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나민원(Minwon Na),박윤영(Yunyoung Park),이성원(Seongwon Lee) 한국IT서비스학회 2020 한국IT서비스학회 학술대회 논문집 Vol.2020 No.1
본 논문에서는 사용자가 대구 지역 내의 출발지, 목적지, 허용시간(사용자가 목적지까지 대중교통으로 이용할 의사가 있는 시간)을 입력하면 허용시간 경계에 있는 주차장을 시 각화하여 보여주고, 주차장을 선택하면 목적지까지 갈 수 있는 주차장 주변 버스, 지하철 정류장 정보를 제공하는 알고리즘을 구현한다. 이를 위하여 교통 공공데이터 중 대구광역시 시내버스 정류소 목록, 시내버스 노선 목록, 버스노선별 정류소, 대구도시철도공사 도시철도 역사정보, 전국 주차장 표준데이터를 활용한다. 알고리즘 구현을 위하여 대중교통(버스, 지하철) 정류장과 노선은 노드(node)와 유향 그래프(directed graph)로 구조화한다. 노드 크기는 정류장을 지나는 노선 개수와 비례하도록 정의하고, 그래프의 엣지(edge) 가중치는 노선이 있을 경우 정류장 사이 직선거리로, 없을 경우 무한대로 정의한다. 이렇게 정의한 그래프에 다익스트라(Dijkstra) 알고리즘을 적용하여 목적지에 도달하는 경로를 제안한다.
Dong-Chan Lee,Sang-kwon Na,Seongwon Kim,Chang Wan Kim 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.9 No.5
Wind farms are typically constructed in off shore areas using floating structures. However, such structures are difficult to maintain, sufficient fatigue life must be ensured during the design process. Therefore, techniques for determining the fatigue load on a floating structure in a marine environment and predicting its lifespan are required. This paper proposes a deterministic fatigue damage analysis method of semi-submersible platform for wind turbines using a hydrodynamic-structure interaction analysis. The process for calculating the fatigue load cycle consists of generating the waves in the time series using the JONSWAP spectrum from probabilistic wave scatter diagrams and decomposes them repeatedly into a number of individual regular waves. This process can simplify calculation of the fatigue load cycle by converting irregular dynamic wave load into a combination of static wave loads. The stress range for fatigue analysis is calculated through hydrodynamic structure interaction analysis. The stress ranges are applied to the S–N curves specified in the DNV-RP-C203 and cumulative fatigue damage is predicted using Miner’s rule. To detailed describe the proposed method, fatigue damage analysis was performed on the DeepCwind semi-submersible developed by the National Renewable Energy Laboratory based on the state of the western sea of Jeju Island, South Korea.
Kyung Won Kim,Jimi Huh,Bushra Urooj,Jeongjin Lee,Jinseok Lee,In-Seob Lee,Hyesun Park,Seongwon Na,Yousun Ko 대한위암학회 2023 Journal of gastric cancer Vol.23 No.3
Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.
Kyung Won Kim,Jimi Huh,Bushra Urooj,Jeongjin Lee,Jinseok Lee,In-Seob Lee,Hyesun Park,Seongwon Na,Yousun Ko The Korean Gastric Cancer Association 2023 대한위암학회지 Vol.23 No.3
Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.