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- 저자 : Feifei Yang (검색결과 21 건)
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Tingting Chen,Chenggong Zeng,Juan Wang,Feifei Sun,Junting Huang,Jia Zhu,Suying Lu,Ning Liao,Xiaohong Zhang,Zaisheng Chen,Xiuli Yuan,Zhen Yang,Haixia Guo,Liangchun Yang,Chuan Wen,Wenlin Zhang,Yang Li,X 대한암학회 2024 Cancer Research and Treatment Vol.56 No.4
Purpose The risk stratification of pediatric anaplastic large cell lymphoma (ALCL) has not been standardized. In this study, new risk factors were included to establish a new risk stratification system for ALCL, and its feasibility in clinical practice was explored. Materials and Methods On the basis of the non-Hodgkin’s lymphoma Berlin–Frankfurt–Munster 95 (NHL-BFM-95) protocol, patients with minimal disseminated disease (MDD), high-risk tumor site (multiple bone, skin, liver, and lung involvement), and small cell/lymphohistiocytic (SC/LH) pathological subtype were enrolled in risk stratification. Patients were treated with a modified NHL-BFM-95 protocol combined with an anaplastic lymphoma kinase inhibitor or vinblastine (VBL). Results A total of 136 patients were enrolled in this study. The median age was 8.8 years. The 3-year event-free survival (EFS) and overall survival of the entire cohort were 77.7% (95% confidence interval [CI], 69.0% to 83.9%) and 92.3% (95% CI, 86.1% to 95.8%), respectively. The 3-year EFS rates of low-risk group (R1), intermediate-risk group (R2), and high-risk group (R3) patients were 100%, 89.5% (95% CI, 76.5% to 95.5%), and 67.9% (95% CI, 55.4% to 77.6%), respectively. The prognosis of patients with MDD (+), stage IV cancer, SC/LH lymphoma, and high-risk sites was poor, and the 3-year EFS rates were 45.3% (95% CI, 68.6% to 19.0%), 65.7% (95% CI, 47.6% to 78.9%), 55.7% (95% CI, 26.2% to 77.5%), and 70.7% (95% CI, 48.6% to 84.6%), respectively. At the end of follow-up, one of the five patients who received maintenance therapy with VBL relapsed, and seven patients receiving anaplastic lymphoma kinase inhibitor maintenance therapy did not experience relapse. Conclusion This study has confirmed the poor prognostic of MDD (+), high-risk site and SC/LH, but patients with SC/LH lymphoma and MDD (+) at diagnosis still need to receive better treatment (ClinicalTrials.gov number, NCT03971305).
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Yang Jiejin,Chen Zeyang,Liu Weipeng,Wang Xiangpeng,Ma Shuai,Jin Feifei,Wang Xiaoying 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.3
Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834–0.877), specificity 67.5% (95% CI: 0.636–0.712), PPV 82.1% (95% CI: 0.797–0.843), NPV 73.0% (95% CI: 0.691–0.766), and AUC 0.771 (95% CI: 0.750–0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541–0.995), specificity 70.0% (95% CI: 0.354–0.919), PPV 75.0% (95% CI: 0.428–0.933), NPV 87.5% (95% CI: 0.467–0.993), and AUC 0.800 (95% CI: 0.563–0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.
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Thermal Model for Power Converters Based on Thermal Impedance
Yang Xu,Hao Chen,Sen Lv,Feifei Huang,Zhentao Hu 전력전자학회 2013 JOURNAL OF POWER ELECTRONICS Vol.13 No.6
In this paper, the superposition principle of a heat sink temperature rise is verified based on the mathematical model of a plate-fin heat sink with two mounted heat sources. According to this, the distributed coupling thermal impedance matrix for a heat sink with multiple devices is present, and the equations for calculating the device transient junction temperatures are given. Then methods to extract the heat sink thermal impedance matrix and to measure the Epoxy Molding Compound (EMC) surface temperature of the power Metal Oxide Semiconductor Field Effect Transistor (MOSFET) instead of the junction temperature or device case temperature are proposed. The new thermal impedance model for the power converters in Switched Reluctance Motor (SRM) drivers is implemented in MATLAB/Simulink. The obtained simulation results are validated with experimental results. Compared with the Finite Element Method (FEM) thermal model and the traditional thermal impedance model, the proposed thermal model can provide a high simulation speed with a high accuracy. Finally, the temperature rise distributions of a power converter with two control strategies, the maximum junction temperature rise, the transient temperature rise characteristics, and the thermal coupling effect are discussed.
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One-dimensional CNN Model of Network Traffic Classification based on Transfer Learning
Lingyun Yang,Yuning Dong,Zaijian Wang,Feifei Gao 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.2
There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.
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Sun, Feifei,Duan, Ningling,Wang, Meng,Yang, Jiaqi Council on Tall Building and Urban Habitat Korea 2021 International journal of high-rise buildings Vol.10 No.3
Dynamic characteristics of tall building structures with double negative stiffness damped outriggers (2NSDO) are parametrically studied using the theoretical formula. Compared with one negative stiffness damped outrigger (1NSDO), 2NSDO can achieve a similar maximal modal damping ratio with a smaller negative stiffness ratio. Besides, the 2NSDO can improve the maximum achievable damping ratio to about 30% with less consumption of an outrigger damping coefficient compared with the double conventional damped outriggers (2CDO). Besides, the responses of structures with 2NSDO under fluctuating wind load are investigated by time-history analysis. Numerical results show that the 2NSDO is effective in reducing structural acceleration under fluctuating wind load, being more efficient than 1NSDO.
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Thermal Model for Power Converters Based on Thermal Impedance
Xu, Yang,Chen, Hao,Lv, Sen,Huang, Feifei,Hu, Zhentao The Korean Institute of Power Electronics 2013 JOURNAL OF POWER ELECTRONICS Vol.13 No.6
In this paper, the superposition principle of a heat sink temperature rise is verified based on the mathematical model of a plate-fin heat sink with two mounted heat sources. According to this, the distributed coupling thermal impedance matrix for a heat sink with multiple devices is present, and the equations for calculating the device transient junction temperatures are given. Then methods to extract the heat sink thermal impedance matrix and to measure the Epoxy Molding Compound (EMC) surface temperature of the power Metal Oxide Semiconductor Field Effect Transistor (MOSFET) instead of the junction temperature or device case temperature are proposed. The new thermal impedance model for the power converters in Switched Reluctance Motor (SRM) drivers is implemented in MATLAB/Simulink. The obtained simulation results are validated with experimental results. Compared with the Finite Element Method (FEM) thermal model and the traditional thermal impedance model, the proposed thermal model can provide a high simulation speed with a high accuracy. Finally, the temperature rise distributions of a power converter with two control strategies, the maximum junction temperature rise, the transient temperature rise characteristics, and the thermal coupling effect are discussed.
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Yukun Tao,Feifei Yang,Ping He,Congshan Li,Yuqi Ji 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.9
This paper presents a distributed adaptive neural tracking consensus control strategy for a class of stochastic nonlinear multiagent systems with whole state time delays, input and output constrains. The considered systems are involved in the existence of whole state delays and stochastic disturbances, which makes the controller design more difficult and complex. Firstly, time delays are related to unknown dynamic interactions with the whole states of the agent systems, and novel Lyapunov-Krasovskii functionals are constructed. Secondly, the smooth asymmetric saturation nonlinearity is given based on Gaussian error function, output constraints are achieved via barrier Lyapunov functions, and neural networks are utilized to deal with the completely unknown nonlinearities and stochastic disturbances. Then, based on Lyapunov stability theory, a delay-independent adaptive controller is developed via Lyapunov-Krasovskii functionals and backstepping technique, and it reduces the complexity of learning parameters. It is proved that the proposed approximation-based controller can guarantee that all closed-loop signals are cooperatively semi-globally uniformly ultimately bounded (CSGUUB), and the tracking errors between the followers and the leaders eventually converge to a small neighbourhood around the origin. Finally, simulation studies are carried out, and the simulation results verify the correctness and effectiveness of the proposed Strategy.
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