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
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
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
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 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 order to improve detection system robustness and reliability, multi-sensors fusion is used in modern air combat. In this paper, a data fusion method based on reinforcement learning is developed for multi-sensors. I...In order to improve detection system robustness and reliability, multi-sensors fusion is used in modern air combat. In this paper, a data fusion method based on reinforcement learning is developed for multi-sensors. Initially, the cubic B-spline interpolation is used to solve time alignment problems of multisource data. Then, the reinforcement learning based data fusion(RLBDF) method is proposed to obtain the fusion results. With the case that the priori knowledge of target is obtained, the fusion accuracy reinforcement is realized by the error between fused value and actual value. Furthermore, the Fisher information is instead used as the reward if the priori knowledge is unable to be obtained. Simulations results verify that the developed method is feasible and effective for the multi-sensors data fusion in air combat.展开更多
In order to improve the detail preservation and target information integrity of different sensor fusion images,an image fusion method of different sensors based on non-subsampling contourlet transform(NSCT)and GoogLeN...In order to improve the detail preservation and target information integrity of different sensor fusion images,an image fusion method of different sensors based on non-subsampling contourlet transform(NSCT)and GoogLeNet neural network model is proposed. First,the different sensors images,i. e.,infrared and visible images,are transformed by NSCT to obtain a low frequency sub-band and a series of high frequency sub-bands respectively.Then,the high frequency sub-bands are fused with the max regional energy selection strategy,the low frequency subbands are input into GoogLeNet neural network model to extract feature maps,and the fusion weight matrices are adaptively calculated from the feature maps. Next,the fused low frequency sub-band is obtained with weighted summation. Finally,the fused image is obtained by inverse NSCT. The experimental results demonstrate that the proposed method improves the image visual effect and achieves better performance in both edge retention and mutual information.展开更多
In this research, a near infrared multi-wavelength noninvasive blood glucose monitoring system with distributed laser multi-sensors is applied to monitor human blood glucose concentration. In order to improve the moni...In this research, a near infrared multi-wavelength noninvasive blood glucose monitoring system with distributed laser multi-sensors is applied to monitor human blood glucose concentration. In order to improve the monitoring accuracy, a multi-sensors information fusion model based on Back Propagation Artificial Neural Network is proposed. The Root- Mean-Square Error of Prediction for noninvasive blood glucose measurement is 0.088mmol/L, and the correlation coefficient is 0.94. The noninvasive blood glucose monitoring system based on distributed multi-sensors information fusion of multi-wavelength NIR is proved to be of great efficient. And the new proposed idea of measurement based on distri- buted multi-sensors, shows better prediction accuracy.展开更多
In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Ela...In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Elatty E.Abd Elgawad Computers,Materials&Continua,2022,Vol.70,No.1,pp.1617–1630.DOI:10.32604/cmc.2022.018621,URL:https://www.techscience.com/cmc/v70n1/44361,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.展开更多
Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model...Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model.展开更多
Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it cha...Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective samples.Additionally,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions.This paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these challenges.The network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation network.Specifically,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information effectively.In order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target detection.Moreover,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter number.Finally,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and PVEL-S.SolarCells and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon dataset.Experimental results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.展开更多
BACKGROUND Ependymoma with lipomatous differentiation is a rare type of ependymoma.The ZFTA fusion-positive supratentorial ependymoma is a novel tumor type in the 2021 World Health Organization classification of centr...BACKGROUND Ependymoma with lipomatous differentiation is a rare type of ependymoma.The ZFTA fusion-positive supratentorial ependymoma is a novel tumor type in the 2021 World Health Organization classification of central nervous system tumors.ZFTA fusion-positive lipomatous ependymoma has not been reported to date.CASE SUMMARY We reported a case of a 15-year-old Chinese male who had a sudden convulsion lasting approximately six minutes.Magnetic resonance imaging showed a round cystic shadow of approximately 1.9 cm×1.5 cm×1.9 cm under the right parieto-occipital cortex.Microscopic examination showed characteristic perivascular pseudorosettes and adipose differentiation in the cytoplasm.Immunohisto-chemical staining showed that the tumor cells were negative for cytokeratin,NeuN,Syn and p53,but positive for GFAP,vimentin and S-100 protein.Signi-ficant punctate intracytoplasmic EMA immunoreactivity was observed.The level of Ki-67 was about 5%.Genetic analysis revealed ZFTA:RELA fusion.A cranio-tomy with total excision of the tumor was performed.The follow-up time was 36 months,no evidence of disease recurrence was found in magnetic resonance imaging.CONCLUSION Based on these findings,the patient was diagnosed as a ependymoma with ZFTA fusion and lipomatous differentiation.This case report provides information on the microscopic morphological features of ependymoma with ZFTA fusion and lipomatous differentiation,which can help pathologists to make a definitive diagnosis of this tumor.展开更多
BACKGROUND The classification of uterine sarcomas is based on distinctive morphological and immunophenotypic characteristics,increasingly supported by molecular genetic diagnostics.Data on neurotrophic tyrosine recept...BACKGROUND The classification of uterine sarcomas is based on distinctive morphological and immunophenotypic characteristics,increasingly supported by molecular genetic diagnostics.Data on neurotrophic tyrosine receptor kinase(NTRK)gene fusionpositive uterine sarcoma,potentially aggressive and morphologically similar to fibrosarcoma,are limited due to its recent recognition.Pan-TRK immunohistochemistry(IHC)analysis serves as an effective screening tool with high sensitivity and specificity for NTRK-fusion malignancies.CASE SUMMARY We report a case of a malignant mesenchymal tumor originating from the uterine cervix,which was pan-TRK IHC-positive but lacked NTRK gene fusions,accompanied by a brief literature review.A 55-year-old woman presented to the emergency department with abdominal pain and distension,exhibiting significant ascites and multiple solid pelvic masses.Pelvic examination revealed a tumor encompassing the uterine cervix,extending to the vagina and uterine corpus.A punch biopsy of the cervix indicated NTRK sarcoma with positive immunochemical pan-TRK stain.However,subsequent next generation sequencing revealed no NTRK gene fusion,leading to a diagnosis of poorly differentiated,advanced-stage sarcoma.CONCLUSION The clinical significance of NTRK gene fusion lies in potential treatment with TRK inhibitors for positive sarcomas.Identifying such rare tumors is crucial due to the potential applicability of tropomyosin receptor kinase inhibitor treatment.展开更多
Successful polyethylene glycol fusion(PEG-fusion)of severed axons following peripheral nerve injuries for PEG-fused axons has been reported to:(1)rapidly restore electrophysiological continuity;(2)prevent distal Walle...Successful polyethylene glycol fusion(PEG-fusion)of severed axons following peripheral nerve injuries for PEG-fused axons has been reported to:(1)rapidly restore electrophysiological continuity;(2)prevent distal Wallerian Degeneration and maintain their myelin sheaths;(3)promote primarily motor,voluntary behavioral recoveries as assessed by the Sciatic Functional Index;and,(4)rapidly produce correct and incorrect connections in many possible combinations that produce rapid and extensive recovery of functional peripheral nervous system/central nervous system connections and reflex(e.g.,toe twitch)or voluntary behaviors.The preceding companion paper describes sensory terminal field reo rganization following PEG-fusion repair of sciatic nerve transections or ablations;howeve r,sensory behavioral recovery has not been explicitly explored following PEG-fusion repair.In the current study,we confirmed the success of PEG-fusion surgeries according to criteria(1-3)above and more extensively investigated whether PEG-fusion enhanced mechanical nociceptive recovery following sciatic transection in male and female outbred Sprague-Dawley and inbred Lewis rats.Mechanical nociceptive responses were assessed by measuring withdrawal thresholds using von Frey filaments on the dorsal and midplantar regions of the hindpaws.Dorsal von Frey filament tests were a more reliable method than plantar von Frey filament tests to assess mechanical nociceptive sensitivity following sciatic nerve transections.Baseline withdrawal thresholds of the sciatic-mediated lateral dorsal region differed significantly across strain but not sex.Withdrawal thresholds did not change significantly from baseline in chronic Unoperated and Sham-operated rats.Following sciatic transection,all rats exhibited severe hyposensitivity to stimuli at the lateral dorsal region of the hindpaw ipsilateral to the injury.However,PEG-fused rats exhibited significantly earlier return to baseline withdrawal thresholds than Negative Control rats.Furthermore,PEG-fused rats with significantly improved Sciatic Functional Index scores at or after 4 weeks postoperatively exhibited yet-earlier von Frey filament recove ry compared with those without Sciatic Functional Index recovery,suggesting a correlation between successful PEG-fusion and both motor-dominant and sensory-dominant behavioral recoveries.This correlation was independent of the sex or strain of the rat.Furthermore,our data showed that the acceleration of von Frey filament sensory recovery to baseline was solely due to the PEG-fused sciatic nerve and not saphenous nerve collateral outgrowths.No chronic hypersensitivity developed in any rat up to 12 weeks.All these data suggest that PEG-fusion repair of transection peripheral nerve injuries co uld have important clinical benefits.展开更多
Video classification is an important task in video understanding and plays a pivotal role in intelligent monitoring of information content.Most existing methods do not consider the multimodal nature of the video,and t...Video classification is an important task in video understanding and plays a pivotal role in intelligent monitoring of information content.Most existing methods do not consider the multimodal nature of the video,and the modality fusion approach tends to be too simple,often neglecting modality alignment before fusion.This research introduces a novel dual stream multimodal alignment and fusion network named DMAFNet for classifying short videos.The network uses two unimodal encoder modules to extract features within modalities and exploits a multimodal encoder module to learn interaction between modalities.To solve the modality alignment problem,contrastive learning is introduced between two unimodal encoder modules.Additionally,masked language modeling(MLM)and video text matching(VTM)auxiliary tasks are introduced to improve the interaction between video frames and text modalities through backpropagation of loss functions.Diverse experiments prove the efficiency of DMAFNet in multimodal video classification tasks.Compared with other two mainstream baselines,DMAFNet achieves the best results on the 2022 WeChat Big Data Challenge dataset.展开更多
Based on the Skyrme energy density functional and reaction Q-value,this study proposed an effective nucleus-nucleus poten-tial for describing the capture barrier in heavy-ion fusion processes.The 443 extracted barrier...Based on the Skyrme energy density functional and reaction Q-value,this study proposed an effective nucleus-nucleus poten-tial for describing the capture barrier in heavy-ion fusion processes.The 443 extracted barrier heights were well reproduced with a root-mean-square(RMS)error of 1.53 MeV,and the RMS deviations with respect to 144 time-dependent Hartree-Fock capture barrier heights were only 1.05 MeV.Coupled with the Siwek-Wilczyński formula,wherein three parameters were determined by the proposed effective potentials,the measured capture cross sections at energies around the barriers were reasonably well reproduced for several fusion reactions induced by nearly spherical nuclei as well as by nuclei with large deformations,such as^(154)Sm and^(238)U.The shallow capture pockets and small values of the average barrier radii resulted in the reduction of the capture cross sections for 52,54Cr-and 64 Ni-induced reactions,which were related to the synthesis of new super-heavy nuclei.展开更多
Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.There...Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.Therefore,it is necessary to establish thunderstorm wind gust identification techniques based on multisource high-resolution observations.This paper introduces a new algorithm,called thunderstorm wind gust identification network(TGNet).It leverages multimodal feature fusion to fuse the temporal and spatial features of thunderstorm wind gust events.The shapelet transform is first used to extract the temporal features of wind speeds from automatic weather stations,which is aimed at distinguishing thunderstorm wind gusts from those caused by synoptic-scale systems or typhoons.Then,the encoder,structured upon the U-shaped network(U-Net)and incorporating recurrent residual convolutional blocks(R2U-Net),is employed to extract the corresponding spatial convective characteristics of satellite,radar,and lightning observations.Finally,by using the multimodal deep fusion module based on multi-head cross-attention,the temporal features of wind speed at each automatic weather station are incorporated into the spatial features to obtain 10-minutely classification of thunderstorm wind gusts.TGNet products have high accuracy,with a critical success index reaching 0.77.Compared with those of U-Net and R2U-Net,the false alarm rate of TGNet products decreases by 31.28%and 24.15%,respectively.The new algorithm provides grid products of thunderstorm wind gusts with a spatial resolution of 0.01°,updated every 10minutes.The results are finer and more accurate,thereby helping to improve the accuracy of operational warnings for thunderstorm wind gusts.展开更多
To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network mo...To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.展开更多
This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and...This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle.展开更多
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.
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 Major Projects for Science and Technology Innovation 2030(2018AA0100800)the Equipment Pre-research Foundation of Laboratory(61425040104)+1 种基金the Joint Fund of China Electronics Technology for Equipment Preresearch(6141B08231110a)the Funding for Short Visit Program of Nanjing University of Aeronautics and Astronautics(NUAA)(190915DF03)。
文摘In order to improve detection system robustness and reliability, multi-sensors fusion is used in modern air combat. In this paper, a data fusion method based on reinforcement learning is developed for multi-sensors. Initially, the cubic B-spline interpolation is used to solve time alignment problems of multisource data. Then, the reinforcement learning based data fusion(RLBDF) method is proposed to obtain the fusion results. With the case that the priori knowledge of target is obtained, the fusion accuracy reinforcement is realized by the error between fused value and actual value. Furthermore, the Fisher information is instead used as the reward if the priori knowledge is unable to be obtained. Simulations results verify that the developed method is feasible and effective for the multi-sensors data fusion in air combat.
基金supported by the National Natural Science Foundation of China(No.61301211)the China Scholarship Council(No.201906835017)
文摘In order to improve the detail preservation and target information integrity of different sensor fusion images,an image fusion method of different sensors based on non-subsampling contourlet transform(NSCT)and GoogLeNet neural network model is proposed. First,the different sensors images,i. e.,infrared and visible images,are transformed by NSCT to obtain a low frequency sub-band and a series of high frequency sub-bands respectively.Then,the high frequency sub-bands are fused with the max regional energy selection strategy,the low frequency subbands are input into GoogLeNet neural network model to extract feature maps,and the fusion weight matrices are adaptively calculated from the feature maps. Next,the fused low frequency sub-band is obtained with weighted summation. Finally,the fused image is obtained by inverse NSCT. The experimental results demonstrate that the proposed method improves the image visual effect and achieves better performance in both edge retention and mutual information.
文摘In this research, a near infrared multi-wavelength noninvasive blood glucose monitoring system with distributed laser multi-sensors is applied to monitor human blood glucose concentration. In order to improve the monitoring accuracy, a multi-sensors information fusion model based on Back Propagation Artificial Neural Network is proposed. The Root- Mean-Square Error of Prediction for noninvasive blood glucose measurement is 0.088mmol/L, and the correlation coefficient is 0.94. The noninvasive blood glucose monitoring system based on distributed multi-sensors information fusion of multi-wavelength NIR is proved to be of great efficient. And the new proposed idea of measurement based on distri- buted multi-sensors, shows better prediction accuracy.
文摘In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Elatty E.Abd Elgawad Computers,Materials&Continua,2022,Vol.70,No.1,pp.1617–1630.DOI:10.32604/cmc.2022.018621,URL:https://www.techscience.com/cmc/v70n1/44361,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.
基金supported by the National Key R&D Program of China (Grant No.2022YFF0503700)the National Natural Science Foundation of China (42074196, 41925018)
文摘Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model.
基金supported in part by the National Natural Science Foundation of China under Grants 62463002,62062021 and 62473033in part by the Guiyang Scientific Plan Project[2023]48–11,in part by QKHZYD[2023]010 Guizhou Province Science and Technology Innovation Base Construction Project“Key Laboratory Construction of Intelligent Mountain Agricultural Equipment”.
文摘Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective samples.Additionally,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions.This paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these challenges.The network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation network.Specifically,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information effectively.In order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target detection.Moreover,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter number.Finally,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and PVEL-S.SolarCells and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon dataset.Experimental results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.
文摘BACKGROUND Ependymoma with lipomatous differentiation is a rare type of ependymoma.The ZFTA fusion-positive supratentorial ependymoma is a novel tumor type in the 2021 World Health Organization classification of central nervous system tumors.ZFTA fusion-positive lipomatous ependymoma has not been reported to date.CASE SUMMARY We reported a case of a 15-year-old Chinese male who had a sudden convulsion lasting approximately six minutes.Magnetic resonance imaging showed a round cystic shadow of approximately 1.9 cm×1.5 cm×1.9 cm under the right parieto-occipital cortex.Microscopic examination showed characteristic perivascular pseudorosettes and adipose differentiation in the cytoplasm.Immunohisto-chemical staining showed that the tumor cells were negative for cytokeratin,NeuN,Syn and p53,but positive for GFAP,vimentin and S-100 protein.Signi-ficant punctate intracytoplasmic EMA immunoreactivity was observed.The level of Ki-67 was about 5%.Genetic analysis revealed ZFTA:RELA fusion.A cranio-tomy with total excision of the tumor was performed.The follow-up time was 36 months,no evidence of disease recurrence was found in magnetic resonance imaging.CONCLUSION Based on these findings,the patient was diagnosed as a ependymoma with ZFTA fusion and lipomatous differentiation.This case report provides information on the microscopic morphological features of ependymoma with ZFTA fusion and lipomatous differentiation,which can help pathologists to make a definitive diagnosis of this tumor.
基金Supported by Grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute,funded by the Ministry of Health&Welfare,Republic of Korea,No.RS-2022-KH129889.
文摘BACKGROUND The classification of uterine sarcomas is based on distinctive morphological and immunophenotypic characteristics,increasingly supported by molecular genetic diagnostics.Data on neurotrophic tyrosine receptor kinase(NTRK)gene fusionpositive uterine sarcoma,potentially aggressive and morphologically similar to fibrosarcoma,are limited due to its recent recognition.Pan-TRK immunohistochemistry(IHC)analysis serves as an effective screening tool with high sensitivity and specificity for NTRK-fusion malignancies.CASE SUMMARY We report a case of a malignant mesenchymal tumor originating from the uterine cervix,which was pan-TRK IHC-positive but lacked NTRK gene fusions,accompanied by a brief literature review.A 55-year-old woman presented to the emergency department with abdominal pain and distension,exhibiting significant ascites and multiple solid pelvic masses.Pelvic examination revealed a tumor encompassing the uterine cervix,extending to the vagina and uterine corpus.A punch biopsy of the cervix indicated NTRK sarcoma with positive immunochemical pan-TRK stain.However,subsequent next generation sequencing revealed no NTRK gene fusion,leading to a diagnosis of poorly differentiated,advanced-stage sarcoma.CONCLUSION The clinical significance of NTRK gene fusion lies in potential treatment with TRK inhibitors for positive sarcomas.Identifying such rare tumors is crucial due to the potential applicability of tropomyosin receptor kinase inhibitor treatment.
基金supported by DOD AFIRMⅢW81XWH-20-2-0029 subcontract,UT POC19-1774-13Neuraptive Therapeutics Inc.26-7724-56+1 种基金NIH R01-NS128086 grantsLone Star Paralysis gift(to GDB)。
文摘Successful polyethylene glycol fusion(PEG-fusion)of severed axons following peripheral nerve injuries for PEG-fused axons has been reported to:(1)rapidly restore electrophysiological continuity;(2)prevent distal Wallerian Degeneration and maintain their myelin sheaths;(3)promote primarily motor,voluntary behavioral recoveries as assessed by the Sciatic Functional Index;and,(4)rapidly produce correct and incorrect connections in many possible combinations that produce rapid and extensive recovery of functional peripheral nervous system/central nervous system connections and reflex(e.g.,toe twitch)or voluntary behaviors.The preceding companion paper describes sensory terminal field reo rganization following PEG-fusion repair of sciatic nerve transections or ablations;howeve r,sensory behavioral recovery has not been explicitly explored following PEG-fusion repair.In the current study,we confirmed the success of PEG-fusion surgeries according to criteria(1-3)above and more extensively investigated whether PEG-fusion enhanced mechanical nociceptive recovery following sciatic transection in male and female outbred Sprague-Dawley and inbred Lewis rats.Mechanical nociceptive responses were assessed by measuring withdrawal thresholds using von Frey filaments on the dorsal and midplantar regions of the hindpaws.Dorsal von Frey filament tests were a more reliable method than plantar von Frey filament tests to assess mechanical nociceptive sensitivity following sciatic nerve transections.Baseline withdrawal thresholds of the sciatic-mediated lateral dorsal region differed significantly across strain but not sex.Withdrawal thresholds did not change significantly from baseline in chronic Unoperated and Sham-operated rats.Following sciatic transection,all rats exhibited severe hyposensitivity to stimuli at the lateral dorsal region of the hindpaw ipsilateral to the injury.However,PEG-fused rats exhibited significantly earlier return to baseline withdrawal thresholds than Negative Control rats.Furthermore,PEG-fused rats with significantly improved Sciatic Functional Index scores at or after 4 weeks postoperatively exhibited yet-earlier von Frey filament recove ry compared with those without Sciatic Functional Index recovery,suggesting a correlation between successful PEG-fusion and both motor-dominant and sensory-dominant behavioral recoveries.This correlation was independent of the sex or strain of the rat.Furthermore,our data showed that the acceleration of von Frey filament sensory recovery to baseline was solely due to the PEG-fused sciatic nerve and not saphenous nerve collateral outgrowths.No chronic hypersensitivity developed in any rat up to 12 weeks.All these data suggest that PEG-fusion repair of transection peripheral nerve injuries co uld have important clinical benefits.
基金Fundamental Research Funds for the Central Universities,China(No.2232021A-10)National Natural Science Foundation of China(No.61903078)+1 种基金Shanghai Sailing Program,China(No.22YF1401300)Natural Science Foundation of Shanghai,China(No.20ZR1400400)。
文摘Video classification is an important task in video understanding and plays a pivotal role in intelligent monitoring of information content.Most existing methods do not consider the multimodal nature of the video,and the modality fusion approach tends to be too simple,often neglecting modality alignment before fusion.This research introduces a novel dual stream multimodal alignment and fusion network named DMAFNet for classifying short videos.The network uses two unimodal encoder modules to extract features within modalities and exploits a multimodal encoder module to learn interaction between modalities.To solve the modality alignment problem,contrastive learning is introduced between two unimodal encoder modules.Additionally,masked language modeling(MLM)and video text matching(VTM)auxiliary tasks are introduced to improve the interaction between video frames and text modalities through backpropagation of loss functions.Diverse experiments prove the efficiency of DMAFNet in multimodal video classification tasks.Compared with other two mainstream baselines,DMAFNet achieves the best results on the 2022 WeChat Big Data Challenge dataset.
基金supported by the National Natural Science Foundation of China(Nos.12265006,12375129,U1867212)the Innovation Project of Guangxi Graduate Education(No.YCSWYCSW2022176)the Guangxi Natural Science Foundation(2017GXNSFGA198001).
文摘Based on the Skyrme energy density functional and reaction Q-value,this study proposed an effective nucleus-nucleus poten-tial for describing the capture barrier in heavy-ion fusion processes.The 443 extracted barrier heights were well reproduced with a root-mean-square(RMS)error of 1.53 MeV,and the RMS deviations with respect to 144 time-dependent Hartree-Fock capture barrier heights were only 1.05 MeV.Coupled with the Siwek-Wilczyński formula,wherein three parameters were determined by the proposed effective potentials,the measured capture cross sections at energies around the barriers were reasonably well reproduced for several fusion reactions induced by nearly spherical nuclei as well as by nuclei with large deformations,such as^(154)Sm and^(238)U.The shallow capture pockets and small values of the average barrier radii resulted in the reduction of the capture cross sections for 52,54Cr-and 64 Ni-induced reactions,which were related to the synthesis of new super-heavy nuclei.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3004104)the National Natural Science Foundation of China(Grant No.U2342204)+4 种基金the Innovation and Development Program of the China Meteorological Administration(Grant No.CXFZ2024J001)the Open Research Project of the Key Open Laboratory of Hydrology and Meteorology of the China Meteorological Administration(Grant No.23SWQXZ010)the Science and Technology Plan Project of Zhejiang Province(Grant No.2022C03150)the Open Research Fund Project of Anyang National Climate Observatory(Grant No.AYNCOF202401)the Open Bidding for Selecting the Best Candidates Program(Grant No.CMAJBGS202318)。
文摘Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.Therefore,it is necessary to establish thunderstorm wind gust identification techniques based on multisource high-resolution observations.This paper introduces a new algorithm,called thunderstorm wind gust identification network(TGNet).It leverages multimodal feature fusion to fuse the temporal and spatial features of thunderstorm wind gust events.The shapelet transform is first used to extract the temporal features of wind speeds from automatic weather stations,which is aimed at distinguishing thunderstorm wind gusts from those caused by synoptic-scale systems or typhoons.Then,the encoder,structured upon the U-shaped network(U-Net)and incorporating recurrent residual convolutional blocks(R2U-Net),is employed to extract the corresponding spatial convective characteristics of satellite,radar,and lightning observations.Finally,by using the multimodal deep fusion module based on multi-head cross-attention,the temporal features of wind speed at each automatic weather station are incorporated into the spatial features to obtain 10-minutely classification of thunderstorm wind gusts.TGNet products have high accuracy,with a critical success index reaching 0.77.Compared with those of U-Net and R2U-Net,the false alarm rate of TGNet products decreases by 31.28%and 24.15%,respectively.The new algorithm provides grid products of thunderstorm wind gusts with a spatial resolution of 0.01°,updated every 10minutes.The results are finer and more accurate,thereby helping to improve the accuracy of operational warnings for thunderstorm wind gusts.
基金supported by National Natural Science Foundation of China(No.61862037)Lanzhou Jiaotong University Tianyou Innovation Team Project(No.TY202002)。
文摘To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.
文摘This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle.
