Large-scale power inspection: A deep reinforcement learning approach

Power inspection plays an important role in ensuring the normal operation of the power grid. However, inspection of transmission lines in an unoccupied area is time-consuming and labor-intensive. Recently, unmanned aerial vehicle (UAV) inspection has attracted remarkable attention in the space-ground collaborative smart grid, where UAVs are able to provide full converge of patrol points on transmission lines without the limitation of communication and manpower. Nevertheless, how to schedule UAVs to traverse numerous, dispersed target nodes in a vast area with the least cost (e.g., time consumption and total distance) has rarely been studied. In this paper, we focus on this challenging and practical issue which can be considered as a family of vehicle routing problems (VRPs) with regard to different constraints, and propose a Diverse Trajectory-driven Deep Reinforcement Learning (DT-DRL) approach with encoder-decoder scheme to tackle it. First, we bring in a threshold unit in our encoder for better state representation. Secondly, we realize that the already visited nodes have no impact on future decisions, and then devise a dynamic-aware context embedding which removes irrelevant nodes to trace the current graph. Finally, we introduce multiply decoders with identical structure but unshared parameters, and design a Kullback-Leibler divergence based regular term to enforce decoders to output diverse trajectories, which expands the search space and enhances the routing performance. Comprehensive experiments on five types of routing problems show that our approach consistently outperforms both DRL and heuristic methods by a clear margin

Cavitation Damage Prediction of Stainless Steels Using an Artificial Neural Network Approach

Cavitation damage has not been well predicted because of its complex relationship of many mechanical and microstructural factors. An artificial neural network approach of the back-propagation network was used to predict cavitation damage of stainless steels, 316L and 420, in terms of the significant influence of cavitation time, roughness, and residual stress on cavitation damage. Mean depth of erosion was used to quantitatively describe cavitation damage of 316L and 420. Prediction accuracy was improved by analyzing the effects of the number and type of input nodes, the number of nodes in the hidden layer, and different activation functions on prediction accuracy. The best performance was in the model with the input nodes of cavitation time and roughness, eleven nodes in the hidden layer, and the activation function of logsig

Notch Effect on the Fatigue Behavior of a TC21 Titanium Alloy in Very High Cycle Regime

The very high cycle fatigue (VHCF) property of TC21 titanium alloy blunt-notched specimens were investigated by using an ultrasonic fatigue test machine with a frequency of 20 kHz. S–N of blunt-notched specimens illustrated a continuous decrease characteristic with a horizontal line over the 105–109 cycle regimes. However, the fatigue life showed a large scatter for blunt-notched specimens. Blunt-notch significantly reduced the fatigue property in the high cycle and very high cycle regimes compared with that of smooth specimens. The crack initiation modes for blunt-notched specimens in the very high cycle regime can be divided into three types: (i) surface initiation, (ii) subsurface with flat facet, and (iii) subsurface with “facet + fine granular area”. The crack initiation mechanism of blunt-notched specimens is discussed in view of the interaction of notch stress gradient distribution and heterogeneous microstructure. Furthermore, the fatigue limit model based on the theory of critical distance (TCD) was modified for the very high cycle regime, and the scatter of the fatigue property of the blunt-notched specimens were well predicted by using this model

Shorter-is-Better: Venue Category Estimation from Micro-Video

10.1145/2964284.2964307ACM Multimedia Conference 20161415-142

Effect of Basketweave Microstructure on Very High Cycle Fatigue Behavior of TC21 Titanium Alloy

This paper discusses the effect of basketweave microstructure on the very high cycle fatigue behavior of TC21 titanium alloy. Ultrasonic fatigue tests at 20 kHz are done on a very high cycle fatigue (VHCF) property of the alloys with 60 μm and 40 μm basketweave size, respectively. Results show that the alloys illustrate step-wise S-N characteristics over the 105–109 cycle regimes and that fatigue fracture in both of the alloys occur beyond the conventional fatigue limit of 107 cycles. Subsurface crack initiation occurs at low stress amplitude. A fine granular area (FGA) is observed along the α lamella at the subsurface crack initiation site. The mechanism for the subsurface crack initiation is revealed using layer-by-layer-polishing, due to the micro-voids that are introduced at the granular α phase. The colony of α lamella is due to the local stress concentrated between them under the cyclic load. The stress intensity factor range at the FGA front is regarded as the threshold value controlling the internal crack propagation. Furthermore, the effect of the baseketweave size on the very high cycle fatigue limits of the TC21 titanium alloy is evaluated based on the Murakami model, which is consistent with the experimental results. The fatigue life of TC21 titanium alloy is well predicted using the energy-based crack nucleation life model

First-Principles Study of Atomic Diffusion by Vacancy Defect of the L1₂-Al₃M (M = Sc, Zr, Er, Y) Phase

Atomic diffusion by the vacancy defect of L12-Al3M (M = Sc, Zr, Er, Y) was investigated based on a first-principles calculation. The point defect formation energies were firstly evaluated. Then, the migration energy for different diffusion paths was obtained by the climbing-image nudged elastic band (CI-NEB) method. The results showed that Al atomic and M atomic diffusions through nearest-neighbor jump (NNJ) mediated by Al vacancy (VAl) were, respectively, the preferred diffusion paths in Al3M phases under both Al-rich and M-rich conditions. The other mechanisms, such as six-jump cycle (6JC) and next-nearest-neighbor jump (NNNJ), were energetically inhibited. The order of activation barriers for NNJ(Al-VAl) was Al3Zr 3Y 3Er 3Sc. The Al3Sc phase had high stability with a high self-diffusion activation barrier, while the Al3Zr and Al3Y phases were relatively unstable with a low self-diffusion activation energy. Moreover, the atomic-diffusion behavior between the core and shell layers of L12-Al3M was also further investigated. Zr atoms were prone to diffusion into the Al3Y core layer, resulting in no stable core-shelled Al3(Y,Zr), which well agreed with experimental observation