• Annual Conference of KIPS
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• 2021.05a
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• pp.245-248
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• 2021

전통적인 신약개발은 평균 15년, 2~3조원의 비용이 소요되나 투자 대비 생산성이 지속적으로 감소하고 있어 패러다임 전환이 절실한 상황이다. 인공지능 기술을 활용하면 기간과 비용의 절감효과와 신약 후보물질 탐색의 성공확률이 높아질 것을 기대할 수 있다. 본 연구는 신약 연구 분야를 중심으로 인공지능 플랫폼 도입에 있어서 플랫폼의 가치에 영향을 미치는 요인들을 분석하여 수용 및 확산을 촉진하는데 필요한 시사점을 도출하고자 한다.

• Annual Conference of KIPS
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• 2021.11a
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• pp.1043-1046
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• 2021

인공지능 무인 감독 시스템을 이용하여 코로나 시대에 다수의 인원이 한 공간에서 시험을 볼 수 없는 상황을 극복하고, 전염병의 확산을 피해 언제 어디서든 시험을 볼 수 있는 시대를 도래한다. 미리 학습된 이미지를 바탕으로 얼굴을 판별하고, Motion recognition 기능을 이용하여 얼굴, 동공, 자세 등의 움직임을 인식하여 분석한다. 이처럼 인공지능 시스템을 이용한다면, 실시간 수업 학생 관리, 범죄 예방 등 타 분야에서 다양한 서비스를 실용화할 수 있다.

• Electronics and Telecommunications Trends
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• v.32 no.1
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• pp.54-60
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• 2017

사물인터넷은 모든 사람과 사물이 인터넷을 통해 서로 소통하고 새로운 가치를 창출할 수 있는 기술이며, 정보의 확산, 연계, 활용을 가능하게 하는 중요한 연결고리이다. 인지 IoT는 이러한 사물인터넷 인프라와 함께 인공지능 기술을 활용하여, 사물이 스스로 생각하고 판단하며, 보다 잘 연결하고 더 똑똑해지도록 하는 사물지능 실현 기술이다. 본고는 인간 두뇌의 기능을 모방하여 인식, 행동, 인지 능력을 재현해내는 대표 인지 컴퓨팅 기술인 IBM 왓슨, 딥 러닝, 뉴로모픽칩 기술을 요약하며, 또한, 사물수준 지능 실현 사례인 IBM 쿼크, CISCO DMo와 D3의 개발 현황을 소개한다.

• Journal of the Korea Society for Simulation
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• v.33 no.3
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• pp.13-26
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• 2024

The CBRN(Chemical, Biological, Radiological, and Nuclear) hazard prediction model is a system that supports commanders in making better decisions by creating contamination distribution and damage prediction areas based on the weapons used, terrain, and weather information in the events of biochemical and radiological accidents. NBC_RAMS(Nuclear, Biological and Chemical Reporting And Modeling S/W System) developed by ADD (Agency for Defense Development) is used not only supporting for decision making plan for various military operations and exercises but also for post analyzing CBRN related events. With the NBC_RAMS's core engine, we introduced a CBR hazard assessment scenario analysis system that can generate contaminant distribution prediction results reflecting various CBR scenarios, and described how to apply it in specific purposes in terms of input information, meteorological data, land data with land coverage and DEM, and building data with pologon form. As a practical use case, a technology development case is addressed that tracks the origin location of contaminant source with artificial intelligence and a technology that selects the optimal location of a CBR detection sensor with score data by analyzing large amounts of data generated using the CBRN scenario analysis system. Through this system, it is possible to generate AI-specialized CBRN related to training and analysis data and support planning of operation and exercise by predicting battle field.

• Journal of Korea Game Society
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• v.20 no.3
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• pp.3-14
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• 2020

The purpose of this paper is to analyze the factors effecting users' satisfaction on game AI for early AI diffusion. For this purpose, we interviewed 20 users who had experiences playing Blade&Soul, made by NCsoft. Interview data had been analyzed through the Semantic Network Analysis program to identify key subject words and their relations. As a result, the paper has found keywords such as patterns, contents, variety, system, and getting new users as factors effecting users satisfaction on game AI.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.795-799
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• 2022

In recent years, with the rapid development of artificial intelligence technology and an explosion of interest in it, education on artificial intelligence is spreading to various fields. As a result, many universities are establishing artificial intelligence-related departments or expanding their quota. In line with this trend, University C has newly established the AI operation department in line with the industrial base in the region. In this paper, a curriculum was developed for the newly established AI operation department, and this curriculum was designed and developed focusing on subjects reflecting the demands of industries based on AIOps (Artificial intelligence for IT Operations). To this end, a consultative body was formed with industry experts, and opinions were collected through a survey.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.809-818
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• 2024

This paper proposes an action plan for local governments to safely utilize artificial intelligence technology in various local government policies. The proposed method analyzes cases of application of artificial intelligence-related laws and policies in Gyeonggi Province, Seoul City, and New York City, and then presents matters that local governments should consider when utilizing AI technology in their policies. This paper applies the AILocalism-Korea analysis methodology, which is a modified version of the AILocalsm analysis methodology[1] presented by TheGovLab at New York University. AILocalism-Korea is an analysis methodology created to analyze the current activities of each local government in the fields of legal system, public procurement, mutual cooperation, and citizen participation, and to suggest practical alternatives in each area. In this paper, we use this analysis methodology to present 9 action plans that local governments should take based on safe and reliable use of artificial intelligence. By utilizing various AI technologies through the proposed plan in local government policies, it will be possible to realize reliable public services.

• Analyses & Alternatives
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• v.8 no.1
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• pp.59-85
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• 2024

This study examines the impact of the development of the artificial intelligence (AI) industry on the economic growth of South Korea. The study uses variables such as the revenue and patent applications of AI-related companies, as well as industry-specific total factor productivity and GDP, to estimate the effects. The results suggest that the growth of the AI industry has a positive effect on the economic growth with a lag of about one year. Specifically, the effect of government AI revenue on GDP growth appears to be greater than that of private companies or consumer-focused AI revenue. This indicates that government policies aimed at promoting the diffusion of the AI industry have had significant effects. The study notes that the period covered by the AI industry survey data is relatively short, and there is a lack of detailed data for the manufacturing sector. I suggest that further improvements and accumulation of data could lead to more robust results.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.51-60
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• 2019

The purpose of this study is to examine the issues that can be expected in the smart intelligent information society led by artificial intelligence and the Internet of Things, and how to resolve the issues in terms of PR.The results were as follows. First, there are three major issues that can be expected Second, in order to resolve the issue, it is necessary to prepare and carry out a public interest campaign to create and participate in a new paradigm for the alienated public. Third, welfare technology can be considered as an alternative to the issues.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.