In this paper, we document the key components for the implementation of Graves & Pitarka's stochastic kinematic fault source model in SPECFEM3D. We verify firstly our implementation with analytical ...seismograms for a double-couple source in a homogenous infinite medium. Then, we compare the displacement distribution excited by Graves & Pitarka's source model in a homogenous semi-infinite medium with fixed source parameters expect the rake angles used to mimic the strike-slip, normal-slip and the reverse-slip fault type. The results show that the level of simulated peak ground velocity corresponding to rupture distance are similar for the strike-slip and normal-slip fault type, which are lower for the reverse-slip fault type, and they are qualitatively consistent with predicted by the ground motion prediction equation in NGA2 project.
Tool face adjustment in oil and gas drilling are vital issues that affect the efficiency and safety of directional drilling. The existing tool face adjustment methods mainly rely on manual real-time ...intervention for continuous adjustment. Affected by manual experience, the effect is unstable and the labor cost is high. With rising energy consumption, the need for intelligent directional drilling is becoming more pressing. However, establishing an autonomous adjustment approach for the tool face remains challenging because of the variety of complex downhole environments encountered during actual drilling operations. This paper proposes a model-free online learning adaptive decision strategy for cross well intelligent adjustment and stability of tool face. A reward function embedded with expert operating experience is designed to learn the directional policy from the driller's corrective actions. Further, to improve the efficiency of online learning, a priority-based experience playback algorithm is developed. A data-driven directional drilling simulation environment is proposed to realize the accurate simulation of the directional drilling process and pre-training of directional strategy. Simulations are carried out to validate the efficacy of the proposed method. The outcomes of field application suggest that the proposed strategy can achieve decision-making goals in a short period.
While self-attention mechanism has shown promising results for many vision tasks, it only considers the current features at a time. We show that such a manner cannot take full advantage of the ...attention mechanism. In this paper, we present Deep Connected Attention Network (DCANet), a novel design that boosts attention modules in a CNN model without any modification of the internal structure. To achieve this, we interconnect adjacent attention blocks, making information flow among attention blocks possible. With DCANet, all attention blocks in a CNN model are trained jointly, which improves the ability of attention learning. Our DCANet is generic. It is not limited to a specific attention module or base network architecture. Experimental results on ImageNet and MS COCO benchmarks show that DCANet consistently outperforms the state-of-the-art attention modules with a minimal additional computational overhead in all test cases. The code is available at: https://github.com/13952522076/DCANet.
We demonstrate a dual-optic imaging-spectrometer for high harmonic generation sources, ideal for coherent diffractive imaging in a tunable monochromatic mode. Our optimization combines record-high ...efficiency, high spectral resolution, low aberrations, broad spectral coverage, and polarization-maintaining.
While self-attention mechanism has shown promising results for many vision tasks, it only considers the current features at a time. We show that such a manner cannot take full advantage of the ...attention mechanism. In this paper, we present Deep Connected Attention Network (DCANet), a novel design that boosts attention modules in a CNN model without any modification of the internal structure. To achieve this, we interconnect adjacent attention blocks, making information flow among attention blocks possible. With DCANet, all attention blocks in a CNN model are trained jointly, which improves the ability of attention learning. Our DCANet is generic. It is not limited to a specific attention module or base network architecture. Experimental results on ImageNet and MS COCO benchmarks show that DCANet consistently outperforms the state-of-the-art attention modules with a minimal additional computational overhead in all test cases. All code and models are made publicly available.
Recently, more and more attention has been paid to the connected object detection for better performance. One of the most interesting fields is learning from multiple resources in a connected ...fashion. In this paper, we present a connected object detection method using multiple cameras for the smart transportation system. The proposed architecture consists of three parts: an alignment framework, a deep multi-view fusion network and an object detection network. Experiments are conducted to illustrate the performance of our proposed architecture.
