According to the well-known models for rubberlike elasticity with strain- stii^ening effects, the unbounded strain energy is generated with the unlimitedly growing stress when the stretch approaches certain limits. To...According to the well-known models for rubberlike elasticity with strain- stii^ening effects, the unbounded strain energy is generated with the unlimitedly growing stress when the stretch approaches certain limits. Toward a solution to this issue, an explicit approach is proposed to derive the multi-axial elastic potentials directly from the uniaxial potentials. Then, a new multi-axial potential is presented to characterize the strain-stiffening effect by prescribing suitable forms of uniaxia] potentials so that the strain energy is always bounded as the stress grows to infinity. Numerical examples show good agreement with a number of test data.展开更多
This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instant...This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore, the effects of the sampling period on the tracking performance are analysed. It turns out that from the view point of the sampling period, there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.展开更多
遥感图像中目标尺度变化大、目标密集分布、相似地物易混淆、背景复杂干扰多以及图像细节不足,现有的旋转目标检测算法通常存在较高的计算负担,并且在精度上仍有提升的空间。针对上述问题,本研究改进当前领先的YOLOv9检测器,开发了一种...遥感图像中目标尺度变化大、目标密集分布、相似地物易混淆、背景复杂干扰多以及图像细节不足,现有的旋转目标检测算法通常存在较高的计算负担,并且在精度上仍有提升的空间。针对上述问题,本研究改进当前领先的YOLOv9检测器,开发了一种高效而准确的遥感图像旋转目标检测器RSO-YOLO(YOLO for Remote Sensing Images with Oriented Bounding Box)。首先,利用一种低照度遥感图像辅助数据增强模块,用于改善弱光、噪点、模糊和对比度不足等问题;其次,设计了一个解耦的角度预测头,使算法拥有对遥感目标方向的感知能力;其次,在模型中引入基于卡尔曼滤波的交并比KFIoU(Kalman Filter Intersection over Union)损失,以解决旋转目标表示引起的角度周期性问题,使用分布焦点损失DFL(Distribution Focal Loss)学习旋转边界框的分布,减少高斯建模方法中近正方形目标的角度不准确问题;再次,创建一种面向旋转目标检测的动态标签分配策略,在分配过程中综合考虑了交并比(IOU)与类别得分(Scores),从而构建更好的能够反映目标特性的样本空间;最后,使用基于海林格距离的概率交并比(ProbIoU)进行非极大值抑制,减少非极大值抑制的计算负担。将本研究提出的遥感图像旋转目标检测器在DIOR-R公开数据集上进行实验验证,与多个典型的旋转目标检测方法进行了比较,结果表明,本研究提出RSO-YOLO方法综合检测精度达到81.1%平均精确率mAP(mean Average Precision),位居第一,且能够保证检测的实时性。此外,使用辅助数据增强模块后可提高1.5%mAP。综上,本研究提出的RSO-YOLO能够同时兼顾旋转目标检测的速度和准确性,对海事与机场监测、城市管理、灾害评估、农林巡检等遥感场景具有工程落地价值与应用潜力,亦为后续面向低照度与复杂背景条件的旋转检测研究提供可复用的模块化方案。。展开更多
In this paper,we studied the approximate sampleddata observer design for a class of stochastic nonlinear systems.Euler-Maruyama approximation was investigated in this paper because it is the basis of other higher prec...In this paper,we studied the approximate sampleddata observer design for a class of stochastic nonlinear systems.Euler-Maruyama approximation was investigated in this paper because it is the basis of other higher precision numerical methods,and it preserves important structures of the nonlinear systems.Also,the form of Euler-Maruyama model is simple and easy to be calculated.The results provide a reference for sampled-data observer design method for such stochastic nonlinear systems,and may be useful to many practical control applications,such as tracking control in mechanical systems.And the effectiveness of the approach is demonstrated by a simulation example.展开更多
The strict bounded real lemma for linear system with finite discrete jumps was considered. Especially, the case where D matrices in the system are not assumed to be zero was dealt. Several versions of the bounded real...The strict bounded real lemma for linear system with finite discrete jumps was considered. Especially, the case where D matrices in the system are not assumed to be zero was dealt. Several versions of the bounded real lemma are presented in terms of solution to Riccati differential equations or inequalities with finite discrete jumps. Both the finite and infinite horizon cases are considered. These results generalize the existed bounded real lemma for linear systems.展开更多
Classification,using the decision tree algorithm,is a widely studied problem in data streams.The challenge is when to split a decision node into multiple leaves.Concentration inequalities,that exploit variance informa...Classification,using the decision tree algorithm,is a widely studied problem in data streams.The challenge is when to split a decision node into multiple leaves.Concentration inequalities,that exploit variance information such as Bernstein's and Bennett's inequalities,are often substantially strict as compared with Hoeffding's bound which disregards variance.Many machine learning algorithms for stream classification such as very fast decision tree(VFDT) learner,AdaBoost and support vector machines(SVMs),use the Hoeffding's bound as a performance guarantee.In this paper,we propose a new algorithm based on the recently proposed empirical Bernstein's bound to achieve a better probabilistic bound on the accuracy of the decision tree.Experimental results on four synthetic and two real world data sets demonstrate the performance gain of our proposed technique.展开更多
In this article, floating quantization effects on multirate sampled-data control systems are studied. It shows that the solutions of multirate digital feedback control systems with nonlinear plant and with floating qu...In this article, floating quantization effects on multirate sampled-data control systems are studied. It shows that the solutions of multirate digital feedback control systems with nonlinear plant and with floating quantization in the controller are uniformly ultimately bounded if the associated linear systems consisting of linearization of the plant and controller with no quantization are Schur stable. Moreover, it also shows that the difference between the response of multirate digital controllers without quantizers and the same plant with floating quantization in the controllers can be made as small as desired by selecting proper quantization level.展开更多
随着全球定位系统的发展和应用,巨量的轨迹数据被实时收集,给数据的传输、存储和分析带来挑战.基于分段线性近似(piecewise linear approximation,PLA)的数据压缩技术因具有简单直观、压缩存储低和传输快的特点被广泛应用和研究.针对现...随着全球定位系统的发展和应用,巨量的轨迹数据被实时收集,给数据的传输、存储和分析带来挑战.基于分段线性近似(piecewise linear approximation,PLA)的数据压缩技术因具有简单直观、压缩存储低和传输快的特点被广泛应用和研究.针对现有轨迹PLA压缩方法不能最优化地在线压缩多维数据的现状,在最大误差限定(maximum error bound,记为L_(∞))下提出多维轨迹数据的最优化PLA压缩问题(记为m DisPLA_(∞)),并给出一种在线MDisPLA算法予以解决.该算法利用“分治-融合”的策略扩展一维最优化PLA算法,以最优化地压缩多维轨迹数据.MDisPLA算法具有线性时间复杂性,可以生成最少的不连续分割,且可以保证生成直线表示的质量,即原始数据点和对应解压缩点之间的同步误差具有上界.通过与基于同步距离锥交(cone intersection using the synchronous Euclidean distance,CISED)的轨迹压缩算法进行理论和实验比较,验证了MDisPLA算法是稳健的,可生成具有保质性的直线表示.MDisPLA算法以更低的内存消耗,较CISED算法提高了14倍左右的处理速度,降低了约48%的分割个数和10.5%的存储个数.MDisPLA算法在保证压缩质量的同时,显著提高了处理速度和降低了存储空间,整体上优于CISED算法.展开更多
基金supported by the National Natural Science Foundation of China(No.11372172)the Start-up Fund from the 211-Project of the Education Committee of China(No.S.15-B002-09-032)the Research Innovation Fund of Shanghai University(No.S.10-0401-12-001)
文摘According to the well-known models for rubberlike elasticity with strain- stii^ening effects, the unbounded strain energy is generated with the unlimitedly growing stress when the stretch approaches certain limits. Toward a solution to this issue, an explicit approach is proposed to derive the multi-axial elastic potentials directly from the uniaxial potentials. Then, a new multi-axial potential is presented to characterize the strain-stiffening effect by prescribing suitable forms of uniaxia] potentials so that the strain energy is always bounded as the stress grows to infinity. Numerical examples show good agreement with a number of test data.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61203147,60973095,60804013,and 61104092)the Fundamental Research Funds for the Central Universities,China(Grant Nos.JUSRP111A44,JUSRP21011, and JUSRP11233)+1 种基金the Foundation of State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology(HUST),China(Grant No.DMETKF2010008)the Humanities and Social Sciences Youth Funds of the Ministry of Education,China(Grant No.12YJCZH218)
文摘This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore, the effects of the sampling period on the tracking performance are analysed. It turns out that from the view point of the sampling period, there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.
文摘遥感图像中目标尺度变化大、目标密集分布、相似地物易混淆、背景复杂干扰多以及图像细节不足,现有的旋转目标检测算法通常存在较高的计算负担,并且在精度上仍有提升的空间。针对上述问题,本研究改进当前领先的YOLOv9检测器,开发了一种高效而准确的遥感图像旋转目标检测器RSO-YOLO(YOLO for Remote Sensing Images with Oriented Bounding Box)。首先,利用一种低照度遥感图像辅助数据增强模块,用于改善弱光、噪点、模糊和对比度不足等问题;其次,设计了一个解耦的角度预测头,使算法拥有对遥感目标方向的感知能力;其次,在模型中引入基于卡尔曼滤波的交并比KFIoU(Kalman Filter Intersection over Union)损失,以解决旋转目标表示引起的角度周期性问题,使用分布焦点损失DFL(Distribution Focal Loss)学习旋转边界框的分布,减少高斯建模方法中近正方形目标的角度不准确问题;再次,创建一种面向旋转目标检测的动态标签分配策略,在分配过程中综合考虑了交并比(IOU)与类别得分(Scores),从而构建更好的能够反映目标特性的样本空间;最后,使用基于海林格距离的概率交并比(ProbIoU)进行非极大值抑制,减少非极大值抑制的计算负担。将本研究提出的遥感图像旋转目标检测器在DIOR-R公开数据集上进行实验验证,与多个典型的旋转目标检测方法进行了比较,结果表明,本研究提出RSO-YOLO方法综合检测精度达到81.1%平均精确率mAP(mean Average Precision),位居第一,且能够保证检测的实时性。此外,使用辅助数据增强模块后可提高1.5%mAP。综上,本研究提出的RSO-YOLO能够同时兼顾旋转目标检测的速度和准确性,对海事与机场监测、城市管理、灾害评估、农林巡检等遥感场景具有工程落地价值与应用潜力,亦为后续面向低照度与复杂背景条件的旋转检测研究提供可复用的模块化方案。。
基金supported by the National High Technology Research and Development Program of China(863 Program)(2014AA06A503)the National Natural Science Foundation of China(61422307,61673361)+3 种基金the Scientific Research Starting Foundation for the Returned Overseas Chinese Scholars and Ministry of Education of Chinasupports from the Youth Top-notch Talent Support Programthe 1000-talent Youth Programthe Youth Yangtze River Scholarship
文摘In this paper,we studied the approximate sampleddata observer design for a class of stochastic nonlinear systems.Euler-Maruyama approximation was investigated in this paper because it is the basis of other higher precision numerical methods,and it preserves important structures of the nonlinear systems.Also,the form of Euler-Maruyama model is simple and easy to be calculated.The results provide a reference for sampled-data observer design method for such stochastic nonlinear systems,and may be useful to many practical control applications,such as tracking control in mechanical systems.And the effectiveness of the approach is demonstrated by a simulation example.
基金National Natural Science Foundation of China(No.60274058)
文摘The strict bounded real lemma for linear system with finite discrete jumps was considered. Especially, the case where D matrices in the system are not assumed to be zero was dealt. Several versions of the bounded real lemma are presented in terms of solution to Riccati differential equations or inequalities with finite discrete jumps. Both the finite and infinite horizon cases are considered. These results generalize the existed bounded real lemma for linear systems.
基金Supported by State Key Program of National Natural Science Foundation of China （60934009） National Natural Science Foundations of China （60801048 60974062）
基金the National Natural Science Foundation of China(Nos.60873108,61175047 and 61152001)the Fundamental Research Funds for the Central Universities of China(No.SWJTU11ZT08)
文摘Classification,using the decision tree algorithm,is a widely studied problem in data streams.The challenge is when to split a decision node into multiple leaves.Concentration inequalities,that exploit variance information such as Bernstein's and Bennett's inequalities,are often substantially strict as compared with Hoeffding's bound which disregards variance.Many machine learning algorithms for stream classification such as very fast decision tree(VFDT) learner,AdaBoost and support vector machines(SVMs),use the Hoeffding's bound as a performance guarantee.In this paper,we propose a new algorithm based on the recently proposed empirical Bernstein's bound to achieve a better probabilistic bound on the accuracy of the decision tree.Experimental results on four synthetic and two real world data sets demonstrate the performance gain of our proposed technique.
基金Supported by National Natural Science Foundation of China(10671069) the program of Shanghai Priority Academic Discipline
文摘In this article, floating quantization effects on multirate sampled-data control systems are studied. It shows that the solutions of multirate digital feedback control systems with nonlinear plant and with floating quantization in the controller are uniformly ultimately bounded if the associated linear systems consisting of linearization of the plant and controller with no quantization are Schur stable. Moreover, it also shows that the difference between the response of multirate digital controllers without quantizers and the same plant with floating quantization in the controllers can be made as small as desired by selecting proper quantization level.
文摘随着全球定位系统的发展和应用,巨量的轨迹数据被实时收集,给数据的传输、存储和分析带来挑战.基于分段线性近似(piecewise linear approximation,PLA)的数据压缩技术因具有简单直观、压缩存储低和传输快的特点被广泛应用和研究.针对现有轨迹PLA压缩方法不能最优化地在线压缩多维数据的现状,在最大误差限定(maximum error bound,记为L_(∞))下提出多维轨迹数据的最优化PLA压缩问题(记为m DisPLA_(∞)),并给出一种在线MDisPLA算法予以解决.该算法利用“分治-融合”的策略扩展一维最优化PLA算法,以最优化地压缩多维轨迹数据.MDisPLA算法具有线性时间复杂性,可以生成最少的不连续分割,且可以保证生成直线表示的质量,即原始数据点和对应解压缩点之间的同步误差具有上界.通过与基于同步距离锥交(cone intersection using the synchronous Euclidean distance,CISED)的轨迹压缩算法进行理论和实验比较,验证了MDisPLA算法是稳健的,可生成具有保质性的直线表示.MDisPLA算法以更低的内存消耗,较CISED算法提高了14倍左右的处理速度,降低了约48%的分割个数和10.5%的存储个数.MDisPLA算法在保证压缩质量的同时,显著提高了处理速度和降低了存储空间,整体上优于CISED算法.
