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Multiple dynamic impact signals are widely used in a variety of engineering scenarios and are difficult to identify accurately and quickly due to the signal adhesion phenomenon caused by nonlinear interference. To address this problem, an intelligent algorithm combining wavelet transforms with lightweight neural networks is proposed. First, the features of multiple impact signals are analyzed by establishing a transfer model for multiple impacts in multibody dynamical systems, and interference is suppressed using wavelet transformation. Second, a lightweight neural network, i.e., fast-activated minimal gated unit (FMGU), is elaborated for multiple impact signals, which can reduce computational complexity and improve real-time performance. Third, the experimental results show that the proposed method maintains excellent feature recognition results compared to gate recurrent unit (GRU) and long short-term memory (LSTM) networks under all test datasets with varying impact speeds, while its metrics for computational complexity are 50% lower than those of the GRU and LSTM. Therefore, the proposed method is of great practical value for weak hardware application platforms that require the accurate identification of multiple dynamic impact signals in real time. [ABSTRACT FROM AUTHOR]

Landslide disasters occur frequently in the western mountainous regions of China and are characterized by high concealment, suddenness, and strong destructiveness. Early identification of potential disaster hazards is the most effective disaster prevention and mitigation measure. The western mountainous areas are mostly in alpine-canyon terrain with a wide range, which is hard or even impossible to reach, where the traditional early identification methods of manual inspection are difficult to implement. Interferometric Synthetic Aperture Radar (InSAR), as an emerging radar remote-sensing method, can efficiently and accurately identify the hidden dangers of landslide disasters. In this study, based on the European Space Agency’s Sentinel-1 synthetic aperture radar data, the time series InSAR technology was used to identify the potential landslide hazards in alpine-canyon terrain along Yajiang County-Muli County of the Yalong River, detecting eight potential geohazards. Based on the historical data of landslide hazards and optical remote sensing interpretation, the early identification results were verified and analyzed, and the danger level of the disaster points was evaluated. The influence of geometric distortion in InSAR technology on the early identification of potential landslide disasters in alpine-canyon terrain is also discussed. This case can provide powerful data and technical support for local disaster prevention and mitigation and provide ideas and references for the early identification of the hidden dangers of landslide disasters in mountain-valley areas.

Sichuan-Tibet Railway spans several watersheds such as Jinsha River and Yalong River, and Potential landslides are frequent along the route, which poses serious hazards to the normal construction and operation of the railroad. The traditional time-series InSAR technology is limited by the number of images and other restrictions, and has a long solution time, making it difficult to obtain information on short-term occurrence of deformation and unable to perform wide-area potential landslides monitor quickly. In this paper, taking a geological hazard-prone area in the Jinsha River basin (or a section of the Sichuan-Tibet line) as an example, based on the Sentinel-1 satellite SAR data provided by the Copernicus program of ESA, the model corrects the interferometric superposition deformation results obtained from a small number of SAR images (less than 7), decodes the corrected rate results, and identifies a total of 13 areas where deformation obviously occurs A total of 13 typical areas with significant deformation were identified. The identified typical areas were time-series solved and their deformation was traced. The method provides a new idea for identification and monitor of Potential landslides in a wide area and further promotes the development of disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Revealing the land subsidence in Beijing, China, induced by the massive groundwater extraction in the past three decades, is important to mitigate the hazards and protect the residences and infrastructure. Many SAR (Synthetic Aperture Radar) datasets have been successfully applied to reveal the land subsidence over Beijing in previous research, while few works were achieved on land subsidence revealed by time-series InSAR (Interferometric Synthetic Aperture Radar) with Gaofen-3 SAR images. In this study, we successfully perform the time-series InSAR analysis with Gaofen-3 SAR images to extract the land subsidence in Beijing from 2020 to 2021. The Sentinel-1 SAR images were used to assess the accuracy of Gaofen-3 images. The subsidence scale and extent are consistent in detected major subsidence bowls between the two datasets. The spatial–temporal evolution and the deceleration of Beijing land subsidence were revealed by comparing with the Sentinel-1 results from 2017 to 2020. Moreover, we evaluated the interferometric performance of Gaofen-3 satellite SAR imagery and analyzed the main factors that mostly influence the coherence and quality of interferograms. Our results proved that the long perpendicular baselines decrease the coherence seriously over the study area, and the artifacts induced by inaccurate orbit information reduce the quality of the Gaofen-3 interferograms. Refining and removing the two main artifacts could improve the quality of interferograms formed by Gaofen-3 SAR images. [ABSTRACT FROM AUTHOR]

Landslides are a major concern in the mountainous regions of southwest China, leading to significant loss of life and property damage. Therefore, it is crucial to identify potential landslides for early warning and mitigation. stacking-InSAR, a technique used for landslide identification in a wide area, has been found to be faster than conventional time-series InSAR. However, the dense vegetation in southwest China mountains has an adverse impact on the coherence of stacking-InSAR, resulting in more noise and inaccuracies in landslide identification. To address this problem, this paper proposes an improved seasonal interferometry stacking-InSAR method. It uses Sentinel-1 satellite data from 2017 to 2022. The study area is the river valley section of the G213 road from Wenchuan County to Mao County. The study reveals the characteristics of seasonal decoherence in the steep mountainous region, and identifies a total of 21 potential landslides using the improved method. Additionally, optical satellite imagery and LiDAR data were used to assist in the identification of potential landslides. The results of the conventional stacking-InSAR method and the improved seasonal interferometry stacking-InSAR method are compared, showing that the latter is more effective in noise suppression caused by low coherence. Their standard deviations were reduced by 46%, 22%, 10%, and 14%, respectively, using the quantitative statistics for the four tested areas. The proposed method provides an efficient and effective approach for detecting potential landslides in the mountainous regions of southwest China. It can serve as a valuable technical reference for future landslide identification studies in this area. [ABSTRACT FROM AUTHOR]

The Guest Editors and the Publisher have retracted this article. This paper has been formally retracted from publication by the authors on ethical grounds. Actually, one of the authors (Mr. Peilian Ran) neglected that a similar paper was already presented and published in a previous conference proceedings. The authors are responsible for this mistake and apologise for it. Request approved by the proceedings Editor and the Publisher on February 2, 2021.

Beijing is one of the largest cities in China, which has suffered from land subsidence for a long time. According to the study from 2005 to 2017, the maximum subsidence rate of Beijing is more than 10 cm/year. This paper will use Sentinel-1A TOPS data for the first time to reveal the land subsidence of Beijing from 2017 to 2018 by using time series interferometry. SBAS-InSAR technology was used for time series analysis. The annual mean subsidence rate and time series subsidence of Beijing were obtained. The results show that the east of Chaoyang district and the northwest of Tongzhou district were the severe subsidence areas in Beijing, and the subsidence rate is more than 10 cm/year, which indicates that the subsidence area in Beijing is continuous in recent years, and corresponding measures should be taken by the government.

A triboelectric nanogenerator (TENG) facilitates the advancement of self‐powered displacement sensors, which are important for many autonomous intelligent microsystems. However, the amplitude‐based displacement sensing of conventional TENG‐based sensors still suffers significantly from varying charge densities in harsh environments. Benefiting from the combination of intelligent signal processing algorithms and direct‐current TENG sensors, this study proposes an environmentally robust character‐based displacement sensing method that eliminates the influences of varying charge density in principle. The experimental results show that under drastically changing air humidity and other harsh environments, the sensing of threshold and maximum displacement has far superior consistency and stability than that of traditional amplitude‐based TENG sensors, providing a novel route to realize reliable self‐powered displacement sensing in environment‐variable applications. [ABSTRACT FROM AUTHOR]