This paper presents a novel quasi-zero stiffness (QZS) isolator designed by combining a disk spring with a vertical linear spring. The static characteristics of the disk spring and the QZS isolator are investigated. The optimal combination of the configurative parameters is derived to achieve a wide displacement range around the equilibrium position in which the stiffness has a low value and changes slightly. By considering the overloaded or underloaded conditions, the dynamic equations are established for both force and displacement excitations. The frequency response curves (FRCs) are obtained by using the harmonic balance method (HBM) and confirmed by the numerical simulation. The stability of the steady-state solution is analyzed by applying Floquet theory. The force, absolute displacement, and acceleration transmissibility are defined to evaluate the isolation performance. Effects of the offset displacement, excitation amplitude, and damping ratio on the QZS isolator and the equivalent system (ELS) are studied. The results demonstrate that the QZS isolator for overloaded or underloaded can exhibit different stiffness characteristics with changing excitation amplitude. If loaded with an appropriate mass, excited by not too large amplitude, and owned a larger damper, the QZS isolator can possess better isolation performance than its ELS in low frequency range.

Land-use classification from remote sensing images has become an important but challenging task. This paper proposes Hierarchical Coding Vectors (HCV), a novel representation based on hierarchically coding structures, for scene level land-use classification. We stack multiple Bag of Visual Words (BOVW) coding layers and one Fisher coding layer to develop the hierarchical feature learning structure. In BOVW coding layers, we extract local descriptors from a geographical image with densely sampled interest points, and encode them using soft assignment (SA). The Fisher coding layer encodes those semi-local features with Fisher vectors (FV) and aggregates them to develop a final global representation. The graphical semantic information is refined by feeding the output of one layer into the next computation layer. HCV describes the geographical images through a high-level representation of richer semantic information by using a hierarchical coding structure. The experimental results on the 21-Class Land Use (LU) and RSSCN7 image databases indicate the effectiveness of the proposed HCV. Combined with the standard FV, our method (FV + HCV) achieves superior performance compared to the state-of-the-art methods on the two databases, obtaining the average classification accuracy of 91.5% on the LU database and 86.4% on the RSSCN7 database.

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Remote sensing image scene classification is an important method for understanding the high-resolution remote sensing images. Based on Convolutional Neural Network, various classification methods have been applied into this field and achieved remarkable results. These methods mainly rely on the semantic information to improve the classification performance. However, as the network goes deeper, the highly abstract and global semantic information makes it difficult for the network to accurately classify scene images with similar layout and structures, limiting the further improvement of classification accuracy. Relying on the semantic information only is not sufficient to effectively classify these similar scene images and the network needs spatial information to enhance the classification capability. To solve this dilemma, this paper proposes a best representation branch model, which reaches the optimal balance point where the network can make use of both the semantic information and spatial information to improve the final classification accuracy. In the proposed method, ResNet50 pretrained on the ImageNet dataset is first divided into four branches with different depths to extract feature maps and Capsule Network is used as the classifier. The Grad-CAM algorithm is adopted to explain the mechanism of the optimal balance point from the perspective of attention and guide the further feature fusion. In addition, ablation studies are conducted to prove the effectiveness of our method and extensive experiments are conducted on three public benchmark remote sensing datasets. The results demonstrate that the proposed method can achieve competitive classification performance compared to the state-ofthe-art methods.