Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster...Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.展开更多
In the complex orchard environment,precise picking point localization is crucial for the automation of fruit picking robots.However,existing methods are prone to positioning errors when dealing with complex scenarios ...In the complex orchard environment,precise picking point localization is crucial for the automation of fruit picking robots.However,existing methods are prone to positioning errors when dealing with complex scenarios such as short peduncles,partial occlusion,or complete misidentification,which can affect the actual work efficiency of the fruit picking robot.This study proposes an enhanced picking point localization method based on semantic reasoning for complex picking scenarios in vineyard.It innovatively designs three modules:the semantic reasoning module(SRM),the ROI threshold adjustment strategy(RTAS),and the picking point location optimization module(PPOM).The SRM is applied to handle the scenarios of grape peduncles being obstructed by obstacles,partial misidentification of peduncles,and complete misidentification of peduncles.The RTAS addresses the issue of low and short peduncles during the picking process.Finally,the PPOM optimizes the final position of the picking point,allowing the robotic arm to perform the picking operation with greater flexibility.Experimental results show that SegFormer achieves an mIoU(mean Intersection over Union)of 84.54%,with B_IoU and P_IoU reaching 73.90%and 75.63%,respectively.Additionally,the success rate of the improved fruit picking point localization algorithm reached 94.96%,surpassing the baseline algorithm by 8.12%.The algorithm's average processing time is 0.5428±0.0063 s,meeting the practical requirements for real-time picking.展开更多
This paper describes the core of an equipment on grid of things. The framework supports sensor, wire management framework less network and intellig offering general functionality based ＂ent control system based on se...This paper describes the core of an equipment on grid of things. The framework supports sensor, wire management framework less network and intellig offering general functionality based ＂ent control system based on services and service features. The framework is more generic than traditional hierarchical equipment management frame- work. It supports sensor, actuator and wireless networks. In the framework, techniques of spontaneous service provi- sion, grid, scene-aware, semantic reasoning, and equipment service management are smoothly synthesized. It ena- bles equipments in grid of things to interact automatically, provides semantic-integration equipment status aware- hess, and supports meta-service management based adaptability and scalability展开更多
基金supported by the National Key Research and Development Program of China(2020YFC1512304).
文摘Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.
基金supported by the National Natural Science Foundation of China,China(32171909,52205254,32301704)the Guangdong Basic and Applied Basic Research Foundation,China(2023A1515011255,2020B1515120050,2024A1515010199)+1 种基金the Key Research Projects of Ordinary Universities in Guangdong Province,China(2024ZDZX1042,2024ZDZX3057)the Guangdong key R&D projects,China(202080404030001).
文摘In the complex orchard environment,precise picking point localization is crucial for the automation of fruit picking robots.However,existing methods are prone to positioning errors when dealing with complex scenarios such as short peduncles,partial occlusion,or complete misidentification,which can affect the actual work efficiency of the fruit picking robot.This study proposes an enhanced picking point localization method based on semantic reasoning for complex picking scenarios in vineyard.It innovatively designs three modules:the semantic reasoning module(SRM),the ROI threshold adjustment strategy(RTAS),and the picking point location optimization module(PPOM).The SRM is applied to handle the scenarios of grape peduncles being obstructed by obstacles,partial misidentification of peduncles,and complete misidentification of peduncles.The RTAS addresses the issue of low and short peduncles during the picking process.Finally,the PPOM optimizes the final position of the picking point,allowing the robotic arm to perform the picking operation with greater flexibility.Experimental results show that SegFormer achieves an mIoU(mean Intersection over Union)of 84.54%,with B_IoU and P_IoU reaching 73.90%and 75.63%,respectively.Additionally,the success rate of the improved fruit picking point localization algorithm reached 94.96%,surpassing the baseline algorithm by 8.12%.The algorithm's average processing time is 0.5428±0.0063 s,meeting the practical requirements for real-time picking.
基金supported by the Northwestern Polytechnical University Basic Research Funds Program under Grant No. JC201121
文摘This paper describes the core of an equipment on grid of things. The framework supports sensor, wire management framework less network and intellig offering general functionality based ＂ent control system based on services and service features. The framework is more generic than traditional hierarchical equipment management frame- work. It supports sensor, actuator and wireless networks. In the framework, techniques of spontaneous service provi- sion, grid, scene-aware, semantic reasoning, and equipment service management are smoothly synthesized. It ena- bles equipments in grid of things to interact automatically, provides semantic-integration equipment status aware- hess, and supports meta-service management based adaptability and scalability
