Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small targe...Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.展开更多
Modular technology can effectively support the rapid design of products, and it is one of the key technologies to realize mass customization design. With the application of product lifecycle management(PLM) system in ...Modular technology can effectively support the rapid design of products, and it is one of the key technologies to realize mass customization design. With the application of product lifecycle management(PLM) system in enterprises, the product lifecycle data have been effectively managed. However, these data have not been fully utilized in module division, especially for complex machinery products. To solve this problem, a product module mining method for the PLM database is proposed to improve the effect of module division. Firstly, product data are extracted from the PLM database by data extraction algorithm. Then, data normalization and structure logical inspection are used to preprocess the extracted defective data. The preprocessed product data are analyzed and expressed in a matrix for module mining. Finally, the fuzzy c-means clustering(FCM) algorithm is used to generate product modules, which are stored in product module library after module marking and post-processing. The feasibility and effectiveness of the proposed method are verified by a case study of high pressure valve.展开更多
Designing product platform could be an effective and efficient solution for manufacturing firms. Product platforms enable firms to provide increased product variety for the marketplace with as little variety between p...Designing product platform could be an effective and efficient solution for manufacturing firms. Product platforms enable firms to provide increased product variety for the marketplace with as little variety between products as possible. Developed consumer products and modules within a firm can further be investigated to find out the possibility of product platform creation. A bottom-up method is proposed for module-based product platform through mapping, clustering and matching analysis. The framework and the parametric model of the method are presented, which consist of three steps:(1) mapping parameters from existing product families to functional modules,(2) clustering the modules within existing module families based on their parameters so as to generate module clusters, and selecting the satisfactory module clusters based on commonality, and(3) matching the parameters of the module clusters to the functional modules in order to capture platform elements. In addition, the parameter matching criterion and mismatching treatment are put forward to ensure the effectiveness of the platform process, while standardization and serialization of the platform element are presented. A design case of the belt conveyor is studied to demonstrate the feasibility of the proposed method.展开更多
基金Supported by the Key Laboratory Fund for Equipment Pre-Research(6142207210202)。
文摘Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.
基金Project(51275362)supported by the National Natural Science Foundation of ChinaProject(2013M542055)supported by China Postdoctoral Science Foundation Funded
文摘Modular technology can effectively support the rapid design of products, and it is one of the key technologies to realize mass customization design. With the application of product lifecycle management(PLM) system in enterprises, the product lifecycle data have been effectively managed. However, these data have not been fully utilized in module division, especially for complex machinery products. To solve this problem, a product module mining method for the PLM database is proposed to improve the effect of module division. Firstly, product data are extracted from the PLM database by data extraction algorithm. Then, data normalization and structure logical inspection are used to preprocess the extracted defective data. The preprocessed product data are analyzed and expressed in a matrix for module mining. Finally, the fuzzy c-means clustering(FCM) algorithm is used to generate product modules, which are stored in product module library after module marking and post-processing. The feasibility and effectiveness of the proposed method are verified by a case study of high pressure valve.
基金Project(9140A18010210KG01)supported by the Departmental Pre-research Fund of China
文摘Designing product platform could be an effective and efficient solution for manufacturing firms. Product platforms enable firms to provide increased product variety for the marketplace with as little variety between products as possible. Developed consumer products and modules within a firm can further be investigated to find out the possibility of product platform creation. A bottom-up method is proposed for module-based product platform through mapping, clustering and matching analysis. The framework and the parametric model of the method are presented, which consist of three steps:(1) mapping parameters from existing product families to functional modules,(2) clustering the modules within existing module families based on their parameters so as to generate module clusters, and selecting the satisfactory module clusters based on commonality, and(3) matching the parameters of the module clusters to the functional modules in order to capture platform elements. In addition, the parameter matching criterion and mismatching treatment are put forward to ensure the effectiveness of the platform process, while standardization and serialization of the platform element are presented. A design case of the belt conveyor is studied to demonstrate the feasibility of the proposed method.
