Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and pro...Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and protecting lives and property.However,the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making.This paper proposes an improved artificial potential field(APF)algorithm to optimize the shortest path for ERRVs in the rescue process.To verify the feasibility of the proposed model,eight tests were carried out in two water areas of the Yangtze River.The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds.The improved APF algorithm performed better in the ERRV’s goal achievement,compared with the traditional algorithm.The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief.It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent,mitigate,and relieve flood disasters.展开更多
Accurate localization of cranial nerves and responsible blood vessels is important for diagnosing trigeminal neuralgia(TN)and hemifacial spasm(HFS).Manual delineation of the nerves and vessels on medical images is tim...Accurate localization of cranial nerves and responsible blood vessels is important for diagnosing trigeminal neuralgia(TN)and hemifacial spasm(HFS).Manual delineation of the nerves and vessels on medical images is time-consuming and labor-intensive.Due to the development of convolutional neural networks(CNNs),the performance of medical image segmentation has been improved.In this work,we investigate the plans for automated segmentation of cranial nerves and responsible vessels for TN and HFS,which has not been comprehensively studied before.Different inputs are given to the CNN to find the best training configuration of segmenting trigeminal nerves,facial nerves,responsible vessels and brainstem,including the image modality and the number of segmentation targets.According to multiple experiments with seven training plans,we suggest training with the combination of three-dimensional fast imaging employing steady-state acquisition(3D-FIESTA)and three-dimensional time-of-flight magnetic resonance angiography(3DTOF-MRA),and separate segmentation of cranial nerves and vessels.展开更多
基金The National Natural Science Foundation of China(Grant No.72274052)the National Natural Science Foundation of China(Grant No.72174173).
文摘Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and protecting lives and property.However,the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making.This paper proposes an improved artificial potential field(APF)algorithm to optimize the shortest path for ERRVs in the rescue process.To verify the feasibility of the proposed model,eight tests were carried out in two water areas of the Yangtze River.The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds.The improved APF algorithm performed better in the ERRV’s goal achievement,compared with the traditional algorithm.The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief.It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent,mitigate,and relieve flood disasters.
文摘Accurate localization of cranial nerves and responsible blood vessels is important for diagnosing trigeminal neuralgia(TN)and hemifacial spasm(HFS).Manual delineation of the nerves and vessels on medical images is time-consuming and labor-intensive.Due to the development of convolutional neural networks(CNNs),the performance of medical image segmentation has been improved.In this work,we investigate the plans for automated segmentation of cranial nerves and responsible vessels for TN and HFS,which has not been comprehensively studied before.Different inputs are given to the CNN to find the best training configuration of segmenting trigeminal nerves,facial nerves,responsible vessels and brainstem,including the image modality and the number of segmentation targets.According to multiple experiments with seven training plans,we suggest training with the combination of three-dimensional fast imaging employing steady-state acquisition(3D-FIESTA)and three-dimensional time-of-flight magnetic resonance angiography(3DTOF-MRA),and separate segmentation of cranial nerves and vessels.
