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- 저자 : Jinhai Fan (검색결과 2 건)
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Preintervention risk stratification of renal pelvic cancer and ureteral cancer should differ
Tao Yang,Nan Zhang,Bo Yang,Dalin He,Junjie Fan,Jinhai Fan 대한비뇨의학회 2020 Investigative and Clinical Urology Vol.61 No.4
Purpose: To identify different preintervention prognostic factors between renal pelvic cancer (RPc) and ureteral cancer (Uc) and to develop different preintervention risk stratifications for each cancer type. Materials and Methods: A total of 1,768 patients with organ-confined upper urinary tract urothelial carcinoma (1,067 patients with RPc and 701 with Uc) who presented between 2004 and 2015 were selected from the Surveillance, Epidemiology, and End Results database. Clinicopathologic characteristics were compared between RPc and Uc. Univariable and multivariable Cox regression models were used to examine the prognostic ability of the clinicopathologic characteristics with respect to oncology outcomes. Results: Age greater than 75 years was significantly associated with cancer-specific survival (CSS) in RPc patients but not in Uc patients. Tumor size had a significant influence on CSS in Uc patients but not in RPc patients; in contrast, age had an influence in RPc but not in Uc. Unlike CSS, age was significantly associated with overall survival (OS) in both RPc and Uc. Tumor size had an effect on OS in Uc patients but not in RPc patients. Conclusions: The preintervention prognostic factors differed between RPc and Uc. Thus, we should develop separate preintervention risk stratification standards for RPc and Uc. Using these specific preintervention risk stratifications, we may be able to select the most appropriate surgical options for patients in the clinic.
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RLS‑based deadbeat predictive current control for dual three‑phase segmented powered linear motors
Shijiong Zhou,Yaohua Li,Liming Shi,Keyu Guo,Manyi Fan,Jinhai Liu 전력전자학회 2024 JOURNAL OF POWER ELECTRONICS Vol.24 No.6
Deadbeat predictive current control (DPCC) is an effective model-based motor control method. However, due to the unbalanced inductance and parameter variations of the segmented powered linear motor stator, the conventional model of linear motors is not accurate, which ultimately affect the performance of the control. This paper proposes a novel DPCC based on the recursive least squares (RLS) method to identify the parameters of the dual three-phase segmented powered linear motor (SP-LM) model. First, the influence of unbalanced inductance caused by the segmented motor stator and parameter variations of the conventional DPCC are analyzed. Second, a discrete RLS model of the dual three-phase SP-LM is established, which is a common model for both linear induction motors (LIMs) and linear synchronous motors (LSMs). Finally, the model parameters are identified by the RLS method and the deadbeat principle is used to predict the current. The proposed method effectively eliminates the influence of unbalanced inductance and the parameter variation, improves the current control performance and reduces the thrust fluctuation. Experiments based on hardware-in-the-loop verify the proposed method.
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