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The dielectric elastomer (DE) has attracted significant attention due to its desired features, including large deformation, fast response, and high energy density. However, for a DE actuator (DEA) utilizing a snap-through deformation mode, most existing theoretical models fail to predict its deformation path. This paper develops a new finite element method (FEM) based on the three-parameter Gent-Gent model suitable for capturing strain-stiffening behaviors. The simulation results are verified by experiments, indicating that the FEM can accurately characterize the snap-through path of a DE. The method proposed in this paper provides theoretical guidance and inspiration for designing and applying DEs and bistable electroactive actuators. [ABSTRACT FROM AUTHOR]

Optical flow is widely used in human action recognition. However, the influence of complex background on optical flow often leads to low recognition efficiency. To deal with this issue, an optical flow-based physical feature-driven action recognition framework is proposed in this paper. We first calculate the original dense optical flow field. Then, for reducing computational burden, joint action relevance that can eliminate the pseudo-optical flow in complex background is developed. After that, a more state flow field is obtained by local spatial–temporal thermal diffusion processing. On this basis, we design a feature descriptor that takes the divergence, curl and gradient features of flow field into consideration. Finally, we adopt Fisher vector to encode descriptors for classification. Experimental on HMDB51, KTH and UCF101 datasets proves that actions in complex background can be recognized accurately by the proposed framework, which outperforms the already developed methods. [ABSTRACT FROM AUTHOR]

Sparse adaptive filtering algorithms are utilized to exploit system sparsity as well as to mitigate interferences in many applications such as channel estimation and system identification. In order to improve the robustness of the sparse adaptive filtering, a novel adaptive filter is developed in this work by incorporating a correntropy-induced metric (CIM) constraint into the least logarithmic absolute difference (LLAD) algorithm. The CIM as an $$l_{0}$$ -norm approximation exerts a zero attraction, and hence, the LLAD algorithm performs well with robustness against impulsive noises. Numerical simulation results show that the proposed algorithm may achieve much better performance than other robust and sparse adaptive filtering algorithms such as the least mean p-power algorithm with $$l_{1}$$ -norm or reweighted $$l_{1}$$ -norm constraints. [ABSTRACT FROM AUTHOR]

Satellite remote sensing and numerical models are widely used to estimate large-scale variations in ocean carbon export, but the relationship between export efficiency (e-ratio) of sinking organic carbon out of the surface ocean and its drivers remains poorly understood, especially in the Southern Ocean. Here, we assess the effects of temperature and primary productivity on e-ratio by combining particulate organic carbon export flux from in situ measurements during 1997–2013, environmental parameters from satellite products, and outputs from ocean biogeochemical models in the Southern Ocean. Results show that "High Productivity Low E-ratio" (HPLE) is a common phenomenon in the Subantarctic Zone and the Polar Frontal Zone, but not the Antarctic Zone. The e-ratio shows little dependence on temperature below 6 °C. Our results support the hypothesis that the HPLE phenomenon is due to the large contribution of non-sinking organic carbon. Both temperature and ballast minerals play less important roles in controlling e-ratio than ecosystem structure at low temperatures. These findings suggest that non-sinking organic carbon, ecosystem structure, and region-specific parameterizations of e-ratio are key factors to quantify the carbon export in the Southern Ocean. [ABSTRACT FROM AUTHOR]

Despite remarkable success has been achieved by convolutional neural networks (CNNs) in no-reference image quality assessment (NR-IQA), there still exist many challenges in improving the performance of IQA for authentically distorted images. An important factor is that the insufficient annotated data limits the training of high-capacity CNNs to accommodate diverse distortions, complicated semantic structures and high-variance quality scores of these images. To address this problem, this paper proposes a hierarchical curriculum learning (HCL) framework for NR-IQA. The main idea of the proposed framework is to leverage the external data to learn the prior knowledge about IQA widely and progressively. Specifically, as a closely-related task with NR-IQA, image restoration is used as the first curriculum to learn the image quality related knowledge (i.e., semantic and distortion information) on massive distorted-reference image pairs. Then multiple lightweight subnetworks are designed to learn human scoring rules on multiple available synthetic IQA datasets independently, and a cross-dataset quality assessment correlation (CQAC) module is proposed to fully explore the similarities and diversities of different scoring rules. Finally, the whole model is fine-tuned on the target authentic IQA dataset to fuse the learned knowledge and adapt to the target data distribution. Experimental results show that our model achieves state-of-the-art performance on multiple standard authentic IQA datasets. Moreover, the generalization of our model is fully validated by the cross-dataset evaluation and the gMAD competition. In addition, extensive analyses prove that the proposed HCL framework is effective in improving the performance of our model. [ABSTRACT FROM AUTHOR]

In China, delivery delays due to a high probability of operational unit failure often occur in perishable manufacturing systems. This paper proposes a rescheduling algorithm known as complete rerouting (CR) for perishable manufacturing systems. The perishable characteristics of agri-products and the stochastic invalidity of operational units are researched. Products and operational units of the system are simulated by agents. In the developed virtual perishable manufacturing system, multiple product agents with different deadlines, values, and due dates search for the best processing route according to scheduling principles. When an operational unit fails stochastically, agents update their status and reroute their path using the rescheduling algorithm. The transition of agents can be described by the Petri net model proposed in this paper. Three basic experiments are investigated, validating the rescheduling algorithm proposed. This study demonstrates that the CR policy causes product agents to search for the best rerouting path compared to other rescheduling policies (i.e., right-shift scheduling and new-job rerouting). The optimal delivery times of products are determined based on penalty cost. Furthermore, this study analyzes the sensitivity of scheduling performance due to different product batches with two failed operational unit statuses (i.e., high frequency, short maintenance, and low frequency, long maintenance). [ABSTRACT FROM AUTHOR]