There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution...There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution process.The sampling strategy of the ultra-sparse angle is an effective method for improving time resolution.Accurate reconstruction under sparse sampling conditions has always been a bottleneck problem.In recent years,convolutional neural networks have shown outstanding advantages in sparse-angle CT reconstruction given the development of deep learning.However,existing ideas did not consider the expression of high-frequency details in neural networks,limiting their application in accurate SR-CT characterization.A novel high-frequency information-constrained deep learning network(HFIC-Net)is proposed in response to this problem.Additional high-frequency information constraints are added to improve the accuracy of the reconstruction results.Further,a series of numerical reconstruction experiments are conducted to verify this new method,and the results indicate that the reconstruction results of HFIC-Net method effectively improve reconstruction quality.This new method uses only eight-angle projections to achieve the reconstruction effect of the filtered backprojection method(FBP)method in 360 projections.The results of the HFIC-Net method demonstrate clear boundaries and accurate detailed structures,correcting the misinformation caused by using other methods.For quantitative evaluation,the SSIM used to evaluate image structure similarity is increased from 0.1951,0.9212,and 0.9308 for FBP,FBP-Conv,and DDC-Net,respectively,to 0.9620 for HFIC-Net.Finally,the results of actual SR-CT experimental data indicate that the new method can suppress artifacts and achieve accurate reconstruction,and it is suitable for the in situ SR-CT accurate characterization of ultxafast evolution process.展开更多
Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicate...Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics.展开更多
Deep learning has been recognized as an effective method for indoor positioning.However,most existing real-valued neural networks(RVNNs)treat the two constituent components of complex-valued channel state information(...Deep learning has been recognized as an effective method for indoor positioning.However,most existing real-valued neural networks(RVNNs)treat the two constituent components of complex-valued channel state information(CSI)as real-valued inputs,potentially discarding useful information embedded in the original CSI.In addition,existing positioning models generally face the contradiction between computational complexity and positioning accuracy.To address these issues,we combine graph neural network(GNN)with complex-valued neural network(CVNN)to construct a lightweight indoor positioning model named CGNet.CGNet employs complexvalued convolution operation to directly process the original CSI data,fully exploiting the correlation between real and imaginary parts of CSI while extracting local features.Subsequently,the feature values are treated as nodes,and conditional position encoding(CPE)module is applied to add positional information.To reduce the number of connections in the graph structure and lower themodel complexity,feature information is mapped to an efficient graph structure through a dynamic axial graph construction(DAGC)method,with global features extracted usingmaximum relative graph convolution(MRConv).Experimental results show that,on the CTW dataset,CGNet achieves a 10%improvement in positioning accuracy compared to existing methods,while the number of model parameters is only 0.8 M.CGNet achieves excellent positioning accuracy with very few parameters.展开更多
The rapid expansion of the low-altitude economy is driving strong demand for highly accurate and reliable positioning technologies to support diverse aerial operations.This review examines core positioning methodologi...The rapid expansion of the low-altitude economy is driving strong demand for highly accurate and reliable positioning technologies to support diverse aerial operations.This review examines core positioning methodologies within the low-altitude intelligent network(LAIN)framework,beginning with an analysis of positioning requirements and performance metrics for low-altitude flight scenarios.It systematically assesses the principles,strengths,and limitations of mainstream positioning systems,including Global Navigation Satellite Systems(GNSS),terrestrial wireless positioning,and autonomous navigation,and it surveys prevalent integrated and cooperative positioning schemes.Our analysis demonstrates that standalone positioning technologies are inadequate in complex low-altitude settings,underscoring the pivotal role of multi-source fusion and unmanned aerial vehicle(UAV)swarm cooperative positioning as future trends.To address infrastructure gaps and high deployment costs in current LAIN systems,we propose a“space−air−ground”integrated and cooperative positioning architecture centered on GNSS and the 5th generation mobile communication technology(5G).The ground layer integrates 5G and GNSS for wide-area enhanced positioning.The aerial layer uses 5G aircraft-to-everything(A2X)and sidelink(SL)communications to build self-organizing networks for cooperative UAV localization.The space layer leverages low Earth orbit(LEO)satellites to overcome coverage limitations in communication and positioning.This hierarchical architecture reduces deployment costs through infrastructure reuse and enables deep integration of communication and navigation capabilities.By supporting collaborative enhancement across all three domains,the framework improves positioning robustness and delivers cost-effective,ubiquitous,and highly reliable positioning services.Finally,we outline promising research directions.This review aims to provide a systematic reference and a novel architectural perspective for the ongoing development of LAIN.展开更多
Precise coseismic displacements in earthquake/tsunamic early warning are necessary to characterize earthquakes in real time in order to enable decision-makers to issue alerts for public safety.Real-time global navigat...Precise coseismic displacements in earthquake/tsunamic early warning are necessary to characterize earthquakes in real time in order to enable decision-makers to issue alerts for public safety.Real-time global navigation satellite systems(GNSSs)have been a valuable tool in monitoring seismic motions,allowing permanent displacement computation to be unambiguously achieved.As a valuable tool presented to the seismic commu nity,the GSeisRT software developed by Wuhan University(China)can realize multi-GNSS precise point positioning with ambiguity resolution(PPP-AR)and achieve centimeterlevel to sub-centimeter-level precision in real time.While the stable maintenance of a global precise point positioning(PPP)service is challenging,this software is capable of estimating satellite clocks and phase biases in real time using a regional GNSS network.This capability makes GSeisRT especially suitable for proprietary GNSS networks and,more importantly,the highest possible positio ning precision and reliability can be obtained.According to real-time results from the Network of the Americas,the mean root mean square(RMS)errors of kinematic PPP-AR over a 24 h span are as low as 1.2,1.3,and 3.0 cm in the east,north,and up components,respectively.Within the few minutes that span a typical seismic event,a horizontal displacement precision of 4 mm can be achieved.The positioning precision of the GSeisRT regional PPP/PPP-AR is 30%-40%higher than that of the global PPP/PPP-AR.Since 2019,GSeisRT has successfully recorded the static,dynamic,and peak ground displacements for the 2020Oaxaca,Mexico moment magnitude(Mw)7.4 event;the 2020 Lone Pine,California Mw 5.8 event;and the 2021 Qinghai,China Mw 7.3 event in real time.The resulting immediate magnitude estimates have an error of around 0.1 only.The GSeisRT software is open to the scientific community and has been applied by the China Earthquake Ne tworks Center,the EarthScope Consortium of the United States,the National Seismological Center of Chile,Institute of Geological and Nuclear Sciences Limited(GNS Science Te PūAo)of New Zealand,and the Geospatial Information Agency of Indonesia.展开更多
Nonalloyed ohmic contacts regrown by metal-organic chemical vapor deposition are performed on AlGaN/GaN high-electron-mobility transistors. Low ohmic contact resistance of 0.15Ω.mm is obtained. It is found that the s...Nonalloyed ohmic contacts regrown by metal-organic chemical vapor deposition are performed on AlGaN/GaN high-electron-mobility transistors. Low ohmic contact resistance of 0.15Ω.mm is obtained. It is found that the sidewall obliquity near the regrown interface induced by the plasma dry etching has great influence on the total contact resistance. The fabricated device with a 100-nm T-shaped gate demonstrates a maximum drain current density of 0.95 A/mm at Vgs = 1 V and a maximum peak extrinsic transcondutance Gm of 216mS/ram. Moreover, a current gain cut-off frequency fT of 115 GHz and a maximum oscillation frequency fmax of 127 GHz are achieved.展开更多
A rock-drilling jumbo is the main piece of tunneling equipment used in the energy and infrastructure industries in various countries.The positioning accuracy of its drilling boom greatly affects tunneling efficiency a...A rock-drilling jumbo is the main piece of tunneling equipment used in the energy and infrastructure industries in various countries.The positioning accuracy of its drilling boom greatly affects tunneling efficiency and section-forming quality of mine roadways and engineering tunnels.In order to improve the drilling-positioning accuracy of a three-boom drilling jumbo,we established a kinematics model of the multi-degree-of-freedom(multi-DOF)multi-boom system,using the improved Denavit-Hartenberg(D-H)method,and obtained the mapping relationship between the end position and the amount of motion of each joint.The error of the inverse kinematics calculation for the drilling boom is estimated by an analytical method and a global search algorithm based on particle swarm optimization(PSO)for a straight blasting hole and an inclined blasting hole.On this basis,we propose a back-propagation(BP)neural network optimized by an improved sparrow search algorithm(ISSA)to predict the positioning error of the drilling booms of a three-boom drilling jumbo.In order to verify the accuracy of the proposed error compensation model,we built an automatic-control test platform for the boom,and carried out a positioning error compensation test on the boom.The results show that the average drilling-positioning error was reduced from 9.79 to 5.92 cm,and the error was reduced by 39.5%.Therefore,the proposed method effectively reduces the positioning error of the drilling boom,and improves the accuracy and efficiency of rock drilling.展开更多
For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Veh...For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Vehicles(SRVs)into CP networks,which is called SRV-aided CP.However,the CP system may split into several sub-clusters that cannot be connected with each other in dense urban environments,in which the sub-clusters with few SRVs will suffer from degradation of CP performance.Since Unmanned Aerial Vehicles(UAVs)have been widely used to aid vehicular communications,we intend to utilize UAVs to assist sub-clusters in CP.In this paper,a UAV-aided CP network is constructed to fully utilize information from SRVs.First,the inter-node connection structure among the UAV and vehicles is designed to share available information from SRVs.After that,the clustering optimization strategy is proposed,in which the UAV cooperates with the high-precision sub-cluster to obtain available information from SRVs,and then broadcasts this positioning-related information to other low-precision sub-clusters.Finally,the Locally-Centralized Factor Graph Optimization(LC-FGO)algorithm is designed to fuse positioning information from cooperators.Simulation results indicate that the positioning accuracy of the CP system could be improved by fully utilizing positioning-related information from SRVs.展开更多
The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction.In satellite laser communication systems,the use of short-wave...The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction.In satellite laser communication systems,the use of short-wave infrared wavelengths as beacon light can reduce atmospheric absorption and signal attenuation.However,there are strong non-uniformity and blind pixels in the short-wave infrared image,which makes the image distorted and leads to the decrease of spot centroid positioning accuracy.Therefore,the high-precision localization of the spot centroid of the short-wave infrared images is of great research significance.A high-precision spot centroid positioning model for short-wave infrared is proposed to correct for non-uniformity and blind pixels in short-wave infrared images and quantify the localization errors caused by the two,further model-based localization error simulations are performed,and a novel spot centroid positioning payload for satellite laser communications has been designed using the latest 640×512 planar array InGaAs shortwave infrared detector.The experimental results show that the non-uniformity of the corrected image is reduced from 7%to 0.6%,the blind pixels rejection rate reaches 100%,the frame rate can be up to 2000 Hz,and the spot centroid localization accuracy is as high as 0.1 pixel point,which realizes high-precision spot centroid localization of high-frame-frequency short-wave infrared images.展开更多
High frequency pulsating voltage injection method is a good candidate for detecting the initial rotor position of permanent magnet synchronous motor.However,traditional methods require a large number of filters,which ...High frequency pulsating voltage injection method is a good candidate for detecting the initial rotor position of permanent magnet synchronous motor.However,traditional methods require a large number of filters,which leads to the deterioration of system stability and dynamic performance.In order to solve these problems,a new signal demodulation method is proposed in this paper.The proposed new method can directly obtain the amplitude of high-frequency current,thus eliminating the use of filters,improving system stability and dynamic performance and saving the work of adjusting filter parameters.In addition,a new magnetic polarity detection method is proposed,which is robust to current measurement noise.Finally,experiments verify the effectiveness of the method.展开更多
The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)den...The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)denial conditions.Therefore,this paper proposes a UAV Clusters Information Geometry Fusion Positioning(UC-IGFP)method using pseudoranges from the LEO satellites.A novel graph model for linking and computing between the UAV clusters and LEO satellites was established.By utilizing probability to describe the positional states of UAVs and sensor errors,the distributed multivariate Probability Fusion Cooperative Positioning(PF-CP)algorithm is proposed to achieve high-precision cooperative positioning and integration of the cluster.Criteria to select the centroid of the cluster were set.A new Kalman filter algorithm that is suitable for UAV clusters was designed based on the global benchmark and Riemann information geometry theory,which overcomes the discontinuity problem caused by the change of cluster centroids.Finally,the UC-IGFP method achieves the LEO continuous highprecision positioning of UAV clusters.The proposed method effectively addresses the positioning challenges caused by the strong direction of signal beams from LEO satellites and the insufficient constraint quantity of information sources at the edge nodes of the cluster.It significantly improves the accuracy and reliability of LEO-UAV cluster positioning.The results of comprehensive simulation experiments show that the proposed method has a 30.5%improvement in performance over the mainstream positioning methods,with a positioning error of 14.267 m.展开更多
Objective: The present research aims to determine if adherence to the Lewinnek safe zone, when exclusively considered, constitutes a pivotal element for ensuring stability in the context of total hip arthroplasty. Thi...Objective: The present research aims to determine if adherence to the Lewinnek safe zone, when exclusively considered, constitutes a pivotal element for ensuring stability in the context of total hip arthroplasty. This is done by examining the acetabular placement in instances of hip dislocation after total hip arthroplasty (THA). Methodology: The authors searched 2653 patient records from 2015 to 2022 looking for patients who had total hip arthroplasty at our facility. For the analysis, 23 patients were culled from 64 individuals who exhibited post-THA dislocations, employing a stringent exclusion criterion, and the resultant acetabular angulation and anteversion were quantified utilizing PEEKMED software (Peek Health S.A., Portugal) upon radiographic evidence. Results: Within the operational timeframe, from the cohort of 2653 subjects, 64 presented with at least a singular incident of displacement. Post-exclusion criterion enforcement, 23 patients were eligible for inclusion. Of these, 10 patients conformed to the safe zone demarcated by Lewinnek for both inclination and anteversion angles, while 13 exhibited deviations from the prescribed anteversion and/or inclination benchmarks. Conclusion: Analysis of the 23 patients reveals that 13 did not confirm to be in the safe zone parameters for anteversion and/or inclination, whereas 10 were within the safe zone as per Lewinnek’s guidelines. This investigative review, corroborated by extant literature, suggests that the isolated consideration of the Lewinnek safe zone does not suffice as a solitary protective factor. It further posits that additional variables are equally critical as acetabular positioning and mandate individual assessment.展开更多
In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based po...In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based positioning error sources.The method focused on overcoming the abnormal observations in satellite observation data caused by railway environment rather than the positioning results.Specifically,the relative positioning experimental platform was built and the zero-baseline method was firstly employed to evaluate the carrier phase data quality,and then,GNSS combined observation models were adopted to construct the detection values,which were applied to judge abnormal-data through the dual-frequency observations.Further,ambiguity fixing optimization was investigated based on observation data selection in partly-blocked environments.The results show that the proposed method can effectively detect and address abnormal observations and improve positioning stability.Cycle slips and gross errors can be detected and identified based on dual-frequency global navigation satellite system data.After adopting the data selection strategy,the ambiguity fixing percentage was improved by 29.2%,and the standard deviation in the East,North,and Up components was enhanced by 12.7%,7.4%,and 12.5%,respectively.The proposed method can provide references for train positioning performance optimization in railway environments from the perspective of positioning error sources.展开更多
Dear Editor,As the Internet of things(IoT)and autonomous driving continue to evolve,positioning technology faces increasing demands for higher accuracy and reliability.Traditional positioning methods often struggle in...Dear Editor,As the Internet of things(IoT)and autonomous driving continue to evolve,positioning technology faces increasing demands for higher accuracy and reliability.Traditional positioning methods often struggle in complex signal environments with multipath interference and non-line-of-sight(NLOS)conditions.Reconfigurable intelligent surfaces(RIS),an innovative technology that can flexibly control signal propagation,offer new possibilities for positioning systems.展开更多
The oil-based mud(OBM) borehole measurement environment presents significant limitations on the application of existing electrical logging instruments in high-resistance formations. In this paper, we propose a novel l...The oil-based mud(OBM) borehole measurement environment presents significant limitations on the application of existing electrical logging instruments in high-resistance formations. In this paper, we propose a novel logging method for detection of high-resistance formations in OBM using highfrequency electrodes. The method addresses the issue of shallow depth of investigation(DOI) in existing electrical logging instruments, while simultaneously ensuring the vertical resolution. Based on the principle of current continuity, the total impedance of the loop is obtained by equating the measurement loop to the series form of a capacitively coupled circuit. and its validity is verified in a homogeneous formation model and a radial two-layer formation model with a mud standoff. Then, the instrument operating frequency and electrode system parameters were preferentially determined by numerical simulation, and the effect of mud gap on impedance measurement was investigated. Subsequently, the DOI of the instrument was investigated utilizing the pseudo-geometric factor defined by the real part of impedance. It was determined that the detection depth of the instrument is 8.74 cm, while the effective vertical resolution was not less than 2 cm. Finally, a focused high-frequency electrode-type instrument was designed by introducing a pair of focused electrodes, which effectively enhanced the DOI of the instrument and was successfully deployed in the Oklahoma formation model. The simulation results demonstrate that the novel method can achieve a detection depth of 17.40 cm in highly-resistive formations drilling with OBM, which is approximately twice the depth of detection of the existing oil-based mud microimager instruments. Furthermore, its effective vertical resolution remains at or above 2 cm,which is comparable to the resolution of the existing OBM electrical logging instrument.展开更多
Objective:To analyze the significance of high-frequency ultrasound in differentiating benign and malignant breast micronodules.Methods:Eighty-five patients with breast micronodules admitted for diagnosis between Octob...Objective:To analyze the significance of high-frequency ultrasound in differentiating benign and malignant breast micronodules.Methods:Eighty-five patients with breast micronodules admitted for diagnosis between October 2022 and October 2024 were selected for high-frequency ultrasound diagnosis.The diagnostic efficacy of high-frequency ultrasound was evaluated by comparing it with the results of surgical pathology.Results:High-frequency ultrasound detected 50 benign nodules,primarily breast fibroadenomas,and 35 malignant nodules,mainly breast ductal carcinoma in situ.Based on surgical pathology results,the diagnostic accuracy of high-frequency ultrasound was 96.47%,specificity was 97.96%,and sensitivity was 94.44%.In high-frequency ultrasound diagnosis,the proportion of grade III and IV blood flow in malignant nodules was higher than that in benign nodules,while the proportion of regular shape and clear margins was lower.The proportion of microcalcifications and posterior echo attenuation was higher in malignant nodules,and the resistance index(RI)and peak blood flow velocity were lower than those in benign nodules(P<0.05).Conclusion:High-frequency ultrasound can effectively differentiate benign and malignant breast micronodules,determine specific nodule types,and exhibits high diagnostic accuracy and sensitivity.Additionally,benign and malignant nodules can be differentiated based on the grading of blood flow signals,sonographic features,and blood flow velocity,providing reasonable guidance for subsequent treatment plans.展开更多
In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deploym...In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind spots.However,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic environments.Nevertheless,research in this area still needs to be conducted.This paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this problem.This algorithm considers the dynamic alterations in obstacle locations within the designated area.It determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage rates.The experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle positions.Experimental results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles change.With 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.展开更多
Global Navigation Satellite System(GNSS)-based continuous and accurate train positioning is one of the key technologies for advanced train operations such as train virtual coupling.However,GNSS-based train positioning...Global Navigation Satellite System(GNSS)-based continuous and accurate train positioning is one of the key technologies for advanced train operations such as train virtual coupling.However,GNSS-based train positioning faces significant challenges in real-world scenarios due to environmental complexities and signal interferences.Considering this issue,this paper presents an approach for modeling and performance analysis of GNSS-based train positioning systems using Colored Petri Nets(CPNs).By systematically modeling the GNSS signal reception and processing process,the performance of the positioning system under various environment scenarios is evaluated.The system model integrates three types of interference signals(i.e.,Amplitude Modulation(AM)signals,Frequency Modulation(FM)signals,and pulse signals)while incorporating environmental factors such as terrain obstructions and tunnel shielding.Additionally,the Extended Kalman Filter(EKF)algorithm is employed to process GNSS observation data,providing accurate train position estimations.The simulation results demonstrate that signal interferences and complex environmental conditions significantly affect the GNSS-based positioning accuracy.This study offers a comprehensive framework for evaluating the performance of GNSS-based train positioning systems in different scenarios,highlighting critical factors that influence positioning accuracy and stability.展开更多
China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and dis...China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and discontinuous,and there is no definite geological law to follow,which seriously threatens the safety of coal mine production and personnel life.Conventional ground geophysical methods have low accuracy in detecting goaf areas affected by mechanical interference from open-pit mines,especially for waterless goaf areas,which cannot be detected by existing methods.This article proposes the use of high-frequency electromagnetic waves for goaf detection.The feasibility of using drilling radar to detect goaf was theoretically analyzed,and a goaf detection model was established.The response characteristics of different fillers in the goaf under different frequencies of high-frequency electromagnetic waves were simulated and analyzed.In a certain open-pit mine in Inner Mongolia,100MHz high-frequency electromagnetic waves were used to detect the goaf through directional drilling on the ground.After detection,excavation verification was carried out,and the location of one goaf detected was verified.The results of engineering practice show that the application of high-frequency electromagnetic waves in goaf detection expands the detection radius of boreholes,has the advantages of high efficiency and accuracy,and has important theoretical and practical significance.展开更多
New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed s...New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed switching of power converters.To address this situation,this paper offers an in-depth review of HF interference problems and challenges originating from power electronic devices.First,the root cause of HF electromagnetic interference,i.e.,the resonant response of the parasitic parameters of the system to high-speed switching transients,is analyzed,and various scenarios of HF interference in power systems are highlighted.Next,the types of HF interference are summarized,with a focus on common-mode interference in grounding systems.This paper thoroughly reviews and compares various suppression methods for conducted HF interference.Finally,the challenges involved and suggestions for addressing emerging HF interference problems from the perspective of both power electronics equipment and power systems are discussed.This review aims to offer a structured understanding of HF interference problems and their suppression techniques for researchers and practitioners.展开更多
基金supported by the National Nature Science Foundation of China(Nos.12027901 and 12041202)Synchrotron Radiation Joint Fund of University of Science and Technology of China(Nos.KY2090000059 and KY2090000054)。
文摘There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution process.The sampling strategy of the ultra-sparse angle is an effective method for improving time resolution.Accurate reconstruction under sparse sampling conditions has always been a bottleneck problem.In recent years,convolutional neural networks have shown outstanding advantages in sparse-angle CT reconstruction given the development of deep learning.However,existing ideas did not consider the expression of high-frequency details in neural networks,limiting their application in accurate SR-CT characterization.A novel high-frequency information-constrained deep learning network(HFIC-Net)is proposed in response to this problem.Additional high-frequency information constraints are added to improve the accuracy of the reconstruction results.Further,a series of numerical reconstruction experiments are conducted to verify this new method,and the results indicate that the reconstruction results of HFIC-Net method effectively improve reconstruction quality.This new method uses only eight-angle projections to achieve the reconstruction effect of the filtered backprojection method(FBP)method in 360 projections.The results of the HFIC-Net method demonstrate clear boundaries and accurate detailed structures,correcting the misinformation caused by using other methods.For quantitative evaluation,the SSIM used to evaluate image structure similarity is increased from 0.1951,0.9212,and 0.9308 for FBP,FBP-Conv,and DDC-Net,respectively,to 0.9620 for HFIC-Net.Finally,the results of actual SR-CT experimental data indicate that the new method can suppress artifacts and achieve accurate reconstruction,and it is suitable for the in situ SR-CT accurate characterization of ultxafast evolution process.
基金Project supported by the Basic Science Research Program through the National Research Foundation(NRF)of Korea funded by the Ministry of Science and ICT(No.RS-2024-00337001)。
文摘Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics.
文摘Deep learning has been recognized as an effective method for indoor positioning.However,most existing real-valued neural networks(RVNNs)treat the two constituent components of complex-valued channel state information(CSI)as real-valued inputs,potentially discarding useful information embedded in the original CSI.In addition,existing positioning models generally face the contradiction between computational complexity and positioning accuracy.To address these issues,we combine graph neural network(GNN)with complex-valued neural network(CVNN)to construct a lightweight indoor positioning model named CGNet.CGNet employs complexvalued convolution operation to directly process the original CSI data,fully exploiting the correlation between real and imaginary parts of CSI while extracting local features.Subsequently,the feature values are treated as nodes,and conditional position encoding(CPE)module is applied to add positional information.To reduce the number of connections in the graph structure and lower themodel complexity,feature information is mapped to an efficient graph structure through a dynamic axial graph construction(DAGC)method,with global features extracted usingmaximum relative graph convolution(MRConv).Experimental results show that,on the CTW dataset,CGNet achieves a 10%improvement in positioning accuracy compared to existing methods,while the number of model parameters is only 0.8 M.CGNet achieves excellent positioning accuracy with very few parameters.
基金supported by the National Key Research&Development Program of China(No.2024YFB3910102).
文摘The rapid expansion of the low-altitude economy is driving strong demand for highly accurate and reliable positioning technologies to support diverse aerial operations.This review examines core positioning methodologies within the low-altitude intelligent network(LAIN)framework,beginning with an analysis of positioning requirements and performance metrics for low-altitude flight scenarios.It systematically assesses the principles,strengths,and limitations of mainstream positioning systems,including Global Navigation Satellite Systems(GNSS),terrestrial wireless positioning,and autonomous navigation,and it surveys prevalent integrated and cooperative positioning schemes.Our analysis demonstrates that standalone positioning technologies are inadequate in complex low-altitude settings,underscoring the pivotal role of multi-source fusion and unmanned aerial vehicle(UAV)swarm cooperative positioning as future trends.To address infrastructure gaps and high deployment costs in current LAIN systems,we propose a“space−air−ground”integrated and cooperative positioning architecture centered on GNSS and the 5th generation mobile communication technology(5G).The ground layer integrates 5G and GNSS for wide-area enhanced positioning.The aerial layer uses 5G aircraft-to-everything(A2X)and sidelink(SL)communications to build self-organizing networks for cooperative UAV localization.The space layer leverages low Earth orbit(LEO)satellites to overcome coverage limitations in communication and positioning.This hierarchical architecture reduces deployment costs through infrastructure reuse and enables deep integration of communication and navigation capabilities.By supporting collaborative enhancement across all three domains,the framework improves positioning robustness and delivers cost-effective,ubiquitous,and highly reliable positioning services.Finally,we outline promising research directions.This review aims to provide a systematic reference and a novel architectural perspective for the ongoing development of LAIN.
基金funded by National Science Foundation of China(42025401)National Key Research and Development Program of China(2022YFB3903800)。
文摘Precise coseismic displacements in earthquake/tsunamic early warning are necessary to characterize earthquakes in real time in order to enable decision-makers to issue alerts for public safety.Real-time global navigation satellite systems(GNSSs)have been a valuable tool in monitoring seismic motions,allowing permanent displacement computation to be unambiguously achieved.As a valuable tool presented to the seismic commu nity,the GSeisRT software developed by Wuhan University(China)can realize multi-GNSS precise point positioning with ambiguity resolution(PPP-AR)and achieve centimeterlevel to sub-centimeter-level precision in real time.While the stable maintenance of a global precise point positioning(PPP)service is challenging,this software is capable of estimating satellite clocks and phase biases in real time using a regional GNSS network.This capability makes GSeisRT especially suitable for proprietary GNSS networks and,more importantly,the highest possible positio ning precision and reliability can be obtained.According to real-time results from the Network of the Americas,the mean root mean square(RMS)errors of kinematic PPP-AR over a 24 h span are as low as 1.2,1.3,and 3.0 cm in the east,north,and up components,respectively.Within the few minutes that span a typical seismic event,a horizontal displacement precision of 4 mm can be achieved.The positioning precision of the GSeisRT regional PPP/PPP-AR is 30%-40%higher than that of the global PPP/PPP-AR.Since 2019,GSeisRT has successfully recorded the static,dynamic,and peak ground displacements for the 2020Oaxaca,Mexico moment magnitude(Mw)7.4 event;the 2020 Lone Pine,California Mw 5.8 event;and the 2021 Qinghai,China Mw 7.3 event in real time.The resulting immediate magnitude estimates have an error of around 0.1 only.The GSeisRT software is open to the scientific community and has been applied by the China Earthquake Ne tworks Center,the EarthScope Consortium of the United States,the National Seismological Center of Chile,Institute of Geological and Nuclear Sciences Limited(GNS Science Te PūAo)of New Zealand,and the Geospatial Information Agency of Indonesia.
基金Supported by the National Natural Science Foundation of China under Grant No 61306113
文摘Nonalloyed ohmic contacts regrown by metal-organic chemical vapor deposition are performed on AlGaN/GaN high-electron-mobility transistors. Low ohmic contact resistance of 0.15Ω.mm is obtained. It is found that the sidewall obliquity near the regrown interface induced by the plasma dry etching has great influence on the total contact resistance. The fabricated device with a 100-nm T-shaped gate demonstrates a maximum drain current density of 0.95 A/mm at Vgs = 1 V and a maximum peak extrinsic transcondutance Gm of 216mS/ram. Moreover, a current gain cut-off frequency fT of 115 GHz and a maximum oscillation frequency fmax of 127 GHz are achieved.
基金National Natural Science Foundation of China(No.12472038)Natural Science Foundation of Jiangsu Province(No.BK20230688)+2 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.22KJB440004)Key Research and Development Program of Xuzhou(No.KC22404)Research Fund for Doctoral Degree Teachers of Jiangsu Normal University of China(No.22XFRS011).
文摘A rock-drilling jumbo is the main piece of tunneling equipment used in the energy and infrastructure industries in various countries.The positioning accuracy of its drilling boom greatly affects tunneling efficiency and section-forming quality of mine roadways and engineering tunnels.In order to improve the drilling-positioning accuracy of a three-boom drilling jumbo,we established a kinematics model of the multi-degree-of-freedom(multi-DOF)multi-boom system,using the improved Denavit-Hartenberg(D-H)method,and obtained the mapping relationship between the end position and the amount of motion of each joint.The error of the inverse kinematics calculation for the drilling boom is estimated by an analytical method and a global search algorithm based on particle swarm optimization(PSO)for a straight blasting hole and an inclined blasting hole.On this basis,we propose a back-propagation(BP)neural network optimized by an improved sparrow search algorithm(ISSA)to predict the positioning error of the drilling booms of a three-boom drilling jumbo.In order to verify the accuracy of the proposed error compensation model,we built an automatic-control test platform for the boom,and carried out a positioning error compensation test on the boom.The results show that the average drilling-positioning error was reduced from 9.79 to 5.92 cm,and the error was reduced by 39.5%.Therefore,the proposed method effectively reduces the positioning error of the drilling boom,and improves the accuracy and efficiency of rock drilling.
基金supported by the National Natural Science Foundation of China(No.62271399)the National Key Research and Development Program of China(No.2022YFB1807102)。
文摘For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Vehicles(SRVs)into CP networks,which is called SRV-aided CP.However,the CP system may split into several sub-clusters that cannot be connected with each other in dense urban environments,in which the sub-clusters with few SRVs will suffer from degradation of CP performance.Since Unmanned Aerial Vehicles(UAVs)have been widely used to aid vehicular communications,we intend to utilize UAVs to assist sub-clusters in CP.In this paper,a UAV-aided CP network is constructed to fully utilize information from SRVs.First,the inter-node connection structure among the UAV and vehicles is designed to share available information from SRVs.After that,the clustering optimization strategy is proposed,in which the UAV cooperates with the high-precision sub-cluster to obtain available information from SRVs,and then broadcasts this positioning-related information to other low-precision sub-clusters.Finally,the Locally-Centralized Factor Graph Optimization(LC-FGO)algorithm is designed to fuse positioning information from cooperators.Simulation results indicate that the positioning accuracy of the CP system could be improved by fully utilizing positioning-related information from SRVs.
基金Supported by the Short-wave Infrared Camera Systems(B025F40622024)。
文摘The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction.In satellite laser communication systems,the use of short-wave infrared wavelengths as beacon light can reduce atmospheric absorption and signal attenuation.However,there are strong non-uniformity and blind pixels in the short-wave infrared image,which makes the image distorted and leads to the decrease of spot centroid positioning accuracy.Therefore,the high-precision localization of the spot centroid of the short-wave infrared images is of great research significance.A high-precision spot centroid positioning model for short-wave infrared is proposed to correct for non-uniformity and blind pixels in short-wave infrared images and quantify the localization errors caused by the two,further model-based localization error simulations are performed,and a novel spot centroid positioning payload for satellite laser communications has been designed using the latest 640×512 planar array InGaAs shortwave infrared detector.The experimental results show that the non-uniformity of the corrected image is reduced from 7%to 0.6%,the blind pixels rejection rate reaches 100%,the frame rate can be up to 2000 Hz,and the spot centroid localization accuracy is as high as 0.1 pixel point,which realizes high-precision spot centroid localization of high-frame-frequency short-wave infrared images.
基金supported by the National Natural Science Foundation of China under Grant 51991384Anhui Provincial Major Science and Technology Project under Grant 202203c08020010。
文摘High frequency pulsating voltage injection method is a good candidate for detecting the initial rotor position of permanent magnet synchronous motor.However,traditional methods require a large number of filters,which leads to the deterioration of system stability and dynamic performance.In order to solve these problems,a new signal demodulation method is proposed in this paper.The proposed new method can directly obtain the amplitude of high-frequency current,thus eliminating the use of filters,improving system stability and dynamic performance and saving the work of adjusting filter parameters.In addition,a new magnetic polarity detection method is proposed,which is robust to current measurement noise.Finally,experiments verify the effectiveness of the method.
基金supported in part by the National Natural Science Foundation of China(Nos.62171375,62271397,62001392,62101458,62173276,61803310 and 61801394)the Shenzhen Science and Technology Innovation ProgramChina(No.JCYJ20220530161615033)。
文摘The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)denial conditions.Therefore,this paper proposes a UAV Clusters Information Geometry Fusion Positioning(UC-IGFP)method using pseudoranges from the LEO satellites.A novel graph model for linking and computing between the UAV clusters and LEO satellites was established.By utilizing probability to describe the positional states of UAVs and sensor errors,the distributed multivariate Probability Fusion Cooperative Positioning(PF-CP)algorithm is proposed to achieve high-precision cooperative positioning and integration of the cluster.Criteria to select the centroid of the cluster were set.A new Kalman filter algorithm that is suitable for UAV clusters was designed based on the global benchmark and Riemann information geometry theory,which overcomes the discontinuity problem caused by the change of cluster centroids.Finally,the UC-IGFP method achieves the LEO continuous highprecision positioning of UAV clusters.The proposed method effectively addresses the positioning challenges caused by the strong direction of signal beams from LEO satellites and the insufficient constraint quantity of information sources at the edge nodes of the cluster.It significantly improves the accuracy and reliability of LEO-UAV cluster positioning.The results of comprehensive simulation experiments show that the proposed method has a 30.5%improvement in performance over the mainstream positioning methods,with a positioning error of 14.267 m.
文摘Objective: The present research aims to determine if adherence to the Lewinnek safe zone, when exclusively considered, constitutes a pivotal element for ensuring stability in the context of total hip arthroplasty. This is done by examining the acetabular placement in instances of hip dislocation after total hip arthroplasty (THA). Methodology: The authors searched 2653 patient records from 2015 to 2022 looking for patients who had total hip arthroplasty at our facility. For the analysis, 23 patients were culled from 64 individuals who exhibited post-THA dislocations, employing a stringent exclusion criterion, and the resultant acetabular angulation and anteversion were quantified utilizing PEEKMED software (Peek Health S.A., Portugal) upon radiographic evidence. Results: Within the operational timeframe, from the cohort of 2653 subjects, 64 presented with at least a singular incident of displacement. Post-exclusion criterion enforcement, 23 patients were eligible for inclusion. Of these, 10 patients conformed to the safe zone demarcated by Lewinnek for both inclination and anteversion angles, while 13 exhibited deviations from the prescribed anteversion and/or inclination benchmarks. Conclusion: Analysis of the 23 patients reveals that 13 did not confirm to be in the safe zone parameters for anteversion and/or inclination, whereas 10 were within the safe zone as per Lewinnek’s guidelines. This investigative review, corroborated by extant literature, suggests that the isolated consideration of the Lewinnek safe zone does not suffice as a solitary protective factor. It further posits that additional variables are equally critical as acetabular positioning and mandate individual assessment.
基金Project(52272339)supported by the National Natural Science Foundation of ChinaProject(2023YFB390730303)supported by the National Key Research and Development Program of China+2 种基金Project(L2023G004)supported by the Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.Project(QZKFKT2023-005)supported by the State Key Laboratory of Heavy-duty and Express High-power Electric Locomotive,ChinaProject(2022JZZ05)supported by the Open Foundation of MOE Key Laboratory of Engineering Structures of Heavy Haul Railway(Central South University),China。
文摘In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based positioning error sources.The method focused on overcoming the abnormal observations in satellite observation data caused by railway environment rather than the positioning results.Specifically,the relative positioning experimental platform was built and the zero-baseline method was firstly employed to evaluate the carrier phase data quality,and then,GNSS combined observation models were adopted to construct the detection values,which were applied to judge abnormal-data through the dual-frequency observations.Further,ambiguity fixing optimization was investigated based on observation data selection in partly-blocked environments.The results show that the proposed method can effectively detect and address abnormal observations and improve positioning stability.Cycle slips and gross errors can be detected and identified based on dual-frequency global navigation satellite system data.After adopting the data selection strategy,the ambiguity fixing percentage was improved by 29.2%,and the standard deviation in the East,North,and Up components was enhanced by 12.7%,7.4%,and 12.5%,respectively.The proposed method can provide references for train positioning performance optimization in railway environments from the perspective of positioning error sources.
基金supported by the Open Fund Project of Key Laboratory of Ocean Observation Technology,MNR(2023klootA01).
文摘Dear Editor,As the Internet of things(IoT)and autonomous driving continue to evolve,positioning technology faces increasing demands for higher accuracy and reliability.Traditional positioning methods often struggle in complex signal environments with multipath interference and non-line-of-sight(NLOS)conditions.Reconfigurable intelligent surfaces(RIS),an innovative technology that can flexibly control signal propagation,offer new possibilities for positioning systems.
基金the National Natural Science Foundation of China(42074134,42474152,42374150)CNPC Innovation Found(2024DQ02-0152).
文摘The oil-based mud(OBM) borehole measurement environment presents significant limitations on the application of existing electrical logging instruments in high-resistance formations. In this paper, we propose a novel logging method for detection of high-resistance formations in OBM using highfrequency electrodes. The method addresses the issue of shallow depth of investigation(DOI) in existing electrical logging instruments, while simultaneously ensuring the vertical resolution. Based on the principle of current continuity, the total impedance of the loop is obtained by equating the measurement loop to the series form of a capacitively coupled circuit. and its validity is verified in a homogeneous formation model and a radial two-layer formation model with a mud standoff. Then, the instrument operating frequency and electrode system parameters were preferentially determined by numerical simulation, and the effect of mud gap on impedance measurement was investigated. Subsequently, the DOI of the instrument was investigated utilizing the pseudo-geometric factor defined by the real part of impedance. It was determined that the detection depth of the instrument is 8.74 cm, while the effective vertical resolution was not less than 2 cm. Finally, a focused high-frequency electrode-type instrument was designed by introducing a pair of focused electrodes, which effectively enhanced the DOI of the instrument and was successfully deployed in the Oklahoma formation model. The simulation results demonstrate that the novel method can achieve a detection depth of 17.40 cm in highly-resistive formations drilling with OBM, which is approximately twice the depth of detection of the existing oil-based mud microimager instruments. Furthermore, its effective vertical resolution remains at or above 2 cm,which is comparable to the resolution of the existing OBM electrical logging instrument.
文摘Objective:To analyze the significance of high-frequency ultrasound in differentiating benign and malignant breast micronodules.Methods:Eighty-five patients with breast micronodules admitted for diagnosis between October 2022 and October 2024 were selected for high-frequency ultrasound diagnosis.The diagnostic efficacy of high-frequency ultrasound was evaluated by comparing it with the results of surgical pathology.Results:High-frequency ultrasound detected 50 benign nodules,primarily breast fibroadenomas,and 35 malignant nodules,mainly breast ductal carcinoma in situ.Based on surgical pathology results,the diagnostic accuracy of high-frequency ultrasound was 96.47%,specificity was 97.96%,and sensitivity was 94.44%.In high-frequency ultrasound diagnosis,the proportion of grade III and IV blood flow in malignant nodules was higher than that in benign nodules,while the proportion of regular shape and clear margins was lower.The proportion of microcalcifications and posterior echo attenuation was higher in malignant nodules,and the resistance index(RI)and peak blood flow velocity were lower than those in benign nodules(P<0.05).Conclusion:High-frequency ultrasound can effectively differentiate benign and malignant breast micronodules,determine specific nodule types,and exhibits high diagnostic accuracy and sensitivity.Additionally,benign and malignant nodules can be differentiated based on the grading of blood flow signals,sonographic features,and blood flow velocity,providing reasonable guidance for subsequent treatment plans.
文摘In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind spots.However,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic environments.Nevertheless,research in this area still needs to be conducted.This paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this problem.This algorithm considers the dynamic alterations in obstacle locations within the designated area.It determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage rates.The experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle positions.Experimental results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles change.With 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.
基金supported by the National Key Research and Development Program of China(2023YFB3907300)the Fundamental Research Funds for the Central Universities(2024JBMC002)the National Natural Science Foundation of China(T2222015,U2268206).
文摘Global Navigation Satellite System(GNSS)-based continuous and accurate train positioning is one of the key technologies for advanced train operations such as train virtual coupling.However,GNSS-based train positioning faces significant challenges in real-world scenarios due to environmental complexities and signal interferences.Considering this issue,this paper presents an approach for modeling and performance analysis of GNSS-based train positioning systems using Colored Petri Nets(CPNs).By systematically modeling the GNSS signal reception and processing process,the performance of the positioning system under various environment scenarios is evaluated.The system model integrates three types of interference signals(i.e.,Amplitude Modulation(AM)signals,Frequency Modulation(FM)signals,and pulse signals)while incorporating environmental factors such as terrain obstructions and tunnel shielding.Additionally,the Extended Kalman Filter(EKF)algorithm is employed to process GNSS observation data,providing accurate train position estimations.The simulation results demonstrate that signal interferences and complex environmental conditions significantly affect the GNSS-based positioning accuracy.This study offers a comprehensive framework for evaluating the performance of GNSS-based train positioning systems in different scenarios,highlighting critical factors that influence positioning accuracy and stability.
文摘China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and discontinuous,and there is no definite geological law to follow,which seriously threatens the safety of coal mine production and personnel life.Conventional ground geophysical methods have low accuracy in detecting goaf areas affected by mechanical interference from open-pit mines,especially for waterless goaf areas,which cannot be detected by existing methods.This article proposes the use of high-frequency electromagnetic waves for goaf detection.The feasibility of using drilling radar to detect goaf was theoretically analyzed,and a goaf detection model was established.The response characteristics of different fillers in the goaf under different frequencies of high-frequency electromagnetic waves were simulated and analyzed.In a certain open-pit mine in Inner Mongolia,100MHz high-frequency electromagnetic waves were used to detect the goaf through directional drilling on the ground.After detection,excavation verification was carried out,and the location of one goaf detected was verified.The results of engineering practice show that the application of high-frequency electromagnetic waves in goaf detection expands the detection radius of boreholes,has the advantages of high efficiency and accuracy,and has important theoretical and practical significance.
基金supported by the science and technology project of State Grid Shanghai Municipal Electric Power Company(No.52094023003L).
文摘New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed switching of power converters.To address this situation,this paper offers an in-depth review of HF interference problems and challenges originating from power electronic devices.First,the root cause of HF electromagnetic interference,i.e.,the resonant response of the parasitic parameters of the system to high-speed switching transients,is analyzed,and various scenarios of HF interference in power systems are highlighted.Next,the types of HF interference are summarized,with a focus on common-mode interference in grounding systems.This paper thoroughly reviews and compares various suppression methods for conducted HF interference.Finally,the challenges involved and suggestions for addressing emerging HF interference problems from the perspective of both power electronics equipment and power systems are discussed.This review aims to offer a structured understanding of HF interference problems and their suppression techniques for researchers and practitioners.
