This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a ...This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real\|time data. A pseudolite (PL) simulation example is given.展开更多
With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagno...With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagnosis technology play a critical role in improving the stability of metal additive manufacturing equipment.However,the limited proportion of fault data during operation challenges the accuracy and efficiency of multi-classification models due to excessive redundant data.A multi-sensor and principal component analysis(PCA)and support vector machine(SVM)asymptotic classification(PCSV)for additive manufacturing fault diagnosis method is proposed,and it divides the fault diagnosis into two steps.In the first step,real-time data are evaluated using the T2 and Q statistical parameters of the PCAmodel to identify potential faults while filtering non-fault data,thereby reducing redundancy and enhancing real-time efficiency.In the second step,the identified fault data are input into the SVM model for precise multi-class classification of fault categories.The PCSV method advances the field by significantly improving diagnostic accuracy and efficiency,achieving an accuracy of 99%,a diagnosis time of 0.65 s,and a training time of 503 s.The experimental results demonstrate the sophistication of the PCSV method for high-precision and high-efficiency fault diagnosis of small fault samples.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
Quadruped robots with body joints exhibit enhanced mobility,however,in outdoor environments,the energy that the robot can carry is limited,necessitating optimization of energy consumption to accomplish more tasks with...Quadruped robots with body joints exhibit enhanced mobility,however,in outdoor environments,the energy that the robot can carry is limited,necessitating optimization of energy consumption to accomplish more tasks within these constraints.Inspired by quadruped animals,this paper proposes an energy-saving strategy for a body joint quadruped robot based on Central Pattern Generator(CPG)with multi-sensor fusion bio-reflexes.First,an energy consumption model for the robot is established,and energy characteristic tests are conducted under different gait parameters.Based on these energy characteristics,optimal energy-efficient gait parameters are determined for various environmental conditions.Second,biological reflex mechanisms are studied,and a motion control model based on multi-sensor fusion biological reflexes is established using CPG as the foundation.By integrating the reflex model and gait parameters,real-time adaptive adjustments to the robot’s motion gait are achieved on complex terrains,reducing energy loss caused by terrain disturbances.Finally,a prototype of the body joint quadruped robot is built for experimental verification.Simulation and experimental results demonstrate that the proposed algorithm effectively reduces the robot’s Cost of Transport(COT)and significantly improves energy efficiency.The related research results can provide a useful reference for the research on energy efficiency of quadruped robots on complex terrain.展开更多
Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joint...Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.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
The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission.The tower’s tilt and severe deformation will cause the buildi...The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission.The tower’s tilt and severe deformation will cause the building to collapse.Many small changes caused the tower’s collapse,but the early staff often could not intuitively notice the changes in the tower’s state.In the current tower online monitoring system,terminal equipment often needs to replace batteries frequently due to premature exhaustion of power.According to the need for real-time measurement of power line tower,this research designed a real-time monitoring device monitoring the transmission tower attitude tilting and foundation state based on the inertial sensor,the acceleration of 3 axis inertial sensor and angular velocity raw data to pole average filtering pre-processing,and then through the complementary filtering algorithm for comprehensive calculation of tilt angle,the system meets the demand for inclined online monitoring of power line poles and towers regarding measurement accuracy,with low cost and power consumption.The optimization multi-sensor cooperative detection and correction measured tilt angle result relative accuracy can reach 1.03%,which has specific promotion and application value since the system has the advantages of unattended and efficient calculation.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
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
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
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
In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utili...In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.展开更多
At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-se...At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter.展开更多
文摘This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real\|time data. A pseudolite (PL) simulation example is given.
基金supported in part by the National Key R&D Program of China Grant 2022YFB4602200.
文摘With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagnosis technology play a critical role in improving the stability of metal additive manufacturing equipment.However,the limited proportion of fault data during operation challenges the accuracy and efficiency of multi-classification models due to excessive redundant data.A multi-sensor and principal component analysis(PCA)and support vector machine(SVM)asymptotic classification(PCSV)for additive manufacturing fault diagnosis method is proposed,and it divides the fault diagnosis into two steps.In the first step,real-time data are evaluated using the T2 and Q statistical parameters of the PCAmodel to identify potential faults while filtering non-fault data,thereby reducing redundancy and enhancing real-time efficiency.In the second step,the identified fault data are input into the SVM model for precise multi-class classification of fault categories.The PCSV method advances the field by significantly improving diagnostic accuracy and efficiency,achieving an accuracy of 99%,a diagnosis time of 0.65 s,and a training time of 503 s.The experimental results demonstrate the sophistication of the PCSV method for high-precision and high-efficiency fault diagnosis of small fault samples.
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(Grant no.52075488)the Natural Science Foundation of Zhejiang Province(LY20E050023).
文摘Quadruped robots with body joints exhibit enhanced mobility,however,in outdoor environments,the energy that the robot can carry is limited,necessitating optimization of energy consumption to accomplish more tasks within these constraints.Inspired by quadruped animals,this paper proposes an energy-saving strategy for a body joint quadruped robot based on Central Pattern Generator(CPG)with multi-sensor fusion bio-reflexes.First,an energy consumption model for the robot is established,and energy characteristic tests are conducted under different gait parameters.Based on these energy characteristics,optimal energy-efficient gait parameters are determined for various environmental conditions.Second,biological reflex mechanisms are studied,and a motion control model based on multi-sensor fusion biological reflexes is established using CPG as the foundation.By integrating the reflex model and gait parameters,real-time adaptive adjustments to the robot’s motion gait are achieved on complex terrains,reducing energy loss caused by terrain disturbances.Finally,a prototype of the body joint quadruped robot is built for experimental verification.Simulation and experimental results demonstrate that the proposed algorithm effectively reduces the robot’s Cost of Transport(COT)and significantly improves energy efficiency.The related research results can provide a useful reference for the research on energy efficiency of quadruped robots on complex terrain.
基金Project(SICGM2023301) supported by the State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology,ChinaProject(SMDPC202202) supported by the Key Laboratory of Mining Disaster Prevention and Control,ChinaProject(U21A2030) supported by the National Natural Science Foundation of China。
文摘Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.
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This work was supported by the National Natural Science Foundation of China(Nos.62172242,51901152)Industry University Cooperation Education Program of the Ministry of Education(No.2020021680113)Shanxi Scholarship Council of China.
文摘The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission.The tower’s tilt and severe deformation will cause the building to collapse.Many small changes caused the tower’s collapse,but the early staff often could not intuitively notice the changes in the tower’s state.In the current tower online monitoring system,terminal equipment often needs to replace batteries frequently due to premature exhaustion of power.According to the need for real-time measurement of power line tower,this research designed a real-time monitoring device monitoring the transmission tower attitude tilting and foundation state based on the inertial sensor,the acceleration of 3 axis inertial sensor and angular velocity raw data to pole average filtering pre-processing,and then through the complementary filtering algorithm for comprehensive calculation of tilt angle,the system meets the demand for inclined online monitoring of power line poles and towers regarding measurement accuracy,with low cost and power consumption.The optimization multi-sensor cooperative detection and correction measured tilt angle result relative accuracy can reach 1.03%,which has specific promotion and application value since the system has the advantages of unattended and efficient calculation.
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.
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.
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supported in part by the National Natural Science Foundation of China(12171124,61933007)the Natural Science Foundation of Heilongjiang Province of China(ZD2022F003)+2 种基金the National High-End Foreign Experts Recruitment Plan of China(G2023012004L)the Royal Society of UKthe Alexander von Humboldt Foundation of Germany
文摘In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.
文摘At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter.
