Electric dipole sheets in BaTiO3_{3}/BaZrO3_{3} superlattices

We investigate two-dimensional electric dipole sheets in the superlattice made of BaTiO$_{3}$ and BaZrO$_{3}$ using first-principles-based Monte-Carlo simulations and density functional calculations. Electric dipole domains and complex patterns are observed and the complex dipole structures with various symmetries (e.g. Pma2, Cmcm and Pmc2_{1}) are further confirmed by density functional calculations, which are found to be almost degenerate in energy with the ferroelectric ground state of the Amm2 symmetry, therefore strongly resembling magnetic sheets. More complex dipole patterns, including vortices and anti-vortices, are also observed, which may constitute the intermediate states that overcome the high energy barrier of different polarization orientations previously predicted by Lebedev\onlinecite{Lebedev2013}. We also show that such system possesses large electrostrictive effects that may be technologically important

On Spectral Graph Embedding: A Non-Backtracking Perspective and Graph Approximation

Graph embedding has been proven to be efficient and effective in facilitating graph analysis. In this paper, we present a novel spectral framework called NOn-Backtracking Embedding (NOBE), which offers a new perspective that organizes graph data at a deep level by tracking the flow traversing on the edges with backtracking prohibited. Further, by analyzing the non-backtracking process, a technique called graph approximation is devised, which provides a channel to transform the spectral decomposition on an edge-to-edge matrix to that on a node-to-node matrix. Theoretical guarantees are provided by bounding the difference between the corresponding eigenvalues of the original graph and its graph approximation. Extensive experiments conducted on various real-world networks demonstrate the efficacy of our methods on both macroscopic and microscopic levels, including clustering and structural hole spanner detection.Comment: SDM 2018 (Full version including all proofs

Correlation analysis of PCB and comparison of test-analysis model reduction methods

AbstractThe validity of correlation analysis between finite element model (FEM) and modal test data is strongly affected by three factors, i.e., quality of excitation and measurement points in modal test, FEM reduction methods, and correlation check techniques. A new criterion based on modified mode participation (MMP) for choosing the best excitation point is presented. Comparison between this new criterion and mode participation (MP) criterion is made by using Case 1 with a simple printed circuit board (PCB). The result indicates that this new criterion produces better results. In Case 2, 35 measurement points are selected to perform modal test and correlation analysis while 9 selected in Case 3. System equivalent reduction expansion process (SEREP), modal assurance criteria (MAC), coordinate modal assurance criteria (CoMAC), pseudo orthogonality check (POC) and coordinate orthogonality check (CORTHOG) are used to show the error introduced by modal test in Cases 2 and 3. Case 2 shows that additional errors which cannot be identified by using CoMAC can be found by using CORTHOG. In both Cases 2 and 3, Guyan reduction, improved reduced system (IRS) method, SEREP and Hybrid reduction are compared for accuracy and robustness. The results suggest that the quality of the reduction process is problem dependent. However, the IRS method is an improvement over the Guyan reduction, and the Hybrid reduction is an improvement over the SEREP reduction

Synthesis of running RMS-induced non-Gaussian random vibration based on Weibull distribution

Gaussian signal is produced by ordinary random vibration controllers to test the products in the laboratory, while the field data usually is non-Gaussian. To synthesize non-Gaussian random vibration, both the probability density function (PDF) and the damage effects must be considered. A new method is presented in this paper to synthesize non-Gaussian random vibration that is characterized by running RMS (root mean square). The essential idea is to model the non-Gaussian signal by a Gaussian signal multiplied by an amplitude modulation function (AMF). A two-parameter Weibull distribution is used to model the PDF of the running RMS and to create the AMF. The shock response spectrum (SRS) is used to detect significant shocks within the non-Gaussian signal. A case study is presented to show that the synthesized non-Gaussian signal has the same power spectral density (PSD), kurtosis, PDF and fatigue damage spectrum (FDS) as the field data

An HMM-Based Framework for Video Semantic Analysis

Video semantic analysis is essential in video indexing and structuring. However, due to the lack of robust and generic algorithms, most of the existing works on semantic analysis are limited to specific domains. In this paper, we present a novel hidden Markove model (HMM)-based framework as a general solution to video semantic analysis. In the proposed framework, semantics in different granularities are mapped to a hierarchical model space, which is composed of detectors and connectors. In this manner, our model decomposes a complex analysis problem into simpler subproblems during the training process and automatically integrates those subproblems for recognition. The proposed framework is not only suitable for a broad range of applications, but also capable of modeling semantics in different semantic granularities. Additionally, we also present a new motion representation scheme, which is robust to different motion vector sources. The applications of the proposed framework in basketball event detection, soccer shot classification, and volleyball sequence analysis have demonstrated the effectiveness of the proposed framework on video semantic analysis

A HMM Based Semantic Analysis Framework for Sports Game Event Detection

Video events detection or recognition is one of important tasks in semantic understanding of video content. Sports game video should be considered as a rule-based sequential signal. Therefore, it is reasonable to model sports events using hidden Markov models. In this paper, we present a generic, scalable and multilayer framework based on HMMs, called SG-HMMs (sports game HMMs), for sports game event detection. At the bottom layer of this framework, event HMMs output basic hypotheses based on low-level features. The upper layers are composed of composition HMMs, which add constraints on those hypotheses of the lower layer. Instead of isolated event recognition, the hypotheses at different layers are optimized in a bottom-up manner and the optimal semantics are determined by top-down process. The experimental results on basketball and volleyball videos have demonstrated the effectiveness of the proposed framework for sports game analysis

Motion Based Event Recognition Using HMM

Motion is an important cue for video understanding and is widely used in many semantic video analyses. We present a new motion representation scheme in which motion in a video is represented by the responses of frames to a set of motion filters. Each of these filters is designed to be most responsive to a type of dominant motion. Then we employ hidden Markov models (HMMs) to characterize the motion patterns based on these features and thus classify basketball video into 16 events. The evaluation by human satisfaction rate to classification result is 75%, demonstrating effectiveness of the proposed approach to recognizing semantic events in video