This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognit...This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognition methods that struggle to adapt in dynamic environments,as they typically rely on post-response mechanisms that fail to detect scene changes before users experience latency.The proposed framework leverages data from multiple smartphone sensors,including acceleration sensors,gyroscopes,magnetic field sensors,and orientation sensors,to identify different communication scenes,such as walking,running,cycling,and various modes of transportation.Extensive experimental comparative analysis with existing methods on the open-source SHL-2018 dataset confirmed the superior performance of our approach in terms of F1 score and processing speed.Additionally,tests using a Microsoft Surface Pro tablet and a self-collected Beijing-2023 dataset have validated the framework's efficiency and generalization capability.The results show that our framework achieved an F1 score of 95.15%on SHL-2018and 94.6%on Beijing-2023,highlighting its robustness across different datasets and conditions.Furthermore,the levels of computational complexity and power consumption associated with the algorithm are moderate,making it suitable for deployment on mobile devices.展开更多
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
Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and ot...Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.展开更多
Multi-sensor measurement iswidely employed in rotatingmachinery to ensure the safety ofmachines.The information provided by the single sensor is not comprehensive.Multi-sensor signals can provide complementary informa...Multi-sensor measurement iswidely employed in rotatingmachinery to ensure the safety ofmachines.The information provided by the single sensor is not comprehensive.Multi-sensor signals can provide complementary information in characterizing the health condition of machines.This paper proposed a multi-sensor fusion convolution neural network(MF-CNN)model.The proposed model adds a 2-D convolution layer before the classical 1-D CNN to automatically extract complementary features of multi-sensor signals and minimize the loss of information.A series of experiments are carried out on a rolling bearing test rig to verify the model.Vibration and sound signals are fused to achieve higher classification accuracy than typical machine learning model.In addition,the model is further applied to gas turbine abnormal detection,and shows great robustness and generalization.展开更多
This paper presents an obstacle detection approach for blind pedestrians by fusing data from camera and laser sensor.For purely vision-based blind guidance system,it is difficult to discriminate low-level obstacles wi...This paper presents an obstacle detection approach for blind pedestrians by fusing data from camera and laser sensor.For purely vision-based blind guidance system,it is difficult to discriminate low-level obstacles with cluttered road surface,while for purely laser-based system,it usually requires to scan the forward environment,which turns out to be very inconvenient.To overcome these inherent problems when using camera and laser sensor independently,a sensor-fusion model is proposed to associate range data from laser domain with edges from image domain.Based on this fusion model,obstacle's position,size and shape can be estimated.The proposed method is tested in several indoor scenes,and its efficiency is confirmed.展开更多
Maneuvering targets tracking is a fundamental task in intelligent vehicle research. Thispaper focuses on the problem of fusion between radar and image sensors in targets tracking. Inorder to improve positioning accura...Maneuvering targets tracking is a fundamental task in intelligent vehicle research. Thispaper focuses on the problem of fusion between radar and image sensors in targets tracking. Inorder to improve positioning accuracy and narrow down the image working area, a novel methodthat integrates radar filter with image intensity is proposed to establish an adaptive vision window.A weighted Hausdor? distance is introduced to define the functional relationship between image andmodel projection, and a modified simulated annealing algorithm is used to find optimum orientationparameter. Furthermore, the global state is estimated, which refers to the distributed data fusionalgorithm. Experiment results show that our method is accurate.展开更多
In recent years,Simultaneous Localization And Mapping(SLAM)technology has prevailed in a wide range of applications,such as autonomous driving,intelligent robots,Augmented Reality(AR),and Virtual Reality(VR).Multi-sen...In recent years,Simultaneous Localization And Mapping(SLAM)technology has prevailed in a wide range of applications,such as autonomous driving,intelligent robots,Augmented Reality(AR),and Virtual Reality(VR).Multi-sensor fusion using the most popular three types of sensors(e.g.,visual sensor,LiDAR sensor,and IMU)is becoming ubiquitous in SLAM,in part because of the complementary sensing capabilities and the inevitable shortages(e.g.,low precision and long-term drift)of the stand-alone sensor in challenging environments.In this article,we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work.Firstly,a brief introduction of the state estimator formation in SLAM is presented.Secondly,the state-of-the-art algorithms of different multi-sensor fusion algorithms are given.Then we analyze the deficiencies associated with the reviewed approaches and formulate some future research considerations.This paper can be considered as a brief guide to newcomers and a comprehensive reference for experienced researchers and engineers to explore new interesting orientations.展开更多
With the advancement of artificial intelligence,the dominance of deep learning(DL)models over ordinary machine learning(ML)algorithms has become a reality in recent years due to its capability of handling complex patt...With the advancement of artificial intelligence,the dominance of deep learning(DL)models over ordinary machine learning(ML)algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition.With the growing demands for power savings,building energy loss reduction could benefit from DL techniques.For buildings/rooms with the varying number of occupants,heating,ventilation,and air conditioning(HVAC)systems are often found in operations without much necessity.To reduce the building’s energy loss,accurate occupancy detection/prediction(ODP)results could be used to control the proper operations of HVACs.However,ODP is a challenging issue due to multiple reasons,such as improper selection/deployment of sensors,inefficient learning algorithms for pattern recognition,varying room conditions,etc.To overcome the above challenges,we propose a DL-based framework,i.e.,Deep Weighted Fusion Learning(DWFL),to detect and predict occupancy counts with optimal multi-sensor fusion structure.DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor.Such weight assignment considers different room conditions and the pros/cons of each type of sensor.To evaluate DWFL model in terms of occupancy prediction accuracy,we have set up an experimental testbed with low-cost cameras,carbon dioxide(CO_(2)),and passive infrared(PIR)sensors.Among the recently proposed occupancy detection models,DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88%accuracy in occupancy count estimation(Xue et al.,2019).Another deep learning-based model MI-PIR achieved 91%accuracy on raw analog data from PIR sensors(Andrews et al.,2020).Our research outcome is 94%.Therefore,the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.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
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
基金supported by National 2011 Collaborative Innovation Center of Wireless Communication Technologies under Grant 2242022k60006。
文摘This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognition methods that struggle to adapt in dynamic environments,as they typically rely on post-response mechanisms that fail to detect scene changes before users experience latency.The proposed framework leverages data from multiple smartphone sensors,including acceleration sensors,gyroscopes,magnetic field sensors,and orientation sensors,to identify different communication scenes,such as walking,running,cycling,and various modes of transportation.Extensive experimental comparative analysis with existing methods on the open-source SHL-2018 dataset confirmed the superior performance of our approach in terms of F1 score and processing speed.Additionally,tests using a Microsoft Surface Pro tablet and a self-collected Beijing-2023 dataset have validated the framework's efficiency and generalization capability.The results show that our framework achieved an F1 score of 95.15%on SHL-2018and 94.6%on Beijing-2023,highlighting its robustness across different datasets and conditions.Furthermore,the levels of computational complexity and power consumption associated with the algorithm are moderate,making it suitable for deployment on mobile devices.
基金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National Key R&D Program of China(No.2021YFB2501102)。
文摘Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.
基金support from the National Natural Science Foundation of China (Grant No.U1809219)the Key Research and Development Project of Zhejiang Province (Grant No.2020C01088).
文摘Multi-sensor measurement iswidely employed in rotatingmachinery to ensure the safety ofmachines.The information provided by the single sensor is not comprehensive.Multi-sensor signals can provide complementary information in characterizing the health condition of machines.This paper proposed a multi-sensor fusion convolution neural network(MF-CNN)model.The proposed model adds a 2-D convolution layer before the classical 1-D CNN to automatically extract complementary features of multi-sensor signals and minimize the loss of information.A series of experiments are carried out on a rolling bearing test rig to verify the model.Vibration and sound signals are fused to achieve higher classification accuracy than typical machine learning model.In addition,the model is further applied to gas turbine abnormal detection,and shows great robustness and generalization.
基金The MSIP(Ministry of Science,ICT&Future Planning),Korea,under the ITRC(Information Technology Research Center) support program(NIPA-2013-H0301-13-2006)supervised by the NIPA(National IT Industry Promotion Agency)
文摘This paper presents an obstacle detection approach for blind pedestrians by fusing data from camera and laser sensor.For purely vision-based blind guidance system,it is difficult to discriminate low-level obstacles with cluttered road surface,while for purely laser-based system,it usually requires to scan the forward environment,which turns out to be very inconvenient.To overcome these inherent problems when using camera and laser sensor independently,a sensor-fusion model is proposed to associate range data from laser domain with edges from image domain.Based on this fusion model,obstacle's position,size and shape can be estimated.The proposed method is tested in several indoor scenes,and its efficiency is confirmed.
基金Supported by the Special Funds for Major State Basic Research Program of P.R.China(2001CB309403)
文摘Maneuvering targets tracking is a fundamental task in intelligent vehicle research. Thispaper focuses on the problem of fusion between radar and image sensors in targets tracking. Inorder to improve positioning accuracy and narrow down the image working area, a novel methodthat integrates radar filter with image intensity is proposed to establish an adaptive vision window.A weighted Hausdor? distance is introduced to define the functional relationship between image andmodel projection, and a modified simulated annealing algorithm is used to find optimum orientationparameter. Furthermore, the global state is estimated, which refers to the distributed data fusionalgorithm. Experiment results show that our method is accurate.
基金supported by the Scientific and Technological Innovation 2030(No.2021ZD0110900).
文摘In recent years,Simultaneous Localization And Mapping(SLAM)technology has prevailed in a wide range of applications,such as autonomous driving,intelligent robots,Augmented Reality(AR),and Virtual Reality(VR).Multi-sensor fusion using the most popular three types of sensors(e.g.,visual sensor,LiDAR sensor,and IMU)is becoming ubiquitous in SLAM,in part because of the complementary sensing capabilities and the inevitable shortages(e.g.,low precision and long-term drift)of the stand-alone sensor in challenging environments.In this article,we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work.Firstly,a brief introduction of the state estimator formation in SLAM is presented.Secondly,the state-of-the-art algorithms of different multi-sensor fusion algorithms are given.Then we analyze the deficiencies associated with the reviewed approaches and formulate some future research considerations.This paper can be considered as a brief guide to newcomers and a comprehensive reference for experienced researchers and engineers to explore new interesting orientations.
基金supported by the Advanced Research Projects Agency - Energy (ARPA-E), USA under award number DE-AR0001316.
文摘With the advancement of artificial intelligence,the dominance of deep learning(DL)models over ordinary machine learning(ML)algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition.With the growing demands for power savings,building energy loss reduction could benefit from DL techniques.For buildings/rooms with the varying number of occupants,heating,ventilation,and air conditioning(HVAC)systems are often found in operations without much necessity.To reduce the building’s energy loss,accurate occupancy detection/prediction(ODP)results could be used to control the proper operations of HVACs.However,ODP is a challenging issue due to multiple reasons,such as improper selection/deployment of sensors,inefficient learning algorithms for pattern recognition,varying room conditions,etc.To overcome the above challenges,we propose a DL-based framework,i.e.,Deep Weighted Fusion Learning(DWFL),to detect and predict occupancy counts with optimal multi-sensor fusion structure.DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor.Such weight assignment considers different room conditions and the pros/cons of each type of sensor.To evaluate DWFL model in terms of occupancy prediction accuracy,we have set up an experimental testbed with low-cost cameras,carbon dioxide(CO_(2)),and passive infrared(PIR)sensors.Among the recently proposed occupancy detection models,DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88%accuracy in occupancy count estimation(Xue et al.,2019).Another deep learning-based model MI-PIR achieved 91%accuracy on raw analog data from PIR sensors(Andrews et al.,2020).Our research outcome is 94%.Therefore,the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.
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.
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.
