In order to ensure safety of long and huge bridges in deep water under earthquake action, it is significance to consider water and bridge pier dynamic interaction. Analysis method of water-bridge pier dynamic interaction under earthquake excitation is established using radiation wave theory, and earthquake induced hydrodynamic pressure apply program is complied. Considering different earthquake wave input, earthquake induced hydrodynamic pressure influence on dynamic responses of bridge pier in deep water is further studied. The results indicate that: Dynamic response of bridge pier in deep water is augmented because of hydrodynamic pressure action. Earthquake induced hydrodynamic pressure influence on seismic responses of bridge piers in deep water will change with different input earthquake wave.
In order to study the basic principles of vibration-excited liquid metal nucleation technology, a coupled model to connect the temperature field calculated by ANSYS Fluent and the dendritic growth simulated by cellular automaton (CA) algorithm was proposed. A two-dimensional CA model for dendrite growth controlled by solute diffusion and local curvature effects with random zigzag capture rule was developed. The proposed model was applied to simulate the temporal evolution of solidification microstructures under different degrees of surface undercooling and vibration frequency of the crystal nucleus generator conditions. The simulation results showed that the predicted columnar dendrites regions were more developed, the ratio of interior equiaxed dendrite reduced and the size of dendrites increased with the increase of the surface undercooling degrees on the crystal nucleus generator. It was caused by a large temperature gradient formed in the melt. The columnar-to-equiaxed transition (CET) was promoted, and the refined grains and homogenized microstructure were also achieved at the high vibration frequency of the crystal nucleus generator. The influences of the different process parameters on the temperature gradient and cooling rates in the mushy zone were investigated in detail. A lower cooling intensity and a uniform temperature gradient distribution could promote nucleation and refine grains. The present research has guiding significance for the process parameter selection in the actual experimental.
Based on mechanical characteristics of the single layer latticed shell member, two buckling types of structural compression members are summarized. The pre-buckling and post-buckling mechanical behaviors of the member are simulated by different calculating model, and the member calculating model are founded by which the likely buckle-straighten processes of the member and the form-disappear processes of the plastic hinge can be simulated. Numerical computing results indicate that based on the member calculating model presented in this paper, the changes of member mechanical behaviors and the consequent complex changes of structural bearing capacity in the seismic dynamic responses process of single layer latticed shell can be calculate accurately, and the refined simulation of the full-range dynamic response process for single layer latticed shell subject to dynamic excitation are realized.
We present a novel online unsupervised anomaly detection method for human activities. The proposed approach is based on one-class support vector machine (OCSVM) clustering, where the novelty detection SVM capabilities are used for the identification of anomalous activities. Particular attention is given to activity classification in absence of a priori information on the distribution of outliers. Activities are represented by variable-length event sequences, but the most commonly used kernels are defined on fixed-dimension spaces. To solve the problem, we develop a novel sequence-similarity kernel, the n-grams kernel. Our kernel is conceptually simple and efficient to compute and performs well in comparison with state-of-the-art methods for anomaly detection. Moreover, most SVM algorithms require large number of memory to store the kernel matrix, or repeated access to the training samples. This makes it infeasible for online anomaly detection. In this paper, we develop simple and computationally efficient online learning algorithms for anomaly detection.
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