Intermittent convective transport at the edge and in the scrape-off layer （SOL） of EAST was investigated by using fast reciprocating Langmuir probe. Holes, as part of plasma structures, were detected for the first t...Intermittent convective transport at the edge and in the scrape-off layer （SOL） of EAST was investigated by using fast reciprocating Langmuir probe. Holes, as part of plasma structures, were detected for the first time inside the shear layer. The amplitude probability distribution function of the turbulence is strongly skewed, with positive skewed events （＂blobs＂） prevailing in the SOL region and negative skewed events （＂holes＂） dominant inside the shear layer. The statistical properties coincide with previous observations from JET. The generation mechanism of blobs and holes is also discussed. In addition burst structure and dynamics character of them are also presented.展开更多
Space-based plasma(i.e.,a highly ionized gas or the fourth state of matter)blobs are isolated pockets of this highly ionized gas made up of charged particles.These blobs are believed to have a substantial impact on th...Space-based plasma(i.e.,a highly ionized gas or the fourth state of matter)blobs are isolated pockets of this highly ionized gas made up of charged particles.These blobs are believed to have a substantial impact on the structure and dynamics of the cosmos and can be seen in a variety of astronomical objects,including stars,galaxies,and the intergalactic medium.Some plasma blobs are connected to intense phenomena like magnetic reconnection,shock waves,and supernovae,while others may be the result of more passive processes like cooling and gravitational collapse.In both astrophysics and plasma physics,there is ongoing research on the characteristics and behavior of plasma blobs.This phenomenon has a very adverse effect on tokamak-based MCF(magnetic confinement fusion),which is the subject of this short review paper.展开更多
A gas puff imaging(GPI) diagnostic has been developed and applied to measure edge plasma turbulence on the HL-2A tokamak.The principle and experimental setup of GPI are described.GPI is applied to investigate blobs in...A gas puff imaging(GPI) diagnostic has been developed and applied to measure edge plasma turbulence on the HL-2A tokamak.The principle and experimental setup of GPI are described.GPI is applied to investigate blobs in the edge and scrape-off layer.Statistical characterizations of GPI line emission intensity are calculated, including the probability density functions(PDFs),skewness, and kurtosis of the intensity, which are found to be consistent with measurements by Langmuir probes.Besides, the track of blob motions is recorded by time sequence of individual frames.The characteristics of the original images and the relatively high-frequency(>10 kHz)/low-frequency(1–10 kHz) component images are illustrated.The observation of the blob’s structures and high-speed motions proves the success and high performance of the GPI diagnostic.展开更多
Now a day’s image searching is still a challenging problem in content based image retrieval (CBIR) system. Most system operates on all images without pre-sorting the images. The image search result contains many unre...Now a day’s image searching is still a challenging problem in content based image retrieval (CBIR) system. Most system operates on all images without pre-sorting the images. The image search result contains many unrelated image. The aim of this research is to propose a new method for content based image indexing and research based on blobs feature extraction and existing edges in the image and classification of image to different type and to search image which are similar the given research.展开更多
Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance I...Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance Imaging(MRI)and Computed Tomography(CT)have become essential tools for diagnosing and assessing kidney disorders.However,accurate analysis of thesemedical images is critical for detecting and evaluating tumor severity.This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images.The proposed framework fuses a customized U-Net and Mask R-CNN using a weighted scheme to achieve semantic and instance-level segmentation.The fused outputs are further refined through edge detection using Stochastic FeatureMapping Neural Networks(SFMNN),while volumetric consistency is ensured through Improved Mini-Batch K-Means(IMBKM)clustering integrated with an Encoder-Decoder Convolutional Neural Network(EDCNN).The outputs of these three stages are combined through a weighted fusion mechanism,with optimal weights determined empirically.Experiments on MRI scans from the TCGA-KIRC dataset demonstrate that the proposed hybrid framework significantly outperforms standalone models,achieving a Dice Score of 92.5%,an IoU of 87.8%,a Precision of 93.1%,a Recall of 90.8%,and a Hausdorff Distance of 2.8 mm.These findings validate that the weighted integration of complementary architectures effectively overcomes key limitations in kidney tumor segmentation,leading to improved diagnostic accuracy and robustness in medical image analysis.展开更多
在废旧锂电池模组的自动化拆解过程中,需要快速地对其表面数量众多的各类螺纹紧固件进行精准位姿识别。针对已有特征匹配方法难以适应紧固件周围复杂背景环境及深度学习方法无法实现紧固件中心精确定位与姿态识别的现状,基于轻量化深度...在废旧锂电池模组的自动化拆解过程中,需要快速地对其表面数量众多的各类螺纹紧固件进行精准位姿识别。针对已有特征匹配方法难以适应紧固件周围复杂背景环境及深度学习方法无法实现紧固件中心精确定位与姿态识别的现状,基于轻量化深度学习模型SqueezeNet与紧固件BLOB(Binary Large Object)特征分析,以由粗到精的识别策略将上述两类方法结合,快速实现紧固件的种类判别与精确定位。并在此基础上进一步提出区域相交法用于准确识别各类紧固件的头部姿态角。实验结果表明:所提方法与其他现有识别模型相比,不仅获得了较高的粗定位精度(94.9%),并且紧固件中心精定位误差与头部姿态角误差分别在0.3 mm与3°之内,能够很好地满足机器人拆卸紧固件的应用需求。展开更多
基金supported by National Natural Science Foundation of China(Nos.11075181,10725523,10721505,10990212,10605028)the 973 Programme(No.2010GB104001)
文摘Intermittent convective transport at the edge and in the scrape-off layer （SOL） of EAST was investigated by using fast reciprocating Langmuir probe. Holes, as part of plasma structures, were detected for the first time inside the shear layer. The amplitude probability distribution function of the turbulence is strongly skewed, with positive skewed events （＂blobs＂） prevailing in the SOL region and negative skewed events （＂holes＂） dominant inside the shear layer. The statistical properties coincide with previous observations from JET. The generation mechanism of blobs and holes is also discussed. In addition burst structure and dynamics character of them are also presented.
文摘Space-based plasma(i.e.,a highly ionized gas or the fourth state of matter)blobs are isolated pockets of this highly ionized gas made up of charged particles.These blobs are believed to have a substantial impact on the structure and dynamics of the cosmos and can be seen in a variety of astronomical objects,including stars,galaxies,and the intergalactic medium.Some plasma blobs are connected to intense phenomena like magnetic reconnection,shock waves,and supernovae,while others may be the result of more passive processes like cooling and gravitational collapse.In both astrophysics and plasma physics,there is ongoing research on the characteristics and behavior of plasma blobs.This phenomenon has a very adverse effect on tokamak-based MCF(magnetic confinement fusion),which is the subject of this short review paper.
基金supported by the National Key Research and Development Program of China (No.2017YFE0300405)National Natural Science Foundation of China (Nos.11575055, 11705052, 11875124, 11475058, and 11475056)+1 种基金the National Key Research and Development Program of China (Nos.2017YFE0301201, 2018YFE0303102, 2018YFE0309103)the Chinese National Fusion Project for ITER (No.2015GB104000)
文摘A gas puff imaging(GPI) diagnostic has been developed and applied to measure edge plasma turbulence on the HL-2A tokamak.The principle and experimental setup of GPI are described.GPI is applied to investigate blobs in the edge and scrape-off layer.Statistical characterizations of GPI line emission intensity are calculated, including the probability density functions(PDFs),skewness, and kurtosis of the intensity, which are found to be consistent with measurements by Langmuir probes.Besides, the track of blob motions is recorded by time sequence of individual frames.The characteristics of the original images and the relatively high-frequency(>10 kHz)/low-frequency(1–10 kHz) component images are illustrated.The observation of the blob’s structures and high-speed motions proves the success and high performance of the GPI diagnostic.
文摘Now a day’s image searching is still a challenging problem in content based image retrieval (CBIR) system. Most system operates on all images without pre-sorting the images. The image search result contains many unrelated image. The aim of this research is to propose a new method for content based image indexing and research based on blobs feature extraction and existing edges in the image and classification of image to different type and to search image which are similar the given research.
基金funded by the Ongoing Research Funding Program-Research Chairs(ORF-RC-2025-2400),King Saud University,Riyadh,Saudi Arabia。
文摘Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance Imaging(MRI)and Computed Tomography(CT)have become essential tools for diagnosing and assessing kidney disorders.However,accurate analysis of thesemedical images is critical for detecting and evaluating tumor severity.This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images.The proposed framework fuses a customized U-Net and Mask R-CNN using a weighted scheme to achieve semantic and instance-level segmentation.The fused outputs are further refined through edge detection using Stochastic FeatureMapping Neural Networks(SFMNN),while volumetric consistency is ensured through Improved Mini-Batch K-Means(IMBKM)clustering integrated with an Encoder-Decoder Convolutional Neural Network(EDCNN).The outputs of these three stages are combined through a weighted fusion mechanism,with optimal weights determined empirically.Experiments on MRI scans from the TCGA-KIRC dataset demonstrate that the proposed hybrid framework significantly outperforms standalone models,achieving a Dice Score of 92.5%,an IoU of 87.8%,a Precision of 93.1%,a Recall of 90.8%,and a Hausdorff Distance of 2.8 mm.These findings validate that the weighted integration of complementary architectures effectively overcomes key limitations in kidney tumor segmentation,leading to improved diagnostic accuracy and robustness in medical image analysis.
文摘在废旧锂电池模组的自动化拆解过程中,需要快速地对其表面数量众多的各类螺纹紧固件进行精准位姿识别。针对已有特征匹配方法难以适应紧固件周围复杂背景环境及深度学习方法无法实现紧固件中心精确定位与姿态识别的现状,基于轻量化深度学习模型SqueezeNet与紧固件BLOB(Binary Large Object)特征分析,以由粗到精的识别策略将上述两类方法结合,快速实现紧固件的种类判别与精确定位。并在此基础上进一步提出区域相交法用于准确识别各类紧固件的头部姿态角。实验结果表明:所提方法与其他现有识别模型相比,不仅获得了较高的粗定位精度(94.9%),并且紧固件中心精定位误差与头部姿态角误差分别在0.3 mm与3°之内,能够很好地满足机器人拆卸紧固件的应用需求。
