Developing the wisdom of rural tourism and promoting the transformation and upgrading of Shangluo rural tourism industry

Tourism industry is one of the industries that promote the development of modern economy. Shangluo rural tourism is also developing slowly. Rural tourism improves the employment rate of farmers and drives the economic development of various places. With the rapid development of information technology in China, tourists travel in a variety of ways, and the needs of tourists are becoming more and more differentiated. Traditional rural tourism is difficult to meet the personalized needs of tourists. In such a social environment, Shangluo rural tourism should develop in the direction of smart tourism, so that we can quickly complete the intelligent development of Shangluo rural tourism and improve the development level of Shangluo rural tourism. Based on the background of the intelligent growth of rural tourism, this paper studies the current situation and problems of the intelligent development of rural tourism in Shangluo. It is concluded that there are still some problems in the intelligent development of Shangluo rural tourism, such as backward infrastructure, single website content, lack of talents related to Shangluo intelligent tourism, backward information technology and poor service quality of scenic spots. We should speed up the construction of Shangluo smart rural tourism and the industrial upgrading of Shangluo rural tourism from the aspects of government departments, tourism management departments, rural tourism communities and other departments

Effect of Low-Stress Fatigue on the Off-Crack-Plane Fracture Energy in Engineered Cementitious Composites

This paper presented an experimental study on the flexural properties of engineered cementitious composites (ECCs). The bending fatigue damage, residual deformation, and damage characteristics were investigated after a certain number of low stress levels in fatigue load. The composite fracture energy and fiber-bridging fracture energy were calculated by the J integral. It is observed that the number of cracks increased with the increment of stress levels, and most of the cracks were formed during the earlier stage of the dynamic test. The deformation capability decreased with the increment of stress levels while the reduction of the ultimate load was minor after the dynamic load. Furthermore, the strain-hardening phenomenon of the specimen enhanced initially and then weakened with the increment of stress levels. The residual equivalent yield strength became smaller with the increase of stress levels. Meanwhile, the trend was mild at low stress levels and then became steep at high stress levels

Fault identification technology for gear tooth surface wear based on MPE method by MI and improved FNN algorithm

Multiscale Permutation Entropy (MPE) is a presented nonlinear dynamic technology for measuring the randomness and detecting the nonlinear dynamic change of time sequences and can be used effectively to extract the nonlinear dynamic wear fault feature of gear tooth surface from vibration signals of gear set. To solve the subjectivity drawback of threshold parameter selection process in MPE method, a joint calculation method based on the Mutual Information (MI) and improved False Nearest Neighbor (FNN) principle for calculating threshold parameters for MPE method was presented in this article. Then, the influence of threshold parameters on the identification accuracy of fault features with the MPE was studied by analyzing simulation data. Through the simulation analysis, the effectiveness of the proposed MPE method is validated. Finally, the wear failure test of spur gear was carried out, and the proposed method was applied to analyze the experimental data of fault signal. Meanwhile, the vibration characteristics of the fault signal are acquired. The analysis results show that the proposed method can effectively realize the fault diagnosis of gear box and has higher fault identification accuracy than the existing methods

Tibetan Word Segmentation as Syllable Tagging Using Conditional Random Field

In this paper, we proposed a novel approach for Tibetan word segmentation using the conditional random field. We reformulate the segmentation as a syllable tagging problem. The approach labels each syllable with a word-internal position tag, and combines syllable(s) into words according to their tags. As there is no public available Tibetan word segmentation corpus, the training corpus is generated by another segmenter which has an F-score of 96.94% on the test set. Two feature template sets namely TMPT-6 and TMPT-10 are used and compared, and the result shows that the former is better. Experiments also show that larger training set improves the performance significantly. Trained on a set of 131,903 sentences, the segmenter achieves an F-score of 95.12% on the test set of 1,000 sentences. © 2011 by Huidan Liu, Minghua Nuo, Longlong Ma, Jian Wu, and Yeping He.In this paper, we proposed a novel approach for Tibetan word segmentation using the conditional random field. We reformulate the segmentation as a syllable tagging problem. The approach labels each syllable with a word-internal position tag, and combines syllable(s) into words according to their tags. As there is no public available Tibetan word segmentation corpus, the training corpus is generated by another segmenter which has an F-score of 96.94% on the test set. Two feature template sets namely TMPT-6 and TMPT-10 are used and compared, and the result shows that the former is better. Experiments also show that larger training set improves the performance significantly. Trained on a set of 131,903 sentences, the segmenter achieves an F-score of 95.12% on the test set of 1,000 sentences. © 2011 by Huidan Liu, Minghua Nuo, Longlong Ma, Jian Wu, and Yeping He

Double Graphs Regularized Multi-view Subspace Clustering

Recent years have witnessed a growing academic interest in multi-view subspace clustering. In this paper, we propose a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to harness both global and local structural information of multi-view data in a unified framework. Specifically, DGRMSC firstly learns a latent representation to exploit the global complementary information of multiple views. Based on the learned latent representation, we learn a self-representation to explore its global cluster structure. Further, Double Graphs Regularization (DGR) is performed on both latent representation and self-representation to take advantage of their local manifold structures simultaneously. Then, we design an iterative algorithm to solve the optimization problem effectively. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method

Multi-task unscented Kalman inversion (MUKI): a derivative-free joint inversion framework and its application to joint inversion of geophysical data

In the geophysical joint inversion, the gradient and Bayesian Markov Chain Monte Carlo (MCMC) sampling-based methods are widely used owing to their fast convergences or global optimality. However, these methods either require the computation of gradients and easily fall into local optimal solutions, or cost much time to carry out the millions of forward calculations in a huge sampling space. Different from these two methods, taking advantage of the recently developed unscented Kalman method in computational mathematics, we extend an iterative gradient-free Bayesian joint inversion framework, i.e., Multi-task unscented Kalman inversion (MUKI). In this new framework, information from various observations is incorporated, the model is iteratively updated in a derivative-free way, and a Gaussian approximation to the posterior distribution of the model parameters is obtained. We apply the MUKI to the joint inversion of receiver functions and surface wave dispersion, which is well-established and widely used to construct the crustal and upper mantle structure of the earth. Based on synthesized and real data, the tests demonstrate that MUKI can recover the model more efficiently than the gradient-based method and the Markov Chain Monte Carlo method, and it would be a promising approach to resolve the geophysical joint inversion problems.Comment: 13 pages, 4 figure

The dependence of Ni-Fe bioxide composites nanoparticles on the FeCl2 solution used

BACKGROUND: Ni(2)O(3)- γ-Fe(2)O(3) composite nanoparticles coated with a layer of 2FeCl(3)·5H(2)O can be prepared by co-precipitation and processing in FeCl(2) solution. Using vibrating sample magnetometer (VSM), X-ray diffraction (XRD), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) diffraction techniques, the dependence of the preparation on the concentration of the FeCl(2) treatment solution is revealed. RESULTS: The magnetization of the as-prepared products varied non-monotonically as the FeCl(2) concentration increased from 0.020 M to 1.000 M. The Experimental results show that for the composite nanoparticles, the size of the γ-Fe(2)O(3) phase is constant at about 8 nm, the Ni(2)O(3) phase decreased and the 2FeCl(3)·5H(2)O phase increased with increasing concentration of FeCl(2) solution. The magnetization of the as-prepared products mainly results from the γ-Fe(2)O(3) core, and the competition between the reduction of the Ni(2)O(3) phase with the increase of the 2FeCl(3)·5H(2)O phase resulted in the apparent magnetization varying non-monotonically. CONCLUSIONS: When the concentration of FeCl(2) treatment solution did not exceed 0.100 M, the products are spherical nanoparticles of size about 11 nm; their magnetization increased monotonically with increasing the concentration of FeCl(2) solution due to the decreasing proportion of Ni(2)O(3) phase

Influence of characteristic parameters of signal on fault feature extraction of singular value method

The detection of mechanical fault signals by singular value decomposition is a commonly used method in fault diagnosis. The delay time of the fault signal time series and the rationality of the value of the phase space embedding dimension, as well as the fluctuation of the characteristic parameters of the fault signal, will cause the singular value decomposition method to have a greater impact on the accuracy of fault feature identification and diagnosis. In this article, the simulation model of the similarity signal is established by the combination of the autocorrelation function method and the Cao’s algorithm. Then, the delay time of the signal sequence and the optimal value of the embedded dimension are obtained through simulation. Next, using this method to study the fluctuation of the characteristic parameters such as the frequency, amplitude and initial phase of the signal, the relationship between the characteristic parameters of the signal and the singular value of the signal is obtained. Finally, through the experimental study of the pitting corrosion of the gear tooth surface, the vibration of the fault feature is obtained. The research shows that the combination of autocorrelation function method and Cao's algorithm can calculate the optimal characteristic parameters for the singular value decomposition method and improve the ability of the method to identify fault features

An ideal mass assignment scheme for measuring the Power Spectrum with FFTs

In measuring the power spectrum of the distribution of large numbers of dark matter particles in simulations, or galaxies in observations, one has to use Fast Fourier Transforms (FFT) for calculational efficiency. However, because of the required mass assignment onto grid points in this method, the measured power spectrum \la |\delta^f(k)|^2\ra obtained with an FFT is not the true power spectrum $P(k)$ but instead one that is convolved with a window function $|W(\vec k)|^2$ in Fourier space. In a recent paper, Jing (2005) proposed an elegant algorithm to deconvolve the sampling effects of the window function and to extract the true power spectrum, and tests using N-body simulations show that this algorithm works very well for the three most commonly used mass assignment functions, i.e., the Nearest Grid Point (NGP), the Cloud In Cell (CIC) and the Triangular Shaped Cloud (TSC) methods. In this paper, rather than trying to deconvolve the sampling effects of the window function, we propose to select a particular function in performing the mass assignment that can minimize these effects. An ideal window function should fulfill the following criteria: (i) compact top-hat like support in Fourier space to minimize the sampling effects; (ii) compact support in real space to allow a fast and computationally feasible mass assignment onto grids. We find that the scale functions of Daubechies wavelet transformations are good candidates for such a purpose. Our tests using data from the Millennium Simulation show that the true power spectrum of dark matter can be accurately measured at a level better than 2% up to $k=0.7k_N$, without applying any deconvolution processes. The new scheme is especially valuable for measurements of higher order statistics, e.g. the bi-spectrum,........Comment: 17 pages, 3 figures, Accepted for publication in ApJ,Matches the accepte