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동일 T 병기 유방암에서 유방의 부피가 예후에 미치는 영향
이중재,전영산,강수환,이수정 한국유방암학회 2009 Journal of breast cancer Vol.12 No.4
Purpose: The aim of this study was to evaluate the prognostic significance of the breast volume in primary breast cancer patients with the same T stage. Methods: The study population consisted of 358 patients with T1 and T2 primary breast cancer, who underwent preoperative mammography and surgery in our institution from March 1992 to December 2006. The patients were divided into three groups based on the calculated breast volume as the following: Group A: <285 cc (n=117), Group B: 285-460 cc (n=121) and Group C: ≥460 cc (n=120). Overall survival (OS) and disease free survival (DFS) of the patients in the three groups in each T stage were analyzed. Results: The mean age was 46.3 years (age range, 22-85 years) and the mean calculated breast volume was 403.1 cc (volume range, 94-1,231 cc). As the age of patients was increased, the breast volume was increased (r=0.184, p<0.001). With a mean follow up period of 80.8 months, there was no significant difference in DFS or OS among patients in Groups A, B, and C (p>0.05). For patients with T1 stage disease, Group A patients showed the highest DFS and OS, and patients in Group C showed the lowest DFS and OS; however, the difference was not statistically significant (p>0.05). For patients with T2 disease, patients in Group C showed the highest DFS and OS, though the difference with the two other groups did not have statistical significance (p>0.05). Conclusion: The breast volume was not a significant predictor of DFS and OS for patients with T1 and T2 breast cancer. However it should be noted that this was the first study to evaluate the correlation between breast volume and survival in breast cancer patients.
네트워크 환경에서의 몰입형 상호작용을 위한 딥러닝 기반 그룹 동기화 기법
이중재,Lee, Joong-Jae 한국정보처리학회 2022 정보처리학회논문지. 컴퓨터 및 통신시스템 Vol.11 No.10
본 논문에서는 네트워크 환경에서 원격사용자들의 몰입형 상호작용을 위한 딥러닝 기반의 그룹 동기화 기법을 제안한다. 그룹 동기화의 목적은 사용자의 몰입감을 높이기 위해서 모든 참여자가 동시에 상호작용이 가능하게 하는 것이다. 기존 방법은 시간 정확도를 향상을 위해 대부분 NTP(Network Time Protocol) 기반의 시간 동기화 방식에 초점이 맞추어져 있다. 동기화 서버에서는 미디어 재생 시간을 제어하기 위해 이동 평균 필터를 사용한다. 그 한 예로서, 지수 가중평균 방법은 입력 데이터의 변화가 크지 않으면 정확하게 재생 시간을 추종하고 예측하나 네트워크, 코덱, 시스템 상태의 급격한 변화가 있을 때는 안정화를 위해 더 많이 시간이 필요하다. 이런 문제점을 개선하기 위해서 데이터의 특성을 반영할 수 있는 딥러닝 기반의 그룹 동기화 기법인 DeepGroupSync를 제안한다. 제안한 딥러닝 모델은 시계열의 재생 지연 시간을 이용하여 최적의 재생 시간을 예측하는 두 개의 GRU(gated recurrent unit) 계층과 하나의 완전 연결 계층으로 구성된다. 실험에서는 기존의 지수 가중평균 기반 방법과 제안한 DeepGroupSync 방법에 대한 성능을 평가한다. 실험 결과로부터 예상하지 못한 급격한 네트워크 조건 변화에 대해서 제안한 방법이 기존 방법보다 더 강건함을 볼 수 있다. This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.
Structural Equation Modeling for One-Way MANOVA Design Using the Amos Graphics Software
이중재,오현숙 한국자료분석학회 2015 Journal of the Korean Data Analysis Society Vol.17 No.2
Bagozzi, Yi (1989) developed powerful procedures for the analysis of MANOVA and MANCOVA designs on the use of structural equation modeling with the Lisrel program. The present paper showed how the Bagozzi, Yi's (1989) procedure for one-way MANOVA and MANCOVA analysis could be accomplished with the Amos program. For the comparison with the Lisrel and easy understanding for the method of Bagozzi, Yi (1989), we used the same illustrative examples as Bagozzi, Yi (1989). The noticeable difference in MANOVA or MANCOVA analysis is that a pseudo variable is present to estimate means in the Lisrel while means are estimated automatically by the option "Estimate means and intercepts" by which the model with the Amos is simpler than the one with the Lisrel. Model equation formulas, assumptions for parameters and appropriate data forms for a given model specification in the Amos were explained by comparing with the Lisrel. This paper showed all the procedures for one-way MANOVAs suggested by Bagozzi, Yi (1989) could be performed easily by using the Amos while another software PLS had some limitation for application of the procedures (Bagozzi, Yi, 1991).