To solve the problem of predicting and evaluating coal mine roof water inrush accidents, based on the background of Hongliu coal mine, the relevant weight values of the main control factors of water inrush were determined based on the water-richness index method, combined with the entropy method and FAHP two-factor method. The grid processing function of GIS and the kriging interpolation method are used to draw thematic maps of the main controlling factors, and the weight values of the water permeability indicators are coupled into the GIS. The FLAC3D numerical simulation software is used to analyze the water-conducting fracture zone after the mining of the Hongliu Coal Seam numerical simulation of development and water pressure distribution changes. The results show that the permeability hazard zoning map obtained by the EM-FAHP two-factor model is in line with the results of the damage height and the increased water pressure zone obtained by the numerical simulation.

Study of roof water inrush forecasting based on EM-FAHP two-factor model.

ABSTRACT In DEM generation using interferometric synthetic aperture radar (InSAR), the ground control points (GCPs) for refinement and reflattening are usually selected by manual selection, field surveying, GPS points and existing basemaps, which may not be completely suitable for consequent processes. We proposed a new method (auto-PS-GCP) of GCP selection based on permanent scatterers, which automatically defines the thresholds for the coherence, amplitude, and amplitude dispersion index to select permanent scatterer as the GCPs. The GCP thinning (auto-PS-GCP-Thin) was further conducted considering the point density, distances among points and terrain conditions. We used a three-stage assessment that includes: (1) phase stability and intensity of the GCPs, (2) RMSEs of the elevations between GCPs and homonymous points in the reference DEM, and (3) generated DEM accuracy. Three areas respectively in the plain, hilly and mountainous regions were selected to verify the proposed methods. The assessment using both SRTM DEM andICESat-2 points shows that the DEM accuracy of auto-PS-GCP-Thin was improved by 20%∼30% for different areas compared to the manual, where the best DEM accuracy of 4.71 m was found in the plain area. It is concluded that the proposed methods are effective and reliable in various areas with different terrain conditions.

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2022.2070678?needAccess=true

Automatic selection of permanent scatterers-based GCPs for refinement and reflattening in InSAR DEM generation

With the continuous advancement of automation technology, autonomous driving assistance systems are gradually sharing the tasks during driving, but the driver still assumes the main driving tasks. In addition to driving activities, the advent of numerous new functions will have an indirect impact on the driver’s mental effort. However, determining the driver’s mental effort remains a difficult issue. In this paper, a method is proposed to assess the mental workload of drivers, combining real driver’s physiological data with the speed of his/her vehicle. The correlation coefficient and significance level are obtained by analyzing the correlation between physiological data and road types. The relevant data is then preprocessed to determine the characteristic index, with the mental workload as the input index. The driver’s mental workload is classified and the mental workload prediction model is constructed on the basis of the combination of the Fuzzy Pattern Recognition Algorithm and Genetic Algorithm. At the same time, the suggested approach is compared to the J48 Classification Algorithm and the Simulated Annealing Optimization Algorithm. The results demonstrate that the proposed method in this paper’s effectiveness for identifying the driver’s mental workload level is evidently better than other algorithms, which provides new theoretical support for assessing the L3+ driver’s mental workload level under the background of the safety of the intended functionality when they take over the control of the drive.

/pdf/detection-of-the-drivers-mental-workload-level-in-smart-and-1v6g2pr2.pdf

Detection of the Driver’s Mental Workload Level in Smart and Autonomous Systems Using Physiological Signals

Abstract. Technology advancement has urged the development of Interferometric Synthetic Aperture Radar (InSAR) to be upgraded and transformed. The main contribution of the InSAR technique is that the surface deformation changes measurements can achieve up to millimetre level precision. Environmental problems such as landslides, volcanoes, earthquakes, excessive underground water production, and other phenomena can cause the earth's surface deformation. Deformation monitoring of a surface is vital as unexpected movement, and future behaviour can be detected and predicted. InSAR time series analysis, known as Persistent Scatterer Interferometry (PSI), has become an essential tool for measuring surface deformation. Therefore, this study provides a review of the PSI techniques used to measure surface deformation changes. An overview of surface deformation and the basic principles of the four techniques that have been developed from the improvement of Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), which is Small Baseline Subset (SBAS), Stanford Method for Persistent Scatterers (StaMPS), SqueeSAR and Quasi Persistent Scatterer (QPS) were summarised to perceive the ability of these techniques in monitoring surface deformation. This study also emphasises the effectiveness and restrictions of each developed technique and how they suit Malaysia conditions and environment. The future outlook for Malaysia in realising the PSI techniques for structural monitoring also discussed in this review. Finally, this review will lead to the implementation of appropriate techniques and better preparation for the country's structural development.


/pdf/a-short-review-on-persistent-scatterer-interferometry-2pgiyszy.pdf

A short review on persistent scatterer interferometry techniques for surface deformation monitoring

Abstract. During the construction and operation of bridges, safety accidents can occur due to improper design, construction, development of structural diseases, and inadequate maintenance measures. Such incidents can lead to significant property damage and casualties. InSAR technology is increasingly playing a critical role in the deformation monitoring of linear projects, such as bridges and roads because it is not affected by light and weather conditions. However, in the process of bridge construction, the instability of the environment often results in low scattering intensity, which can make it challenging to obtain a sufficient number of permanent scatterer (PS) points. This study proposes a method to optimize the identification of PS points by combining amplitude dispersion index, statistical median of amplitude mean, and temporal coherence coefficient, based on TerraSAR-X data. We then employed PS-InSAR technology to monitor the deformation of the under-construction bridge over the JingXiong Expressway-Yongding River, China. Furthermore, we conducted temporal subsidence analysis for different structural locations of the bridge while combining temperature data from weather stations to carry out a sensitivity analysis of the bridge deformation. The study results demonstrated that (1) the proposed method for optimizing the identification of PS points outperformed the two traditional methods in terms of increasing the selection of PS candidates by 14% and 10% respectively, while also leading to better quality identification of the PS candidates; (2) the main body of the under-construction bridge appeared relatively stable at present, with the deformation of the bored cast-in-situ piles and the connection of the approach span and abutment showing greater magnitude compared to other areas. Thus, it is essential to conduct regular monitoring of these regions and implement preventive measures in the future; and (3) the deformation of the under-construction bridge is correlated with the ambient temperature, and different structural locations display varying sensitivity to temperature changes. The findings of this study can help guide the subsequent construction and safe operation of the under-construction bridge after completion.


Deformation monitoring and sensitivity analysis of under-construction bridges considering ps optimization

Persistent Scatterer SAR Interferometry (PSInSAR) is known as one of the most effective technique for monitoring and analyzing ground deformation. It is a key step that how to identify Persistent Scatterers (PS) effectively and automatically from time-series SAR images. In the past research, one pixel will be classified to "PS" set or "no-PS" set clearly by one or more threshold rules for PS features. However, it is easy to fall into the 'either this or that' logical paradox in some cases because the covered objects in study area usually possess ambiguous boundary for interested characteristics. In this paper, a fuzzy PS concept is present and a fuzzy identification method is designed based on fuzzy set theory by taking the fuzzy characteristics of the pixels into account. Two groups of real data tests indicate that the new method can not only recognize more effective and reliable PS, but also can obtain the better quality results of selected PS, which can be used to evaluate the result of deformation and further improve the PSInSAR technology.

A fuzzy identification method for persistent scatterers in PSInSAR technology

Phase unwrapping is one of the most critical steps in InSAR data processing. Filter-based phase unwrapping methods can perform phase unwrapping and noise removal simultaneously, and show a clear advantage in dealing with high noise and density fringe interferograms. Based on the precision estimation theory of nonlinear functions in Extended Kalman Filter (EKF) and Cubature Kalman Filter (CKF) algorithms, we derive and analyze in detail the estimated precision of the EKF and the CKF on nonlinear observation equations in the phase unwrapping model. Results show that both EKF and CKF algorithms have similar precision in areas with better phase quality. However, in poorer quality areas, the precision of the two shows a certain difference and there is no consistent superiority for each algorithm. Experiments using simulated and measurement data confirm the validity of our theoretical analysis.

Performance Evaluation of Cubature Kalman Filtering and Extended Kalman Filtering Based Phase Unwrapping For Insar

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A fuzzy identification method for persistent scatterers in PSInSAR technology

Performance Evaluation of Cubature Kalman Filtering and Extended Kalman Filtering Based Phase Unwrapping For Insar