• 해양환경안전학회지
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• 제2권1호
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• pp.143-143
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• 1996

Recent Class requirements and IMO recommendations concerning Hull Strength Monitoring (HSM) have prompted an increasing number of shipowner to adopt monitoring systems on bulk carriers and tanker. Such systems are designed to give warning when stress levels and the frequency and magnitude of ship motions approach levels which require corrective action. When fitted these systems provide enhanced operational safety and efficiency. This paper describes a development beyond the standard BMT HSM system through the integration of stress, motion and radar-based sea state monitoring with powerful, on-board, artificial intelligence (AI) tools. The latter utilises conceptual clustering techniques as an aid to pattern recognition in stress, fatigue. motion and sea state data clusters. This, in turn, provides additional operational guidance for ship's staff. Feedback from applications of the standard BMT HSM and extended HSM systems on board the British Steel Bulk Shipping fleet is described.

• 전자통신동향분석
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• 제36권1호
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• pp.89-98
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• 2021

The Ministry of National Defense aims to create an environment in which transparent and reasonable defense policies can be implemented in real time by establishing the vision of smart defense innovation based on the Fourth Industrial Revolution and promoting innovation in technology-based defense operation systems. Artificial intelligence (AI) based defense technology is at the level of basic research worldwide, includes no domestic tasks, and involves classified military operation data and command control/decision information. Further, it is needed to secure independent technologies specialized for our military. In the army, military power continues to decline due to aging and declining population. In addition, it is expected that there will be more than 500,000 units should be managed simultaneously, to recognize the battle situation in real time on the future battlefields. Such a complex battlefield, command decisions will be limited by the experience and expertise of individual commanders. Accordingly, the study of AI core technologies supporting real-time combat command is actively pursued at home and abroad. It is necessary to strengthen future defense capabilities by identifying potential threats that commanders are likely to miss, improving the viability of the combat system, ensuring smart commanders always win conflicts and providing reasonable AI digital staff based on data science. This paper describes the recent research trends in AI military staff technology supporting commander decision-making, broken down into five key areas.

• 한국멀티미디어학회논문지
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• 제25권5호
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• pp.757-768
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• 2022

In this paper, we proposed a system that visualizes a hologram device in 3D by utilizing the CT image segmentation function based on artificial intelligence deep learning. The input axial CT medical image is converted into Sagittal and Coronal, and the input image and the converted image are divided into 3D volumes using ResUNet, a deep learning model. In addition, the volume is created by segmenting the tumor region in the segmented liver image. Each result is integrated into one 3D volume, displayed in a medical image viewer, and converted into a video. When the converted video is transmitted to the hologram device and output from the device, a 3D image with a sense of space can be checked. As for the performance of the deep learning model, in Axial, the basic input image, DSC showed 95.0% performance in liver region segmentation and 67.5% in liver tumor region segmentation. If the system is applied to a real-world care environment, additional physical contact is not required, making it safer for patients to explain changes before and after surgery more easily. In addition, it will provide medical staff with information on liver and liver tumors necessary for treatment or surgery in a three-dimensional manner, and help patients manage them after surgery by comparing and observing the liver before and after liver resection.

• 한국융합학회논문지
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• 제9권9호
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• pp.431-440
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• 2018

본 연구에서는 인공지능 기술 중 구글에서 개발하여 오픈소스로 제공하고 있는 텐서플로우(Tensorflow) 활용하여 신규간호사 이직률을 예측해 보았고, 이를 통해 전략적 인적자원관리 방안을 제시하였다. 부산지역 한 대학병원의 2010년에서 2017년 사이 퇴직한 간호사 데이터 1,018건을 수집하였다. 학습에 사용된 자료는 순서를 임의로 재배열 한 뒤 전체 데이터의 80%를 학습에, 나머지 20%를 테스트에 이용하였다. 활용된 알고리즘은 다중신경망회로(multiple neural network)로서 입력층과 출력층, 3개 층의 은닉층을 가지도록 설계 되었다. 본 연구의 결과 텐서플로우 플랫폼을 활용하여 1년 이내 이직률을 88.7%, 3년 이내 조기 이직률은 79.8%의 정확도로 예측하였고, 대상자들의 퇴직 시 연령은 20대 후반부터 30대에 집중되어 있었다. 가장 높은 빈도를 차지한 이직 사유로는 '결혼, 출산, 육아, 가정 및 개인사정'이었으나, 근무기간 1년 이하 대상자 들의 가장 높은 이직사유는 '업무 부적응 및 대인관계 문제'로 나타났다.

• 융합보안논문지
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• 제23권4호
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• pp.122-129
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• 2023

제4차 산업혁명 기술의 혁신적 발전에 따라 전쟁의 패러다임이 변화하고 있다. 특히, 미군의 군사혁신 측면에서 제안된 모자이크전은 다양한 무기, 플랫폼, 정보시스템, 인공지능 등 다양한 자원과 능력을 조합하여 유동적인 작전 수행과 상황에 대응하는 능력을 강화하는 것을 목표로 한다. 이러한 개념의 도입은 AI 참모와 인간 지휘자의 결합으로 효과적이고 신속한 지휘통제를 촉진할 수 있다. 모자이크전은 이미 러시아의 침공에 대응하기 위해 우크라이나군의 작전에 도입된 바 있다. 본 논문은 미래전의 모델로 제안되고 있는 모자이크전 개념을 중심으로 전장 패러다임 변화에 따른 한국형 모자이크전 개념 도입을 위한 조건을 도출하고 전략을 제시한다.

• 로봇학회논문지
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• 제16권2호
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• pp.172-178
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• 2021

The COVID-19 pandemic has been reshaping the world by accelerating non-contact services and technologies in various domains. Hospitals as a healthcare system lie at the center of the dramatic change because of their fundamental roles: medical diagnosis and treatments. Leading experts in health, science, and technologies have predicted that robotics and artificial intelligence (AI) can drive such a hospital transformation. Accordingly, several government-led projects have been developed and started toward smarter hospitals, where robots and AI replace or support healthcare personnel, particularly in the diagnosis and non-surgical treatment procedures. This article inspects the remaining element of healthcare services, i.e., surgical treatment, focusing on evaluating whether or not currently available laparoscopic surgical robotic systems are sufficiently preparing for the era of post-COVID-19 when contactless is the new normal. Challenges and future directions towards an effective, fully non-contact surgery are identified and summarized, including remote surgery assistance, domain-expansion of robotic surgery, and seamless integration with smart operating rooms, followed by emphasis on robot tranining for surgical staff.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1080-1099
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• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.11-23
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• 2024

Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients' information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization.

• 인터넷정보학회논문지
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• 제22권6호
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• pp.141-150
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• 2021

인공지능(Artificial Intelligence) 기술을 활용하여 인공지능 기반의 전쟁 (AI-enabled warfare)가 미래전의 핵심이 될 것으로 예상한다. 자연어 처리 기술은 이러한 AI 기술의 핵심 기술로 지휘관 및 참모들이 자연어로 작성된 보고서, 정보 및 첩보를 일일이 열어확인하는 부담을 줄이는데 획기적으로 기여할 수 있다. 본 논문에서는 지휘관 및 참모의 정보 처리 부담을 줄이고 신속한 지휘결심을 지원하기 위해 언어 모델 기반의 다출처 정보 통합 (Language model-based Multi-source Information Integration, LAMII) 프레임워크를 제안한다. 제안된 LAMII 프레임워크는 자기지도 학습법을 활용한 언어 모델에 기반한 표현학습과 오토인코더를 활용한 문서 통합의 핵심 단계로 구성되어 있다. 첫 번째 단계에서는, 자기지도 학습 기법을 활용하여 구조적으로 이질적인 두 문장간의 유사 관계를 식별할 수 있는 표현학습을 수행한다. 두 번째 단계에서는, 앞서 학습된 모델을 활용하여 다출처로부터 비슷한 내용 혹은 토픽을 함양하는 문서들을 발견하고 이들을 통합한다. 이 때, 중복되는 문장을 제거하기 위해 오토인코더를 활용하여 문장의 중복성을 측정한다. 본 논문의 우수성을 입증하기 위해, 우리는 언어모델들과 이의 성능을 평가할 때 활용되는 대표적인 벤치마크 셋들을 함께 활용하여 이질적인 문장간의 유사 관계를 예측의 비교 실험하였다. 실험 결과, 제안된 LAMII 프레임워크가 다른 언어 모델에 비하여 이질적인 문장 구조간의 유사 관계를 효과적으로 예측할 수 있음을 입증하였다.

• 치위생과학회지
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• 제22권4호
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• pp.233-240
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• 2022

Background: In modern society, rapid changes in the medical environment have required medical staff to access various information and be competent in active and effective problem-solving through collegial interactions. In line with these changes, universities are aiming to connect education. This study aimed to provide basic data of connected-convergence education by survey the awareness and needs of college students in health-related fields. Methods: This study included 122 college students from the health field. A survey regarding "the awareness and need of connected-convergence education" was conducted and general characteristics of the participants were collected from June to July 2022. Results: The awareness of connected-convergence education was low at 19.7%, but the intention to participate was high at 74.6%. Subject requirements were 18.0% for medical psychology, 13.5% for communication and counseling, 13.5% for medical artificial intelligence technology convergence, and 10.4% for sports health management. In the group showing high satisfaction with the major curriculum, the demand for connected education was also high. For efficient operation, it was investigated that it was necessary to secure specialized training courses, recognition of liberal arts credits, the right to register for courses equal to those of major students, and secure dedicated classrooms. Conclusion: Although the awareness and experience of connected-convergence education among the participants were low, the intention to participate was high. As such a plan to revitalize the university curriculum was required. It is timely to discuss the nurturing of convergence-type talents and multidisciplinary thinking skills. It is meaningful to provide basic data necessary for connected-convergence education in health-related fields at university. Universities should strive to enhance job competency in the health field by providing connected-convergence education based on student demands.