker preferences, content, and major conversation patterns, and provides guidelines for developing services that meet children's eye level.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.215-235
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• 2024

As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.52-57
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• 2005

In the foe of global competition, the requirements for the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a mai or change on steel manufacturing industries. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI(Artificial Intelligence) techniques. The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. In this paper, an on-line training neural network for both long-term teaming and short-term teaming was developed in order to improve the prediction of rolling force in hot rolling mill. This analysis shows that the predicted rolling force is very closed to the actual rolling force, and the thickness error of the strip is considerably reduced.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.86-93
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• 2019

The population of domestic companion animals is estimated to be about 10 million now. In recent years, the domestic pet market has been launching a wide range of products and services for high quality, smart and well-being. As a result, the market size will increase from 900 billion won in 2012 to 2.3 trillion won in 2016, which has more than doubled in five years. The industry expects to reach 6 trillion won by 2020, expecting 3 trillion won this year. In particular, domestic dogs and cats market is estimated at 275.5 billion won, accounting for 19% of the domestic animal market and 1.425 billion won for the world market. However, despite the growing market for companion animals products, unfortunately the import dependence on related industrial goods is still high and the quality of service is very low. Unlike Europe and the United States, 90% of companion animals are housed in apartments, often causing problems in the health and safety of companions and companions. The purpose of this study is to develop a smart house for companion animals with environmental friendliness and AI function that can be won in competition with products of developed countries. The results of this study are expected to contribute to the creation of a new value - added base for the related industries through the strengthening of the competitiveness of the related SMEs and further the effect of employment increase and import substitution.

• Animal Bioscience
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• v.37 no.2_spc
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• pp.337-345
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• 2024

Ruminants possess a specialized four-compartment forestomach, consisting of the reticulum, rumen, omasum, and abomasum. The rumen, the primary fermentative chamber, harbours a dynamic ecosystem comprising bacteria, protozoa, fungi, archaea, and bacteriophages. These microorganisms engage in diverse ecological interactions within the rumen microbiome, primarily benefiting the host animal by deriving energy from plant material breakdown. These interactions encompass symbiosis, such as mutualism and commensalism, as well as parasitism, predation, and competition. These ecological interactions are dependent on many factors, including the production of diverse molecules, such as those involved in quorum sensing (QS). QS is a density-dependent signalling mechanism involving the release of autoinducer (AIs) compounds, when cell density increases AIs bind to receptors causing the altered expression of certain genes. These AIs are classified as mainly being N-acyl-homoserine lactones (AHL; commonly used by Gram-negative bacteria) or autoinducer-2 based systems (AI-2; used by Gram-positive and Gram-negative bacteria); although other less common AI systems exist. Most of our understanding of QS at a gene-level comes from pure culture in vitro studies using bacterial pathogens, with much being unknown on a commensal bacterial and ecosystem level, especially in the context of the rumen microbiome. A small number of studies have explored QS in the rumen using 'omic' technologies, revealing a prevalence of AI-2 QS systems among rumen bacteria. Nevertheless, the implications of these signalling systems on gene regulation, rumen ecology, and ruminant characteristics are largely uncharted territory. Metatranscriptome data tracking the colonization of perennial ryegrass by rumen microbes suggest that these chemicals may influence transitions in bacterial diversity during colonization. The likelihood of undiscovered chemicals within the rumen microbial arsenal is high, with the identified chemicals representing only the tip of the iceberg. A comprehensive grasp of rumen microbial chemical signalling is crucial for addressing the challenges of food security and climate targets.