The alignment coupling between optical waveguide chips and optical fiber arrays is the basis of the alignment coupling of planar optical waveguide devices, and the precise position detection with angle and spacing adj...The alignment coupling between optical waveguide chips and optical fiber arrays is the basis of the alignment coupling of planar optical waveguide devices, and the precise position detection with angle and spacing adjustments is one of the key steps of alignment coupling. A methodology for position detection, and angle and spacing adjustment was proposed for optical waveguide chips and optical fiber arrays based on machine vision. The experimental results show angle detection precision levels higher than 0.05°, line detection precision levels higher than 0.1 μm, and detection time less than 2 s. Therefore, the system developed herein meets the precise requirements necessary for position detection, and angle and spacing adjustments for optical waveguide chips and optical fiber arrays.展开更多
Pulse-coupled neural network （PCNN） is a novel neural network, which has been widely used in image segmentation. However, there are still some limitations, such as trial-and-error parameter settings and manual selec...Pulse-coupled neural network （PCNN） is a novel neural network, which has been widely used in image segmentation. However, there are still some limitations, such as trial-and-error parameter settings and manual selection of the optimal results. This paper puts forward a new method based on the simplified PCNN model for automatic image segmentation. By calculating the un- iformity measure of the corresponding image at each process of iteration, the optimal segmentation result is obtained when the max- imum value of the uniformity measure is achieved. Experimental results show that the proposed method can automatically achieve better segmentation result and has a common adaptability.展开更多
To meet the challenges in software testing for automated vehicles,such as increasing system complexity and an infinite number of operating scenarios,new simulation methods must be developed.Closed-loop simulations for...To meet the challenges in software testing for automated vehicles,such as increasing system complexity and an infinite number of operating scenarios,new simulation methods must be developed.Closed-loop simulations for automated driving(AD)require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context.For the realization of such models,different simulation domains must be coupled with co-simulation.However,widely supported model integration standards such as functional mock-up interface(FMI)lack native support for distributed platforms,which is a key feature for AD due to the computational intensity and platform exclusivity of certain models.The newer FMI companion standard distributed co-simulation protocol(DCP)introduces platform coupling but must still be used in conjunction with AD co-simulations.As part of an assessment framework for AD,this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform.A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave.A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models.A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP.Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA.The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors.This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability,reuse potential,and performance.展开更多
基金Projects(51475479,51075402)supported by the National Natural Science Foundation of ChinaProject(2012AA040406)supported by the National High Technology Research and Development Program of China+2 种基金Project(20110162130004)supported by the Research Fund for the Doctoral Program of Higher Education of ChinaProject(14JJ2010)supported by the Natural Science Foundation of Hunan Province,ChinaProject(GZKF-201401)supported by the Open Project of Stage Key Laboratory of Fluid Power Transmission and Control(Zhejiang University),China
文摘The alignment coupling between optical waveguide chips and optical fiber arrays is the basis of the alignment coupling of planar optical waveguide devices, and the precise position detection with angle and spacing adjustments is one of the key steps of alignment coupling. A methodology for position detection, and angle and spacing adjustment was proposed for optical waveguide chips and optical fiber arrays based on machine vision. The experimental results show angle detection precision levels higher than 0.05°, line detection precision levels higher than 0.1 μm, and detection time less than 2 s. Therefore, the system developed herein meets the precise requirements necessary for position detection, and angle and spacing adjustments for optical waveguide chips and optical fiber arrays.
文摘Pulse-coupled neural network （PCNN） is a novel neural network, which has been widely used in image segmentation. However, there are still some limitations, such as trial-and-error parameter settings and manual selection of the optimal results. This paper puts forward a new method based on the simplified PCNN model for automatic image segmentation. By calculating the un- iformity measure of the corresponding image at each process of iteration, the optimal segmentation result is obtained when the max- imum value of the uniformity measure is achieved. Experimental results show that the proposed method can automatically achieve better segmentation result and has a common adaptability.
基金Open Access funding enabled and organized by Projekt DEAL.This work was supported in part by the German Ministry of Education and Research(BMBF)under grant 01IS16043.
文摘To meet the challenges in software testing for automated vehicles,such as increasing system complexity and an infinite number of operating scenarios,new simulation methods must be developed.Closed-loop simulations for automated driving(AD)require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context.For the realization of such models,different simulation domains must be coupled with co-simulation.However,widely supported model integration standards such as functional mock-up interface(FMI)lack native support for distributed platforms,which is a key feature for AD due to the computational intensity and platform exclusivity of certain models.The newer FMI companion standard distributed co-simulation protocol(DCP)introduces platform coupling but must still be used in conjunction with AD co-simulations.As part of an assessment framework for AD,this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform.A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave.A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models.A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP.Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA.The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors.This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability,reuse potential,and performance.
文摘目的建立一种简便、快速的鱼、虾等生物样品中重金属元素的分析方法。方法以10.0μg/L的Rh作为内标,采用硝酸-过氧化氢-高氯酸体系,用全自动石墨消解仪消解鱼、虾等生物样品,建立石墨消解-电感耦合等离子体质谱法(inductively coupled plasma mass spectrometry,ICP-MS)同时测定样品中铜、镉、锌、铬4种金属元素的方法。结果各元素的检出限在0.001~0.200μg/L之间,相对标准偏差在0~2.30%之间,加标回收率为在91.4%~113.4%之间。对大虾标准品(GSB-28)进行分析的测定值与标准值吻合良好。结论本研究所建立的方法简单、快速,灵敏度高,重现性好,是同时分析大批生物样品中多种金属元素的可靠、高效方法。
