Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(...Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(FIR)”method for multi-sourced depth data fusion,and used it to merge the electronic nautical chart dataset(referred to as Chart2014 in this paper)with the global digital elevation dataset(referred to as Globalbath2002 in this paper).Compared to the traditional fusion of two datasets by direct combination and interpolation,the new Grid-DEM formed by FIR can better represent the data characteristics of Chart2014,reduce the calculation difficulty,and be more intuitive,and,the choice of different interpolation methods in FIR and the influence of the“exclusion radius R”parameter were discussed.FIR avoids complex calculations of spatial distances among points from different sources,and instead uses spatial exclusion map to perform one-step screening based on the exclusion radius R,which greatly improved the fusion status of a reliable dataset.The fusion results of different experiments were analyzed statistically with root mean square error and mean relative error,showing that the interpolation methods based on Delaunay triangulation are more suitable for the fusion of nautical chart depth of China,and factors such as the point density distribution of multiple source data,accuracy,interpolation method,and various terrain conditions should be fully considered when selecting the exclusion radius R.展开更多
Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classification...Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.展开更多
The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains thr...The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.展开更多
Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly ...Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening(PS), principal component analysis(PCA), Gram-Schmidt(GS), and wavelet fusion(WF), are utilized to fuse MS and PAN images of GF-1.The results of subjective and objective evaluation methods application indicate that GS performs the best,followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.展开更多
Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion, are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coeff...Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion, are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.展开更多
The control rod drive mechanism(CRDM)is an essential part of the control and safety protection system of pressurized water reactors.Current CRDM simulations are mostly performed collectively using a single method,igno...The control rod drive mechanism(CRDM)is an essential part of the control and safety protection system of pressurized water reactors.Current CRDM simulations are mostly performed collectively using a single method,ignoring the influence of multiple motion units and the differences in various features among them,which strongly affect the efficiency and accuracy of the simulations.In this study,we constructed a flow field fusion simulation method based on model features by combining key motion unit analysis and various simulation methods and then applied the method to the CRDM simulation process.CRDM performs motion unit decomposition through the structural hierarchy of function-movement-action method,and the key meta-actions are identified as the nodes in the flow field simulation.We established a fused feature-based multimethod simulation process and processed the simulation methods and data according to the features of the fluid domain space and the structural complexity to obtain the fusion simulation results.Compared to traditional simulation methods and real measurements,the simulation method provides advantages in terms of simulation efficiency and accuracy.展开更多
In cabin-type component alignment, digital measurement technology is usually adopted to provide guidance for assembly. Depending on the system of measurement, the alignment process can be divided into measurement-assi...In cabin-type component alignment, digital measurement technology is usually adopted to provide guidance for assembly. Depending on the system of measurement, the alignment process can be divided into measurement-assisted assembly(MAA) and force-driven assembly. In MAA,relative pose between components is directly measured to guide assembly, while in force-driven assembly, only contact state can be recognized according to measured six-dimensional force and torque(6 D F/T) and the process is completed based on preset assembly strategy. Aiming to improve the efficiency of force-driven cabin-type component alignment, this paper proposed a heuristic alignment method based on multi-source data fusion. In this method, measured 6 D F/T, pose data and geometric information of components are fused to calculate the relative pose between components and guide the movement of pose adjustment platform. Among these data types, pose data and measured 6 D F/T are combined as data set. To collect the data sets needed for data fusion, dynamic gravity compensation method and hybrid motion control method are designed. Then the relative pose calculation method is elaborated, which transforms collected data sets into discrete geometric elements and calculates the relative poses based on the geometric information of components.Finally, experiments are conducted in simulation environment and the results show that the proposed alignment method is feasible and effective.展开更多
Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques ar...Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques are often involved in such multi-method fusion metrics so that its output would be more consistent with human visual perceptions. On the other hand, the robustness and generalization ability of these multi-method fusion metrics are questioned because of the scarce of images with mean opinion scores. In order to comprehensively validate whether or not the generalization ability of such multi-method fusion IQA metrics are satisfying, we construct a new image database which contains up to 60 reference images. The newly built image database is then used to test the generalization ability of different multi-method fusion IQA metrics. Cross database validation experiment indicates that in our new image database, the performances of all the multi-method fusion IQA metrics have no statistical significant different with some single-method IQA metrics such as FSIM and MAD. In the end, a thorough analysis is given to explain why the performance of multi-method fusion IQA framework drop significantly in cross database validation.展开更多
The current life-prediction models for lithium-ion batteries have several problems, such as the construction of complex feature structures, a high number of feature dimensions, and inaccurate prediction results. To ov...The current life-prediction models for lithium-ion batteries have several problems, such as the construction of complex feature structures, a high number of feature dimensions, and inaccurate prediction results. To overcome these problems, this paper proposes a deep-learning model combining an autoencoder network and a long short-term memory network. First, this model applies the characteristics of the autoencoder to reduce the dimensionality of the high-dimensional features extracted from the battery data set and realize the fusion of complex time-domain features, which overcomes the problems of redundant model information and low computational efficiency. This model then uses a long short-term memory network that is sensitive to time-series data to solve the long-path dependence problem in the prediction of battery life. Lastly, the attention mechanism is used to give greater weight to features that have a greater impact on the target value, which enhances the learning effect of the model on the long input sequence. To verify the efficacy of the proposed model, this paper uses NASA's lithium-ion battery cycle life data set.展开更多
Fusion and fission are two important phenomena that have been experimentally observed in many real physical models.In this paper,we investigate the two phenomena in the(2+1)-dimensional Hirota-Satsuma-Ito equation via...Fusion and fission are two important phenomena that have been experimentally observed in many real physical models.In this paper,we investigate the two phenomena in the(2+1)-dimensional Hirota-Satsuma-Ito equation via the physics-informed neural networks(PINN)method.By choosing suitable physically constrained initial boundary conditions,the data-driven fusion and fission solutions are obtained for the first time.Dynamical behaviors and error analysis of these solutions are investigated via illustratively numerical figures,which show that good results are achieved.It is pointed out that the PINN method adopted here can be effectively used to construct the data-driven fusion and fission solutions for other nonlinear integrable equations.Based on the powerful predictive capability of the PINN method and wide applications of fusion and fission in many physical areas,it is hoped that the data-driven solutions obtained here will be helpful for experts to predict or explain related physical phenomena.展开更多
In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in thi...In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.展开更多
Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized charact...Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.展开更多
Sample preparation by fusion for XRF analysis is all about knowing the exact weights of the sample and the flux (sample-to-flux ratio). The whole analytical chain, including the weighing step in sample preparation pri...Sample preparation by fusion for XRF analysis is all about knowing the exact weights of the sample and the flux (sample-to-flux ratio). The whole analytical chain, including the weighing step in sample preparation prior to fusion, is of crucial importance to get precise and accurate x-ray fluorescence (XRF) results. Consequently, the weighing method will affect the quality of the analytical results given by the spectrometer. In this study, the effects of different weighing methods on the precision (RSD) of the obtained XRF results are compared to determine the best weighing method for sample preparation by fusion in terms of comparable precisions in the XRF results.展开更多
The project-based learning method simply means that the students disassemble the English knowledge under the guidance of the teachers, and complete the teaching project with their own thinking, analysis and processing...The project-based learning method simply means that the students disassemble the English knowledge under the guidance of the teachers, and complete the teaching project with their own thinking, analysis and processing of the text. In the process of continuous disassembly, the students can not only deeply understand the meaning contained in the text, but also effectively play the effect of English teaching. Based on this, this paper mainly analyzes the importance of project-based teaching method in secondary vocational English teaching, and puts forward the application effect of project-based teaching method in secondary vocational English teaching. In order to achieve the effective integration effect, the following actively explores the countermeasures for reference.展开更多
While automatic image captioning systems have made notable progress in the past few years,generating captions that fully convey sentiment remains a considerable challenge.Although existing models achieve strong perfor...While automatic image captioning systems have made notable progress in the past few years,generating captions that fully convey sentiment remains a considerable challenge.Although existing models achieve strong performance in visual recognition and factual description,they often fail to account for the emotional context that is naturally present in human-generated captions.To address this gap,we propose the Sentiment-Driven Caption Generator(SDCG),which combines transformer-based visual and textual processing withmulti-level fusion.RoBERTa is used for extracting sentiment from textual input,while visual features are handled by the Vision Transformer(ViT).These features are fused using several fusion approaches,including Concatenation,Attention,Visual-Sentiment Co-Attention(VSCA),and Cross-Attention.Our experiments demonstrate that SDCG significantly outperforms baseline models such as the Generalized Image Transformer(GIT),which achieves 82.01%,and Bootstrapping Language-Image Pre-training(BLIP),which achieves 83.07%,in sentiment accuracy.While SDCG achieves 94.52%sentiment accuracy and improves scores in BLEU and ROUGE-L,the model demonstrates clear advantages.More importantly,the captions aremore natural,as they incorporate emotional cues and contextual awareness,making them resemble those written by a human.展开更多
Semi-crystalline polymer laser powder bed fusion(L-PBF)has recently attracted increasing interest due to its potential for fabricating complex geometry.However,a more comprehensive understanding of the underlying phys...Semi-crystalline polymer laser powder bed fusion(L-PBF)has recently attracted increasing interest due to its potential for fabricating complex geometry.However,a more comprehensive understanding of the underlying physics during L-PBF is required to better control the properties of the final part.This work proposed a multi-layer numerical model to study the temperature and phase evolution during the polyamide-12(PA12)L-PBF process.The Descend and Parallel Chord methods were introduced to improve the convergence of the non-linear thermal solver.The level-set-based mesh adaptation strategy,governed by multi-physical fields,was applied to alleviate the calculation and accurately track the phase evolution.The processing simulation on the dog-bone model revealed that preheating temperature significantly influences the crystallization behavior.Finally,the multi-layer simulation demonstrated that such a developed numerical model can be used to study the phase transformation during powder layer updating and the cyclic laser sintering phenomena.Moreover,the numerical study suggested that crystallization occurs slowly during the L-PBF process.展开更多
This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was...This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.展开更多
Tungsten is considered the most promising plasma-facing material for fusion reactors with exceptional performance.Under certain conditions,activated tungsten dust can be generated through plasma–wall interactions and...Tungsten is considered the most promising plasma-facing material for fusion reactors with exceptional performance.Under certain conditions,activated tungsten dust can be generated through plasma–wall interactions and released into the atmosphere.Activated tungsten migrates downward in the soil after atmospheric deposition.However,effective methods for evaluating the environmental dose of gamma rays emitted by activated tungsten are still lacking.Consequently,a method for evaluating the air-absorbed dose rate of activated tungsten dust was proposed considering soil attenuation.Key parameters including the mass attenuation coefficient and energy absorption build-up factor were determined for the main gamma ray energies of radionuclides within the activated tungsten dust.Additionally,air-absorbed dose rates were calculated by assuming that radioactive sources were located at different soil depths and radii.It was found that a soil depth of 50 cm significantly attenuated the environmental dose by 99.9%,whereas the air-absorbed dose rates within the horizontal distance of 500 cm accounted for 91%of the total dose rate.Therefore,this study underscored the importance of soil attenuation in environmental dose assessments,which must be carefully re-examined for the safety analysis of fusion reactors.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-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative.Current research on 3D reconstruction using high-resolution satellite data primarily focuse...High-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative.Current research on 3D reconstruction using high-resolution satellite data primarily focuses on two approaches:Multi-stereo fusion and multi-view matching.While algorithms based on these two methodologies for multi-view image 3D reconstruction have reached relative maturity,no systematic comparison has been conducted specifically on satellite data to evaluate the relative merits of multi-stereo fusion versus multi-view matching methods.This paper conducts a comparative analysis of the practical accuracy of both approaches using high-resolution satellite datasets from diverse geographical regions.To ensure fairness in accuracy comparison,both methodologies employ non-local dense matching for cost optimization.Results demonstrate that the multi-stereo fusion method outperforms multi-view matching in all evaluation metrics,exhibiting approximately 1.2%higher average matching accuracy and 10.7%superior elevation precision in the experimental datasets.Therefore,for 3D modeling applications using satellite data,we recommend adopting the multi-stereo fusion approach for digital surface model(DSM)product generation.展开更多
基金Supported by the National Key R&D Program of China (No.2023YFC3008100)the National Natural Science Foundation of China (No.U23A2033)
文摘Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(FIR)”method for multi-sourced depth data fusion,and used it to merge the electronic nautical chart dataset(referred to as Chart2014 in this paper)with the global digital elevation dataset(referred to as Globalbath2002 in this paper).Compared to the traditional fusion of two datasets by direct combination and interpolation,the new Grid-DEM formed by FIR can better represent the data characteristics of Chart2014,reduce the calculation difficulty,and be more intuitive,and,the choice of different interpolation methods in FIR and the influence of the“exclusion radius R”parameter were discussed.FIR avoids complex calculations of spatial distances among points from different sources,and instead uses spatial exclusion map to perform one-step screening based on the exclusion radius R,which greatly improved the fusion status of a reliable dataset.The fusion results of different experiments were analyzed statistically with root mean square error and mean relative error,showing that the interpolation methods based on Delaunay triangulation are more suitable for the fusion of nautical chart depth of China,and factors such as the point density distribution of multiple source data,accuracy,interpolation method,and various terrain conditions should be fully considered when selecting the exclusion radius R.
基金The National High Technology Research and Develop-ment Program of China(863Program)(No.2006AA04Z416)the Na-tional Science Fund for Distinguished Young Scholars(No.50725828)the Excellent Dissertation Program for Doctoral Degree of Southeast University(No.0705)
文摘Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.
基金financially supported by the National Natural Science Foundation of China(Grant No.51879049)DK-I Dynamic Positioning System Console Project
文摘The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.
基金The National Key Research and Development Program of China under contract Nos 2016YFC1400901 and 2018YFC1406600the National Natural Science Foundation of China under contract No.40706057+1 种基金the Environmental Protection and Science and Technology Plan Project of Zhejiang Province of China under contract No.2013A021the Research Center for Air Pollution and Health of Zhejiang University
文摘Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening(PS), principal component analysis(PCA), Gram-Schmidt(GS), and wavelet fusion(WF), are utilized to fuse MS and PAN images of GF-1.The results of subjective and objective evaluation methods application indicate that GS performs the best,followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.
文摘Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion, are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.
基金supported by the National Natural Science Foundation of China (No. 52075350)the Special City School Strategic Cooperation Project of Sichuan University and Zigong (No.2021CDZG-3)
文摘The control rod drive mechanism(CRDM)is an essential part of the control and safety protection system of pressurized water reactors.Current CRDM simulations are mostly performed collectively using a single method,ignoring the influence of multiple motion units and the differences in various features among them,which strongly affect the efficiency and accuracy of the simulations.In this study,we constructed a flow field fusion simulation method based on model features by combining key motion unit analysis and various simulation methods and then applied the method to the CRDM simulation process.CRDM performs motion unit decomposition through the structural hierarchy of function-movement-action method,and the key meta-actions are identified as the nodes in the flow field simulation.We established a fused feature-based multimethod simulation process and processed the simulation methods and data according to the features of the fluid domain space and the structural complexity to obtain the fusion simulation results.Compared to traditional simulation methods and real measurements,the simulation method provides advantages in terms of simulation efficiency and accuracy.
基金co-supported by the Special Research on Civil Aircraft of China (No.MJZ-2017-J-96)the Defense Industrial Technology Development Program of China (No.JCKY2016206B009)。
文摘In cabin-type component alignment, digital measurement technology is usually adopted to provide guidance for assembly. Depending on the system of measurement, the alignment process can be divided into measurement-assisted assembly(MAA) and force-driven assembly. In MAA,relative pose between components is directly measured to guide assembly, while in force-driven assembly, only contact state can be recognized according to measured six-dimensional force and torque(6 D F/T) and the process is completed based on preset assembly strategy. Aiming to improve the efficiency of force-driven cabin-type component alignment, this paper proposed a heuristic alignment method based on multi-source data fusion. In this method, measured 6 D F/T, pose data and geometric information of components are fused to calculate the relative pose between components and guide the movement of pose adjustment platform. Among these data types, pose data and measured 6 D F/T are combined as data set. To collect the data sets needed for data fusion, dynamic gravity compensation method and hybrid motion control method are designed. Then the relative pose calculation method is elaborated, which transforms collected data sets into discrete geometric elements and calculates the relative poses based on the geometric information of components.Finally, experiments are conducted in simulation environment and the results show that the proposed alignment method is feasible and effective.
基金supported by “the Fundamental Research Funds for the Central Universities” No.2018CUCTJ081
文摘Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques are often involved in such multi-method fusion metrics so that its output would be more consistent with human visual perceptions. On the other hand, the robustness and generalization ability of these multi-method fusion metrics are questioned because of the scarce of images with mean opinion scores. In order to comprehensively validate whether or not the generalization ability of such multi-method fusion IQA metrics are satisfying, we construct a new image database which contains up to 60 reference images. The newly built image database is then used to test the generalization ability of different multi-method fusion IQA metrics. Cross database validation experiment indicates that in our new image database, the performances of all the multi-method fusion IQA metrics have no statistical significant different with some single-method IQA metrics such as FSIM and MAD. In the end, a thorough analysis is given to explain why the performance of multi-method fusion IQA framework drop significantly in cross database validation.
基金supported by the National Natural Science Foundation of China (No.61871350)the Zhejiang Science and Technology Plan Project (No.2019C011123)the Zhejiang Province Basic Public Welfare Research Project (No.LGG19F030011)。
文摘The current life-prediction models for lithium-ion batteries have several problems, such as the construction of complex feature structures, a high number of feature dimensions, and inaccurate prediction results. To overcome these problems, this paper proposes a deep-learning model combining an autoencoder network and a long short-term memory network. First, this model applies the characteristics of the autoencoder to reduce the dimensionality of the high-dimensional features extracted from the battery data set and realize the fusion of complex time-domain features, which overcomes the problems of redundant model information and low computational efficiency. This model then uses a long short-term memory network that is sensitive to time-series data to solve the long-path dependence problem in the prediction of battery life. Lastly, the attention mechanism is used to give greater weight to features that have a greater impact on the target value, which enhances the learning effect of the model on the long input sequence. To verify the efficacy of the proposed model, this paper uses NASA's lithium-ion battery cycle life data set.
基金supported by the National Natural Science Foundation of China under grant Nos.12371250 and 12205154Jiangsu Provincial Natural Science Foundation under grant Nos.BK20221508 and BK20210380Jiangsu Qinglan High-level Talent Project and High-level Personnel Project under grant No.JSSCBS20210277.
文摘Fusion and fission are two important phenomena that have been experimentally observed in many real physical models.In this paper,we investigate the two phenomena in the(2+1)-dimensional Hirota-Satsuma-Ito equation via the physics-informed neural networks(PINN)method.By choosing suitable physically constrained initial boundary conditions,the data-driven fusion and fission solutions are obtained for the first time.Dynamical behaviors and error analysis of these solutions are investigated via illustratively numerical figures,which show that good results are achieved.It is pointed out that the PINN method adopted here can be effectively used to construct the data-driven fusion and fission solutions for other nonlinear integrable equations.Based on the powerful predictive capability of the PINN method and wide applications of fusion and fission in many physical areas,it is hoped that the data-driven solutions obtained here will be helpful for experts to predict or explain related physical phenomena.
基金sponsored by National Key R&D Program of China(2018YFC1504504)Youth Foundation of Yunnan Earthquake Agency(2021K01)Project of Yunnan Earthquake Agency“Chuan bang dai”(CQ3-2021001).
文摘In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.
基金funded by National Natural Science Foundation of China(Grant Nos.42272333,42277147).
文摘Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.
文摘Sample preparation by fusion for XRF analysis is all about knowing the exact weights of the sample and the flux (sample-to-flux ratio). The whole analytical chain, including the weighing step in sample preparation prior to fusion, is of crucial importance to get precise and accurate x-ray fluorescence (XRF) results. Consequently, the weighing method will affect the quality of the analytical results given by the spectrometer. In this study, the effects of different weighing methods on the precision (RSD) of the obtained XRF results are compared to determine the best weighing method for sample preparation by fusion in terms of comparable precisions in the XRF results.
文摘The project-based learning method simply means that the students disassemble the English knowledge under the guidance of the teachers, and complete the teaching project with their own thinking, analysis and processing of the text. In the process of continuous disassembly, the students can not only deeply understand the meaning contained in the text, but also effectively play the effect of English teaching. Based on this, this paper mainly analyzes the importance of project-based teaching method in secondary vocational English teaching, and puts forward the application effect of project-based teaching method in secondary vocational English teaching. In order to achieve the effective integration effect, the following actively explores the countermeasures for reference.
基金funded by the Committee of Science of the Ministry of Science andHigher Education of the Republic of Kazakhstan(Grant No.BR24993166).
文摘While automatic image captioning systems have made notable progress in the past few years,generating captions that fully convey sentiment remains a considerable challenge.Although existing models achieve strong performance in visual recognition and factual description,they often fail to account for the emotional context that is naturally present in human-generated captions.To address this gap,we propose the Sentiment-Driven Caption Generator(SDCG),which combines transformer-based visual and textual processing withmulti-level fusion.RoBERTa is used for extracting sentiment from textual input,while visual features are handled by the Vision Transformer(ViT).These features are fused using several fusion approaches,including Concatenation,Attention,Visual-Sentiment Co-Attention(VSCA),and Cross-Attention.Our experiments demonstrate that SDCG significantly outperforms baseline models such as the Generalized Image Transformer(GIT),which achieves 82.01%,and Bootstrapping Language-Image Pre-training(BLIP),which achieves 83.07%,in sentiment accuracy.While SDCG achieves 94.52%sentiment accuracy and improves scores in BLEU and ROUGE-L,the model demonstrates clear advantages.More importantly,the captions aremore natural,as they incorporate emotional cues and contextual awareness,making them resemble those written by a human.
文摘Semi-crystalline polymer laser powder bed fusion(L-PBF)has recently attracted increasing interest due to its potential for fabricating complex geometry.However,a more comprehensive understanding of the underlying physics during L-PBF is required to better control the properties of the final part.This work proposed a multi-layer numerical model to study the temperature and phase evolution during the polyamide-12(PA12)L-PBF process.The Descend and Parallel Chord methods were introduced to improve the convergence of the non-linear thermal solver.The level-set-based mesh adaptation strategy,governed by multi-physical fields,was applied to alleviate the calculation and accurately track the phase evolution.The processing simulation on the dog-bone model revealed that preheating temperature significantly influences the crystallization behavior.Finally,the multi-layer simulation demonstrated that such a developed numerical model can be used to study the phase transformation during powder layer updating and the cyclic laser sintering phenomena.Moreover,the numerical study suggested that crystallization occurs slowly during the L-PBF process.
基金supported by the National Natural Science Foundation of China (grant numbers 42293351, and U2468221)。
文摘This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.
基金supported by the National Natural Science Foundation of China(No.12375314)。
文摘Tungsten is considered the most promising plasma-facing material for fusion reactors with exceptional performance.Under certain conditions,activated tungsten dust can be generated through plasma–wall interactions and released into the atmosphere.Activated tungsten migrates downward in the soil after atmospheric deposition.However,effective methods for evaluating the environmental dose of gamma rays emitted by activated tungsten are still lacking.Consequently,a method for evaluating the air-absorbed dose rate of activated tungsten dust was proposed considering soil attenuation.Key parameters including the mass attenuation coefficient and energy absorption build-up factor were determined for the main gamma ray energies of radionuclides within the activated tungsten dust.Additionally,air-absorbed dose rates were calculated by assuming that radioactive sources were located at different soil depths and radii.It was found that a soil depth of 50 cm significantly attenuated the environmental dose by 99.9%,whereas the air-absorbed dose rates within the horizontal distance of 500 cm accounted for 91%of the total dose rate.Therefore,this study underscored the importance of soil attenuation in environmental dose assessments,which must be carefully re-examined for the safety analysis of fusion reactors.
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.
文摘High-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative.Current research on 3D reconstruction using high-resolution satellite data primarily focuses on two approaches:Multi-stereo fusion and multi-view matching.While algorithms based on these two methodologies for multi-view image 3D reconstruction have reached relative maturity,no systematic comparison has been conducted specifically on satellite data to evaluate the relative merits of multi-stereo fusion versus multi-view matching methods.This paper conducts a comparative analysis of the practical accuracy of both approaches using high-resolution satellite datasets from diverse geographical regions.To ensure fairness in accuracy comparison,both methodologies employ non-local dense matching for cost optimization.Results demonstrate that the multi-stereo fusion method outperforms multi-view matching in all evaluation metrics,exhibiting approximately 1.2%higher average matching accuracy and 10.7%superior elevation precision in the experimental datasets.Therefore,for 3D modeling applications using satellite data,we recommend adopting the multi-stereo fusion approach for digital surface model(DSM)product generation.
