Let α≥ 0 and 0 〈 ρ ≤ n/2, the boundedness of hypersingular parameterized Marcinkiewicz integrals μΩ,α^ρ with variable kernels on Sobolev spaces Lα^ρ and HardySobolev spaces Hα^ρ is established.
In this paper,the parameterized Marcinkiewicz integrals with variable kernels defined by μΩ^ρ（f）（x）=（∫0^∞│∫│1-y│≤t Ω（x,x-y）/│x-y│^n-p f（y）dy│^2dt/t1＋2p）^1/2 are investigated.It is proved that ...In this paper,the parameterized Marcinkiewicz integrals with variable kernels defined by μΩ^ρ（f）（x）=（∫0^∞│∫│1-y│≤t Ω（x,x-y）/│x-y│^n-p f（y）dy│^2dt/t1＋2p）^1/2 are investigated.It is proved that if Ω∈ L∞（R^n） × L^r（S^n-1）（r〉（n-n1p＇/n） is an odd function in the second variable y,then the operator μΩ^ρ is bounded from L^p（R^n） to L^p（R^n） for 1 〈 p ≤ max{（n＋1）/2,2}.It is also proved that,if Ω satisfies the L^1-Dini condition,then μΩ^ρ is of type（p,p） for 1 〈 p ≤ 2,of the weak type（1,1） and bounded from H1 to L1.展开更多
针对废墟环境下红外图像人体检测任务中存在的图像分辨率低且人体特征不明显的问题,基于YOLO框架设计了一种包含重参数化(re-parameterization)和多尺度大核卷积(multi-scale large kernel convolution)的红外图像人体检测网络RML-YOLO(...针对废墟环境下红外图像人体检测任务中存在的图像分辨率低且人体特征不明显的问题,基于YOLO框架设计了一种包含重参数化(re-parameterization)和多尺度大核卷积(multi-scale large kernel convolution)的红外图像人体检测网络RML-YOLO(re-parameterization multi-scale large kernel convolution)。该网络通过空间和通道重构注意力模块,将注意值集中到对检测任务更重要的区域。通过Sobel算子强化边缘特征,提高对不同姿态人体的检测能力。RML-YOLO的有效性在自制数据集上得到验证。在只有1.8×10~6可学习参数的情况下,模型的AP50和AP50-75分别达到了91.2%和87.3%,与参数量相近的YOLOv8-n相比分别提高了4.4%和5.3%。结果表明,RML-YOLO显著提高了利用红外图像进行废墟环境下人体检测的精度。展开更多
Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal ke...Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.展开更多
We assessed chemical composition and variation in oil content and seed weight of 40 wild-growing almonds（Prunus L. spp.） accessions collected from different parts of Iran. There were significant differences in kerne...We assessed chemical composition and variation in oil content and seed weight of 40 wild-growing almonds（Prunus L. spp.） accessions collected from different parts of Iran. There were significant differences in kernel weight and oil parameters. Accessions ranged from0.20 to 1.5 g in kernel weight, 0.2–3.0 mm in shell thickness, and 16–55 % in oil content. The predominant vegetable oil components of kernels were 4.6–9.5 % palmitic acid, 0.4–0.8 % palmitoleic acid, 1.0–3.4 % stearic acid,48.8–88.4 % oleic acid and 11.3–33.2 % linoleic acid.Linolenic acid was detected in 15 accessions. High heritability was recorded for all studied traits and was maximum for shell thickness（98.5 %） and minimum for oil content（97.1 %）. Maximum and minimum ‘Euclidean＇pair wise dissimilarities were 17.9 and 0.5, respectively.All 40 accessions were grouped into two major clusters.展开更多
The support vector machine （SVM） is a novel machine learning tool in data mining. In this paper, the geometric approach based on the compressed convex hull （CCH） with a mathematical framework is introduced to solv...The support vector machine （SVM） is a novel machine learning tool in data mining. In this paper, the geometric approach based on the compressed convex hull （CCH） with a mathematical framework is introduced to solve SVM classification problems. Compared with the reduced convex hull （RCH）, CCH preserves the shape of geometric solids for data sets; meanwhile, it is easy to give the necessary and sufficient condition for determining its extreme points. As practical applications of CCH, spare and probabilistic speed-up geometric algorithms are developed. Results of numerical experiments show that the proposed algorithms can reduce kernel calculations and display nice performances.展开更多
基金Supported by the National Natural Science Foundation of China(1057115610871173)
文摘Let α≥ 0 and 0 〈 ρ ≤ n/2, the boundedness of hypersingular parameterized Marcinkiewicz integrals μΩ,α^ρ with variable kernels on Sobolev spaces Lα^ρ and HardySobolev spaces Hα^ρ is established.
基金Supported by the National Natural Science Foundation of China (1057115610871173)
文摘In this paper,the parameterized Marcinkiewicz integrals with variable kernels defined by μΩ^ρ（f）（x）=（∫0^∞│∫│1-y│≤t Ω（x,x-y）/│x-y│^n-p f（y）dy│^2dt/t1＋2p）^1/2 are investigated.It is proved that if Ω∈ L∞（R^n） × L^r（S^n-1）（r〉（n-n1p＇/n） is an odd function in the second variable y,then the operator μΩ^ρ is bounded from L^p（R^n） to L^p（R^n） for 1 〈 p ≤ max{（n＋1）/2,2}.It is also proved that,if Ω satisfies the L^1-Dini condition,then μΩ^ρ is of type（p,p） for 1 〈 p ≤ 2,of the weak type（1,1） and bounded from H1 to L1.
文摘针对废墟环境下红外图像人体检测任务中存在的图像分辨率低且人体特征不明显的问题,基于YOLO框架设计了一种包含重参数化(re-parameterization)和多尺度大核卷积(multi-scale large kernel convolution)的红外图像人体检测网络RML-YOLO(re-parameterization multi-scale large kernel convolution)。该网络通过空间和通道重构注意力模块,将注意值集中到对检测任务更重要的区域。通过Sobel算子强化边缘特征,提高对不同姿态人体的检测能力。RML-YOLO的有效性在自制数据集上得到验证。在只有1.8×10~6可学习参数的情况下,模型的AP50和AP50-75分别达到了91.2%和87.3%,与参数量相近的YOLOv8-n相比分别提高了4.4%和5.3%。结果表明,RML-YOLO显著提高了利用红外图像进行废墟环境下人体检测的精度。
基金supported by National Natural Science Foundation under Grant No.50875247Shanxi Province Natural Science Foundation under Grant No.2009011026-1
文摘Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.
基金financially supported by Payam-e-Noor University
文摘We assessed chemical composition and variation in oil content and seed weight of 40 wild-growing almonds（Prunus L. spp.） accessions collected from different parts of Iran. There were significant differences in kernel weight and oil parameters. Accessions ranged from0.20 to 1.5 g in kernel weight, 0.2–3.0 mm in shell thickness, and 16–55 % in oil content. The predominant vegetable oil components of kernels were 4.6–9.5 % palmitic acid, 0.4–0.8 % palmitoleic acid, 1.0–3.4 % stearic acid,48.8–88.4 % oleic acid and 11.3–33.2 % linoleic acid.Linolenic acid was detected in 15 accessions. High heritability was recorded for all studied traits and was maximum for shell thickness（98.5 %） and minimum for oil content（97.1 %）. Maximum and minimum ‘Euclidean＇pair wise dissimilarities were 17.9 and 0.5, respectively.All 40 accessions were grouped into two major clusters.
基金Supported by the National Natural Science Foundation of China under Grant Nos.6043302060773111(国家自然科学基金)+1 种基金the Program for New Century Excellent Talents in University of China under Grant No.NCET-05-0683(新世纪优秀人才支持计划)the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant No.IRT0661(长江学者和创新团队发展计划)
基金Supported by the National Natural Science Foundation of China (No. 30571059)the National High-Tech Research and Development Program of China (No. 2006AA02Z190)Shanghai Leading Academic Discipline Project (No. 530405)
文摘The support vector machine （SVM） is a novel machine learning tool in data mining. In this paper, the geometric approach based on the compressed convex hull （CCH） with a mathematical framework is introduced to solve SVM classification problems. Compared with the reduced convex hull （RCH）, CCH preserves the shape of geometric solids for data sets; meanwhile, it is easy to give the necessary and sufficient condition for determining its extreme points. As practical applications of CCH, spare and probabilistic speed-up geometric algorithms are developed. Results of numerical experiments show that the proposed algorithms can reduce kernel calculations and display nice performances.
