This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two algorithms: the weighted av...This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two algorithms: the weighted average and the cosine simplex algorithm. The first approach identifies binding constraints by using the weighted average of each constraint, whereas the second algorithm is based on the cosine similarity between the vector of the objective function and the constraints. These two approaches are complementary, and when used together, they locate the essential subset of initial constraints required for solving medium and large-scale linear programming problems. After reducing the dimension of the linear programming problem using the subset of the essential constraints, the solution method can be chosen from any suitable method for linear programming. The proposed approach was applied to a set of well-known benchmarks as well as more than 2000 random medium and large-scale linear programming problems. The results are promising, indicating that the new approach contributes to the reduction of both the size of the problems and the total number of iterations required. A tree-based classification model also confirmed the need for combining the two approaches. A detailed numerical example, the general numerical results, and the statistical analysis for the decision tree procedure are presented.展开更多
In the present paper,three dimensional analyses of some general constraint parameters and fracture parameters near the crack tip of Mode I CT specimens in two different thicknesses are carried out by employing ADINA p...In the present paper,three dimensional analyses of some general constraint parameters and fracture parameters near the crack tip of Mode I CT specimens in two different thicknesses are carried out by employing ADINA program.The results reveal that the constraints along the thickness direction are obviously separated into two parts:the keeping similar high constraint field(Z_(1))and rapid reducing constraints one(Z_(2)).The two fields are experimentally confiremed to correspond to the smooth region and the shear lip on the fracture face respectively.So the three dimensional stress structure of Mode I specimens can be derived through discussing the two fields respectively.The distribution of the Crack Tip Opening Displacement(CTOD)along the thickness direction and the three dimensional distribution of the void growth ratio(V_(g))near the crack tip are also obtained.The two fracture parameters are in similar trends along the thickness direction,and both of them can reflect the effect of thickness and that of the loading level to a certain degree.展开更多
目的:探讨数据挖掘中决策树模型在结直肠癌患者肝脏CT图像分类中的应用。方法:分别选取结直肠癌患者肝转移、单纯性肝囊肿以及正常肝脏的CT增强图像各20例。对该60例肝脏CT增强图像分别进行灰度直方图、灰度共生矩阵以及图像变换的纹理...目的:探讨数据挖掘中决策树模型在结直肠癌患者肝脏CT图像分类中的应用。方法:分别选取结直肠癌患者肝转移、单纯性肝囊肿以及正常肝脏的CT增强图像各20例。对该60例肝脏CT增强图像分别进行灰度直方图、灰度共生矩阵以及图像变换的纹理特征提取,然后采用朴素贝叶斯分类器和决策树归纳分类器对图像进行分类。最终分类结果与临床事实分类对照,利用十折交叉验证法验证两种分类模型的有效性。结果:基于数据挖掘的决策树模型对结直肠癌患者肝脏CT图像进行分类准确性较高。决策树归纳的分类准确性远高于朴素贝叶斯分类器(准确性96.7%vs 76.7%,Kappa值0.95 vs 0.65,P<0.05)。结论:基于数据挖掘的决策树模型可以对结直肠癌患者肝脏CT图像进行分类,不仅能够判断肝脏有无相关病灶,而且仅依据图像的基本特性,可以自动识别肝脏乏血供转移瘤与单纯性肝囊肿,为未来计算机辅助诊疗疾病提供有效的参考信息及途径。展开更多
文摘This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two algorithms: the weighted average and the cosine simplex algorithm. The first approach identifies binding constraints by using the weighted average of each constraint, whereas the second algorithm is based on the cosine similarity between the vector of the objective function and the constraints. These two approaches are complementary, and when used together, they locate the essential subset of initial constraints required for solving medium and large-scale linear programming problems. After reducing the dimension of the linear programming problem using the subset of the essential constraints, the solution method can be chosen from any suitable method for linear programming. The proposed approach was applied to a set of well-known benchmarks as well as more than 2000 random medium and large-scale linear programming problems. The results are promising, indicating that the new approach contributes to the reduction of both the size of the problems and the total number of iterations required. A tree-based classification model also confirmed the need for combining the two approaches. A detailed numerical example, the general numerical results, and the statistical analysis for the decision tree procedure are presented.
文摘In the present paper,three dimensional analyses of some general constraint parameters and fracture parameters near the crack tip of Mode I CT specimens in two different thicknesses are carried out by employing ADINA program.The results reveal that the constraints along the thickness direction are obviously separated into two parts:the keeping similar high constraint field(Z_(1))and rapid reducing constraints one(Z_(2)).The two fields are experimentally confiremed to correspond to the smooth region and the shear lip on the fracture face respectively.So the three dimensional stress structure of Mode I specimens can be derived through discussing the two fields respectively.The distribution of the Crack Tip Opening Displacement(CTOD)along the thickness direction and the three dimensional distribution of the void growth ratio(V_(g))near the crack tip are also obtained.The two fracture parameters are in similar trends along the thickness direction,and both of them can reflect the effect of thickness and that of the loading level to a certain degree.
文摘目的:探讨数据挖掘中决策树模型在结直肠癌患者肝脏CT图像分类中的应用。方法:分别选取结直肠癌患者肝转移、单纯性肝囊肿以及正常肝脏的CT增强图像各20例。对该60例肝脏CT增强图像分别进行灰度直方图、灰度共生矩阵以及图像变换的纹理特征提取,然后采用朴素贝叶斯分类器和决策树归纳分类器对图像进行分类。最终分类结果与临床事实分类对照,利用十折交叉验证法验证两种分类模型的有效性。结果:基于数据挖掘的决策树模型对结直肠癌患者肝脏CT图像进行分类准确性较高。决策树归纳的分类准确性远高于朴素贝叶斯分类器(准确性96.7%vs 76.7%,Kappa值0.95 vs 0.65,P<0.05)。结论:基于数据挖掘的决策树模型可以对结直肠癌患者肝脏CT图像进行分类,不仅能够判断肝脏有无相关病灶,而且仅依据图像的基本特性,可以自动识别肝脏乏血供转移瘤与单纯性肝囊肿,为未来计算机辅助诊疗疾病提供有效的参考信息及途径。
