Vertical vibration directly impacts the measurement accuracy of a zero-length spring relative gravimeter. Vibration isolation has limited suppression effect in complicated vibration situations, and an effective and reliable vibration compensation method is crucial. The mechanism by which the vibration environment affects the gravimeter is first analyzed in this letter. In addition, an improved alternating direction multiplier method (ADMM) based on the inexact criterion and golden ratio proximity is proposed to find the optimal model coefficients of the time-delay gain simplified model of the vibration transfer function and compared with the sparrow search algorithm (SSA). The simulation and experimental results show that the method proposed in this letter has the best vibration compensation effect, and the standard deviation (STD) and mean absolute error (MAE) can reach up to 0.1424 and 0.0965 mGal, respectively, which is of great significance for improving the environmental adaptability of gravity measurement.

In this paper, we proposed a novel motion model with Helmholtz decomposition for complex fluid flows in a filtering-based optical flow framework, where the optimization of the regularization term is treated as a filtering process, and different motion patterns can be captured by finding appropriate filter kernels based on the designed filters. In this framework, we introduce a novel optical flow method with a joint spatial filter, which is based on the Helmholtz decomposition theorem that assumes a local motion field is composed of a curl field and a divergence field. By adjusting the scale of the weights in the filter kernels and combining a curl filter with a divergence filter in a certain ratio, it can simulate different motion patterns. In addition, if the correlation between the horizontal and vertical components of the optical flow field in the filter kernel is eliminated, it will be transformed into a linear motion model. Based on this linear motion model, we also develop a novel optical flow method with an adaptive guided filter. By finding an adaptive filter kernel driven by both the input image and the guided motion field for the designed filter, it can successfully capture different motion patterns, and yield an edge-preserving smoothing optical flow field. Most importantly, the proposed optical flow model provides a new way to design the regularizers for capturing different motion patterns in complex fluid flow. In particular, the designed optical flow method with an adaptive guided filter significantly outperforms the current state-of-the-art optical flow methods in predicting complex fluid flows.

Optical flow computation for video under the dynamic illumination is a challenging issue in video multimedia applications. In this paper, we solve this issue by introducing an illumination-invariant framework for variational optical flow estimation. It consists of an illumination-invariance model that handles complex illumination changes and a data enhancement model that guarantees highly accurate optical flow estimation. In this framework, we design a log-correlation descriptor for the data term, which handles complex illumination changes by eliminating the common parameters shared by the neighboring pixels in the corresponding illumination change model while improving the accuracy of optical flow estimation by enhancing the discriminability of the data term matching. We also introduce a novel optical flow model with$L_{0}$norm regularization, which reconstructs optical flow field by a sparse flow gradient counting scheme. Different from other edge-preserving regularizers, it does not depend on local motion features, but locates important flow edges globally. Therefore, it will not cause edge blurriness due to avoiding local filtering or average operation. It is particularly effective for enhancing major flow edges while eliminating a manageable degree of low-amplitude motion structures to control smoothing and reduce oversegmentation artifacts. Even small-scale motion structures with high contrast can be preserved remarkably well. The experimental results show our method significantly outperforms previous illumination-robust optical flow methods in handling complex illumination changes, and achieves competitive evaluation results on the challenging MPI-Sintel and Kitti datasets.

Data outsourcing is a promising service for data owners, where their data are stored on a cloud storage provider. Since the cloud is not fully trusted, data access control has become a challenging issue in the Cloud Storage System (CSS). Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a feasible technique for ensuring access control in the CSS, where an attribute authority is responsible to manage attributes and distribute keys. In this paper, we propose a novel revocable Multi-Authority CP-ABE scheme, in which the access policy can be constructed as an arbitrary tree rather than a matrix used by existing schemes. The tree-like policy makes our scheme more flexible. Consequently, the encryption, decryption and attribute revocation operations are also more efficient. Our scheme is also proved to be secure under the standard assumption. It can resist user collusion attack, while the attribute revocation operation also achieves both forward security and backward security. Simulation results show that our scheme is highly efficient.

The primary objective of this paper was to incorporate the reinforcement learning technique in variable speed limit (VSL) control strategies to reduce system travel time at freeway bottlenecks. A Q-learning (QL)-based VSL control strategy was proposed. The controller included two components: a QL-based offline agent and an online VSL controller. The VSL controller was trained to learn the optimal speed limits for various traffic states to achieve a long-term goal of system optimization. The control effects of the VSL were evaluated using a modified cell transmission model for a freeway recurrent bottleneck. A new parameter was introduced in the cell transmission model to account for the overspeed of drivers in unsaturated traffic conditions. Two scenarios that considered both stable and fluctuating traffic demands were evaluated. The effects of the proposed strategy were compared with those of the feedback-based VSL strategy. The results showed that the proposed QL-based VSL strategy outperformed the feedback-based VSL strategy. More specifically, the proposed VSL control strategy reduced the system travel time by 49.34% in the stable demand scenario and 21.84% in the fluctuating demand scenario.