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  • 11
    Online Resource
    Online Resource
    How Biochar Derived from Pond Cypress (Taxodium Ascendens) Evolved with Pyrolysis Temperature and Time and Their End Efficacy Evaluation
    MDPI AG ; 2022
    In:  International Journal of Environmental Research and Public Health Vol. 19, No. 18 ( 2022-09-06), p. 11205-
    Details
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 19, No. 18 ( 2022-09-06), p. 11205-
    Abstract: Biomass type, pyrolysis temperature, and duration can affect biochar properties simultaneously. To further clarify the mechanism of this interaction, the branch and leaf parts of Pond cypress (Taxodium ascendens) were separately pyrolyzed at four peak temperatures (350 °C, 450 °C, 650 °C, and 750 °C) for three different durations (0.5 h, 1 h, and 2 h) in this study. The resulting biochar properties were measured, which included the yield, specific surface area (SSA), pH, EC (electricity conductivity), the bulk and surface elemental composition, and the contents of moisture, ash, fixed carbon, and volatile matter. The results showed that the pyrolysis temperature was more determinant for the modification of all biochar, but the residence time had a significant effect on the yield, pH, and SSA of branch-based biochar (B-biochar) at specific temperatures. However, such a phenomenon only happened on the pH of leaf-based biochar (L-biochar). Results: (1) With the temperature at 350 and 650 °C, the residence time had a significant effect on the yield of B-biochar. (2) The pH of B-biochar and L-biochar varied considerably between durations when the heating temperature hit 650 and 750 °C. (3) The SSA of B-biochar possessed an obvious fluctuation with the time during the pyrolysis from 650 to 750 °C. According to the properties measured above, the principal component and the cluster analysis classified the 24 types of biochar made in this experiment into four groups and revealed that an obvious disparity existed between B-biochar and L-biochar that were pyrolyzed at temperatures ranging from 450 to 750 °C, which suggested that biomass type was the primary factor for biochar-making. All this information can provide valuable references for the optimization of biochar-making in the real world.
    Type of Medium: Online Resource
    ISSN: 1660-4601
    URL: Article
    DOI: 10.3390/ijerph191811205
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2175195-X
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  • 12
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    An Adaptive Moving Target Imaging Method for Bistatic Forward-Looking SAR Using Keystone Transform and Optimization NLCS
    MDPI AG ; 2017
    In:  Sensors Vol. 17, No. 12 ( 2017-01-23), p. 216-
    Details
    In: Sensors, MDPI AG, Vol. 17, No. 12 ( 2017-01-23), p. 216-
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s17010216
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2052857-7
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  • 13
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    Automated Characterization of Yardangs Using Deep Convolutional Neural Networks
    MDPI AG ; 2021
    In:  Remote Sensing Vol. 13, No. 4 ( 2021-02-17), p. 733-
    Details
    In: Remote Sensing, MDPI AG, Vol. 13, No. 4 ( 2021-02-17), p. 733-
    Abstract: The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional distribution and evolution process. However, the existing automated methods to characterize yardangs are of limited generalization that may only be feasible for specific types of yardangs in certain areas. Deep learning methods, which are superior in representation learning, provide potential solutions for mapping yardangs with complex and variable features. In this study, we apply Mask region-based convolutional neural networks (Mask R-CNN) to automatically delineate and classify yardangs using very high spatial resolution images from Google Earth. The yardang field in the Qaidam Basin, northwestern China is selected to conduct the experiments and the method yields mean average precisions of 0.869 and 0.671 for intersection of union (IoU) thresholds of 0.5 and 0.75, respectively. The manual validation results on images of additional study sites show an overall detection accuracy of 74%, while more than 90% of the detected yardangs can be correctly classified and delineated. We then conclude that Mask R-CNN is a robust model to characterize multi-scale yardangs of various types and allows for the research of the morphological and evolutionary aspects of aeolian landform.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs13040733
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2513863-7
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  • 14
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    Optimization Research of Machining Parameters for Cutting GH4169 Based on Tool Vibration and Surface Roughness under High-Pressure Cooling
    MDPI AG ; 2021
    In:  Materials Vol. 14, No. 24 ( 2021-12-18), p. 7861-
    Details
    In: Materials, MDPI AG, Vol. 14, No. 24 ( 2021-12-18), p. 7861-
    Abstract: The nickel-based superalloy is widely used in aerospace. It is a typical difficult-to-cut material with poor plasticity. During the cutting process, the fluctuation of the cutting force caused by the change of cutting conditions can aggravate tool vibration, thereby reducing the surface quality of the machined workpiece. However, the emergence of high-pressure cooling technology provides technical support for overcoming the difficulty in superalloy processing. Therefore, it is of great significance to optimize the tool vibration and surface roughness of cutting GH4169 under high-pressure cooling. Taking GH4169 as the research object, the single-factor and orthogonal high-pressure cooling cutting experiments were conducted firstly in this paper. Then, the methods of the main effect diagram and response surface were applied to analyze the impact of cutting speed, feed rate, cutting depth, and cooling pressure on the three-way tool vibration. Next, MATLAB was adopted to draw the frequency spectrum of radial tool vibration at different cutting speeds, and the relationship between chip morphology, tool vibration, and workpiece surface roughness at different cutting speeds was discussed. Based on this, a mathematical model of radial tool cutting vibration and surface roughness related to the cutting amount and cooling pressure was established. Support vector machine (SVM) was applied to make predictions. Meanwhile, the non-dominated sorting genetic algorithm with an elitist strategy was adopted for multi-objective optimization, and the optimization results were verified through experiments. The results indicated that the feed rate and cutting depth had a great impact on the tool vibration and surface roughness. The established mathematical model was accurate and effective for optimizing the cutting parameters. These results are of great significance to improve the cutting stability and the quality of machined surface.
    Type of Medium: Online Resource
    ISSN: 1996-1944
    URL: Article
    DOI: 10.3390/ma14247861
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2487261-1
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  • 15
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    Research and Application Validation of a Feature Wavelength Selection Method Based on Acousto-Optic Tunable Filter (AOTF) and Automatic Machine Learning (AutoML)
    MDPI AG ; 2022
    In:  Materials Vol. 15, No. 8 ( 2022-04-12), p. 2826-
    Details
    In: Materials, MDPI AG, Vol. 15, No. 8 ( 2022-04-12), p. 2826-
    Abstract: Near-infrared spectroscopy has been widely applied in various fields such as food analysis and agricultural testing. However, the conventional method of scanning the full spectrum of the sample and then invoking the model to analyze and predict results has a large amount of collected data, redundant information, slow acquisition speed, and high model complexity. This paper proposes a feature wavelength selection approach based on acousto-optical tunable filter (AOTF) spectroscopy and automatic machine learning (AutoML). Based on the programmable selection of sub nm center wavelengths achieved by the AOTF, it is capable of rapid acquisition of combinations of feature wavelengths of samples selected using AutoML algorithms, enabling the rapid output of target substance detection results in the field. The experimental setup was designed and application validation experiments were carried out to verify that the method could significantly reduce the number of NIR sampling points, increase the sampling speed, and improve the accuracy and predictability of NIR data models while simplifying the modelling process and broadening the application scenarios.
    Type of Medium: Online Resource
    ISSN: 1996-1944
    URL: Article
    DOI: 10.3390/ma15082826
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2487261-1
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  • 16
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    Numerical and Experimental Investigation of the Seismic Effect of a Two-Stage Seismic Isolation Method
    MDPI AG ; 2023
    In:  Sustainability Vol. 15, No. 6 ( 2023-03-09), p. 4883-
    Details
    In: Sustainability, MDPI AG, Vol. 15, No. 6 ( 2023-03-09), p. 4883-
    Abstract: In order to improve the post-earthquake resilience of bridge structures, a Two-Stage seismic isolation method is proposed in this paper. According to the method, the restoring force and horizontal stiffness are smaller in the first stage and become much larger in the second stage. Therefore, a new kind of seismic isolation device, Two-Stage Friction Pendulum Bearing (TSFPB for short), is invented based on the traditional friction pendulum bearing (FPB for short). In this paper, the geometry configuration, sliding states and hysteresis characteristics of the bearing are first introduced with a theoretical approach. Then the hysteresis curve of the TSFPB is verified experimentally and the simulation method of the bearing in an FEM software is proposed. Last, a numerical analysis for an actual highway girder bridge is carried out to compare the seismic design method recommended in this paper with the conventional seismic isolation method. It is found that the Two-Stage seismic isolation method has an adaptive restoring force, horizontal stiffness and energy dissipation mechanism for different seismic intensity levels and better seismic performance compared with a conventional seismic isolation method. In addition, bridges with TSFPBs have smaller residual displacements and better post-earthquake resilience than those with traditional FPBs.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su15064883
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2518383-7
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  • 17
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    An Efficient Backprojection Algorithm Based on Wavenumber-Domain Spectral Splicing for Monostatic and Bistatic SAR Configurations
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 8 ( 2022-04-14), p. 1885-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 8 ( 2022-04-14), p. 1885-
    Abstract: This paper introduces a fast backprojection synthetic aperture radar (SAR) imaging algorithm based on wavenumber-domain spectral splicing. The traditional fast backprojection (FBP) algorithm establishes the polar coordinate system with the center of the sub-aperture as the origin. Therefore, the coordinates of the image obtained from each sub-aperture are different. Sub-aperture images must be projected to a uniform coordinate system before they can be coherently superimposed to form the final image, which requires a large amount of calculation. In order to deal with this problem, this paper proposes a novel imaging method, which uses the same polar coordinate system for each sub-aperture. The sub-aperture images are then spliced in the wavenumber-domain, and directly added after upsampling. This method avoids the projection from each sub-aperture to the uniform coordinate system, thus improving the imaging accuracy and efficiency. At the same time, the algorithm is suitable for various configurations, and can achieve good imaging results for bistatic forward-looking SAR and high-speed mobile platform. Finally, simulations are presented to demonstrate the effectiveness of the algorithm.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14081885
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 18
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    An Estimation and Compensation Method for Motion Trajectory Error in Bistatic SAR
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 21 ( 2022-11-02), p. 5522-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 21 ( 2022-11-02), p. 5522-
    Abstract: Bistatic synthetic aperture radar (BiSAR) has drawn increasing attention in recent studies benefiting from its ability for forward-looking imaging, its capability of receiver radio silence and its resistance to jamming. However, the motion trajectory error compensation of BiSAR is a challenging task due to multiple error sources and complex effects. In this paper, an estimation and compensation method for three-dimensional (3D) motion trajectory error of BiSAR is proposed. In this method, the Doppler error of multiple scattering points is estimated firstly by using the time–frequency analysis method. Next, a local autofocus process is introduced to improve the Doppler error estimation accuracy. Then, the 3D trajectory error of BiSAR is estimated by solving a series of linear equations of the trajectory error and the Doppler error with the least squares method, and a well-focused BiSAR image is produced by using the corrected 3D trajectories. Finally, simulation and experiment results are presented to demonstrate the effectiveness of the proposed method.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14215522
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 19
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    Focusing Translational Variant Bistatic Forward-Looking SAR Using Keystone Transform and Extended Nonlinear Chirp Scaling
    MDPI AG ; 2016
    In:  Remote Sensing Vol. 8, No. 10 ( 2016-10-13), p. 840-
    Details
    In: Remote Sensing, MDPI AG, Vol. 8, No. 10 ( 2016-10-13), p. 840-
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs8100840
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
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  • 20
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    When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 23 ( 2020-11-25), p. 3863-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 23 ( 2020-11-25), p. 3863-
    Abstract: With the recent advances of deep learning, automatic target recognition (ATR) of synthetic aperture radar (SAR) has achieved superior performance. By not being limited to the target category, the SAR ATR system could benefit from the simultaneous extraction of multifarious target attributes. In this paper, we propose a new multi-task learning approach for SAR ATR, which could obtain the accurate category and precise shape of the targets simultaneously. By introducing deep learning theory into multi-task learning, we first propose a novel multi-task deep learning framework with two main structures: encoder and decoder. The encoder is constructed to extract sufficient image features in different scales for the decoder, while the decoder is a tasks-specific structure which employs these extracted features adaptively and optimally to meet the different feature demands of the recognition and segmentation. Therefore, the proposed framework has the ability to achieve superior recognition and segmentation performance. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, experimental results show the superiority of the proposed framework in terms of recognition and segmentation.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12233863
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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