High-pressure is a mechanical method to regulate the structure and internal interaction of materials. Therefore, observation of properties' change can be realized in a relatively pure environment. Furthermore, high-pressure affects the delocalization of wavefunction among materials' atoms and thus their dynamics process. Dynamics results are essential data for understanding the physical and chemical characteristics, which is valuable for materials application and development. Ultrafast spectroscopy is a powerful tool to investigate dynamics process and becoming a necessary characterization method for materials investigation. The combination of high-pressure with ultrafast spectroscopy in the nanocosecond∼femtosecond scale enables us to investigate the influence of the enhanced interaction between particles on the physical and chemical properties of materials, such as energy transfer, charge transfer, Auger recombination, etc. Base on this point of view, this review summarizes recent progress in the ultrafast dynamics under high-pressure for various materials, in which new phenomena and new mechanisms are observed. In this review, we describe in detail the principles ofin situhigh pressure ultrafast dynamics probing technology and its field of application. On this basis, the progress of the study of dynamic processes under high-pressure in different material systems is summarized. An outlook onin situhigh-pressure ultrafast dynamics research is also provided.
Performing reliable target detection in reverberation background is a critical problem demanding prompt solutions. Researching on the statistical characterization of reverberation envelope is significantly important for not only the CFAR detection, but also the environment detection and seafloor classification. Identified as a robust statistical model for shallow water reverberation envelope, K-distribution, which is introduced from radar clutter processing to sonar engineering, provides a physical interpretation of its parameters which are relevant to the environment parameters. Because different sonar data processing methods may lead to different results, we have to research on the reliable statistical approaches of processing sonar data, in order to obtain objective and credible conclusions. In this paper, we generate pseudo-random variables of K-distribution and introduce a series of statistical procedures to handle the random data. Then the distribution fitting, the parameter estimation of the distribution and the evaluation of fitting goodness could be made on the basis of these steps. Supported by an underwater experiment, we sampled a set of real data by a planar array. We illustrate the approach using the real data, and verify that the reverberation envelope of shallow water fits the K-distribution model well.
Underwater target detection (UTD) is widely applied in ocean exploration. A key issue for time-of-arrival (ToA)-based UTD algorithm is to localize the first direct path in underwater multipath channel. However, the conventional Matched Filter (MF) suffers from a deterioration of the estimation accuracy since the direct arrival of the received signal is usually non-strongest. Therefore, this paper proposes a multi-feature fusion ToA estimation algorithm. First, a multi-decision strategy is proposed to realize a balance between computational complexity and estimation accuracy, including the coarse ToA range estimation and the fine ToA estimation. Second, we realize the coarse ToA range estimation by multi-level spectrum analysis. Then, the coarse ToA range is used to localize the fine ToA utilizing the multi-feature fusion ToA estimation. Finally, the simulation and experimental results are provided to validate the effectiveness of the proposed ToA estimation algorithm. It clearly shows that without training data, the proposed algorithm can deal with the multipath acoustic channel problem. The estimation accuracy is improved and compared with other algorithms.
Grouting can effectively seal and reinforce broken rock masses in deep geotechnical engineering, which have an important impact on groundwater-related disaster prevention and control. Based on multi-field coupling mechanics and rotational viscosity experiments, an advance grouting migration model of cement slurry in tunnels with high-stress broken surrounding rock is built against the background of the Xianglushan Tunnel for water diversion in central Yunnan Province. The influence characteristics of water–cement ratio, grouting pressure, and initial permeability on the process of grouting material migration are analyzed by combining classical column theory and spherical theory. The results show the following: Overall, the growth rate of grouting radius is fast during the earlier 5 min and slows down later. At the fifth minute, the normal grouting ranges are 22 cm, 51 cm, and 58 cm, at water–cement ratios 0.6, 0.8, and 1.0, respectively, while the normal grouting ranges are 58 cm, 51 cm, and 36 cm at grouting pressures 2 MPa, 1 MPa, and 0.5 MPa, respectively; the normal grouting ranges are 58 cm, 24 cm, and 11 cm at initial permeabilities 5D, 0.5D, and 0.05D, respectively. At the 60th minute, the normal grouting ranges are 47 cm, 133 cm, and 155 cm at water–cement ratios 0.6, 0.8, and 1.0, respectively; the normal grouting ranges are 155 cm, 131 cm, and 96 cm at grouting pressures 2 MPa, 1 MPa, and 0.5 MPa, respectively; meanwhile, the normal grouting ranges are 155 cm, 63 cm, and 29 cm at initial permeabilities 5D, 0.5D, and 0.05D, respectively. This study can provide theoretical guidance for on-site grouting design in unfavorable geological treatment projects.
Detecting low-speed quiet targets such as divers and underwater-unmanned-vehicles in littoral environment by using active sonar is becoming increasingly attractive. When using a Doppler insensitive pulsed linearly frequency modulated signal, the high-level clutters which might arise from underwater physical scatterers will lead to excessive false alarm rates and limit the detection performance. However, Doppler sensitive waveforms such as binary phase shift keying have the capability of filtering clutters and degrading false alarms. Thus, the question how to estimate the clutter-suppressing performance of waveforms is essential for sonar system design. In this study, the clutter-rejecting principle of waveform is theoretically introduced firstly. Then, based on the reverberation statistical features, this study proposes two methods, the Doppler-statistic method and envelope-statistic method, separately, to estimate and evaluate the clutter suppressing performance of waveforms. Finally, the methods are verified by lake experiments. It is proved that the first method has the capacity of calculating the confidence probability of suppressing clutters by a waveform, and through the second method, the clutter rejecting performance of waveforms can be evaluated and verified. The methods can be used for selecting and designing waveforms to reduce false alarms and improve detection performance.
Predicting the susceptibility of a specific part of a landslide (SSPL) involves predicting the likelihood that the part of the landslide (e.g., the entire landslide, the source area, or the scarp) will form in a given area. When predicting SSPL, the landslide samples are far less than the non-landslide samples. This class imbalance makes it difficult to predict the SSPL. This paper proposes an advanced artificial intelligence (AI) model based on the dice-cross entropy (DCE) loss function and XGBoost (XGBDCE) or Light Gradient Boosting Machine (LGBDCE) to ameliorate the class imbalance in the SSPL prediction. We select the earthquake-induced landslides from the 2018 Hokkaido earthquake as a case study to evaluate our proposed method. First, six different datasets with 24 landslide influencing factors and 10,422 samples of a specific part of the landslides are established using remote sensing and geographic information system technologies. Then, based on each of the six datasets, four landslide susceptibility algorithms (XGB, LGB, random-forest (RF) and linear discriminant analysis (LDA)) and four class balancing methods (non-balance (NB), equal-quantity sampling (EQS), inverse landslide-frequency weighting (ILW), and DCE loss) are applied to predict the SSPL. The results show that the non-balanced method underestimates landslide susceptibility, and the ILW or EQS methods overestimate the landslide susceptibility, while the DCE loss method produces more balanced results. The prediction performance of the XGBDCE (average area under the receiver operating characteristic curve (0.970) surpasses that of RF (0.956), LGB (0.962), and LDA (0.921). Our proposed methods produce more unbiased and precise results than the existing models, and have a great potential to produce accurate general (e.g., predicting the entire landslide) and detailed (e.g., combining the prediction of the landslide source area with the landslide run-out modeling) landslide susceptibility assessments, which can be further applied to landslide hazard and risk assessments.
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