The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflect...The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.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
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
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
Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning b...Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning based model,for the types identification.However,traditional approaches such as convolutional neural networks(CNNs)encounter difficulties in capturing global contextual information.In addition,they are computationally expensive,which restricts their usability in resource-limited environments.To tackle these issues,we present the Cloud Vision Transformer(CloudViT),a lightweight model that integrates CNNs with Transformers.The integration enables an effective balance between local and global feature extraction.To be specific,CloudViT comprises two innovative modules:Feature Extraction(E_Module)and Downsampling(D_Module).These modules are able to significantly reduce the number of model parameters and computational complexity while maintaining translation invariance and enhancing contextual comprehension.Overall,the CloudViT includes 0.93×10^(6)parameters,which decreases more than ten times compared to the SOTA(State-of-the-Art)model CloudNet.Comprehensive evaluations conducted on the HBMCD and SWIMCAT datasets showcase the outstanding performance of CloudViT.It achieves classification accuracies of 98.45%and 100%,respectively.Moreover,the efficiency and scalability of CloudViT make it an ideal candidate for deployment inmobile cloud observation systems,enabling real-time cloud image classification.The proposed hybrid architecture of CloudViT offers a promising approach for advancing ground-based cloud image classification.It holds significant potential for both optimizing performance and facilitating practical deployment scenarios.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
Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation...Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation technologies are mostly based on target magnitudes for simulations,making it difficult to meet image simulation requirements for different signal-to-noise ratio(SNR)needs.Therefore,design of a simulation method that generates target image sequences with various SNRs based on the optical detection system parameters will be important for faint space target detection research.Addressing the SNR calculation issue in optical observation systems,this paper proposes a ground-based detection image SNR calculation method using the optical system parameters.This method calculates the SNR of an observed image precisely using radiative transfer theory,the optical system parameters,and the observation environment parameters.An SNR-based target sequence image simulation method for ground-based detection scenarios is proposed.This method calculates the imaging SNR using the optical system parameters and establishes a model for conversion between the target’s apparent magnitude and image grayscale values,thereby enabling generation of target sequence simulation images with corresponding SNRs for different system parameters.Experiments show that the SNR obtained using this calculation method has an average calculation error of<1 dB when compared with the theoretical SNR of the actual optical system.Additionally,the simulation images generated by the imaging simulation method show high consistency with real images,which meets the requirements of faint space target detection algorithm research and provides reliable data support for development of related technologies.展开更多
As global efforts to combat climate change intensify,offshore wind farms have emerged as scalable and sustainable solutions.However,their deployment depends heavily on the availability of specialized vessels with Dyna...As global efforts to combat climate change intensify,offshore wind farms have emerged as scalable and sustainable solutions.However,their deployment depends heavily on the availability of specialized vessels with Dynamic Positioning(DP)systems such as Wind Turbine Installation Vessels(WTIVs)and Service Operation Vessels(SOVs).Despite their importance,long-term demand forecasting for such vessels remains underexplored,especially in South Korea.This study presents the dDP-W model,a System Dynamics(SD)-based framework that simulates the evolving demand for DP vessels under varying technological,policy,and environmental conditions.Unlike conventional methods based on historical extrapolation,the model uses feedback-driven causality and scenario-based simulations aligned with South Korea’s offshore wind roadmap(2026-2036).Three WTIV demand scenarios—baseline,optimistic,and pessimistic—were constructed based on vessel productivity and weather-related downtime.SOV demand was estimated using cumulative turbine counts and fixed vessel coverage ratios.The simulations indicate that WTIV demand peaks in the early 2030s,requiring 6 to 7 vessels depending on conditions,while SOV demand increases steadily,reaching nearly 70 vessels by 2036.These findings highlight the need for early vessel procurement,infrastructure investment,and workforce preparation.By integrating technical,logistical,and policy factors into a dynamic model,this study provides a practical decision-support tool for stakeholders in shipbuilding and offshore energy.The results offer strategic insights to address potential vessel shortages and ensure alignment with national renewable energy goals.展开更多
Reliable and accurate cooperative positioning is vital to intelligent connected vehicles(ICVs),in which vehicle-vehicle relative measurements are integrated to provide stable locationaware services.However,in zero-tru...Reliable and accurate cooperative positioning is vital to intelligent connected vehicles(ICVs),in which vehicle-vehicle relative measurements are integrated to provide stable locationaware services.However,in zero-trust autonomous driving environments,the possibility of measurement failures and malicious communication attacks tends to reduce positioning performance.With this in mind,this paper presents an ultra-wide bandwidth(UWB)based cooperative positioning system with the specific objective of ICV localization in zero-trust driving environments.Firstly,to overcome measurement degradation under non-line-ofsight(NLOS)propagation conditions,this study proposes a decentralized 3D cooperative positioning method based on a distributed Kalman filter(DKF)by integrating relative rangeazimuth-elevation measurements,unlike the state-of-the-art methods that rely on only one single relative range information to update motion states.More specifically,in contrast to pioneering studies that mainly focus on the positioning problem arising from only one single type of communication attack(either false data injection(FDI)or denial of service(DoS)),we consider a more challenging case of secure cooperative state estimation under mixed FDI and DoS attacks.To this end,a singular-value decomposition(SVD)-assisted decoupled DKF algorithm is proposed in this work,in which a novel update-triggered inter-vehicular communication mechanism is introduced to ensure robust positioning performance against communication attacks while maintaining low transmission load between individuals.To verify the effectiveness in practical 3D NLOS scenarios,we design an intelligent connected multi-robot platform based on a robot operating system(ROS)and UWB technology.Consequently,extensive experimental results demonstrate its superiority and feasibility by achieving a high positioning accuracy of 0.68 m under adverse attacks,especially in the case of hybrid FDI and DoS attacks.In addition,several critical discussions,including the impact of attack parameters,resilience assessment,and a comparison with event-triggered methods,are provided in this work.Moreover,a demo video has been uploaded in the supplementary materials for a detailed presentation.展开更多
Numerous arthropods evolve and optimize sensory systems, enabling them to effectively adapt complex and competitive habitats. Typically, scorpions can precisely perceive the prey location with the lowest metabolic rat...Numerous arthropods evolve and optimize sensory systems, enabling them to effectively adapt complex and competitive habitats. Typically, scorpions can precisely perceive the prey location with the lowest metabolic rate among invertebrates. This biological phenomenon contrasts sharply with engineered systems, which generally associates high accuracy with substantial energy consumption. Inspired by the Scorpion Compound Slit Sensilla (SCSS) with a stress field modulation strategy, a bionic positioning sensor with superior precision and minimal power consumption is developed for the first time, which utilizes the particular Minimum Positioning Units (MPUs) to efficiently locate vibration signals. The single MPU of the SCSS can recognize the direction of collinear loads by regulating the stress field distribution and further, the coupling action of three MPUs can realize all-angle vibration monitoring in plane. Experiments demonstrate that the bionic positioning sensor achieves 1.43 degrees of angle-error-free accuracy without additional energy supply. As a proof of concept, two bionic positioning sensors and machine learning algorithm are integrated to provide centimeter (cm)-accuracy target localization, ideally suited for the man-machine interaction. The novel design offers a new mechanism for the design of traditional positioning devices, improving precision and efficiency in both the meta-universe and real-world Internet-connected systems.展开更多
Aiming at the terminal defense problem of aircraft,this paper proposes a method to simultaneously achieve terminal defense and seize the dominant position.The method employs aλ-return based reinforcement learning alg...Aiming at the terminal defense problem of aircraft,this paper proposes a method to simultaneously achieve terminal defense and seize the dominant position.The method employs aλ-return based reinforcement learning algorithm,which can be applied to the flight assistance decision-making system to improve the pilot’s survivability.First,we model the environment to simulate the interaction between air-to-air missiles and aircraft.Subsequently,we propose aλ-return based approach to improve the deep Q learning network(DQN),deep advantageous actor criticism(A2C),and proximity policy optimization(PPO)algorithms used to train manoeuvre strategies.The method employs an action space containing nine manoeuvres and defines the off-target distance at the end of the scene as a sparse reward for algorithm training.Simulation results show that the convergence speed of the three improved algorithms is significantly improved when using theλ-return method.Moreover,the effect of the fetch value on the convergence speed is verified by ablation experiments.In order to solve the illegal behavior problem in the training process,we also design a backtracking-based illegal behavior masking mechanism,which improves the data generation efficiency of the environment model and promotes effective algorithm training.展开更多
The spatiotemporal variations of sound speed, particularly the drastic variation in depth, significantly affect seafloor geodetic positioning precision. For this reason, the global navigation satellite system-acoustic...The spatiotemporal variations of sound speed, particularly the drastic variation in depth, significantly affect seafloor geodetic positioning precision. For this reason, the global navigation satellite system-acoustic(GNSS-A) positioning technology typically uses in-situ sound speed profiles(SSPs) and considers the impact of these variations at the data post-processing stage. However, in-situ SSP measurement is costly and somewhat hinders the timeliness of seafloor geodetic monitoring. We generalize the bilinear SSP(BL-SSP) to be a piecewise-linear SSP, whose model parameters are estimated from GNSS-A observations. In addition, we construct a set of constraints based on a priori marine environment observation to stabilize SSP inversion and propose an algorithm to recursively conduct the inversion, e.g.,the trilinear SSP(TL-SSP) inversion is initialized using the BL-SSP inversion result. The proposed model is verified by long-term GNSS-A seafloor geodetic observations. It shows that the root mean square error(RMSE) of the TL-SSP inversion result is 10.87 m/s, compared to 11.08 m/s for the traditional BL-SSP, with significant improvements observed in shallow and middle water layers. Furthermore, when replacing the in-situ SSP with the inverted SSP for precise seafloor geodetic positioning and incorporating the acoustic delay parameters, the TL-SSP-based positioning demonstrates higher accuracy than the BL-SSP-based approach. Relative to the positioning result based on the in-situ SSP, the mean bias, standard deviation and RMSE of the horizontal positioning error are better than 0.003 m, 0.005 m,and 0.006 m, respectively, while those of the vertical positioning error are better than 0.03 m, 0.04 m, and 0.04 m,respectively. Compared with BL-SSP, TL-SSP can achieve a positioning error reduction along the E-direction, Ndirection, and U-direction by 16.7%, 15.0%, and 5.5%, respectively.展开更多
Visual indoor positioning methods have the potential for widespread application in complex large-scale indoor environments,such as shopping centers and hospitals.However,during the visual positioning process,passing p...Visual indoor positioning methods have the potential for widespread application in complex large-scale indoor environments,such as shopping centers and hospitals.However,during the visual positioning process,passing pedestrians may cause occlusion in the visual image,leading to large deviations in the visual positioning results.Aiming at the problem of feature occlusion in visual images caused by pedestrians,this paper proposes a visual indoor positioning system that combines semantic segmentation and image restoration.The paper proposes a method called the fast image segmentation repair(FISR),which segments and rapidly repairs the selected image to eliminate the influence of pedestrians on image feature extraction and improve positioning accuracy.In addition,the paper proposes a method called local feature based bag-of-visual-words combined with high-level semantic information(LFHS)for image retrieval.LFHS uses both local features and high-level semantic information to obtain more comprehensive and accurate representations of image features.This approach improves the accuracy and robustness of image retrieval by harnessing the combined power of local features and high-level semantic information.Experimental results show that the proposed positioning method reduces the average positioning error by 0.35 m compared with NetVLAD and 0.49 m compared with MixVPR,significantly improving the performance of visual positioning technology.展开更多
This study aimed to explore an effective nursing plan for peripherally inserted central catheter(PICC)placement guided by endocardial electrocardiogram(ECG)positioning technology and to analyze related nursing problem...This study aimed to explore an effective nursing plan for peripherally inserted central catheter(PICC)placement guided by endocardial electrocardiogram(ECG)positioning technology and to analyze related nursing problems and countermeasures.A total of 96 patients who underwent PICC placement using ECG-guided positioning technology in our hospital from January 2024 to December 2024 were selected and randomly divided into an experimental group(n=48)and a control group(n=48)using a random number table.The control group received routine basic nursing care,while the experimental group was provided with comprehensive high-quality nursing care.The results showed that the success rate of one-time catheter placement was significantly higher in the experimental group than in the control group(P<0.05),while the incidence of complications was notably lower in the experimental group(P<0.05).These findings suggest that comprehensive high-quality nursing care during PICC placement guided by ECG positioning technology can effectively enhance the success rate of catheter placement and reduce complications,indicating that this nursing approach is valuable for clinical promotion and application.展开更多
基金funded by the National Key Research and Development Program of China(Grant No.2023YFB3907500)the National Natural Science Foundation(Grant No.42330602)the“Fengyun Satellite Remote Sensing Product Validation and Verification”Youth Innovation Team of the China Meteorological Administration(Grant No.CMA2023QN12)。
文摘The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.
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 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funded by Innovation and Development Special Project of China Meteorological Administration(CXFZ2022J038,CXFZ2024J035)Sichuan Science and Technology Program(No.2023YFQ0072)+1 种基金Key Laboratory of Smart Earth(No.KF2023YB03-07)Automatic Software Generation and Intelligent Service Key Laboratory of Sichuan Province(CUIT-SAG202210).
文摘Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning based model,for the types identification.However,traditional approaches such as convolutional neural networks(CNNs)encounter difficulties in capturing global contextual information.In addition,they are computationally expensive,which restricts their usability in resource-limited environments.To tackle these issues,we present the Cloud Vision Transformer(CloudViT),a lightweight model that integrates CNNs with Transformers.The integration enables an effective balance between local and global feature extraction.To be specific,CloudViT comprises two innovative modules:Feature Extraction(E_Module)and Downsampling(D_Module).These modules are able to significantly reduce the number of model parameters and computational complexity while maintaining translation invariance and enhancing contextual comprehension.Overall,the CloudViT includes 0.93×10^(6)parameters,which decreases more than ten times compared to the SOTA(State-of-the-Art)model CloudNet.Comprehensive evaluations conducted on the HBMCD and SWIMCAT datasets showcase the outstanding performance of CloudViT.It achieves classification accuracies of 98.45%and 100%,respectively.Moreover,the efficiency and scalability of CloudViT make it an ideal candidate for deployment inmobile cloud observation systems,enabling real-time cloud image classification.The proposed hybrid architecture of CloudViT offers a promising approach for advancing ground-based cloud image classification.It holds significant potential for both optimizing performance and facilitating practical deployment scenarios.
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.
基金supported by Open Fund of National Key Laboratory of Deep Space Exploration(NKDSEL2024014)by Civil Aerospace Pre-research Project of State Administration of Science,Technology and Industry for National Defence,PRC(D040103).
文摘Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation technologies are mostly based on target magnitudes for simulations,making it difficult to meet image simulation requirements for different signal-to-noise ratio(SNR)needs.Therefore,design of a simulation method that generates target image sequences with various SNRs based on the optical detection system parameters will be important for faint space target detection research.Addressing the SNR calculation issue in optical observation systems,this paper proposes a ground-based detection image SNR calculation method using the optical system parameters.This method calculates the SNR of an observed image precisely using radiative transfer theory,the optical system parameters,and the observation environment parameters.An SNR-based target sequence image simulation method for ground-based detection scenarios is proposed.This method calculates the imaging SNR using the optical system parameters and establishes a model for conversion between the target’s apparent magnitude and image grayscale values,thereby enabling generation of target sequence simulation images with corresponding SNRs for different system parameters.Experiments show that the SNR obtained using this calculation method has an average calculation error of<1 dB when compared with the theoretical SNR of the actual optical system.Additionally,the simulation images generated by the imaging simulation method show high consistency with real images,which meets the requirements of faint space target detection algorithm research and provides reliable data support for development of related technologies.
文摘As global efforts to combat climate change intensify,offshore wind farms have emerged as scalable and sustainable solutions.However,their deployment depends heavily on the availability of specialized vessels with Dynamic Positioning(DP)systems such as Wind Turbine Installation Vessels(WTIVs)and Service Operation Vessels(SOVs).Despite their importance,long-term demand forecasting for such vessels remains underexplored,especially in South Korea.This study presents the dDP-W model,a System Dynamics(SD)-based framework that simulates the evolving demand for DP vessels under varying technological,policy,and environmental conditions.Unlike conventional methods based on historical extrapolation,the model uses feedback-driven causality and scenario-based simulations aligned with South Korea’s offshore wind roadmap(2026-2036).Three WTIV demand scenarios—baseline,optimistic,and pessimistic—were constructed based on vessel productivity and weather-related downtime.SOV demand was estimated using cumulative turbine counts and fixed vessel coverage ratios.The simulations indicate that WTIV demand peaks in the early 2030s,requiring 6 to 7 vessels depending on conditions,while SOV demand increases steadily,reaching nearly 70 vessels by 2036.These findings highlight the need for early vessel procurement,infrastructure investment,and workforce preparation.By integrating technical,logistical,and policy factors into a dynamic model,this study provides a practical decision-support tool for stakeholders in shipbuilding and offshore energy.The results offer strategic insights to address potential vessel shortages and ensure alignment with national renewable energy goals.
基金supported in part by the National Natural Science Foundation of China(62273065,62003064,62303386)the Natural Science Foundation of Chongqing(CSTB2023NSCQ-LZX0014)+1 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(KJZDK201800701,KJQN202000717)Sichuan Science and Technology Program(2024NSFSC0525).
文摘Reliable and accurate cooperative positioning is vital to intelligent connected vehicles(ICVs),in which vehicle-vehicle relative measurements are integrated to provide stable locationaware services.However,in zero-trust autonomous driving environments,the possibility of measurement failures and malicious communication attacks tends to reduce positioning performance.With this in mind,this paper presents an ultra-wide bandwidth(UWB)based cooperative positioning system with the specific objective of ICV localization in zero-trust driving environments.Firstly,to overcome measurement degradation under non-line-ofsight(NLOS)propagation conditions,this study proposes a decentralized 3D cooperative positioning method based on a distributed Kalman filter(DKF)by integrating relative rangeazimuth-elevation measurements,unlike the state-of-the-art methods that rely on only one single relative range information to update motion states.More specifically,in contrast to pioneering studies that mainly focus on the positioning problem arising from only one single type of communication attack(either false data injection(FDI)or denial of service(DoS)),we consider a more challenging case of secure cooperative state estimation under mixed FDI and DoS attacks.To this end,a singular-value decomposition(SVD)-assisted decoupled DKF algorithm is proposed in this work,in which a novel update-triggered inter-vehicular communication mechanism is introduced to ensure robust positioning performance against communication attacks while maintaining low transmission load between individuals.To verify the effectiveness in practical 3D NLOS scenarios,we design an intelligent connected multi-robot platform based on a robot operating system(ROS)and UWB technology.Consequently,extensive experimental results demonstrate its superiority and feasibility by achieving a high positioning accuracy of 0.68 m under adverse attacks,especially in the case of hybrid FDI and DoS attacks.In addition,several critical discussions,including the impact of attack parameters,resilience assessment,and a comparison with event-triggered methods,are provided in this work.Moreover,a demo video has been uploaded in the supplementary materials for a detailed presentation.
基金supported by the National Natural Science Foundation of China(No.52175269)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.52021003)+2 种基金Science and Technology Research Project of Education Department of Jilin Province(JJKH20231146KJ,JJKH20241262KJ)Project ZR2024ME104 supported by Shandong Provincial Natural Science FoundationChina Postdoctoral Science Foundation(No.2024M751086).
文摘Numerous arthropods evolve and optimize sensory systems, enabling them to effectively adapt complex and competitive habitats. Typically, scorpions can precisely perceive the prey location with the lowest metabolic rate among invertebrates. This biological phenomenon contrasts sharply with engineered systems, which generally associates high accuracy with substantial energy consumption. Inspired by the Scorpion Compound Slit Sensilla (SCSS) with a stress field modulation strategy, a bionic positioning sensor with superior precision and minimal power consumption is developed for the first time, which utilizes the particular Minimum Positioning Units (MPUs) to efficiently locate vibration signals. The single MPU of the SCSS can recognize the direction of collinear loads by regulating the stress field distribution and further, the coupling action of three MPUs can realize all-angle vibration monitoring in plane. Experiments demonstrate that the bionic positioning sensor achieves 1.43 degrees of angle-error-free accuracy without additional energy supply. As a proof of concept, two bionic positioning sensors and machine learning algorithm are integrated to provide centimeter (cm)-accuracy target localization, ideally suited for the man-machine interaction. The novel design offers a new mechanism for the design of traditional positioning devices, improving precision and efficiency in both the meta-universe and real-world Internet-connected systems.
文摘Aiming at the terminal defense problem of aircraft,this paper proposes a method to simultaneously achieve terminal defense and seize the dominant position.The method employs aλ-return based reinforcement learning algorithm,which can be applied to the flight assistance decision-making system to improve the pilot’s survivability.First,we model the environment to simulate the interaction between air-to-air missiles and aircraft.Subsequently,we propose aλ-return based approach to improve the deep Q learning network(DQN),deep advantageous actor criticism(A2C),and proximity policy optimization(PPO)algorithms used to train manoeuvre strategies.The method employs an action space containing nine manoeuvres and defines the off-target distance at the end of the scene as a sparse reward for algorithm training.Simulation results show that the convergence speed of the three improved algorithms is significantly improved when using theλ-return method.Moreover,the effect of the fetch value on the convergence speed is verified by ablation experiments.In order to solve the illegal behavior problem in the training process,we also design a backtracking-based illegal behavior masking mechanism,which improves the data generation efficiency of the environment model and promotes effective algorithm training.
基金The National Key Research and Development Program under contract No. 2024YFB3909702the National Natural Science Foundation of China under contract Nos 42474014, 41931076, and 42388102+2 种基金the Scientific and Technology Inmo-vation Program of Laoshan Laboratory under contract Nos LSKJ202205100 and LSKJ202205105the Independent Research Project of State Key Laboratory of Geo-information Engineering under contract SKLGIE2023-ZZ-8the Scientific Research Project of Chinese Academy of Surveying and Mapping under contract No. AR2501。
文摘The spatiotemporal variations of sound speed, particularly the drastic variation in depth, significantly affect seafloor geodetic positioning precision. For this reason, the global navigation satellite system-acoustic(GNSS-A) positioning technology typically uses in-situ sound speed profiles(SSPs) and considers the impact of these variations at the data post-processing stage. However, in-situ SSP measurement is costly and somewhat hinders the timeliness of seafloor geodetic monitoring. We generalize the bilinear SSP(BL-SSP) to be a piecewise-linear SSP, whose model parameters are estimated from GNSS-A observations. In addition, we construct a set of constraints based on a priori marine environment observation to stabilize SSP inversion and propose an algorithm to recursively conduct the inversion, e.g.,the trilinear SSP(TL-SSP) inversion is initialized using the BL-SSP inversion result. The proposed model is verified by long-term GNSS-A seafloor geodetic observations. It shows that the root mean square error(RMSE) of the TL-SSP inversion result is 10.87 m/s, compared to 11.08 m/s for the traditional BL-SSP, with significant improvements observed in shallow and middle water layers. Furthermore, when replacing the in-situ SSP with the inverted SSP for precise seafloor geodetic positioning and incorporating the acoustic delay parameters, the TL-SSP-based positioning demonstrates higher accuracy than the BL-SSP-based approach. Relative to the positioning result based on the in-situ SSP, the mean bias, standard deviation and RMSE of the horizontal positioning error are better than 0.003 m, 0.005 m,and 0.006 m, respectively, while those of the vertical positioning error are better than 0.03 m, 0.04 m, and 0.04 m,respectively. Compared with BL-SSP, TL-SSP can achieve a positioning error reduction along the E-direction, Ndirection, and U-direction by 16.7%, 15.0%, and 5.5%, respectively.
基金Supported by the National Natural Science Foundation of China(No.61971162,61771186)the Natural Science Foundation of Heilongjiang Province(No.PL2024F025)+2 种基金the Open Research Fund of National Mobile Communications Research Laboratory Southeast University(No.2023D07)the Outstanding Youth Program of Natural Science Foundation of Heilongjiang Province(No.YQ2020F012)the Fundamental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
文摘Visual indoor positioning methods have the potential for widespread application in complex large-scale indoor environments,such as shopping centers and hospitals.However,during the visual positioning process,passing pedestrians may cause occlusion in the visual image,leading to large deviations in the visual positioning results.Aiming at the problem of feature occlusion in visual images caused by pedestrians,this paper proposes a visual indoor positioning system that combines semantic segmentation and image restoration.The paper proposes a method called the fast image segmentation repair(FISR),which segments and rapidly repairs the selected image to eliminate the influence of pedestrians on image feature extraction and improve positioning accuracy.In addition,the paper proposes a method called local feature based bag-of-visual-words combined with high-level semantic information(LFHS)for image retrieval.LFHS uses both local features and high-level semantic information to obtain more comprehensive and accurate representations of image features.This approach improves the accuracy and robustness of image retrieval by harnessing the combined power of local features and high-level semantic information.Experimental results show that the proposed positioning method reduces the average positioning error by 0.35 m compared with NetVLAD and 0.49 m compared with MixVPR,significantly improving the performance of visual positioning technology.
文摘This study aimed to explore an effective nursing plan for peripherally inserted central catheter(PICC)placement guided by endocardial electrocardiogram(ECG)positioning technology and to analyze related nursing problems and countermeasures.A total of 96 patients who underwent PICC placement using ECG-guided positioning technology in our hospital from January 2024 to December 2024 were selected and randomly divided into an experimental group(n=48)and a control group(n=48)using a random number table.The control group received routine basic nursing care,while the experimental group was provided with comprehensive high-quality nursing care.The results showed that the success rate of one-time catheter placement was significantly higher in the experimental group than in the control group(P<0.05),while the incidence of complications was notably lower in the experimental group(P<0.05).These findings suggest that comprehensive high-quality nursing care during PICC placement guided by ECG positioning technology can effectively enhance the success rate of catheter placement and reduce complications,indicating that this nursing approach is valuable for clinical promotion and application.
