This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognit...This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognition methods that struggle to adapt in dynamic environments,as they typically rely on post-response mechanisms that fail to detect scene changes before users experience latency.The proposed framework leverages data from multiple smartphone sensors,including acceleration sensors,gyroscopes,magnetic field sensors,and orientation sensors,to identify different communication scenes,such as walking,running,cycling,and various modes of transportation.Extensive experimental comparative analysis with existing methods on the open-source SHL-2018 dataset confirmed the superior performance of our approach in terms of F1 score and processing speed.Additionally,tests using a Microsoft Surface Pro tablet and a self-collected Beijing-2023 dataset have validated the framework's efficiency and generalization capability.The results show that our framework achieved an F1 score of 95.15%on SHL-2018and 94.6%on Beijing-2023,highlighting its robustness across different datasets and conditions.Furthermore,the levels of computational complexity and power consumption associated with the algorithm are moderate,making it suitable for deployment on mobile devices.展开更多
Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and ot...Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.展开更多
With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation sa...With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.展开更多
Multi-sensor measurement iswidely employed in rotatingmachinery to ensure the safety ofmachines.The information provided by the single sensor is not comprehensive.Multi-sensor signals can provide complementary informa...Multi-sensor measurement iswidely employed in rotatingmachinery to ensure the safety ofmachines.The information provided by the single sensor is not comprehensive.Multi-sensor signals can provide complementary information in characterizing the health condition of machines.This paper proposed a multi-sensor fusion convolution neural network(MF-CNN)model.The proposed model adds a 2-D convolution layer before the classical 1-D CNN to automatically extract complementary features of multi-sensor signals and minimize the loss of information.A series of experiments are carried out on a rolling bearing test rig to verify the model.Vibration and sound signals are fused to achieve higher classification accuracy than typical machine learning model.In addition,the model is further applied to gas turbine abnormal detection,and shows great robustness and generalization.展开更多
Although laser powder bed fusion(LPBF)technology is considered one of the most promising additive man-ufacturing techniques,the fabricated parts still suffer from porosity defects,which can severely impact their mecha...Although laser powder bed fusion(LPBF)technology is considered one of the most promising additive man-ufacturing techniques,the fabricated parts still suffer from porosity defects,which can severely impact their mechanical performance.Monitoring the printing process using a variety of sensors to collect process signals can realize a comprehensive capture of the processing status;thus,the monitoring accuracy can be improved.However,existing multi-sensing signals are mainly optical and acoustic,and camera-based signals are mostly layer-wise images captured after printing,preventing real-time monitoring.This paper proposes a real-time melt-pool-based in-situ quality monitoring method for LPBF using multiple sensors.High-speed cameras,photodiodes,and microphones were used to collect signals during the experimental process.All three types of signals were transformed from one-dimensional time-domain signals into corresponding two-dimensional grayscale images,which enabled the capture of more localized features.Based on an improved LeNet-5 model and the weighted Dempster-Shafer evidence theory,single-sensor,dual-sensor and triple-sensor fusion monitoring models were in-vestigated with the three types of signals,and their performances were compared.The results showed that the triple-sensor fusion monitoring model achieved the highest recognition accuracy,with accuracy rates of 97.98%,92.63%,and 100%for high-,medium-,and low-quality samples,respectively.Hence,a multi-sensor fusion based melt pool monitoring system can improve the accuracy of quality monitoring in the LPBF process,which has the potential to reduce porosity defects.Finally,the experimental analysis demonstrates that the convolutional neural network proposed in this study has better classification accuracy compared to other machine learning models.展开更多
The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results ...The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.展开更多
In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And th...In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.展开更多
Cable-driven soft robots exhibit complex deformations,making state estimation challenging.Hence,this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficien...Cable-driven soft robots exhibit complex deformations,making state estimation challenging.Hence,this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients.These coefficients combine measurements from proprioceptive sensors,such as resistive flex sensors,to determine the bending angle.Additionally,the fusion strategy adopted provides robust state estimates,overcoming mismatches between the flex sensors and soft robot dimensions.Furthermore,a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter.A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors,which affect the accuracy of state estimation and control.The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot.The controller incorporates the nonlinear differentiator and drift compensation,enhancing tracking performance.Experimental results validate the effectiveness of the integrated approach,demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.展开更多
The construction of high-precision urban rail maps is crucial for the safe and efficient operation of railway transportation systems.However,the repetitive features and sparse textures in urban rail environments pose ...The construction of high-precision urban rail maps is crucial for the safe and efficient operation of railway transportation systems.However,the repetitive features and sparse textures in urban rail environments pose challenges for map construction with high-precision.Motivated by this,this paper proposes a high-precision urban rail map construction algorithm based on multi-sensor fusion.The algorithm integrates laser radar and Inertial Measurement Unit(IMU)data to construct the geometric structure map of the urban rail.It utilizes image point-line features and color information to improve map accuracy by minimizing photometric errors and incorporating color information,thus generating high-precision maps.Experimental results on a real urban rail dataset demonstrate that the proposed algorithm achieves root mean square errors of 0.345 and 1.033m for ground and tunnel scenes,respectively,representing a 19.31%and 56.80%improvement compared to state-ofthe-art methods.展开更多
This paper presents an obstacle detection approach for blind pedestrians by fusing data from camera and laser sensor.For purely vision-based blind guidance system,it is difficult to discriminate low-level obstacles wi...This paper presents an obstacle detection approach for blind pedestrians by fusing data from camera and laser sensor.For purely vision-based blind guidance system,it is difficult to discriminate low-level obstacles with cluttered road surface,while for purely laser-based system,it usually requires to scan the forward environment,which turns out to be very inconvenient.To overcome these inherent problems when using camera and laser sensor independently,a sensor-fusion model is proposed to associate range data from laser domain with edges from image domain.Based on this fusion model,obstacle's position,size and shape can be estimated.The proposed method is tested in several indoor scenes,and its efficiency is confirmed.展开更多
Maneuvering targets tracking is a fundamental task in intelligent vehicle research. Thispaper focuses on the problem of fusion between radar and image sensors in targets tracking. Inorder to improve positioning accura...Maneuvering targets tracking is a fundamental task in intelligent vehicle research. Thispaper focuses on the problem of fusion between radar and image sensors in targets tracking. Inorder to improve positioning accuracy and narrow down the image working area, a novel methodthat integrates radar filter with image intensity is proposed to establish an adaptive vision window.A weighted Hausdor? distance is introduced to define the functional relationship between image andmodel projection, and a modified simulated annealing algorithm is used to find optimum orientationparameter. Furthermore, the global state is estimated, which refers to the distributed data fusionalgorithm. Experiment results show that our method is accurate.展开更多
Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhan...Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.展开更多
This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer mult...This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.展开更多
A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5w...A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.展开更多
Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collect...Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.展开更多
BACKGROUND Lumbar interbody fusion(LIF)is the primary treatment for lumbar degenerative diseases.Elderly patients are prone to anxiety and depression after undergoing surgery,which affects their postoperative recovery...BACKGROUND Lumbar interbody fusion(LIF)is the primary treatment for lumbar degenerative diseases.Elderly patients are prone to anxiety and depression after undergoing surgery,which affects their postoperative recovery speed and quality of life.Effective prevention of anxiety and depression in elderly patients has become an urgent problem.AIM To investigate the trajectory of anxiety and depression levels in elderly patients after LIF,and the influencing factors.METHODS Random sampling was used to select 239 elderly patients who underwent LIF from January 2020 to December 2024 in Shenzhen Pingle Orthopedic Hospital.General information and surgery-related indices were recorded,and participants completed measures of psychological status,lumbar spine dysfunction,and quality of life.A latent class growth model was used to analyze the post-LIF trajectory of anxiety and depression levels,and unordered multi-categorical logistic regression was used to analyze the influencing factors.RESULTS Three trajectories of change in anxiety level were identified:Increasing anxiety(n=26,10.88%),decreasing anxiety(n=27,11.30%),and stable anxiety(n=186,77.82%).Likewise,three trajectories of change in depression level were identified:Increasing depression(n=30,12.55%),decreasing depression(n=26,10.88%),and stable depression(n=183,76.57%).Regression analysis showed that having no partner,female sex,elevated Oswestry dysfunction index(ODI)scores,and reduced 36-Item Short Form Health Survey scores all contributed to increased anxiety levels,whereas female sex,postoperative opioid use,and elevated ODI scores all contributed to increased depression levels.CONCLUSION During clinical observation,combining factors to predict anxiety and depression in post-LIF elderly patients enables timely intervention,quickens recovery,and enhances quality of life.展开更多
BACKGROUND Salvage of the infected long stem revision total knee arthroplasty is challenging due to the presence of well-fixed ingrown or cemented stems.Reconstructive options are limited.Above knee amputation(AKA)is ...BACKGROUND Salvage of the infected long stem revision total knee arthroplasty is challenging due to the presence of well-fixed ingrown or cemented stems.Reconstructive options are limited.Above knee amputation(AKA)is often recommended.We present a surgical technique that was successfully used on four such patients to convert them to a knee fusion(KF)using a cephalomedullary nail.CASE SUMMARY Four patients with infected long stem revision knee replacements that refused AKA had a single stage removal of their infected revision total knee followed by a KF.They were all treated with a statically locked antegrade cephalomedullary fusion nail,augmented with antibiotic impregnated bone cement.All patients had successful limb salvage and were ambulatory with assistive devices at the time of last follow-up.All were infection free at an average follow-up of 25.5 months(range 16-31).CONCLUSION Single stage cephalomedullary nailing can result in a successful KF in patients with infected long stem revision total knees.展开更多
With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a compl...With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has been well researched, the methods developed for the structured road of city scenes may fail in an off-road environment because of its uncertainty and diversity.A single type of sensor finds it hard to satisfy the needs of obstacle detection because of the sensing limitations in range, signal features, and working conditions of detection, and this motivates researchers and engineers to develop multi-sensor fusion and system integration methodology. This survey aims at summarizing the main considerations for the onboard multi-sensor configuration of intelligent ground vehicles in the off-road environments and providing users with a guideline for selecting sensors based on their performance requirements and application environments.State-of-the-art multi-sensor fusion methods and system prototypes are reviewed and associated to the corresponding heterogeneous sensor configurations. Finally, emerging technologies and challenges are discussed for future study.展开更多
In recent years,Simultaneous Localization And Mapping(SLAM)technology has prevailed in a wide range of applications,such as autonomous driving,intelligent robots,Augmented Reality(AR),and Virtual Reality(VR).Multi-sen...In recent years,Simultaneous Localization And Mapping(SLAM)technology has prevailed in a wide range of applications,such as autonomous driving,intelligent robots,Augmented Reality(AR),and Virtual Reality(VR).Multi-sensor fusion using the most popular three types of sensors(e.g.,visual sensor,LiDAR sensor,and IMU)is becoming ubiquitous in SLAM,in part because of the complementary sensing capabilities and the inevitable shortages(e.g.,low precision and long-term drift)of the stand-alone sensor in challenging environments.In this article,we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work.Firstly,a brief introduction of the state estimator formation in SLAM is presented.Secondly,the state-of-the-art algorithms of different multi-sensor fusion algorithms are given.Then we analyze the deficiencies associated with the reviewed approaches and formulate some future research considerations.This paper can be considered as a brief guide to newcomers and a comprehensive reference for experienced researchers and engineers to explore new interesting orientations.展开更多
The smart toolholder is the core component in the development of intelligent and precise manufacturing.It enables in situ monitoring of cutting data and machining accuracy evolution and has become a focal point in aca...The smart toolholder is the core component in the development of intelligent and precise manufacturing.It enables in situ monitoring of cutting data and machining accuracy evolution and has become a focal point in academic research and industrial applications.However,current table and rotational dynamometers for milling force,vibration,and temperature testing suffer from cumbersome installation and provide only a single acquisition signal,which limits their use in laboratory settings.In this study,we propose a wireless smart toolholder with multi-sensor fusion for simultaneous sensing of milling force,vibration,and temperature signals.We select force,vibration,and temperature sensors suitable for smart toolholder fusion to adapt to the cutting environment.Thereafter,structural design,circular runout,dynamic balancing,static stiffness,and dynamic inherent frequency tests are conducted to assess its dynamic and static performance.Finally,the smart toolholder is tested for accuracy and repeatability in terms of force,vibration,and temperature.Experimental results demonstrate that the smart toolholder accurately captures machining data with a relative deviation of less than 1.5%compared with existing force gauges and provides high repeatability of milling temperature and vibration signals.Therefore,it is a smart solution for machining condition monitoring.展开更多
基金supported by National 2011 Collaborative Innovation Center of Wireless Communication Technologies under Grant 2242022k60006。
文摘This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognition methods that struggle to adapt in dynamic environments,as they typically rely on post-response mechanisms that fail to detect scene changes before users experience latency.The proposed framework leverages data from multiple smartphone sensors,including acceleration sensors,gyroscopes,magnetic field sensors,and orientation sensors,to identify different communication scenes,such as walking,running,cycling,and various modes of transportation.Extensive experimental comparative analysis with existing methods on the open-source SHL-2018 dataset confirmed the superior performance of our approach in terms of F1 score and processing speed.Additionally,tests using a Microsoft Surface Pro tablet and a self-collected Beijing-2023 dataset have validated the framework's efficiency and generalization capability.The results show that our framework achieved an F1 score of 95.15%on SHL-2018and 94.6%on Beijing-2023,highlighting its robustness across different datasets and conditions.Furthermore,the levels of computational complexity and power consumption associated with the algorithm are moderate,making it suitable for deployment on mobile devices.
基金National Key R&D Program of China(No.2021YFB2501102)。
文摘Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.
基金supported in part by the Guangxi Power Grid Company’s 2023 Science and Technol-ogy Innovation Project(No.GXKJXM20230169)。
文摘With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.
基金support from the National Natural Science Foundation of China (Grant No.U1809219)the Key Research and Development Project of Zhejiang Province (Grant No.2020C01088).
文摘Multi-sensor measurement iswidely employed in rotatingmachinery to ensure the safety ofmachines.The information provided by the single sensor is not comprehensive.Multi-sensor signals can provide complementary information in characterizing the health condition of machines.This paper proposed a multi-sensor fusion convolution neural network(MF-CNN)model.The proposed model adds a 2-D convolution layer before the classical 1-D CNN to automatically extract complementary features of multi-sensor signals and minimize the loss of information.A series of experiments are carried out on a rolling bearing test rig to verify the model.Vibration and sound signals are fused to achieve higher classification accuracy than typical machine learning model.In addition,the model is further applied to gas turbine abnormal detection,and shows great robustness and generalization.
基金supported by Key Research and Development Pro-gram of Jiangsu Province(Grant Nos.BE2022069-1 and BE2022069-2)Natural Science Research Project of Jiangsu Higher Education Institu-tions(Grant Nos.22KJB460030 and 22KJB460004)+2 种基金Suzhou Science and Technology Development Plan(Grant No.SYC2022020)startup fund-ing at the Nanjing Normal University(Grant No.184080H202B318)2022 Nanjing Carbon Peak and Neutrality Technology Innovation Special Fund(Grant No.202211017).
文摘Although laser powder bed fusion(LPBF)technology is considered one of the most promising additive man-ufacturing techniques,the fabricated parts still suffer from porosity defects,which can severely impact their mechanical performance.Monitoring the printing process using a variety of sensors to collect process signals can realize a comprehensive capture of the processing status;thus,the monitoring accuracy can be improved.However,existing multi-sensing signals are mainly optical and acoustic,and camera-based signals are mostly layer-wise images captured after printing,preventing real-time monitoring.This paper proposes a real-time melt-pool-based in-situ quality monitoring method for LPBF using multiple sensors.High-speed cameras,photodiodes,and microphones were used to collect signals during the experimental process.All three types of signals were transformed from one-dimensional time-domain signals into corresponding two-dimensional grayscale images,which enabled the capture of more localized features.Based on an improved LeNet-5 model and the weighted Dempster-Shafer evidence theory,single-sensor,dual-sensor and triple-sensor fusion monitoring models were in-vestigated with the three types of signals,and their performances were compared.The results showed that the triple-sensor fusion monitoring model achieved the highest recognition accuracy,with accuracy rates of 97.98%,92.63%,and 100%for high-,medium-,and low-quality samples,respectively.Hence,a multi-sensor fusion based melt pool monitoring system can improve the accuracy of quality monitoring in the LPBF process,which has the potential to reduce porosity defects.Finally,the experimental analysis demonstrates that the convolutional neural network proposed in this study has better classification accuracy compared to other machine learning models.
基金the National Key R&D Program of China(2018AAA0103103).
文摘The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.
文摘In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.
基金financial support from the National Natural Science Foundation of China(62103039,62073030)the Joint Fund of Ministry of Education for Equipment Pre-Research(8091B03032303).
文摘Cable-driven soft robots exhibit complex deformations,making state estimation challenging.Hence,this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients.These coefficients combine measurements from proprioceptive sensors,such as resistive flex sensors,to determine the bending angle.Additionally,the fusion strategy adopted provides robust state estimates,overcoming mismatches between the flex sensors and soft robot dimensions.Furthermore,a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter.A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors,which affect the accuracy of state estimation and control.The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot.The controller incorporates the nonlinear differentiator and drift compensation,enhancing tracking performance.Experimental results validate the effectiveness of the integrated approach,demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.
基金supported by the Beijing Natural Science Foundation(No.L221003).
文摘The construction of high-precision urban rail maps is crucial for the safe and efficient operation of railway transportation systems.However,the repetitive features and sparse textures in urban rail environments pose challenges for map construction with high-precision.Motivated by this,this paper proposes a high-precision urban rail map construction algorithm based on multi-sensor fusion.The algorithm integrates laser radar and Inertial Measurement Unit(IMU)data to construct the geometric structure map of the urban rail.It utilizes image point-line features and color information to improve map accuracy by minimizing photometric errors and incorporating color information,thus generating high-precision maps.Experimental results on a real urban rail dataset demonstrate that the proposed algorithm achieves root mean square errors of 0.345 and 1.033m for ground and tunnel scenes,respectively,representing a 19.31%and 56.80%improvement compared to state-ofthe-art methods.
基金The MSIP(Ministry of Science,ICT&Future Planning),Korea,under the ITRC(Information Technology Research Center) support program(NIPA-2013-H0301-13-2006)supervised by the NIPA(National IT Industry Promotion Agency)
文摘This paper presents an obstacle detection approach for blind pedestrians by fusing data from camera and laser sensor.For purely vision-based blind guidance system,it is difficult to discriminate low-level obstacles with cluttered road surface,while for purely laser-based system,it usually requires to scan the forward environment,which turns out to be very inconvenient.To overcome these inherent problems when using camera and laser sensor independently,a sensor-fusion model is proposed to associate range data from laser domain with edges from image domain.Based on this fusion model,obstacle's position,size and shape can be estimated.The proposed method is tested in several indoor scenes,and its efficiency is confirmed.
基金Supported by the Special Funds for Major State Basic Research Program of P.R.China(2001CB309403)
文摘Maneuvering targets tracking is a fundamental task in intelligent vehicle research. Thispaper focuses on the problem of fusion between radar and image sensors in targets tracking. Inorder to improve positioning accuracy and narrow down the image working area, a novel methodthat integrates radar filter with image intensity is proposed to establish an adaptive vision window.A weighted Hausdor? distance is introduced to define the functional relationship between image andmodel projection, and a modified simulated annealing algorithm is used to find optimum orientationparameter. Furthermore, the global state is estimated, which refers to the distributed data fusionalgorithm. Experiment results show that our method is accurate.
文摘Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.
基金supported by the National Natural Science Foundation of China (Nos. 62276204, 62203343)。
文摘This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.
基金Shaanxi Province Qin Chuangyuan“Scientist+Engineer”Team Construction Project(2022KXJ-071)2022 Qin Chuangyuan Achievement Transformation Incubation Capacity Improvement Project(2022JH-ZHFHTS-0012)+8 种基金Shaanxi Province Key Research and Development Plan-“Two Chains”Integration Key Project-Qin Chuangyuan General Window Industrial Cluster Project(2023QCY-LL-02)Xixian New Area Science and Technology Plan(2022-YXYJ-003,2022-XXCY-010)2024 Scientific Research Project of Shaanxi National Defense Industry Vocational and Technical College(Gfy24-07)Shaanxi Vocational and Technical Education Association 2024 Vocational Education Teaching Reform Research Topic(2024SZX354)National Natural Science Foundation of China(U24A20115)2024 Shaanxi Provincial Education Department Service Local Special Scientific Research Program Project-Industrialization Cultivation Project(24JC005,24JC063)Shaanxi Province“14th Five-Year Plan”Education Science Plan,2024 Project(SGH24Y3181)National Key Research and Development Program of China(2023YFB4606400)Longmen Laboratory Frontier Exploration Topics Project(LMQYTSKT003)。
文摘A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.
基金funded by the Jilin Provincial Department of Science and Technology,grant number 20230101208JC.
文摘Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.
基金Supported by the Scientific Research Projects of the Health System in Pingshan District,No.2023122.
文摘BACKGROUND Lumbar interbody fusion(LIF)is the primary treatment for lumbar degenerative diseases.Elderly patients are prone to anxiety and depression after undergoing surgery,which affects their postoperative recovery speed and quality of life.Effective prevention of anxiety and depression in elderly patients has become an urgent problem.AIM To investigate the trajectory of anxiety and depression levels in elderly patients after LIF,and the influencing factors.METHODS Random sampling was used to select 239 elderly patients who underwent LIF from January 2020 to December 2024 in Shenzhen Pingle Orthopedic Hospital.General information and surgery-related indices were recorded,and participants completed measures of psychological status,lumbar spine dysfunction,and quality of life.A latent class growth model was used to analyze the post-LIF trajectory of anxiety and depression levels,and unordered multi-categorical logistic regression was used to analyze the influencing factors.RESULTS Three trajectories of change in anxiety level were identified:Increasing anxiety(n=26,10.88%),decreasing anxiety(n=27,11.30%),and stable anxiety(n=186,77.82%).Likewise,three trajectories of change in depression level were identified:Increasing depression(n=30,12.55%),decreasing depression(n=26,10.88%),and stable depression(n=183,76.57%).Regression analysis showed that having no partner,female sex,elevated Oswestry dysfunction index(ODI)scores,and reduced 36-Item Short Form Health Survey scores all contributed to increased anxiety levels,whereas female sex,postoperative opioid use,and elevated ODI scores all contributed to increased depression levels.CONCLUSION During clinical observation,combining factors to predict anxiety and depression in post-LIF elderly patients enables timely intervention,quickens recovery,and enhances quality of life.
文摘BACKGROUND Salvage of the infected long stem revision total knee arthroplasty is challenging due to the presence of well-fixed ingrown or cemented stems.Reconstructive options are limited.Above knee amputation(AKA)is often recommended.We present a surgical technique that was successfully used on four such patients to convert them to a knee fusion(KF)using a cephalomedullary nail.CASE SUMMARY Four patients with infected long stem revision knee replacements that refused AKA had a single stage removal of their infected revision total knee followed by a KF.They were all treated with a statically locked antegrade cephalomedullary fusion nail,augmented with antibiotic impregnated bone cement.All patients had successful limb salvage and were ambulatory with assistive devices at the time of last follow-up.All were infection free at an average follow-up of 25.5 months(range 16-31).CONCLUSION Single stage cephalomedullary nailing can result in a successful KF in patients with infected long stem revision total knees.
基金Project supported by the National Natural Science Foundation of China(Nos.61603303,61803309,and 61703343)the Natural Science Foundation of Shaanxi Province,China(No.2018JQ6070)+1 种基金the China Postdoctoral Science Foundation(No.2018M633574)the Fundamental Research Funds for the Central Universities,China(Nos.3102019ZDHKY02 and3102018JCC003)。
文摘With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has been well researched, the methods developed for the structured road of city scenes may fail in an off-road environment because of its uncertainty and diversity.A single type of sensor finds it hard to satisfy the needs of obstacle detection because of the sensing limitations in range, signal features, and working conditions of detection, and this motivates researchers and engineers to develop multi-sensor fusion and system integration methodology. This survey aims at summarizing the main considerations for the onboard multi-sensor configuration of intelligent ground vehicles in the off-road environments and providing users with a guideline for selecting sensors based on their performance requirements and application environments.State-of-the-art multi-sensor fusion methods and system prototypes are reviewed and associated to the corresponding heterogeneous sensor configurations. Finally, emerging technologies and challenges are discussed for future study.
基金supported by the Scientific and Technological Innovation 2030(No.2021ZD0110900).
文摘In recent years,Simultaneous Localization And Mapping(SLAM)technology has prevailed in a wide range of applications,such as autonomous driving,intelligent robots,Augmented Reality(AR),and Virtual Reality(VR).Multi-sensor fusion using the most popular three types of sensors(e.g.,visual sensor,LiDAR sensor,and IMU)is becoming ubiquitous in SLAM,in part because of the complementary sensing capabilities and the inevitable shortages(e.g.,low precision and long-term drift)of the stand-alone sensor in challenging environments.In this article,we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work.Firstly,a brief introduction of the state estimator formation in SLAM is presented.Secondly,the state-of-the-art algorithms of different multi-sensor fusion algorithms are given.Then we analyze the deficiencies associated with the reviewed approaches and formulate some future research considerations.This paper can be considered as a brief guide to newcomers and a comprehensive reference for experienced researchers and engineers to explore new interesting orientations.
基金the National Key R&D Program of China(Grant No.2022YFB3206700)the Fundamental Research Funds for the Central Universities,China(Grant No.2022CDJKYJH060)the Graduate Research and Innovation Foundation of Chongqing,China(Grant No.CYB23017).
文摘The smart toolholder is the core component in the development of intelligent and precise manufacturing.It enables in situ monitoring of cutting data and machining accuracy evolution and has become a focal point in academic research and industrial applications.However,current table and rotational dynamometers for milling force,vibration,and temperature testing suffer from cumbersome installation and provide only a single acquisition signal,which limits their use in laboratory settings.In this study,we propose a wireless smart toolholder with multi-sensor fusion for simultaneous sensing of milling force,vibration,and temperature signals.We select force,vibration,and temperature sensors suitable for smart toolholder fusion to adapt to the cutting environment.Thereafter,structural design,circular runout,dynamic balancing,static stiffness,and dynamic inherent frequency tests are conducted to assess its dynamic and static performance.Finally,the smart toolholder is tested for accuracy and repeatability in terms of force,vibration,and temperature.Experimental results demonstrate that the smart toolholder accurately captures machining data with a relative deviation of less than 1.5%compared with existing force gauges and provides high repeatability of milling temperature and vibration signals.Therefore,it is a smart solution for machining condition monitoring.
