Additive manufacturing(AM)is an advanced production method for layer-by-layer fabrication,offering a paradigm shift in manufacturing.However,the sustainability of AM processes is poor,since suppliers recommend reusing...Additive manufacturing(AM)is an advanced production method for layer-by-layer fabrication,offering a paradigm shift in manufacturing.However,the sustainability of AM processes is poor,since suppliers recommend reusing 50%-70%of reprocessed powder,contributing to a significant increase in material disposal.To explore the possibility of fully reusing the polymeric material,we conduct a comprehensive characterisation of the powder particulates,in combination with analysis of the final prints.Utilizing optical and scanning electron microscopes,we statistically evaluate the size,morphology,and shape of the particles.Furthermore,tensile strength and deformation of printed bars is evaluated,showcasing the impact of aging on the print properties.The findings reveal that consecutive reuse of used powder significantly influences dimensional accuracy of the printed parts.We detect a 30.63%relative value of shrinkage after six printing iterations,which corresponds to an absolute shrinkage increase by 0.98%.This is significant considering the standard shrinkage for the material used is already 3.2%.Additionally,parts that are printed with reused material exhibit a small increase in elongation at yield,as well as an unexpected rise in tensile strength.Significant agglomeration of small particles is observed in the aged powder,since there are particles of less than 10μm,which are not found in the virgin powder.These results contribute to a better understanding of the issues related to the reusing of aged material,and offer invaluable insights for mitigating the environmental impact that is associated with material disposal in AM.展开更多
This paper investigates the finite time blow-up of nonnegative solutions for a nonlinear diffusion system with a more complicated source term, which is a product of localized source, local source, and weight function,...This paper investigates the finite time blow-up of nonnegative solutions for a nonlinear diffusion system with a more complicated source term, which is a product of localized source, local source, and weight function, and complemented by homogeneous Dirichlet boundary conditions. The criteria are proposed to identify simultaneous and nonsimultaneous blow-up solutions. Moreover, the related classification for the four parameters in the model is optimal and complete. The results extend those in Zhang and Yang [12].展开更多
A new distributed fusion method of radar/infrared （IR） tracking system based on separation and combination of the measurements is proposed by analyzing the influence of rate measurement. The rate information separat...A new distributed fusion method of radar/infrared （IR） tracking system based on separation and combination of the measurements is proposed by analyzing the influence of rate measurement. The rate information separated from the radar measurements together with measurements of IR form a pseudo vector of IR, and the corresponding filter is designed. The results indicate that the method not only makes a great improvement to the local tracker＇s performance, but also improves the global tracking precision efficiently.展开更多
This paper describes the analysis and design of an assistive device for elderly people under development at the EgyptJapan University of Science and Technology（E-JUST） named E-JUST assistive device（EJAD）.Several e...This paper describes the analysis and design of an assistive device for elderly people under development at the EgyptJapan University of Science and Technology（E-JUST） named E-JUST assistive device（EJAD）.Several experiments were carried out using a motion capture system（VICON） and inertial sensors to identify the human posture during the sit-to-stand motion.The EJAD uses only two inertial measurement units（IMUs） fused through an adaptive neuro-fuzzy inference systems（ANFIS） algorithm to imitate the real motion of the caregiver.The EJAD consists of two main parts,a robot arm and an active walker.The robot arm is a 2-degree-of-freedom（2-DOF） planar manipulator.In addition,a back support with a passive joint is used to support the patient s back.The IMUs on the leg and trunk of the patient are used to compensate for and adapt to the EJAD system motion depending on the obtained patient posture.The ANFIS algorithm is used to train the fuzzy system that converts the IMUs signals to the right posture of the patient.A control scheme is proposed to control the system motion based on practical measurements taken from the experiments.A computer simulation showed a relatively good performance of the EJAD in assisting the patient.展开更多
A Layered Interactive Neural-fuzzy Fusion System, which is a general fusion model is presented with its structure and algorithm studied systematically. The system, according to the layering technique, is logically com...A Layered Interactive Neural-fuzzy Fusion System, which is a general fusion model is presented with its structure and algorithm studied systematically. The system, according to the layering technique, is logically composed of a hierarchical set of subsystems. Subsystems with the same rank make up a specific layer. Corresponding fusion techniques are adopted for each layer. Thus a general scheme from the whole to the detail is obtained for the design of tile fusion system. Furthermore, since the element of the bottom layer can be defined by object-oriented analyzing method, the flexibility of the fusion system is consequently improved. A practical neural-fuzzy fusion system is developed for data processing problem and its performance is proved to be better than the old ones.展开更多
For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system ...For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.展开更多
With the rapid spread of the coronavirus epidemic all over the world,educational and other institutions are heading towards digitization.In the era of digitization,identifying educational e-platform users using ear an...With the rapid spread of the coronavirus epidemic all over the world,educational and other institutions are heading towards digitization.In the era of digitization,identifying educational e-platform users using ear and iris based multi-modal biometric systems constitutes an urgent and interesting research topic to pre-serve enterprise security,particularly with wearing a face mask as a precaution against the new coronavirus epidemic.This study proposes a multimodal system based on ear and iris biometrics at the feature fusion level to identify students in electronic examinations(E-exams)during the COVID-19 pandemic.The proposed system comprises four steps.Thefirst step is image preprocessing,which includes enhancing,segmenting,and extracting the regions of interest.The second step is feature extraction,where the Haralick texture and shape methods are used to extract the features of ear images,whereas Tamura texture and color histogram methods are used to extract the features of iris images.The third step is feature fusion,where the extracted features of the ear and iris images are combined into one sequential fused vector.The fourth step is the matching,which is executed using the City Block Dis-tance(CTB)for student identification.Thefindings of the study indicate that the system’s recognition accuracy is 97%,with a 2%False Acceptance Rate(FAR),a 4%False Rejection Rate(FRR),a 94%Correct Recognition Rate(CRR),and a 96%Genuine Acceptance Rate(GAR).In addition,the proposed recognition sys-tem achieved higher accuracy than other related systems.展开更多
Introduction: The TruFUSE lumbar facet fusion system is a unique allograft milled bone dowel used to fuse facet joints. We evaluated subjects undergoing TruFUSE fusion for stable grade I spondylolisthesis and stenosis...Introduction: The TruFUSE lumbar facet fusion system is a unique allograft milled bone dowel used to fuse facet joints. We evaluated subjects undergoing TruFUSE fusion for stable grade I spondylolisthesis and stenosis comparing operative time, length of stay, blood loss and outcome to a similar literature-based cohort of patients undergoing pedicle screw fusion (PSF). Methods: From 2009 to 2011, 41 subjects (17 M,24 F, aver. age 69.5 yr) underwent TruFUSE facet fusion along with transverse process bone fusion and laminectomy. Length of stay, operative time, blood loss and outcomes were compared to eight literature-based cohort that analyzed similar parameters following pedicle screw fusion. Results: The 41 subjects’ mean operative time for laminectomy, transverse process fusion and TruFUSE facet fusion was 106 min, with a mean blood loss of145 cm3, and a mean hospital stay of 1.7 days (77% one day). A follow-up at average six months, 33 (80%) subjects reported subjective outcomes of “excellent” or “somewhat improved”, four (10%) “unchanged” and four (10%) “worse”. Flexion and extension radiographs showed 39 of the 41 patients (95%) had spinal stability at an average six months post-op and all (100%) had signs of early fusion. Discussion: TruFUSE subjects had significantly (p - 19 days range). Mean estimated blood loss (EBL) was significantly lower (p 3 compared to321 cm3 and1082 cm3 range for PSF). Subjective outcome and radiographic stability were comparable between groups. Conclusion: This comparison using the TruFUSE lumbar facet fusion system demonstrates improvements in length of stay, surgical blood loss, and operative time in our selected patient population compared to several published lumbar pedicle screw fusion systems outcomes. There may be potential economic benefits as a result of these improvements.展开更多
This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on th...This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.展开更多
Let p be a prime and F be a fusion system over a finite p-group S.The fusion system F is said to be nilpotent if F=Fs(S).Using F-strongly closed subgroups,we provide a new criterion for a saturated fusion system F to ...Let p be a prime and F be a fusion system over a finite p-group S.The fusion system F is said to be nilpotent if F=Fs(S).Using F-strongly closed subgroups,we provide a new criterion for a saturated fusion system F to be nilpotent,which may be viewed as extending the Glauberman-Thompson p-nilpotency criterion to fusion systems.展开更多
This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysi...This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysis,software design analysis,and simulation analysis,all supported by data fusion technology.Hopefully,this analysis can provide some reference for the rational application of data fusion technology to meet the actual design and application requirements of the system.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)den...The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)denial conditions.Therefore,this paper proposes a UAV Clusters Information Geometry Fusion Positioning(UC-IGFP)method using pseudoranges from the LEO satellites.A novel graph model for linking and computing between the UAV clusters and LEO satellites was established.By utilizing probability to describe the positional states of UAVs and sensor errors,the distributed multivariate Probability Fusion Cooperative Positioning(PF-CP)algorithm is proposed to achieve high-precision cooperative positioning and integration of the cluster.Criteria to select the centroid of the cluster were set.A new Kalman filter algorithm that is suitable for UAV clusters was designed based on the global benchmark and Riemann information geometry theory,which overcomes the discontinuity problem caused by the change of cluster centroids.Finally,the UC-IGFP method achieves the LEO continuous highprecision positioning of UAV clusters.The proposed method effectively addresses the positioning challenges caused by the strong direction of signal beams from LEO satellites and the insufficient constraint quantity of information sources at the edge nodes of the cluster.It significantly improves the accuracy and reliability of LEO-UAV cluster positioning.The results of comprehensive simulation experiments show that the proposed method has a 30.5%improvement in performance over the mainstream positioning methods,with a positioning error of 14.267 m.展开更多
Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images,ultrasound scans,patient records,and genetic tests—but most AI tools look at only one of these at a time...Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images,ultrasound scans,patient records,and genetic tests—but most AI tools look at only one of these at a time,which limits their ability to produce accurate and comprehensive decisions.In recent years,multimodal learning has emerged,enabling the integration of heterogeneous data to improve performance and diagnostic accuracy.However,doctors cannot always see how or why these AI tools make their choices,which is a significant bottleneck in their reliability,along with adoption in clinical settings.Hence,people are adding explainable AI techniques that show the steps the model takes.This review investigates previous work that has employed multimodal learning and XAI for the diagnosis of breast cancer.It discusses the types of data,fusion techniques,and XAI models employed.It was done following the PRISMA guidelines and included studies from 2021 to April 2025.The literature search was performed systematically and resulted in 61 studies.The review highlights a gradual increase in current studies focusing on multimodal fusion and XAI,particularly in the years 2023–2024.It found that studies using multi-modal data fusion achieved the highest accuracy by 5%–10%on average compared to other studies that used single-modality data,an intermediate fusion strategy,and modern fusion techniques,such as cross attention,achieved the highest accuracy and best performance.The review also showed that SHAP,Grad-CAM,and LIME techniques are the most used in explaining breast cancer diagnostic models.There is a clear research shift toward integrating multimodal learning and XAI techniques into the breast cancer diagnostics field.However,several gaps were identified,including the scarcity of public multimodal datasets.Lack of a unified explainable framework in multimodal fusion systems,and lack of standardization in evaluating explanations.These limitations call for future research focused on building more shared datasets and integrating multimodal data and explainable AI techniques to improve decision-making and enhance transparency.展开更多
To solve the problems of traditional garbage classification relying on manual work,low recognition accuracy,and unhygienic contact-based disposal,an inductive intelligent garbage recycling system based on a micro-cont...To solve the problems of traditional garbage classification relying on manual work,low recognition accuracy,and unhygienic contact-based disposal,an inductive intelligent garbage recycling system based on a micro-controller is designed.The system takes STM32F103ZET6 as the core,integrates speech recognition,visual recognition,and infrared induction modules,and adopts a dual recognition fusion strategy of“speech priority+visual verification”to realize con-tactless induction lid opening,accurate garbage classification,and remote status monitoring functions.Speech recognition completes keyword matching through the LD3320 module,visual recognition realizes image feature extraction and classification based on the BP neural network,and the ESP8266 module is responsible for uploading device status to the Cloud platform.It is applicable to multiple scenarios such as households and communities,effectively improving the efficiency and convenience of garbage classification,and has good practical value.展开更多
This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer mult...This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.展开更多
High-performance 24CrNiMo steel was fabricated using Laser Powder Bed Fusion (LPBF). Subsequent quenching treatment was applied and the influence of quenching temperatures on micro-structure evolution and properties w...High-performance 24CrNiMo steel was fabricated using Laser Powder Bed Fusion (LPBF). Subsequent quenching treatment was applied and the influence of quenching temperatures on micro-structure evolution and properties was systematically characterised and analysed. The micro-structure of the as-built steel consisted of two parts. The first part comprised martensite with twins combined with ω-Fe nano-particles, and the second part consisted of lower bainite in the molten pool, as well as upper bainite, granular bainite and tempered martensite in the heat-affected zone. With the quenching temperatures varying from 800℃ to 950℃, the micro-structure gradually transformed from acicular ferrite + martensite to tempered martensite +θ-Fe3C carbides, and the grain size exhibited noticeable growth. Moreover, quenching treatments could eliminate the anisotropy and inhomogeneity of the micro-structure. The rod-shaped nanosized η-Fe2C and θ-Fe3C precipitates were clearly observed, which were converted from ω-Fe and distributed at multiple angles in the lath. The size and number of nano-precipitates, triggered by the high self-tempering degree of martensite, gradually increased. The relationships among grain size, the twins, dislocation density and nano-precipitation and the dramatically improved performance of quenched samples were analysed using strengthening mechanisms. After quenching at 850℃, the as-built 24CrNiMo steel attained ultra-high mechanical properties including hardness, Ultimate Tensile Strength (UTS), Elongation (El) and impact energy with values of 480.9 HV_(1), 1611.4 MPa, 9.8% and 42.8 J, respectively. Meanwhile, both the wear and thermal fatigue resistance increased by approximately 40%. This study demonstrated that LPBF-fabricated 24CrNiMo steel, with matching good performances, can be achieved using a subsequent one-step quenching process.展开更多
Mounds of spatter are generated in laser powder-bed fusion(L-PBF)additive manufacturing,which reduces build quality and laser lifetime.Due to the lack of supplemental airflow above the chamber,the conventional build c...Mounds of spatter are generated in laser powder-bed fusion(L-PBF)additive manufacturing,which reduces build quality and laser lifetime.Due to the lack of supplemental airflow above the chamber,the conventional build chamber with a single gas inlet exhibits a pronounced tendency for gas to flow upward near the outlet.This phenomenon results in the formation of a large vortex within the build chamber.The vortex leads to the chaotic motion trajectory of the spatter in the build chamber.The design defects of the existing build chamber based on dual gas inlets are shown in this paper.We established a coupled computational fluid dynamics-discrete phase model(CFD-DPM)model to optimize the build chamber by adjusting the position and structure of the second gas inlet.The homogeneity of the flow is increased with a distance of 379 mm between the two inlets and a wider-reaching second inlet.The Coanda effect is also crucial in the spatter-removal process.The Coanda effect is reduced by modifying the right sidewall of the build chamber and increasing the pressure difference between the inlet and outlet.Finally,we found that the spatter-removal rate rose from 8.9%to 76.1%between the conventional build chamber with a single gas inlet and the optimized build chamber with two gas inlets.展开更多
文摘Additive manufacturing(AM)is an advanced production method for layer-by-layer fabrication,offering a paradigm shift in manufacturing.However,the sustainability of AM processes is poor,since suppliers recommend reusing 50%-70%of reprocessed powder,contributing to a significant increase in material disposal.To explore the possibility of fully reusing the polymeric material,we conduct a comprehensive characterisation of the powder particulates,in combination with analysis of the final prints.Utilizing optical and scanning electron microscopes,we statistically evaluate the size,morphology,and shape of the particles.Furthermore,tensile strength and deformation of printed bars is evaluated,showcasing the impact of aging on the print properties.The findings reveal that consecutive reuse of used powder significantly influences dimensional accuracy of the printed parts.We detect a 30.63%relative value of shrinkage after six printing iterations,which corresponds to an absolute shrinkage increase by 0.98%.This is significant considering the standard shrinkage for the material used is already 3.2%.Additionally,parts that are printed with reused material exhibit a small increase in elongation at yield,as well as an unexpected rise in tensile strength.Significant agglomeration of small particles is observed in the aged powder,since there are particles of less than 10μm,which are not found in the virgin powder.These results contribute to a better understanding of the issues related to the reusing of aged material,and offer invaluable insights for mitigating the environmental impact that is associated with material disposal in AM.
基金Supported by the National Natural Science Foundation of China(11071100),supported by National Natural Science Foundation of ChinaNatural Science Foundation of Guangxi(2011jjA10044),Natural Science Foundation of Guangxi
文摘This paper investigates the finite time blow-up of nonnegative solutions for a nonlinear diffusion system with a more complicated source term, which is a product of localized source, local source, and weight function, and complemented by homogeneous Dirichlet boundary conditions. The criteria are proposed to identify simultaneous and nonsimultaneous blow-up solutions. Moreover, the related classification for the four parameters in the model is optimal and complete. The results extend those in Zhang and Yang [12].
基金supported by the National Natural Science Foundation of China (60574022).
文摘A new distributed fusion method of radar/infrared （IR） tracking system based on separation and combination of the measurements is proposed by analyzing the influence of rate measurement. The rate information separated from the radar measurements together with measurements of IR form a pseudo vector of IR, and the corresponding filter is designed. The results indicate that the method not only makes a great improvement to the local tracker＇s performance, but also improves the global tracking precision efficiently.
基金supported in part by a scholarship provided by the Mission DepartmentMinistry of Higher Education of the Government of Egypt
文摘This paper describes the analysis and design of an assistive device for elderly people under development at the EgyptJapan University of Science and Technology（E-JUST） named E-JUST assistive device（EJAD）.Several experiments were carried out using a motion capture system（VICON） and inertial sensors to identify the human posture during the sit-to-stand motion.The EJAD uses only two inertial measurement units（IMUs） fused through an adaptive neuro-fuzzy inference systems（ANFIS） algorithm to imitate the real motion of the caregiver.The EJAD consists of two main parts,a robot arm and an active walker.The robot arm is a 2-degree-of-freedom（2-DOF） planar manipulator.In addition,a back support with a passive joint is used to support the patient s back.The IMUs on the leg and trunk of the patient are used to compensate for and adapt to the EJAD system motion depending on the obtained patient posture.The ANFIS algorithm is used to train the fuzzy system that converts the IMUs signals to the right posture of the patient.A control scheme is proposed to control the system motion based on practical measurements taken from the experiments.A computer simulation showed a relatively good performance of the EJAD in assisting the patient.
文摘A Layered Interactive Neural-fuzzy Fusion System, which is a general fusion model is presented with its structure and algorithm studied systematically. The system, according to the layering technique, is logically composed of a hierarchical set of subsystems. Subsystems with the same rank make up a specific layer. Corresponding fusion techniques are adopted for each layer. Thus a general scheme from the whole to the detail is obtained for the design of tile fusion system. Furthermore, since the element of the bottom layer can be defined by object-oriented analyzing method, the flexibility of the fusion system is consequently improved. A practical neural-fuzzy fusion system is developed for data processing problem and its performance is proved to be better than the old ones.
文摘For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.
文摘With the rapid spread of the coronavirus epidemic all over the world,educational and other institutions are heading towards digitization.In the era of digitization,identifying educational e-platform users using ear and iris based multi-modal biometric systems constitutes an urgent and interesting research topic to pre-serve enterprise security,particularly with wearing a face mask as a precaution against the new coronavirus epidemic.This study proposes a multimodal system based on ear and iris biometrics at the feature fusion level to identify students in electronic examinations(E-exams)during the COVID-19 pandemic.The proposed system comprises four steps.Thefirst step is image preprocessing,which includes enhancing,segmenting,and extracting the regions of interest.The second step is feature extraction,where the Haralick texture and shape methods are used to extract the features of ear images,whereas Tamura texture and color histogram methods are used to extract the features of iris images.The third step is feature fusion,where the extracted features of the ear and iris images are combined into one sequential fused vector.The fourth step is the matching,which is executed using the City Block Dis-tance(CTB)for student identification.Thefindings of the study indicate that the system’s recognition accuracy is 97%,with a 2%False Acceptance Rate(FAR),a 4%False Rejection Rate(FRR),a 94%Correct Recognition Rate(CRR),and a 96%Genuine Acceptance Rate(GAR).In addition,the proposed recognition sys-tem achieved higher accuracy than other related systems.
文摘Introduction: The TruFUSE lumbar facet fusion system is a unique allograft milled bone dowel used to fuse facet joints. We evaluated subjects undergoing TruFUSE fusion for stable grade I spondylolisthesis and stenosis comparing operative time, length of stay, blood loss and outcome to a similar literature-based cohort of patients undergoing pedicle screw fusion (PSF). Methods: From 2009 to 2011, 41 subjects (17 M,24 F, aver. age 69.5 yr) underwent TruFUSE facet fusion along with transverse process bone fusion and laminectomy. Length of stay, operative time, blood loss and outcomes were compared to eight literature-based cohort that analyzed similar parameters following pedicle screw fusion. Results: The 41 subjects’ mean operative time for laminectomy, transverse process fusion and TruFUSE facet fusion was 106 min, with a mean blood loss of145 cm3, and a mean hospital stay of 1.7 days (77% one day). A follow-up at average six months, 33 (80%) subjects reported subjective outcomes of “excellent” or “somewhat improved”, four (10%) “unchanged” and four (10%) “worse”. Flexion and extension radiographs showed 39 of the 41 patients (95%) had spinal stability at an average six months post-op and all (100%) had signs of early fusion. Discussion: TruFUSE subjects had significantly (p - 19 days range). Mean estimated blood loss (EBL) was significantly lower (p 3 compared to321 cm3 and1082 cm3 range for PSF). Subjective outcome and radiographic stability were comparable between groups. Conclusion: This comparison using the TruFUSE lumbar facet fusion system demonstrates improvements in length of stay, surgical blood loss, and operative time in our selected patient population compared to several published lumbar pedicle screw fusion systems outcomes. There may be potential economic benefits as a result of these improvements.
文摘This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.
基金supported in part by the Natural Science Foundation of China(No.12071181).
文摘Let p be a prime and F be a fusion system over a finite p-group S.The fusion system F is said to be nilpotent if F=Fs(S).Using F-strongly closed subgroups,we provide a new criterion for a saturated fusion system F to be nilpotent,which may be viewed as extending the Glauberman-Thompson p-nilpotency criterion to fusion systems.
基金Chongqing Engineering University Undergraduate Innovation and Entrepreneurship Training Program Project:Wireless Fire Automatic Alarm System(Project No.:CXCY2024017)Chongqing Municipal Education Commission Science and Technology Research Project:Development and Research of Chongqing Wireless Fire Automatic Alarm System(Project No.:KJQN202401906)。
文摘This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysis,software design analysis,and simulation analysis,all supported by data fusion technology.Hopefully,this analysis can provide some reference for the rational application of data fusion technology to meet the actual design and application requirements of the system.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
基金supported in part by the National Natural Science Foundation of China(Nos.62171375,62271397,62001392,62101458,62173276,61803310 and 61801394)the Shenzhen Science and Technology Innovation ProgramChina(No.JCYJ20220530161615033)。
文摘The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)denial conditions.Therefore,this paper proposes a UAV Clusters Information Geometry Fusion Positioning(UC-IGFP)method using pseudoranges from the LEO satellites.A novel graph model for linking and computing between the UAV clusters and LEO satellites was established.By utilizing probability to describe the positional states of UAVs and sensor errors,the distributed multivariate Probability Fusion Cooperative Positioning(PF-CP)algorithm is proposed to achieve high-precision cooperative positioning and integration of the cluster.Criteria to select the centroid of the cluster were set.A new Kalman filter algorithm that is suitable for UAV clusters was designed based on the global benchmark and Riemann information geometry theory,which overcomes the discontinuity problem caused by the change of cluster centroids.Finally,the UC-IGFP method achieves the LEO continuous highprecision positioning of UAV clusters.The proposed method effectively addresses the positioning challenges caused by the strong direction of signal beams from LEO satellites and the insufficient constraint quantity of information sources at the edge nodes of the cluster.It significantly improves the accuracy and reliability of LEO-UAV cluster positioning.The results of comprehensive simulation experiments show that the proposed method has a 30.5%improvement in performance over the mainstream positioning methods,with a positioning error of 14.267 m.
基金supported by the Deanship of Scientific Research,King Saud University through the Vice Deanship of Scientific Research Chairs,Chair of Pervasive and Mobile Computing.
文摘Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images,ultrasound scans,patient records,and genetic tests—but most AI tools look at only one of these at a time,which limits their ability to produce accurate and comprehensive decisions.In recent years,multimodal learning has emerged,enabling the integration of heterogeneous data to improve performance and diagnostic accuracy.However,doctors cannot always see how or why these AI tools make their choices,which is a significant bottleneck in their reliability,along with adoption in clinical settings.Hence,people are adding explainable AI techniques that show the steps the model takes.This review investigates previous work that has employed multimodal learning and XAI for the diagnosis of breast cancer.It discusses the types of data,fusion techniques,and XAI models employed.It was done following the PRISMA guidelines and included studies from 2021 to April 2025.The literature search was performed systematically and resulted in 61 studies.The review highlights a gradual increase in current studies focusing on multimodal fusion and XAI,particularly in the years 2023–2024.It found that studies using multi-modal data fusion achieved the highest accuracy by 5%–10%on average compared to other studies that used single-modality data,an intermediate fusion strategy,and modern fusion techniques,such as cross attention,achieved the highest accuracy and best performance.The review also showed that SHAP,Grad-CAM,and LIME techniques are the most used in explaining breast cancer diagnostic models.There is a clear research shift toward integrating multimodal learning and XAI techniques into the breast cancer diagnostics field.However,several gaps were identified,including the scarcity of public multimodal datasets.Lack of a unified explainable framework in multimodal fusion systems,and lack of standardization in evaluating explanations.These limitations call for future research focused on building more shared datasets and integrating multimodal data and explainable AI techniques to improve decision-making and enhance transparency.
基金2024 Ministry of Education Supply-Demand Docking Employment and Education Projects(Project No.:2024101679202,Project No.:2024121116066)2023 Ministry of Education Supply-Demand Docking Employment and Education Projects(Project No.:2023122927732,Project No.:2023122925618)+1 种基金2024“Innovation Strong Institute Project”Construction Project(Project No.:2024CQ-29)Talent Project of the Open University of Guangdong:Research on key technologies for improving the performance of blockchain application platforms(Project No.:2021F001).
文摘To solve the problems of traditional garbage classification relying on manual work,low recognition accuracy,and unhygienic contact-based disposal,an inductive intelligent garbage recycling system based on a micro-controller is designed.The system takes STM32F103ZET6 as the core,integrates speech recognition,visual recognition,and infrared induction modules,and adopts a dual recognition fusion strategy of“speech priority+visual verification”to realize con-tactless induction lid opening,accurate garbage classification,and remote status monitoring functions.Speech recognition completes keyword matching through the LD3320 module,visual recognition realizes image feature extraction and classification based on the BP neural network,and the ESP8266 module is responsible for uploading device status to the Cloud platform.It is applicable to multiple scenarios such as households and communities,effectively improving the efficiency and convenience of garbage classification,and has good practical value.
基金supported by the National Natural Science Foundation of China (Nos. 62276204, 62203343)。
文摘This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.
基金co-supported by the National Key Research and Development Program of China (No. 2022YFB4600500)the National Natural Science Foundation of China (No. 52235006)
文摘High-performance 24CrNiMo steel was fabricated using Laser Powder Bed Fusion (LPBF). Subsequent quenching treatment was applied and the influence of quenching temperatures on micro-structure evolution and properties was systematically characterised and analysed. The micro-structure of the as-built steel consisted of two parts. The first part comprised martensite with twins combined with ω-Fe nano-particles, and the second part consisted of lower bainite in the molten pool, as well as upper bainite, granular bainite and tempered martensite in the heat-affected zone. With the quenching temperatures varying from 800℃ to 950℃, the micro-structure gradually transformed from acicular ferrite + martensite to tempered martensite +θ-Fe3C carbides, and the grain size exhibited noticeable growth. Moreover, quenching treatments could eliminate the anisotropy and inhomogeneity of the micro-structure. The rod-shaped nanosized η-Fe2C and θ-Fe3C precipitates were clearly observed, which were converted from ω-Fe and distributed at multiple angles in the lath. The size and number of nano-precipitates, triggered by the high self-tempering degree of martensite, gradually increased. The relationships among grain size, the twins, dislocation density and nano-precipitation and the dramatically improved performance of quenched samples were analysed using strengthening mechanisms. After quenching at 850℃, the as-built 24CrNiMo steel attained ultra-high mechanical properties including hardness, Ultimate Tensile Strength (UTS), Elongation (El) and impact energy with values of 480.9 HV_(1), 1611.4 MPa, 9.8% and 42.8 J, respectively. Meanwhile, both the wear and thermal fatigue resistance increased by approximately 40%. This study demonstrated that LPBF-fabricated 24CrNiMo steel, with matching good performances, can be achieved using a subsequent one-step quenching process.
基金supported by the Natural Science Foundation of Jiangxi Province(Nos.20224BAB214061 and 20224ACB214008)the National Natural Science Foundation of China(Nos.52165043 and 52166002)+2 种基金the Jiangxi Provincial Cultivation Program for Academic and Technical Leaders of Major Subjects(No.20225BCJ23008)the Anhui Provincial Natural Science Foundation(No.2308085ME171)the University Synergy Innovation Program of Anhui Province(Nos.GXXT-2023-025 and GXXT-2023-026),China。
文摘Mounds of spatter are generated in laser powder-bed fusion(L-PBF)additive manufacturing,which reduces build quality and laser lifetime.Due to the lack of supplemental airflow above the chamber,the conventional build chamber with a single gas inlet exhibits a pronounced tendency for gas to flow upward near the outlet.This phenomenon results in the formation of a large vortex within the build chamber.The vortex leads to the chaotic motion trajectory of the spatter in the build chamber.The design defects of the existing build chamber based on dual gas inlets are shown in this paper.We established a coupled computational fluid dynamics-discrete phase model(CFD-DPM)model to optimize the build chamber by adjusting the position and structure of the second gas inlet.The homogeneity of the flow is increased with a distance of 379 mm between the two inlets and a wider-reaching second inlet.The Coanda effect is also crucial in the spatter-removal process.The Coanda effect is reduced by modifying the right sidewall of the build chamber and increasing the pressure difference between the inlet and outlet.Finally,we found that the spatter-removal rate rose from 8.9%to 76.1%between the conventional build chamber with a single gas inlet and the optimized build chamber with two gas inlets.
