To improve the density of information storage,this paper introduces a kind of annular binary filters with super-resolution,Several of these filters have been designed based on the genetic algorithm,the simulations dem...To improve the density of information storage,this paper introduces a kind of annular binary filters with super-resolution,Several of these filters have been designed based on the genetic algorithm,the simulations demonstrate that the transverse gain of the filters can reach the value of 1.37.Thus they can remarkably decrease the recording spot size,which is helpful to improve the density of information storage and to make the depth of focus longer,and therefore they can avoid the mistake caused by the small undulation of the optical disk in the process of recording/reading the information.展开更多
Many existing product family design methods assume a given platform, However, it is not an in-tuitive task to select the platform and unique variable within a product family. Meanwhile, most approaches are single-plat...Many existing product family design methods assume a given platform, However, it is not an in-tuitive task to select the platform and unique variable within a product family. Meanwhile, most approaches are single-platform methods, in which design variables are either shared across all product variants or not at all. While in multiple-platform design, platform variables can have special value with regard to a subset of product variants within the product family, and offer opportunities for superior overall design. An information theoretical approach incorporating fuzzy clustering and Shannon's entropy was proposed for platform variables selection in multiple-platform product family. A 2-level chromosome genetic algorithm (2LCGA) was proposed and developed for optimizing the corresponding product family in a single stage, simultaneously determining the optimal settings for the product platform and unique variables. The single-stage approach can yield im-provements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform and values of unique variables are found for each product in the second stage. An example of design of a family of universal motors was used to verify the proposed method.展开更多
Soil organic carbon （SOC） pool has the potential to mitigate or enhance climate change by either acting as a sink, or a source of atmospheric carbon dioxide （CO2） and also plays a fundamental role in the health an...Soil organic carbon （SOC） pool has the potential to mitigate or enhance climate change by either acting as a sink, or a source of atmospheric carbon dioxide （CO2） and also plays a fundamental role in the health and proper functioning of soils to sustain life on Earth. As such, the objective of this study was to investigate the applicability of a novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system （ANFIS-EG） in predicting and mapping the spatial patterns of SOC stocks in the Eastern Mau Forest Reserve, Kenya. Field measurements and auxiliary data reflecting the soil-forming factors were used to design an ANFIS-EG model, which was then implemented to predict and map the areal differentiation of SOC stocks in the Eastern Mau Forest Reserve. This was achieved with a reasonable level of uncertainty （i.e., root mean square error of 15.07 Mg C ha-l）, hence demonstrating the applicability of the ANFIS-EG in SOC mapping studies. There is potential for improving the model performance, as indicated by the current ratio of performance to deviation （1.6）. The mapping also revealed marginally higher SOC stocks in the forested ecosystems （i.e., an average of 109.78 M C ha-1） than in the aro-ecosvstems （i.e., an average of 95.9 Mg C ha-l）.展开更多
Sacrificial multi-piece molds can be used for producing complex parts. To obtain the optimal design of molds automatically, a multi-objective optimal approach is proposed. Mold pieces number, material utilization and ...Sacrificial multi-piece molds can be used for producing complex parts. To obtain the optimal design of molds automatically, a multi-objective optimal approach is proposed. Mold pieces number, material utilization and partitioning area are taken as the objective functions, and the machinability of each mold piece is taken as constraint condition. Genetic algorithm (GA) is adopted to realize global optimization of partitioning process. Each mold piece in optimal scheme can be manufactured by milling and drilling operations, which reduce the tooling cost and shorten product cycle obviously. Using the proposed approach, mold design can be significantly automated for making complex parts.展开更多
文摘To improve the density of information storage,this paper introduces a kind of annular binary filters with super-resolution,Several of these filters have been designed based on the genetic algorithm,the simulations demonstrate that the transverse gain of the filters can reach the value of 1.37.Thus they can remarkably decrease the recording spot size,which is helpful to improve the density of information storage and to make the depth of focus longer,and therefore they can avoid the mistake caused by the small undulation of the optical disk in the process of recording/reading the information.
基金the National Natural Science Founda-tion of China (No. 70471022)Joint Research Scheme ofthe National Natural Science Foundation of China andthe Hong Kong Research Grant Council (No. 70418013)
文摘Many existing product family design methods assume a given platform, However, it is not an in-tuitive task to select the platform and unique variable within a product family. Meanwhile, most approaches are single-platform methods, in which design variables are either shared across all product variants or not at all. While in multiple-platform design, platform variables can have special value with regard to a subset of product variants within the product family, and offer opportunities for superior overall design. An information theoretical approach incorporating fuzzy clustering and Shannon's entropy was proposed for platform variables selection in multiple-platform product family. A 2-level chromosome genetic algorithm (2LCGA) was proposed and developed for optimizing the corresponding product family in a single stage, simultaneously determining the optimal settings for the product platform and unique variables. The single-stage approach can yield im-provements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform and values of unique variables are found for each product in the second stage. An example of design of a family of universal motors was used to verify the proposed method.
文摘Soil organic carbon （SOC） pool has the potential to mitigate or enhance climate change by either acting as a sink, or a source of atmospheric carbon dioxide （CO2） and also plays a fundamental role in the health and proper functioning of soils to sustain life on Earth. As such, the objective of this study was to investigate the applicability of a novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system （ANFIS-EG） in predicting and mapping the spatial patterns of SOC stocks in the Eastern Mau Forest Reserve, Kenya. Field measurements and auxiliary data reflecting the soil-forming factors were used to design an ANFIS-EG model, which was then implemented to predict and map the areal differentiation of SOC stocks in the Eastern Mau Forest Reserve. This was achieved with a reasonable level of uncertainty （i.e., root mean square error of 15.07 Mg C ha-l）, hence demonstrating the applicability of the ANFIS-EG in SOC mapping studies. There is potential for improving the model performance, as indicated by the current ratio of performance to deviation （1.6）. The mapping also revealed marginally higher SOC stocks in the forested ecosystems （i.e., an average of 109.78 M C ha-1） than in the aro-ecosvstems （i.e., an average of 95.9 Mg C ha-l）.
文摘Sacrificial multi-piece molds can be used for producing complex parts. To obtain the optimal design of molds automatically, a multi-objective optimal approach is proposed. Mold pieces number, material utilization and partitioning area are taken as the objective functions, and the machinability of each mold piece is taken as constraint condition. Genetic algorithm (GA) is adopted to realize global optimization of partitioning process. Each mold piece in optimal scheme can be manufactured by milling and drilling operations, which reduce the tooling cost and shorten product cycle obviously. Using the proposed approach, mold design can be significantly automated for making complex parts.
