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Acoustic signals of the underwater targets are susceptible to noise, reverberation, submarine topography and biology, therefore it is difficult to precisely locate underwater targets. This paper proposes a new underwater Hanbury Brown-Twiss (HBT) interference passive localization method. This study aims to achieve precise location of the underwater acoustic targets.

The principle of HBT interference with ultrasensitive detection characteristics in optical measurements was introduced in the field of hydroacoustics. The coherence of the underwater target signal was analyzed using the HBT interference measurement principle, and the corresponding relationship between the signal coherence and target position was obtained. Consequently, an HBT interference localization model was established, and its validity was verified through simulations and experiments.

The effects of different array structures on the localization performance were obtained by simulation analysis, and the simulations confirmed that the HBT method exhibited a higher positioning accuracy than conventional beamforming. In addition, the experimental analysis demonstrated the excellent positioning performance of the HBT method, which verified the feasibility of the proposed method.

This study provides a new method for the passive localization of underwater targets, which may be widely used in the field of oceanic explorations.

Auxetics metamaterials show high performance in their specific characteristics, while the absolute stiffness and strength are much weaker due to substantial porosity. This paper aims to propose a novel auxetic honeycomb structure manufactured using selective laser melting and study the enhanced mechanical performance when subjected to in-plane compression loading.

A novel composite structure was designed and fabricated on the basis of an arrowhead auxetic honeycomb and filled with polyurethane foam. The deformation mechanism and mechanical responses of the structure with different structural parameters were investigated experimentally and numerically. With the verified simulation models, the effects of parameters on compression strength and energy absorption characteristics were further discussed through parametric analysis.

A good agreement was achieved between the experimental and simulation results, showing an evidently enhanced compression strength and energy absorption capacity. The interaction between the auxetic honeycomb and foam reveals to exploit a reinforcement effect on the compression performance. The parametric analysis indicates that the composite with smaller included angel and higher foam density exhibits higher plateau stress and better specific energy absorption, while increasing strut thickness is undesirable for high energy absorption efficiency.

The results of this study served to demonstrate an enhanced mechanical performance for the foam filled auxetic honeycomb, which is expected to be exploited with applications in aerospace, automobile, civil engineering and protective devices. The findings of this study can provide numerical and experimental references for the design of structural parameters.

The purpose of this study is to create a mathematical model, a numerical algorithm and a computer program for studying the vibration of composite pipelines based on the theory of beams used in the oil and gas industry, agriculture and water management, housing and communal services and other areas.

A mathematical model of vibration of a viscoelastic pipeline based on the theory of beams with a pulsating fluid flowing through it was developed. Using the Bubnov-Galerkin method, based on the polynomial approximation of deflections, the problem is reduced to the study of systems of ordinary integro-differential equations, the solution of which is found by a numerical method. A computational algorithm was developed for solving problems of vibrations of composite pipelines conveying pulsating liquid.

The stability and amplitude-time characteristics of vibration of composite pipelines with a pulsating fluid flowing in it are studied for wide range of changes in the parameters of deformable systems and fluid flow. The critical velocities of fluid flow at which the viscoelastic pipe loses its rectilinear equilibrium shape are found. The effect of singularity in the kernels of heredity on the vibrations of structures with viscoelastic properties was numerically studied. It is shown that with an increase in the viscosity parameter of the pipeline material, the critical flow velocity decreases. It was determined that an increase in the value of the fluid pulsation frequency and the excitation coefficient leads to a decrease in the critical velocity of the fluid flow. It was established that an increase in the parameters of the Winkler foundation and the rigidity parameter of the continuous layer leads to an increase in the critical flow velocity.

The study of the vibration of pipelines made of composite materials is of great theoretical and applied interest. The solution to this problem is an effective application of the theory of viscoelasticity to real processes. Therefore, the methods and problems of pipeline vibrations attract much attention from researchers. This study is devoted to solving the above problems and therefore its subject is relevant. The paper considers the results of numerical simulation of the processes of vibration of a composite pipeline based on the theory of shells during the flow of a pulsating liquid through it. A mathematical model of vibration of a composite pipeline was developed. A computational algorithm was developed for solving problems of vibrations of composite pipelines conveying pulsating liquid.

This study aims to investigate the content of hotel brands’ mission statements (MSs) and their relationship with selected attributes of hotel brands.

Content analysis of hotel brands’ MSs was used to detect the MSs’ key words, which were further processed by methods of social network analysis, complemented by clustering techniques and correspondence analysis on the generalized aggregated lexical tables, a special type of correspondence analysis.

Hotel brands operating in luxurious markets more often emphasize experiences than those in midscale markets. Furthermore, hotel brands with longer traditions and those with a large number of controlled rooms communicate words in their MSs that represent a rather traditional approach to hospitality. Younger hotel brands with fewer controlled rooms chose words that indicate a more commercially oriented approach. Finally, cluster analysis revealed four dimensions of hotel brands’ MSs, instead of the nine most typically used in mission statement component models.

Understanding the frequencies and networks of keywords, and their relationship with hotel brand attributes, will help create more focussed MSs. This will strengthen hotel brands, raise their revenues and subsequently increase company performance.

The analysis provides valuable insight into MSs in the specific tourism context of hotel brands. The authors have achieved this with the use of a wide range of advanced network analytic methods. These insights can guide hotel brands to better position themselves in the competitive tourism accommodation market.

Conventional learning-based visual odometry (VO) systems usually use convolutional neural networks (CNN) to extract features, where some important context-related and attention-holding global features might be ignored. Without essential global features, VO system will be sensitive to various environmental perturbations. The purpose of this paper is to design a novel learning-based framework that aims to improve accuracy of learning-based VO without decreasing the generalization ability.

Instead of CNN, a context-gated convolution is adopted to build an end-to-end learning framework, which enables convolutional layers that dynamically capture representative local patterns and composes local features of interest under the guidance of global context. In addition, an attention mechanism module is introduced to further improve learning ability and enhance robustness and generalization ability of the VO system.

The proposed system is evaluated on the public data set KITTI and the self-collected data sets of our college building, where it shows competitive performance compared with some classical and state-of-the-art learning-based methods. Quantitative experimental results on the public data set KITTI show that compared with CNN-based VO methods, the average translational error and rotational error of all the test sequences are reduced by 45.63% and 37.22%, respectively.

The main contribution of this paper is that an end-to-end deep context gate convolutional VO system based on lightweight attention mechanism is proposed, which effectively improves the accuracy compared with other learning-based methods.