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김형석,신만식,김창종,유선형,YOO TAE KYUNG R,엄용화,채병주,송병주 한국유방암학회 2017 Journal of breast cancer Vol.20 No.4
Purpose: Pathological complete response (pCR) of axillary lymph node (LN) is frequently achieved in patients with clinically nodepositive breast cancer after neoadjuvant chemotherapy (NAC). Treatment of the axilla after NAC is not well established and the value of sentinel LN biopsy following NAC remains unclear. This study investigated the predictive value of axillary response following NAC and evaluated the predictive value of a model based on axillary response. Methods: Data prospectively collected on 201 patients with clinically node-positive breast cancer who were treated with NAC and underwent axillary LN dissection (ALND) were retrieved. A model predictive of axillary pCR was developed based on clinicopathologic variables. The overall predictive ability between models was compared by receiver operating characteristic (ROC) curve analysis. Results: Of 201 patients who underwent ALND after NAC, 68 (33.8%) achieved axillary pCR. Multivariate analysis using axillary LN pCR after NAC as the dependent variable showed that higher histologic grade (p= 0.031; odds ratio [OR], 2.537; 95% confidence interval [CI], 1.087–5.925) and tumor response rate ≥47.1% (p=0.001; OR, 3.212; 95% CI, 1.584–6.515) were significantly associated with an increased probability of achieving axillary pCR. The area under the ROC curve for estimating axillary pCR was significantly higher in the model that included tumor response rate than in the model that excluded this rate (0.732 vs. 0.649, p=0.022). Conclusion: Tumor response rate was the most significant independent predictor of axillary pCR in response to NAC. The model that included tumor response rate was a significantly better predictor of axillary pCR than the model that excluded tumor response rate.
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유턴을 고려한 교통축 적용 수요대응 자율주행 대중교통(DRDTO)의 실시간 동적 경로 생성 알고리즘
김현(KIM, Hyun),유선형(YOO, Seonhyung),이진우(LEE, Jinwoo),백범열(BAEK, Beomyeol),신정희(SHIN, Junghee) 대한교통학회 2022 대한교통학회지 Vol.40 No.2
최근 자율주행 차량이 대중교통분야로 진출하여 관련 연구들이 활발히 진행되고 있다. 본 연구에서는 시공간적으로 다양하게 발생하는 실시간 호출수요에 대응하여 자율주행 차량에 동적 경로를 제시하여 효율적인 대중교통 서비스를 제공할 수 있는 알고리즘을 개발했다. 실시간 호출수요는 다수의 출발지와 다수의 목적지로 구성되어 복잡하고 다양한 경우의 수를 고려하여 최적 경로를 제시할 수 있는 방안이 필수적이다. 복잡한 계산과정은 오프라인 과정에서 선행할 수 있는 방법으로 머신러닝의 강화학습 기술을 사용하여 제시하였다. 개발된 자율주행 대중교통(DTO)를 위한 실시간 호출 수요대응 동적경로 알고리즘은 테스트베드를 선정하여 시뮬레이션 실험이 이루어 졌다. 시뮬레이션 결과는 개발된 실시간 호출 수요대응 동적경로 생성 알고리즘을 고정노선의 운행결과 대비하여 효율적인 운행결과가 나타났다. 이는 실시간 호출수요량이 적을수록 효과가 크게 나타나지만 호출 수요량이 증가하면서 그 효과 폭이 점차적으로 감소했다. Recently, autonomous vehicle is receiving much attention in various sectors including transportation and public transportation. This study presents an adaptive routing algorithm for the real-time demand response service of autonomous transit vehicles. The experimental setting includes the real-time demand occurring randomly over time within the study area. Each demand (service call) request the transit service from designated origin to destination. The routing algorithm is designed to make U-turn and skip stops to improve service and reduce the wait time of users. This study adapts Reinforcement Learning, one of the machine learning techniques, namely reinforcement learning, which can precede a complex calculation process in an offline process. Simulation experiments conducted on a testbed of the Chungju campus of the Korea National University of Transportation. The simulation results show that the proposed routing algorithm can improve the adaptive transit service over the fixed operation in selected performance indicators.
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