Kinematic semantics is often an important content of a CAD model(it refers to a single part/solid model in this work)in many applications,but it is usually not the belonging of the model,especially for the one retriev...Kinematic semantics is often an important content of a CAD model(it refers to a single part/solid model in this work)in many applications,but it is usually not the belonging of the model,especially for the one retrieved from a common database.Especially,the effective and automatic method to reconstruct the above information for a CAD model is still rare.To address this issue,this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics.First,as the geometry of an assembly interface is formed by one or more adjacent faces on each model,a face-attributed adjacency graph integrated with face structure fingerprint is proposed.This can describe each CAD model as well as its assembly interfaces uniformly.After that,aided by the above descriptor,an improved graph attention network is developed based on a new dual-level anti-interference filtering mechanism,which makes it have the great potential to identify all representative kinds of assembly interface faces with high accuracy that have various geometric shapes but consistent kinematic semantics.Moreover,based on the abovementioned graph and face-adjacent relationships,each assembly interface on a model can be identified.Finally,experiments on representative CAD models are implemented to verify the effectiveness and characteristics of the proposed approach.The results show that the average assembly-interface-face-identification accuracy of the proposed approach can reach 91.75%,which is about 2%–5%higher than those of the recent-representative graph neural networks.Besides,compared with the state-of-the-art methods,our approach is more suitable to identify the assembly interfaces(with various shapes)for each individual CAD model that has typical kinematic pairs.展开更多
A common feature of previous studies about the application of data envelopment analysis(DEA) to determine environmental and economic efficiencies is that the two were analyzed in separate models or frameworks. The pur...A common feature of previous studies about the application of data envelopment analysis(DEA) to determine environmental and economic efficiencies is that the two were analyzed in separate models or frameworks. The purpose of this paper is to analyze the economic efficiency and environmental efficiency with a single model. This paper proposes an integrated DEA model, based on a modification of the directional distance function, which allows us to decompose the eco-efficiency(EE) into the economic efficiency(ECE) and environmental efficiency(ENE). The ECE characterizes the ability of gaining economic benefits while the ENE characterizes the ability to control pollutant emissions in production activities. Identification of ECE and ENE can help decision makers of different regions detect what kind of factor(economic inefficiency or environmental inefficiency) is the main source of eco-inefficiency. This can help decision makers more targeted to improve EE. To illustrate the feasibility of our approach, a case study of 30 regions in China is presented. The empirical results show that almost all regions have very high economic efficiencies. The environmental inefficiency is the main source of eco-inefficiency. The differences of environmental efficiencies lead to the differences of eco-efficiencies in the east, central and west areas, while the economic efficiencies do not have significant differences among these areas. The economic efficiencies showed an opposite "V" shape and the environmental efficiencies showed a decreasing trend during the period 2010–2014.展开更多
基金supported by the National Natural Science Foundation of China[61702147]the Zhejiang Provincial Science and Technology Program in China[2021C03137].
文摘Kinematic semantics is often an important content of a CAD model(it refers to a single part/solid model in this work)in many applications,but it is usually not the belonging of the model,especially for the one retrieved from a common database.Especially,the effective and automatic method to reconstruct the above information for a CAD model is still rare.To address this issue,this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics.First,as the geometry of an assembly interface is formed by one or more adjacent faces on each model,a face-attributed adjacency graph integrated with face structure fingerprint is proposed.This can describe each CAD model as well as its assembly interfaces uniformly.After that,aided by the above descriptor,an improved graph attention network is developed based on a new dual-level anti-interference filtering mechanism,which makes it have the great potential to identify all representative kinds of assembly interface faces with high accuracy that have various geometric shapes but consistent kinematic semantics.Moreover,based on the abovementioned graph and face-adjacent relationships,each assembly interface on a model can be identified.Finally,experiments on representative CAD models are implemented to verify the effectiveness and characteristics of the proposed approach.The results show that the average assembly-interface-face-identification accuracy of the proposed approach can reach 91.75%,which is about 2%–5%higher than those of the recent-representative graph neural networks.Besides,compared with the state-of-the-art methods,our approach is more suitable to identify the assembly interfaces(with various shapes)for each individual CAD model that has typical kinematic pairs.
基金Supported by the National Natural Science Foundation of China(71301158)the Humanities and Social Sciences Foundation of the Ministry of Education of China(13YJCZH134)the Project Founded by Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘A common feature of previous studies about the application of data envelopment analysis(DEA) to determine environmental and economic efficiencies is that the two were analyzed in separate models or frameworks. The purpose of this paper is to analyze the economic efficiency and environmental efficiency with a single model. This paper proposes an integrated DEA model, based on a modification of the directional distance function, which allows us to decompose the eco-efficiency(EE) into the economic efficiency(ECE) and environmental efficiency(ENE). The ECE characterizes the ability of gaining economic benefits while the ENE characterizes the ability to control pollutant emissions in production activities. Identification of ECE and ENE can help decision makers of different regions detect what kind of factor(economic inefficiency or environmental inefficiency) is the main source of eco-inefficiency. This can help decision makers more targeted to improve EE. To illustrate the feasibility of our approach, a case study of 30 regions in China is presented. The empirical results show that almost all regions have very high economic efficiencies. The environmental inefficiency is the main source of eco-inefficiency. The differences of environmental efficiencies lead to the differences of eco-efficiencies in the east, central and west areas, while the economic efficiencies do not have significant differences among these areas. The economic efficiencies showed an opposite "V" shape and the environmental efficiencies showed a decreasing trend during the period 2010–2014.
