• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.56.1-56.1
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• 2020

한국의 고문헌 중에는 다양한 고천문 기록들이 한문 형태로 존재하며, 이를 학술적으로 활용하기 위해서는 전문 번역가 투입에 따른 많은 비용과 시간이 요구된다. 이에 인공신경망 기계학습에 의한 인공지능 번역기를 개발하여 비록 초벌 번역 수준일지라도 문장 형태의 한문을 한글로 자동번역해 주는 학술 도구를 소개하고자 한다. 이 자동번역기는 한국천문연구원이 한국정보화진흥원이 주관하는 2019년도 Information and Communication Technology 기반 공공서비스 촉진사업에 한국고전번역원과 공동 참여하여 개발 완료한 것이다. 이 연구는 고천문 도메인에 특화된 인공지능 기계학습용 데이터인 천문 고전 코퍼스를 구축하여 이를 기반으로 천문 고전 특화 자동번역 모델을 개발하고 번역 서비스하는 것을 목적으로 한다. 이를 위해 구축되는 시스템은 크게 세 가지이다. 첫째, 로그인이 필요 없이 누구나 웹 접속을 통해 사용이 가능한 클라우드 기반의 고문헌 자동번역 대국민서비스 시스템이다. 둘째, 참여 기관별로 구축된 코퍼스와 도메인 특화된 번역 모델의 생성 및 관리할 수 있는 클라우드 기반의 대기관 서비스 플랫폼 구축이다. 셋째, 개발된 자동번역 Applied Programmable Interface를 활용한 한국천문연구원 내 자체 서비스가 가능한 AITHA 시스템이다. 연구 결과로서 먼저 구축된 천문 고전 코퍼스 60,760건에 대한 샘플링 검수 결과는 품질 순도 99.9% 이상이다. 아울러 도출된 천문 고전 특화 번역 모델 총 20개 중 대표 모델에 대한 성능 평가 결과는 기계 번역 텍스트 품질 평가 알고리즘인 Bilingual Evaluation Understudy 평가에서 40.02점이며, 전문가에 의한 휴먼 평가에서 5.0 만점 중 4.05점이다. 이는 당초 연구 목표로 삼았던 초벌 번역 수준에 충분하며, 현재 개발된 시스템들은 자체 시험 운영 중이다. 이 연구는 특수 고문헌에 해당되는 고천문 기록들의 번역 장벽을 낮춰 관련 연구자들의 학술적 접근 및 다양한 연구에 도움을 줄 수 있다는 점에서 의의가 있다. 또한 고천문 분야가 인공지능 자동번역 확산 플랫폼 시범의 첫 케이스로써 추후 타 학문 분야 참여 시 시너지 효과도 기대해 볼 수 있다. 고문헌 자동번역기는 점차 더 많은 학습 데이터와 학습량이 쌓일수록 더 좋은 학술 도구로 진화할 것이다.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.169-178
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• 2019

Now a day, we can perform various predictions by applying machine learning, which is a field of artificial intelligence; however, the finding of best algorithm in the field is always the problem. This paper predicts monthly power trading amount, monthly power trading amount of money, monthly index of production extension, final consumption of energy, and diesel for automotive using machine learning supervised algorithms. Then, we find most fit algorithm among them for each case. To do this we show the probability of predicting the value for monthly power trading amount and monthly power trading amount of money, monthly index of production extension, final consumption of energy, and diesel for automotive. Then, we try to average each predicting values. Finally, we confirm which algorithm is the most superior algorithm among them.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.205-210
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• 2022

Drones, which have rapidly grown along with the 4th industrial revolution, spread over industries and also widely used for military purposes. In recent wars in Europe, drones are being evaluated as a game changer on the battlefield, and their importance for military use is being highlighted. The Republic of Korea Army also planned drone-bot systems including various drones suitable for echelons and missions of the military as future defense forces. The keyword of these drone-bot systems is autonomy by artificial intelligence. In addition, common use of operating platforms is required for the rapid development of various types of drones. In this paper, we propose software framework that applies diverse artificial intelligence technologies such as multi-agent system, cognitive architecture and knowledge-based context reasoning for mission autonomy and common use of military drones.

• Industry Promotion Research
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• v.8 no.2
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• pp.21-27
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• 2023

This study studies the policies and use cases of the government and the financial sector for artificial intelligence, and the future policy tasks of the financial sector. want to derive According to Gartner, noteworthy technologies leading the financial industry in 2022 include 'generative AI', 'autonomous system', 'Privacy Enhanced Computation (PEC) was selected. The financial sector is developing new technologies such as artificial intelligence, big data, and blockchain. Developments are spurring innovation in the financial sector. Data loss due to the spread of telecommuting after the corona pandemic As interests in sharing and personal information protection increase, companies are expected to change in new digital technologies. Global financial companies also utilize new digital technology to develop products or manage and operate existing businesses. I n order to promote process innovation, I T expenses are being expanded. The financial sector utilizes new digital technology to prevent money laundering, improve work efficiency, and strengthen personal information protection. are applying In the era of Big Blur, where the boundaries between industries are disappearing, the competitive edge in the challenge of new entrants In order to preoccupy the market, financial institutions must actively utilize new technologies in their work.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.279-286
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• 2021

Recently, We are carrying out a policy of physical distancing of at least 1m from each other to prevent the spreading of COVID-19 disease in public places. In this paper, we propose a method for measuring distances between people in real time and an automation system that recognizes objects that are within 1 meter of each other from stereo images acquired by drones or CCTVs according to the estimated distance. A problem with existing methods used to estimate distances between multiple objects is that they do not obtain three-dimensional information of objects using only one CCTV. his is because three-dimensional information is necessary to measure distances between people when they are right next to each other or overlap in two dimensional image. Furthermore, they use only the Bounding Box information to obtain the exact coordinates of human existence. Therefore, in this paper, to obtain the exact two-dimensional coordinate value in which a person exists, we extract a person's key point to detect the location, convert it to a three-dimensional coordinate value using Stereo Vision and Camera Calibration, and estimate the Euclidean distance between people. As a result of performing an experiment for estimating the accuracy of 3D coordinates and the distance between objects (persons), the average error within 0.098m was shown in the estimation of the distance between multiple people within 1m.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Land and Housing Review
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• v.14 no.3
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• pp.125-135
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• 2023

In South Korea, it has been increased the necessity of supplying housing services to meet the needs and desires of various residents by reflecting various demographic and social changes. In particular, various smart device has been widely utilized in South Korea and the smart technologies, such as artificial intelligence and the Internet of Things has been developed rapidly. These smart technologies could support smart housing that allows residents to easily and comfortably employ residential services. However, it is necessary to improve the awareness of users in order to spread the smart housing residential services connected to smart technologies. For this reason, this study observed changes in users' perceptions of smart housing residential service technology using Living Lab. As a result, after experiencing the Living Lab, users' awareness of smart housing housing service increased, and it was observed that the preferred housing service technology was more detailed than before the Living Lab experience. This study shows that it is important to raise users' awareness for the dissemination of smart housing residential service technology, and that Living Lab can be an effective means for this purpose.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.