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您的检索式：作者名="Chengguang Su"
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| 1 | Long-term outcomes of intensity-modulated radiotherapy for 868 patients with nasopharyngeal carcinoma: An analysis of survival and treatment toxicities显示文摘 | Xueming Sun Shengfa Su Chunyan Chen Fei Han Chong Zhao Weiwei Xiao Xiaowu Deng Shaomin Huang Chengguang Lin Taixiang Lu | 2013 | Radiotherapy and Oncology2013，，： | 1 |
| 2 | Fatigue be- havior of CA mortar in CRTS - I ballastless track under train load显示文摘 | DU Huayang LIU Guan SU Chengguang | 2013 | Applied Mechanics and Materials2013，405，8： | 1 |
| 3 | Autofocus technique for ISAR imaging of uniformly rotating targets based on the ExCoV method显示文摘The inverse synthetic aperture radar(ISAR) imaging can be converted into a sparse reconstruction problem and solved by the l_1-norm minimization algorithm. The basis matrix in sparse ISAR imaging is usually characterized by the unknown rotation rate of a moving target, thus the rotation rate and the sparse signal should be jointly estimated. Especially due to the imperfect coarse motion compensation, we consider the phase error correction problem in the context of the sparse signal reconstruction. To address this issue, we propose an iterative reweighted method,which jointly estimates the rotation rate, corrects the phase error and reconstructs a high resolution ISAR image. The proposed method gives a gradual and interweaved iterative process to refine the unknown parameters to achieve the best sparse representation for the ISAR signals. Particularly, in ISAR image reconstruction,the l_1-norm minimization algorithm is sensitive to user parameters.Setting these user parameters are not trivial and the reconstruction performance depends significantly on their choices. Then, we consider an expansion-compression variance-component(ExCoV) based method, which is automatic and demands no prior knowledge about signal-sparsity or measurement-noise levels. Both numerical and electromagnetic data experiments are implemented to show the effectiveness of the proposed method. It is shown that the proposed method can estimate the rotation rate and correct the phase errors simultaneously, and its superior performance is proved in terms of high resolution ISAR image. | Chengguang Wu Hongqiang Wang Bin Deng Yuliang Qin Wuge Su | 2017 | Journal of Systems Engineering and Electronics2017，28，2： | 1 |
| 4 | Temperature field test and prediction using a GA-BP neural network for CRTS Ⅱ slab tracks显示文摘The CRTS Ⅱ slab track, which is connected in a longitudinal direction, is one of the main ballastless tracks in China, with approximately 7365 km of operational track. Temperature loading is a very vital factor leading to slab track damages such as warping and cracking. While existing research on temperature distribution rests on either site tests in special environments or theoretical analysis, the long-term temperature field characteristics are not clear. Therefore, a long-term temperature field test for the CRTS Ⅱ slab track on bridge-subgrade transition section was conducted to analyze the temperature field. A GA-BP(genetic algorithm optimized back propagation) neural network was trained on the test data to predict the temperature field. The vertical and lateral temperature distributions in four typical days were carried out. We found that the temperature along the track was distributed in a nonlinear manner. This was particularly distinct in the vertical direction for depths of less than 300 mm. The highest and lowest daily temperatures and the daily range of the temperature were analyzed. With the increasing depth, the daily highest temperatures and range of the temperature were smaller, the daily lowest temperatures were higher, and the time corresponding to this peak value appeared later in the day. Both the highest and lowest daily temperature could be predicted using the GA-BP neural network, though the accuracy in predicting the highest temperature was higher than that in predicting the lowest temperature. | Dan Liu Chengguang Su Rongshan Yang Juanjuan Ren Xueyi Liu | 2023 | Railway Engineering Science2023，31，4： | 0 |
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