By using high-temperature deep-level transient spectroscopy （HT-DLTS） and other electrical measurement techniques, localized deep levels in n-type AlxGal xN epitaxial films with various A1 compositions （x = 0, 0.14...By using high-temperature deep-level transient spectroscopy （HT-DLTS） and other electrical measurement techniques, localized deep levels in n-type AlxGal xN epitaxial films with various A1 compositions （x = 0, 0.14, 0.24, 0.33, and 0.43） have been investigated. It is found that there are three distinct deep levels in AlxGal-xN films, whose level position with respect to the conduction band increases as AI composition increases. The dominant defect level with the activation energy deeper than 1.0 eV below the conduction band closely follows the Fermi level stabilization energy, indicating that its origin may be related to the defect complex, including the anti-site defects and divacancies in AlxGa1-xN films.展开更多
The paper discusses a new drawing technology, based on a synchronized movement of ram and cushion with multiple bending operations in alternating directions called ＂bi-directional deep drawing（BDD）.＂ The goal is t...The paper discusses a new drawing technology, based on a synchronized movement of ram and cushion with multiple bending operations in alternating directions called ＂bi-directional deep drawing（BDD）.＂ The goal is to avoid local thinning by strengthening the weak point using local hardening. BDD operations are realized before the conventional deep drawing process. This results in a local strain hardening at the weak point of the workpiece, which is usually located at the bottom punch radius. Two major aspects have to be given attention due to the high number of process parameters. On the one hand, for process design, it is helpful to have a tool by means of which it is possible to simultaneously create both the machine program for the servo press and the initial configuration for the process simulation. From the authors＇ point of view, this complexity can only be represented by a numerical analysis method, on the other hand. Consequently, both aspects are given attention in this paper.展开更多
We present a study on the single event transient （SET） induced by a pulsed laser in different silicon-germanium （SiGe） heterojunction bipolar transistors （HBTs） with the structure of local oxidation of silicon ...We present a study on the single event transient （SET） induced by a pulsed laser in different silicon-germanium （SiGe） heterojunction bipolar transistors （HBTs） with the structure of local oxidation of silicon （LOCOS） and deep trench isolation （DTI）. The experimental results are discussed in detail and it is demonstrated that a SiGe HBT with the structure of LOCOS is more sensitive than the DTI SiGe HBT in the SET. Because of the limitation of the DTI structure, the charge collection of diffusion in the DTI SiGe HBT is less than that of the LOCOS SiGe HBT. The SET sensitive area of the LOCOS SiGe HBT is located in the eollector-substrate （C/S） junction, while the sensitive area of the DTI SiGe HBT is located near to the collector electrodes.展开更多
Simultaneous Localization and Mapping(SLAM)has been widely used in emergency response,self-driving and city-scale 3D mapping and navigation.Recent deep-learning based feature point extractors have demonstrated superio...Simultaneous Localization and Mapping(SLAM)has been widely used in emergency response,self-driving and city-scale 3D mapping and navigation.Recent deep-learning based feature point extractors have demonstrated superior performance in dealing with the complex environmental challenges(e.g.extreme lighting)while the traditional extractors are struggling.In this paper,we have successfully improved the robustness and accuracy of a monocular visual SLAM system under various complex scenes by adding a deep learning based visual localization thread as an augmentation to the visual SLAM framework.In this thread,our feature extractor with an efficient lightweight deep neural network is used for absolute pose and scale estimation in real time using the highly accurate georeferenced prior map database at 20cm geometric accuracy created by our in-house and low-cost LiDAR and camera integrated device.The closed-loop error provided by our SLAM system with and without this enhancement is 1.03m and 18.28m respectively.The scale estimation of the monocular visual SLAM is also significantly improved(0.01 versus 0.98).In addition,a novel camera-LiDAR calibration workflow is also provided for large-scale 3D mapping.This paper demonstrates the application and research potential of deep-learning based vision SLAM with image and LiDAR sensors.展开更多
Microseismic source localization plays a critical role in monitoring mining-induced dynamic disasters,assessing rock mass stability,and analyzing excavation-induced disturbances.With increasing monitoring accuracy and...Microseismic source localization plays a critical role in monitoring mining-induced dynamic disasters,assessing rock mass stability,and analyzing excavation-induced disturbances.With increasing monitoring accuracy and data volume,various localization techniques have emerged to suit different scenarios.We systematically review the development of current microseismic location methods,which can be broadly categorized into three types:(1)Pickingbased methods,such as the Geiger and double-difference algorithms,which are suitable for well-constrained velocity models;(2)Waveform stacking-based localization methods,such as the source scanning algorithm(SSA)and cross-correlation stacking,which eliminate the need for arrival-time picking.These techniques exhibit strong noise resistance and are particularly well-suited for environments with low signal-to-noise ratios(SNR);and(3)Deep learning-based automatic localization approaches,such as PhaseNet and LOCFLOW,which are suitable for large-scale,intelligent monitoring.Furthermore,key factors affecting localization accuracy,such as sensor array geometry,arrival-time picking errors,and velocity model uncertainties,are discussed,along with optimization strategies including 3D velocity tomography,non-predefined velocity inversion,and time-varying velocity modeling.Finally,we explore future directions,including multi-station collaborative deep learning models,intelligent denoising techniques,and risk prediction frameworks constrained by statistical seismology,aiming to advance microseismic localization toward higher precision and robustness.展开更多
基金supported by the National Basic Research Program of China(Grant No.2012CB619300)the National Natural Science Foundation of China(Grant Nos.11174008 and 61361166007)
文摘By using high-temperature deep-level transient spectroscopy （HT-DLTS） and other electrical measurement techniques, localized deep levels in n-type AlxGal xN epitaxial films with various A1 compositions （x = 0, 0.14, 0.24, 0.33, and 0.43） have been investigated. It is found that there are three distinct deep levels in AlxGal-xN films, whose level position with respect to the conduction band increases as AI composition increases. The dominant defect level with the activation energy deeper than 1.0 eV below the conduction band closely follows the Fermi level stabilization energy, indicating that its origin may be related to the defect complex, including the anti-site defects and divacancies in AlxGa1-xN films.
基金financed by the ‘‘Arbeitsgemeinschaft industrieller Forschungsvereinigungen-Otto von Guericke e. V.’’ (AiF) as part of the program to support ‘‘Industrial Community Research and Development’’ (IGF) with funds from the ‘‘Federal Ministry for Economic Affairs and Energy’’ (BMWi) following an order by the German Federal Parliament
文摘The paper discusses a new drawing technology, based on a synchronized movement of ram and cushion with multiple bending operations in alternating directions called ＂bi-directional deep drawing（BDD）.＂ The goal is to avoid local thinning by strengthening the weak point using local hardening. BDD operations are realized before the conventional deep drawing process. This results in a local strain hardening at the weak point of the workpiece, which is usually located at the bottom punch radius. Two major aspects have to be given attention due to the high number of process parameters. On the one hand, for process design, it is helpful to have a tool by means of which it is possible to simultaneously create both the machine program for the servo press and the initial configuration for the process simulation. From the authors＇ point of view, this complexity can only be represented by a numerical analysis method, on the other hand. Consequently, both aspects are given attention in this paper.
基金Supported by the National Natural Science Foundation of China under Grant Nos 61274106
文摘We present a study on the single event transient （SET） induced by a pulsed laser in different silicon-germanium （SiGe） heterojunction bipolar transistors （HBTs） with the structure of local oxidation of silicon （LOCOS） and deep trench isolation （DTI）. The experimental results are discussed in detail and it is demonstrated that a SiGe HBT with the structure of LOCOS is more sensitive than the DTI SiGe HBT in the SET. Because of the limitation of the DTI structure, the charge collection of diffusion in the DTI SiGe HBT is less than that of the LOCOS SiGe HBT. The SET sensitive area of the LOCOS SiGe HBT is located in the eollector-substrate （C/S） junction, while the sensitive area of the DTI SiGe HBT is located near to the collector electrodes.
基金supported by the National Key Research and Development Program of China under[Grant number 2019YFC1511304]supported by the Pilot Fund of Frontier Science and Disruptive Technology of Aerospace Information Research Institute,Chinese Academy of Sciences under[Grant number E0Z21101].
文摘Simultaneous Localization and Mapping(SLAM)has been widely used in emergency response,self-driving and city-scale 3D mapping and navigation.Recent deep-learning based feature point extractors have demonstrated superior performance in dealing with the complex environmental challenges(e.g.extreme lighting)while the traditional extractors are struggling.In this paper,we have successfully improved the robustness and accuracy of a monocular visual SLAM system under various complex scenes by adding a deep learning based visual localization thread as an augmentation to the visual SLAM framework.In this thread,our feature extractor with an efficient lightweight deep neural network is used for absolute pose and scale estimation in real time using the highly accurate georeferenced prior map database at 20cm geometric accuracy created by our in-house and low-cost LiDAR and camera integrated device.The closed-loop error provided by our SLAM system with and without this enhancement is 1.03m and 18.28m respectively.The scale estimation of the monocular visual SLAM is also significantly improved(0.01 versus 0.98).In addition,a novel camera-LiDAR calibration workflow is also provided for large-scale 3D mapping.This paper demonstrates the application and research potential of deep-learning based vision SLAM with image and LiDAR sensors.
基金funded by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(2024ZD1004505)Gansu Provincial Joint Research Fund for the Year 2024(24JRRA803)+1 种基金Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology(2022B1212010002)the National Natural Science Foundation of China(42174128).
文摘Microseismic source localization plays a critical role in monitoring mining-induced dynamic disasters,assessing rock mass stability,and analyzing excavation-induced disturbances.With increasing monitoring accuracy and data volume,various localization techniques have emerged to suit different scenarios.We systematically review the development of current microseismic location methods,which can be broadly categorized into three types:(1)Pickingbased methods,such as the Geiger and double-difference algorithms,which are suitable for well-constrained velocity models;(2)Waveform stacking-based localization methods,such as the source scanning algorithm(SSA)and cross-correlation stacking,which eliminate the need for arrival-time picking.These techniques exhibit strong noise resistance and are particularly well-suited for environments with low signal-to-noise ratios(SNR);and(3)Deep learning-based automatic localization approaches,such as PhaseNet and LOCFLOW,which are suitable for large-scale,intelligent monitoring.Furthermore,key factors affecting localization accuracy,such as sensor array geometry,arrival-time picking errors,and velocity model uncertainties,are discussed,along with optimization strategies including 3D velocity tomography,non-predefined velocity inversion,and time-varying velocity modeling.Finally,we explore future directions,including multi-station collaborative deep learning models,intelligent denoising techniques,and risk prediction frameworks constrained by statistical seismology,aiming to advance microseismic localization toward higher precision and robustness.
