目的:探讨典型病例资料库的建立结合PBL(problem based learning))教学模式在整形外科教学中的应用及效果评价。方法:将2012-2014年在广东医学院附属医院实习的108名学生按照随机数字表法分为试验组和对照组,每组54名,试验组采用典型病...目的:探讨典型病例资料库的建立结合PBL(problem based learning))教学模式在整形外科教学中的应用及效果评价。方法:将2012-2014年在广东医学院附属医院实习的108名学生按照随机数字表法分为试验组和对照组,每组54名,试验组采用典型病例资料库结合PBL教学模式,对照组采用传统教学模式。通过出科考试、临床问题的解决以及问卷调查的形式评价教学效果。结果:学生出科考试传统试题两组平均成绩比较差异无统计学意义(P>0.05);在实践及病例分析能力考核中,试验组明显优于对照组,差异有统计学意义(P<0.05);问卷调查表明90.74%以上的学生认为典型病例资料库结合PBL教学模式能够提高学习兴趣、知识记忆的牢固性、临床问题的分析及解决能力、创新意识及临床思维能力。结论:采用典型病例资料库PBL教学模式较传统模式具有较好的教学效果及可行性。展开更多
The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the tr...The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.展开更多
文摘目的:探讨典型病例资料库的建立结合PBL(problem based learning))教学模式在整形外科教学中的应用及效果评价。方法:将2012-2014年在广东医学院附属医院实习的108名学生按照随机数字表法分为试验组和对照组,每组54名,试验组采用典型病例资料库结合PBL教学模式,对照组采用传统教学模式。通过出科考试、临床问题的解决以及问卷调查的形式评价教学效果。结果:学生出科考试传统试题两组平均成绩比较差异无统计学意义(P>0.05);在实践及病例分析能力考核中,试验组明显优于对照组,差异有统计学意义(P<0.05);问卷调查表明90.74%以上的学生认为典型病例资料库结合PBL教学模式能够提高学习兴趣、知识记忆的牢固性、临床问题的分析及解决能力、创新意识及临床思维能力。结论:采用典型病例资料库PBL教学模式较传统模式具有较好的教学效果及可行性。
基金supported by The National 973 program (No. 2007 CB209505)Basic Research Project of PetroChina's 12th Five Year Plan (No. 2011A-3601)RIPED Youth Innovation Foundation (No. 2010-A-26-01)
文摘The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.
