Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ...Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.展开更多
In this paper, exact static conditions at the corner points for the bending of thickrectangular ptates are strictly. derived from the theorem of minimum potentialenerg[1].
This paper discusses by energy theorem the methodof approximate computation for the lowest eigenfrequencies of rechmguhir plates,on which there are symmetrical concentrated masses,supported at corner points,In the cas...This paper discusses by energy theorem the methodof approximate computation for the lowest eigenfrequencies of rechmguhir plates,on which there are symmetrical concentrated masses,supported at corner points,In the case of seseral concentrated masses,by using the prineiple of superposition we mayfiml the reduneed coefficients of masses comveniently.llence we can louain the lowest eigenfrequencies of thin plates.In the paper a good mamy mmerical caleuhting eximples are inustrated展开更多
This paper reviews, implements and compares two corner detection algorithms for mining corner points on the generic shapes. These corner detectors detect corners by using combination of one rectangle (R) and two ellip...This paper reviews, implements and compares two corner detection algorithms for mining corner points on the generic shapes. These corner detectors detect corners by using combination of one rectangle (R) and two ellipses (EE). These algorithms have been used with different combinations: REE and EER together with different parameter settings in their descriptions. REE and EER combinations slide along the boundary of the shape and record number of boundary points in each rectangle and ellipses. REE and EER setup represent both local and global views of the image outlines and present natural corner detection methodologies to detect and mine all true corners accurately. A comparative study demonstrates the superiority of the REE and EER over some of the existing algorithms.展开更多
This paper designs and implements a corner detection algorithm for mining corner points on the generic shapes. The proposed corner detector detects corners by using combination of one rectangle and two ellipses (REE) ...This paper designs and implements a corner detection algorithm for mining corner points on the generic shapes. The proposed corner detector detects corners by using combination of one rectangle and two ellipses (REE) with different parameter settings in their descriptions. REE combination slides along the boundary of the shape and records number of boundary points in each rectangle and ellipses. REE setup represents both local and global views of the image outline. The proposed technique presents a natural corners detection methodology to detect all true corners accurately. This technique is consistent with human vision system.展开更多
基金financially supported by the National Key Research and Development Program(Grant No.2022YFE0107000)the General Projects of the National Natural Science Foundation of China(Grant No.52171259)the High-Tech Ship Research Project of the Ministry of Industry and Information Technology(Grant No.[2021]342)。
文摘Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.
文摘In this paper, exact static conditions at the corner points for the bending of thickrectangular ptates are strictly. derived from the theorem of minimum potentialenerg[1].
文摘This paper discusses by energy theorem the methodof approximate computation for the lowest eigenfrequencies of rechmguhir plates,on which there are symmetrical concentrated masses,supported at corner points,In the case of seseral concentrated masses,by using the prineiple of superposition we mayfiml the reduneed coefficients of masses comveniently.llence we can louain the lowest eigenfrequencies of thin plates.In the paper a good mamy mmerical caleuhting eximples are inustrated
文摘This paper reviews, implements and compares two corner detection algorithms for mining corner points on the generic shapes. These corner detectors detect corners by using combination of one rectangle (R) and two ellipses (EE). These algorithms have been used with different combinations: REE and EER together with different parameter settings in their descriptions. REE and EER combinations slide along the boundary of the shape and record number of boundary points in each rectangle and ellipses. REE and EER setup represent both local and global views of the image outlines and present natural corner detection methodologies to detect and mine all true corners accurately. A comparative study demonstrates the superiority of the REE and EER over some of the existing algorithms.
文摘This paper designs and implements a corner detection algorithm for mining corner points on the generic shapes. The proposed corner detector detects corners by using combination of one rectangle and two ellipses (REE) with different parameter settings in their descriptions. REE combination slides along the boundary of the shape and records number of boundary points in each rectangle and ellipses. REE setup represents both local and global views of the image outline. The proposed technique presents a natural corners detection methodology to detect all true corners accurately. This technique is consistent with human vision system.
