• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.405-406
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• 2023

In the era of AI, intelligent construction safety technologies are being introduced to the construction safety environment, but the application of AI has limitations due to the lack of accident images to learn in complex construction sites. In order to overcome this, we will introduce an intelligent risk detection system that dramatically improves risk detection accuracy by combining AI with digital twin technology, and introduce various cases.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.307-308
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• 2023

본 논문에서는 공사현장 작업자의 실질적 영향을 미치는 요소들을 고려하여 인공지능기반의 실시간 위험성 추정 기법을 개발하였다. 기존 재난안전학 또는 안전공학에서는 위험성평가를 실시하여 강도·빈도에 의하여 위험성을 추정하고 도출된 값을 통해 위험한 정도를 분류하는 경우가 많다. 그러나 대부분의 공사현장에서는 정적인 위험성평가를 통해 해당 유해위험요인을 근거로 형식적인 위험성평가가 이루어지고 있기 때문에 대부분의 사고 후 증빙 자료로만 활용되고 있는 것이 현실이다. 이 위험성평가를 진행하면서 변화하는 환경의 유해위험을 실시간으로 반영할 수 없게 되고, 이는 실질적인 작업자의 안전을 보장해 주지 못하는 문제를 발생시킨다. 이러한 점에서 위험성평가와 더불어 실질적인 안전한 작업장을 만들기 위해서 작업자의 건강정보 및 실시간 공사현장의 영상정보에 기반한 위험성 추정 기법은 실질적 안전사고를 예방하는 방법론 중 하나가 될 수 있다. 작업자의 건강정보는 개개인 맞춤형 건강 데이터에 근거한 해석이 가능하고 실시간 공사현장 영상은 작업 중 발생할 수 있는 돌발상황에 대비하기위해 사용된다. 이러한 위험성 추정 방법은 다양한 공사현장의 위험성 추정 기법으로 사용될 수 있다. 이로 인해 높은 정확도의 위험성 추정 수치를 얻을 수 있으며, 이는 실질적안 안전 예방활동에 있어서 매우 중요하게 작용한다. 위험성 추정 수치 예제를 통하여 본 논문에서 제시된 위험성 추정 방법론이 타당함을 확인하였다. 본 논문에는 기존 위험성평가에 더해 작업자의 건강정보 및 공사현장 실시간 영상정보를 이용하여 실질적인 작업자 안전을 위한 예방 방법을 제시하였다.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.1-14
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• 2022

This study tried to suggest crisis management compliance to prevent personal information infringement accidents that may occur in the process because the data including personal information is being processed in the artificial intelligence (AI) service process. To this end, first, the AI service provision process is divided into 3 processes such as service planning/data design and collection process, data pre-processing and purification process, and algorithm development and utilization process. And 3 processes are subdivided into 9 stages following to personal information processing stages to infringe personal information. All processes were investigated with literature and experts' Delphi. Second, the investigated personal information infringement factors were selected through FGI, Delphi, etc. for experts. Third, a survey was conducted with experts on the severity and possibility of each personal information infringement factor, and the validity and adequacy of the 94 responses were verified. Fourth, to present appropriate risk management compliance for personal information infringement factors in AI services, a method for calculating the risk level of personal information infringement is prepared by utilizing the asset value of personal information, personal information infringement factors, and the possibility of infringement accidents. Through this, the countermeasures for personal information infringement incidents were suggested according to the scored risk level.

• Journal of the Korean Society of Radiology
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• v.83 no.4
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• pp.759-770
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• 2022

The importance of ethics in research and the use of artificial intelligence (AI) is increasingly recognized not only in the field of healthcare but throughout society. This article intends to provide domestic readers with practical points regarding the ethical issues of using radiological images for AI research, focusing on data security and privacy protection and the right to data. Therefore, this article refers to related domestic laws and government policies. Data security and privacy protection is a key ethical principle for AI, in which proper de-identification of data is crucial. Sharing healthcare data to develop AI in a way that minimizes business interests is another ethical point to be highlighted. The need for data sharing makes the data security and privacy protection even more important as data sharing increases the risk of data breach.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.49-52
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• 2022

건물 외벽에 발생하는 균열은 시설물 구조 안전에 영향을 미치며 그 크기에 따라 위험도가 달라진다. 이에 따라 전문검사관의 현장 점검을 통해 발생 균열 두께를 정밀하게 측정할 필요가 있고 최근에는 이러한 현장 안전점검에 인공지능을 도입하려는 추세다. 그러나 기존의 균열 데이터셋은 주로 콘크리트에만 한정되어 다양한 외벽에 강인한 모델을 구축하기 어렵고 균열 두께를 측정하기 위해 정확한 마스크(Mask) 정보가 필요하나 이를 만족하는 데이터셋이 부재하다. 본 논문에서는 다양한 외벽에 강인한 균열 구획화 모델을 목적으로 2,744장의 이미지를 촬영하고 매직 완드 기법으로 라벨링을 진행해 데이터셋을 구축 후, 이를 바탕으로 딥러닝 기반 균열 구획화 모델을 개발했다. UNet-ResNet50을 최종모델로 선정 및 개발 결과, 테스트 데이터셋에 대해 81.22%의 class IoU 성능을 보였다. 본 연구의 기술을 바탕으로 균열 두께를 측정하여 건축물 안전점검에 활용될 수 있기를 기대한다.

• The Science & Technology
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• v.29 no.6 s.325
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• pp.71-74
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• 1996

21세기에는 어린이들이 집안의 어디서 놀고 있는가, 그리고 혹시 위험한 장난을 하고 있다면 이런 사실을 언제나 주부에게 알려 주는 주택이 등장한다. 또 중요한 모임이 있을때는 회의에 방해가 되지 않게 걸려 오는 전화나 방문객 등 모든 것을 알아서 처리해 주는 사무실도 등장한다. 21세기에는 운전자가 과로때문에 사고를 낼지 모른다고 감지하면 운전자에게 차를 주행선 밖으로 빼라고 경고할 수 있는 승용차도 선을 보일 것이다.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.106-116
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• 2024

Purpose: This paper conducts IoT and CCTV-based safety monitoring to analyze accidents and potential risks occurring at construction sites, and detect and analyze risks such as falls and collisions or abnormalities and to establish a system for early warning using devices like a walkie-talkie and chatbot service. Method: A safety management service model is presented through smart construction technology case studies at the construction site and review a relevant literature analysis. Result: According to 'Construction Accident Statistics,' in 2021, there were 26,888 casualties in the construction industry, accounting for 26.3% of all reported accidents. Fatalities in construction-related accidents amounted to 417 individuals, representing 50.5% of all industrial accident-related deaths. This study suggests implementing AI chatbot services for construction site safety management utilizing IoT-based health monitoring technologies in smart construction practices. Construction sites where stakeholders such as workers participate were demonstrated by implementing an artificial intelligence chatbot system by selecting major risk areas within the workplace, such as scaffolding processes, openings, and access to hazardous machinery. Conclusion: The possibility of commercialization was confirmed by receiving more than 90 points in the satisfaction survey of participating workers regarding the empirical results of the artificial intelligence chatbot service at construction sites.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.447-463
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• 2022

Artificial Intelligence (AI) technology is a major technology that develops smart ships into autonomous ships in the marine industry. Autonomous ships recognize a situation with the information collected without human judgment which allow them to operate on their own. Existing ship systems, like control systems on land, are not designed for security against cyberattacks. As a result, there are infringements on numerous data collected inside and outside the ship and potential cyber threats to AI technology to be applied to the ship. For the safety of autonomous ships, it is necessary to focus not only on the cybersecurity of the ship system, but also on the cybersecurity of AI technology. In this paper, we analyzed potential cyber threats that could arise in AI technologies to be applied to existing ship systems and autonomous ships, and derived categories that require security risks and the security of autonomous ships. Based on the derived results, it presents future directions for cybersecurity research on autonomous ships and contributes to improving cybersecurity.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.