Publications

Finite Element Analysis of the Vertical Bearing Capacity of Single Piles in Tianjin Soft Soil Areas

The ABAQUS finite element software is used to calculate the vertical bearing capacity of single piles in Tianjin, and the finite element results are compared with the data gained through the in-situ static load pile foundation tests. It is found that the Young’s modulus of the soil at the bottom of the pile could have a important role in improving the vertical bearing capacity of single piles.

Micro-seismic monitoring in mines based on cross wavelet transform

Time Delay of Arrival (TDOA) estimation methods based on correlation function analysis play an important role in the micro-seismic event monitoring. It makes full use of the similarity in the recorded signals that are from the same source. However, those methods are subjected to the noise effect, particularly when the global similarity of the signals is low. This paper proposes a new approach for micro-seismic monitoring based on cross wavelet transform. The cross wavelet transform is utilized to analyse the measured signals under micro-seismic events, and the cross wavelet power spectrum is used to measure the similarity of two signals in a multi-scale dimension and subsequently identify TDOA. The offset time instant associated with the maximum cross wavelet transform spectrum power is identified as TDOA, and then the location of micro-seismic event can be identified. Individual and statistical identification tests are performed with measurement data from an in-field mine. Experimental studies demonstrate that the proposed approach significantly improves the robustness and accuracy of micro-seismic source locating in mines compared to several existing methods, such as the cross-correlation, multi-correlation, STA/LTA and Kurtosis methods.

A Short Review on Data Modelling for Vector Fields

Machine learning methods based on statistical principles have proven highly successful in dealing with a wide variety of data analysis and analytics tasks. Traditional data models are mostly concerned with independent identically distributed data. The recent success of end-to-end modelling scheme using deep neural networks equipped with effective structures such as convolutional layers or skip connections allows the extension to more sophisticated and structured practical data, such as natural language, images, videos, etc. On the application side, vector fields are an extremely useful type of data in empirical sciences, as well as signal processing, e.g. non-parametric transformations of 3D point clouds using 3D vector fields, the modelling of the fluid flow in earth science, and the modelling of physical fields. This review article is dedicated to recent computational tools of vector fields, including vector data representations, predictive model of spatial data, as well as applications in computer vision, signal processing, and empirical sciences.

Similarity Evaluation for Magnetic Materials Sintering

ABSTRACT. The evaluation of the sintering quality of magnetic materials is an important issue. Similarity measures between curves have a broad prospect in the prediction of process industry. A new algorithm was presented in this paper to calculate the similarity between the designed process curve and the acquired one. The limitation of traditional similarity measures was avoided and the characteristics of process and the curve form were taken into account. Then, the similarity measure was applied to valuate the actual process performance of magnetic materials sintering. It shows that the proposed similarity evaluation methods can be used to effectively predict or analyze the control performance.

Out-of-plane full-field vibration displacement measurement with monocular computer vision

Vibration displacement of civil structures is crucial information for structural health monitoring (SHM). The challenges and costs associated with traditional physical sensors make displacement measurement not always straightforward owing to difficulties such as inaccessibility. While recent computer vision based methods for displacement measurements offer simplicity, unfortunately they lag in terms of accuracy and robustness. This paper introduces a monocular camera system designed to measure out-of-plane vibration displacement. Compared to existing monocular-camera based methods, the proposed monocular vision-based measurement technique significantly enhances accuracy and robustness. This boost can be attributed to the generation of a vast and precise dataset and augmented by employing advanced techniques for object segmentation and background elimination. Experimental tests are conducted in the laboratory to investigate the feasibility of the proposed system. The results demonstrate that the proposed monocular 3D displacement system can produce highly accurate full-field out-of-plane displacement measurement.

Multi‐channel response reconstruction using transformer based generative adversarial network

Abstract Accurate measurement data are a basic prerequisite for effective structural health monitoring (SHM). However, data loss are inevitable in the long‐term monitoring of large‐scale structures. To solve this problem, this research proposes a transformer‐based generative adversarial network (GAN) to reconstruct lost measurements from observed measurements. The generator of GAN is an encoder‐decoder structure using transformer as the backbone combined with discrete wavelet transform. Skip connections are used between the encoder part and decoder part to promote multi‐scale information flow. A novel discriminator is designed to assess the reality of wavelet spectra of reconstructed samples. To deceive the discriminator, the generator must generate samples that are accurate over the full frequency band. The developed model is used to reconstruct linear responses of a footbridge under pedestrian excitations and nonlinear responses of a suspension bridge under typhoon events. Experimental results demonstrate that lost responses can be reconstructed accurately, even when a large proportion of data are lost. The effectiveness of the proposed method is further verified by comparing the reconstruction accuracy of the proposed model with those of other three state‐of‐the‐art models. The results demonstrate that an improved performance of applying the proposed approach for dynamic structural response reconstruction is achieved and validated with in‐field testing data under ambient and extreme excitation conditions.

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DPG-Net: Densely progressive-growing network for point cloud completion

The integer ambiguity resolution of BDS triple-frequency between long range stations with GEO satellite constraints

Research on rock breaking mechanism of rotary-percussion drilling in marine hard rock strata and the influence of engineering and tool parameters on ROP

Localized surface plasmon resonance induced spatial self-phase modulation of Cu₂-xS in the near-infrared region

Cloud Computing Based Solution to Decision Making

Decision support systems (DSSs) are a basic component in the development of business intelligence architecture. They are a specific class of computerized information system that supports business and organizational decision-making activities. The paper first listed its current problems, and presented a cloud solution to provide an effective DSS. It can not only save the capital expenditures, but also provide feasible services, and performance analyses are given to prove its effects.

A Novel Interpretable Relationship Model for Scr Inlet Gas Temperature Prediction Under Deep Peak Load Regulation

To shave peaks and fill valleys, the thermal power units are required to increase their operating time in the low-load range which negatively affects the denitrification of selective catalytic reduction (SCR). To quantify the effect, it is necessary to make an accurate prediction on the selective catalytic reduction inlet gas temperature (SCRIGT). First, the extreme gradient boosting (XGBoost) model is adopted using the unit operating parameters as inputs and load∙SCRIGT -1 as output. The prediction result shows the maximum absolute errors of 23.78% and 20.79% from two thermal power units with different type of boiler. Then, local interpretable model agnostic explanations (LIME) is applied to explain the prediction result from the XGBoost model thereby finding a linear relationship between load and load∙SCRIGT -1 with R-squared 0.994, based on which an interpretable relationship model is constructed. Finally, comparison analysis shows that the mean absolute percentage errors from the interpretable relationship model are improved from 3.07% and 2.49% to 0.68% and 0.97%, and the maximum absolute error are decreased by 85.11% and 85.98%, making the interpretable relationship model more practical to detect the SCR operation status under deep peak load regulation.

Slope reliability evaluation using an improved Kriging active learning method with various active learning functions

Further properties of null-additive fuzzy measure on metric spaces (Mathematical Theory and Applications of Uncertainty Sciences and Decision Making)

Study on DC Transmission Line Loss

The influence of transmission loss on the economic and social benefits of DC transmission project can not be ignored.It also plays a key role in voltage and conductor cross-section selection for the DC transmission project.On the basis of research on the relationship between line energy loss ratio and line power loss ratio,DC energy loss estimation method based on practical data is proposed.Through case study,the effect of conductor size selection on transmission loss is explained.And it also indicates that the loss reduction to DC transmission project with long distance and high availability hour is more prominent by using large sized conductor.

Study on the two points-pasted reinforcement corrosion monitoring method based on FBG

Prototype Debugging on Four-Step Operation Mode of TGP Lock Under Impounded Level 156m During Completion Stage