This paper provides evidence for the relationship between various forms of real options in infrastructure projects and the types and levels of government supports to the infrastructure investments. It analyzes the com...This paper provides evidence for the relationship between various forms of real options in infrastructure projects and the types and levels of government supports to the infrastructure investments. It analyzes the common real options and real options-based strategic investments and aligns them with the common types of public-private partnership （PPP） infrastructure projects. It then develops models to show that the real options incorporated into the different types of PPP infrastructure projects affect the level of direct government cash supports to the projects and hence the viabilities of such projects. The paper however shows that the relationship between the embedded real options and viabilities of infrastructure projects can be influenced by such factors as contract period and percentage of private sector contributions to the projects.展开更多
Context-aware facial recognition regards the recognition of faces in association with their respective environments.This concept is useful for the domestic robot which interacts with humans when performing specific fu...Context-aware facial recognition regards the recognition of faces in association with their respective environments.This concept is useful for the domestic robot which interacts with humans when performing specific functions in indoor environments.Deep learning models have been relevant in solving facial and place recognition challenges;however,they require the procurement of training images for optimal performance.Pre-trained models have also been offered to reduce training time significantly.Regardless,for classification tasks,custom data must be acquired to ensure that learning models are developed from other pre-trained models.This paper proposes a place recognition model that is inspired by the graph cut energy function,which is specifically designed for image segmentation.Common objects in the considered environment are identified and thereafter they are passed over to a graph cut inspired model for indoor environment classification.Additionally,faces in the considered environment are extracted and recognised.Finally,the developed model can recognise a face together with its environment.The strength of the proposed model lies in its ability to classify indoor environments without the usual training process(es).This approach differs from what is obtained in traditional deep learning models.The classification capability of the developed model was compared to state-of-theart models and exhibited promising outcomes.展开更多
文摘This paper provides evidence for the relationship between various forms of real options in infrastructure projects and the types and levels of government supports to the infrastructure investments. It analyzes the common real options and real options-based strategic investments and aligns them with the common types of public-private partnership （PPP） infrastructure projects. It then develops models to show that the real options incorporated into the different types of PPP infrastructure projects affect the level of direct government cash supports to the projects and hence the viabilities of such projects. The paper however shows that the relationship between the embedded real options and viabilities of infrastructure projects can be influenced by such factors as contract period and percentage of private sector contributions to the projects.
文摘Context-aware facial recognition regards the recognition of faces in association with their respective environments.This concept is useful for the domestic robot which interacts with humans when performing specific functions in indoor environments.Deep learning models have been relevant in solving facial and place recognition challenges;however,they require the procurement of training images for optimal performance.Pre-trained models have also been offered to reduce training time significantly.Regardless,for classification tasks,custom data must be acquired to ensure that learning models are developed from other pre-trained models.This paper proposes a place recognition model that is inspired by the graph cut energy function,which is specifically designed for image segmentation.Common objects in the considered environment are identified and thereafter they are passed over to a graph cut inspired model for indoor environment classification.Additionally,faces in the considered environment are extracted and recognised.Finally,the developed model can recognise a face together with its environment.The strength of the proposed model lies in its ability to classify indoor environments without the usual training process(es).This approach differs from what is obtained in traditional deep learning models.The classification capability of the developed model was compared to state-of-theart models and exhibited promising outcomes.
