肥胖症及减重后不能维持健康体质量的核心因素多为食物成瘾,食物成瘾在神经影像学中表现为奖赏网络与认知控制网络间神经环路的失衡。实时功能磁共振成像神经反馈(real time functional magnetic resonance imaging neurofeedback,rtfMR...肥胖症及减重后不能维持健康体质量的核心因素多为食物成瘾,食物成瘾在神经影像学中表现为奖赏网络与认知控制网络间神经环路的失衡。实时功能磁共振成像神经反馈(real time functional magnetic resonance imaging neurofeedback,rtfMRI-NF)作为一种新型生物反馈技术,已被应用于其他物质成瘾领域的临床研究和治疗中。在食物成瘾肥胖症中,rtfMRI-NF同样具有重塑异常脑功能、改善摄食行为并达到减重效果的潜力。本综述总结了肥胖患者食物成瘾的功能磁共振脑成像模型,探讨应用rtfMRI-NF作为其潜在治疗工具的可行神经靶点,并回顾了rtfMRI-NF在肥胖应用中的最新研究进展,为未来rtfMRI-NF在肥胖中的治疗策略和临床指导提供参考。展开更多
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
文摘肥胖症及减重后不能维持健康体质量的核心因素多为食物成瘾,食物成瘾在神经影像学中表现为奖赏网络与认知控制网络间神经环路的失衡。实时功能磁共振成像神经反馈(real time functional magnetic resonance imaging neurofeedback,rtfMRI-NF)作为一种新型生物反馈技术,已被应用于其他物质成瘾领域的临床研究和治疗中。在食物成瘾肥胖症中,rtfMRI-NF同样具有重塑异常脑功能、改善摄食行为并达到减重效果的潜力。本综述总结了肥胖患者食物成瘾的功能磁共振脑成像模型,探讨应用rtfMRI-NF作为其潜在治疗工具的可行神经靶点,并回顾了rtfMRI-NF在肥胖应用中的最新研究进展,为未来rtfMRI-NF在肥胖中的治疗策略和临床指导提供参考。
基金supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
基金supported in part by the Shanghai Aerospace Science and Technology Innovation Foundation(No.SAST 2021-026)the Fund of Prospec⁃tive Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics(NUAA).
文摘随着空间技术的飞速发展,空间态势感知能力需求不断增加。与传统光学传感器相比,逆合成孔径雷达(Inverse synthetic aperture radar,ISAR)具有全天候、远距离高分辨率成像的能力,且成像不受光照条件的影响。此外,空间态势感知系统需要对周围航天器进行准确的评估,因此对空间目标部件识别能力的需求日益迫切。本文提出了一种基于YOLOv5结构的Multitask⁃YOLO网络,用于卫星ISAR图像中卫星帆板的识别和分割。首先,本文添加了分割解耦头来实现网络的分割功能。然后用空间金字塔池快速算法(Spatial pyramid pooling fast,SPPF)和距离交并比算法(Distance intersection over union,DIoU)代替原有结构,避免图像失真,加快收敛速度。通过在通道中引入注意机制,提高了分割和识别的准确性。最后使用模拟卫星的ISAR图像进行实验。结果表明,所提出的Multitask⁃YOLO网络高效、准确地实现了部件的识别和分割。与其他的识别和分割网络相比,该网络的平均精度(mean Average precision,mAP)和平均交并比(mean Intersection over union,mIoU)提高了约5%。此外,该网络的运行速度高达16.4 GFLOP,优于传统的多任务网络的性能。
