Research on the Permeability and Earthquake Damage of an Earth Dam Foundation

The experiences on geological investigation, permeability test, prediction of seepage failure patterns and the earthquake damage of an earth dam foundation are presented in this paper. Basing on the monitoring data and seismic records observed from the seismic station on the dam, the prediction of reservoir induced earthquake and possibility of liquefaction are analysed

JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

The long-lasting optical afterglow plateau of short burst GRB 130912A

The short burst GRB 130912A was detected by Swift, Fermi satellites and several ground-based optical telescopes. Its X-ray light curve decayed with time normally. The optical emission, however, displayed a long term plateau, which is the longest one in current short GRB observations. In this work we examine the physical origin of the X-ray and optical emission of this peculiar event. We find that the canonical forward shock afterglow emission model can account for the X-ray and optical data self-consistently and the energy injection model that has been widely adopted to interpret the shallowly-decaying afterglow emission is not needed. We also find that the burst was born in a very-low density interstellar medium, consistent with the compact object merger model. Significant fractions of the energy of the forward shock have been given to accelerate the non-thermal electrons and amplify the magnetic fields (i.e., $\epsilon_{\rm e}\sim 0.37$ and $\epsilon_{\rm B}\sim 0.16$, respectively), which are much larger than those inferred in most short burst afterglow modeling and can explain why the long-lasting optical afterglow plateau is rare in short GRBs.Comment: 5 pages, 2 figure

Dynamics Model of Carrier-based Aircraft Landing Gears Landed on Dynamic Deck

AbstractIn order to study the carrier-based aircraft landing laws landed on the carrier, the dynamics model of carrier-based aircraft landing gears landed on dynamic deck is built. In this model, the interactions of the carrier-based aircraft landing attitude and the damping force acting on landing gears are considered, and the influence of dynamic deck is introduced into the model through the deck normal vectors. The wheel-deck coordinate system is put forward to solve the complex simulation problem of force-on-wheel which comes from the dynamic deck. At last, by simulation, it is demonstrated that the model can be applied to landing attitude when the carrier-based aircraft is landing on the dynamic deck, it is also proved that the model is comprehensive and suitable for any abnormal landing situation

Embedded Applications of MS-PSO-BP on Wind/Storage Power Forecasting

Higher proportion wind power penetration has great impact on grid operation and dispatching, intelligent hybrid algorithm is proposed to cope with inaccurate schedule forecast. Firstly, hybrid algorithm of MS-PSO-BP (Mathematical Statistics, Particle Swarm Optimization, Back Propagation neural network) is proposed to improve the wind power system prediction accuracy. MS is used to optimize artificial neural network training sample, PSO-BP (particle swarm combined with back propagation neural network) is employed on prediction error dynamic revision. From the angle of root mean square error (RMSE), the mean absolute error (MAE) and convergence rate, analysis and comparison of several intelligent algorithms (BP, RBP, PSO-BP, MS-BP, MS-RBP, MS-PSO-BP) are done to verify the availability of the proposed prediction method. Further, due to the physical function of energy storage in improving accuracy of schedule pre-fabrication, a mathematical statistical method is proposed to determine the optimal capacity of the storage batteries in power forecasting based on the historical statistical data of wind farm. Algorithm feasibility is validated by application of experiment simulation and comparative analysis