基于结构组稀疏表示的遥感图像融合

目的 稀疏表示在遥感图像融合上取得引人注目的良好效果,但由于经典稀疏表示没有考虑图像块与块之间的相似性,导致求解出的稀疏系数不够准确及字典学习的计算复杂度高。为提高稀疏表示遥感图像融合算法的效果和快速性,提出一种基于结构组稀疏表示的遥感图像融合方法。方法 首先,将相似图像块组成结构组,再通过组稀疏表示算法分别计算亮度分量和全色图像的自适应组字典和组稀疏系数;然后,根据绝对值最大规则进行全色图像稀疏系数的部分替换得到新的稀疏系数,利用全色图像的组字典和新的稀疏系数重构出高空间分辨率亮度图像;最后,应用通用分量替换（GCOS）框架计算融合后的高分辨率多光谱图像。结果 针对3组不同类型遥感图像的全色图像和多光谱图像分别进行了退化和未退化遥感融合实验,实验结果表明：在退化融合实验中,本文方法的相关系数、均方根误差、相对全局融合误差、通用图像质量评价指标和光谱角等评价指标比传统的融合算法更优越,其中相对全局融合误差分别是2.326 1、1.888 5和1.816 8均远低于传统融合算法;在未退化融合实验中,除了在绿色植物融合效果上略差于AWLP（additive wavelet luminance proportional）方法外,其他融合结果仍占有优势。与经典稀疏表示方法相比,由于字典学习的优越性,计算复杂度上要远低于经典稀疏表示的遥感图像融合算法。结论 本文算法更能保持图像的光谱特性和空间信息,适用于不同类型遥感图像的全色图像和多光谱图像融合。     

飞机起落架收放系统建模与故障仿真

针对某型支线客机的起落架收放系统,分析了其主要故障类型和故障原因。应用AMESim仿真平台建立了起落架收放系统模型,对该系统的正常收放过程进行了仿真,得到作动筒行程和液压泵出口压力、流量变化情况,并进行了结果分析,仿真结果满足起落架收放系统的基本性能指标。研究了液压泵泄漏、作动筒泄漏、油滤堵塞、节流阀堵塞等对系统工作特性影响较大的故障模式。仿真并分析了不同部件性能参数变化对系统工作性能的影响,为起落架收放系统故障诊断和健康状态监测在工程中的应用提供了有益的参考。     

基于邻域关系模糊粗糙集的医学图像分类研究

对医学图像进行分类时,特征选择是影响分类准确率的非常重要的因素。针对医学图像的特殊性,以及目前提出的特征选择算法在应用于医学图像分类时效果不够理想等问题,提出一种基于邻域关系的模糊粗糙集模型,基于该模型给出特征选择算法,并将其应用于乳腺X光图像。实验结果表明,同已有的算法相比,该方法能有效选择特征,分类精度有较大的提升。     

Power of the Swipe: Why Mobile Websites Should Add Horizontal Swiping to Tapping,Clicking, and Scrolling Interaction Techniques

Horizontal swiping has become a common interaction technique on smartphones, yet many mobile websites do not incorporate this functionality, instead requiring users to scroll down or click on buttons, arrow keys, and other hotspots. This is perhaps because user studies comparing swiping with other interaction techniques are scarce. An experiment was conducted (N = 252) to inform this issue by investigating whether and how adding the swiping functionality to a tap-only mobile website influences users’ intentions to use the website. The data revealed that the addition of swiping technique positively affected behavioral intentions to use the website. Perceived enjoyment was a key variable that explained this effect. These findings have design implications for mobile websites.     

Multi-label classification via learning a unified object-label graph with sparse representation

Automatic annotation is an essential technique for effectively handling and organizing Web objects (e.g., Web pages), which have experienced an unprecedented growth over the last few years. Automatic annotation is usually formulated as a multi-label classification problem. Unfortunately, labeled data are often time-consuming and expensive to obtain. Web data also accommodate much richer feature space. This calls for new semi-supervised approaches that are less demanding on labeled data to be effective in classification. In this paper, we propose a graph-based semi-supervised learning approach that leverages random walks and ? ₁ sparse reconstruction on a mixed object-label graph with both attribute and structure information for effective multi-label classification. The mixed graph contains an object-affinity subgraph, a label-correlation subgraph, and object-label edges with adaptive weight assignments indicating the assignment relationships. The object-affinity subgraph is constructed using ? ₁ sparse graph reconstruction with extracted structural meta-text, while the label-correlation subgraph captures pairwise correlations among labels via linear combination of their co-occurrence similarity and kernel-based similarity. A random walk with adaptive weight assignment is then performed on the constructed mixed graph to infer probabilistic assignment relationships between labels and objects. Extensive experiments on real Yahoo! Web datasets demonstrate the effectiveness of our approach.     

Tracking Performance Bound with Finite Control Energy and Erasure Channel Energy Constraint

This paper studies the tracking performance of the single‐input single‐output (SISO), finite dimensional, linear and time‐invariant (LTI) system under the channel input energy constraint over the Erasure channel. A new performance index is proposed which is minimized over all two‐degree‐of‐freedom stabilizing controllers. The explicit expressions of the lower bound of the performance index and the minimum of the signal‐to‐noise ratio are obtained. The results show that the performance bound is correlated to unstable poles, non‐minimum phase zeros and the packet loss probability. Finally, examples are given to validate the conclusions derived.     

Adaptive Tracking Control for Nonlinear Systems with a Class of Input Nonlinearities

An adaptive tracking control approach is presented for nonlinear systems with a class of input nonlinearities. A generalized model has been developed for a class of non‐smooth nonlinearities that include dead‐zone, backlash and ‘backlash‐like’ hysteresis. By using the developed model and Nussbaum‐gain technique, the problem of input nonlinearity is solved perfectly. The proposed method is available even when the designer is uncertain about the type of input nonlinearities mentioned above, and the knowledge on the bounds of these nonlinearity parameters is not required. Furthermore, it is proved that all closed‐loop signals are bounded and the tracking error converges to a small residual set asymptotically. Two simulation examples are provided to demonstrate the effectiveness of the proposed method.     

Yang–Baxter equations and quantum entanglements

In this paper some results associated with a new type of Yang–Baxter equation (YBE) are reviewed. The braiding matrix of Kauffman–Lomonaco has been extended to the solution (called type-II) of Yang–Baxter equation (YBE) and the related chain Hamiltonian is given. The Lorentz additivity for spectral parameters is found, rather than the Galilean rule for the familiar solutions (called type-I) of YBE associated with the usually exact solvable models. Based on the topological basis, the N-dimensional solution of YBE is found to be the Wigner D-functions. The explicit examples for spin-\(\frac{1}{2}\) and spin-1 have been shown. The extremes of \(\ell _1\)-norm of \(D\)-functions are introduced to distinguish the type-I from type-II of braiding matrices that also correspond to those of von Neumann entropy for quantum information.     

Protection of quantum correlations against decoherence

The protection of different quantum correlations, such as Bell nonlocality, quantum discord, and geometric quantum discord as trace distance against noise, is explored. By weak measurement and quantum measurement reversal, we show that the mentioned quantum correlations can be effectively preserved probabilistically from the decoherence due to amplitude damping. The results will play an important role in the experiments using the quantum correlations as resource.