When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ...When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.展开更多
For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information...For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.展开更多
This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigati...This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.展开更多
Efficiently performing high-resolution direction of arrival(DOA)estimation under low signal-to-noise ratio(SNR)conditions has always been a challenge task in the literatures.Obvi-ously,in order to address this problem...Efficiently performing high-resolution direction of arrival(DOA)estimation under low signal-to-noise ratio(SNR)conditions has always been a challenge task in the literatures.Obvi-ously,in order to address this problem,the key is how to mine or reveal as much DOA related in-formation as possible from the degraded array outputs.However,it is certain that there is no per-fect solution for low SNR DOA estimation designed in the way of winner-takes-all.Therefore,this paper proposes to explore in depth the complementary DOA related information that exists in spa-tial spectrums acquired by different basic DOA estimators.Specifically,these basic spatial spec-trums are employed as the input of multi-source information fusion model.And the multi-source in-formation fusion model is composed of three heterogeneous meta learning machines,namely neural networks(NN),support vector machine(SVM),and random forests(RF).The final meta-spec-trum can be obtained by performing a final decision-making method.Experimental results illus-trate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estim-ators.Even under low SNR conditions,promising DOA estimation performance can be achieved.展开更多
To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode ...To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.展开更多
The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even ...The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile teaching.Therefore,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,respectively.By integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is constructed.According to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple perspectives.Finally,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.展开更多
In the field of target recognition based on the temporal-spatial information fusion,evidence the-ory has received extensive attention.To achieve accurate and efficient target recognition by the evi-dence theory,an ada...In the field of target recognition based on the temporal-spatial information fusion,evidence the-ory has received extensive attention.To achieve accurate and efficient target recognition by the evi-dence theory,an adaptive temporal-spatial information fusion model is proposed.Firstly,an adaptive evaluation correction mechanism is constructed by the evidence distance and Deng entropy,which realizes the credibility discrimination and adaptive correction of the spatial evidence.Secondly,the credibility decay operator is introduced to obtain the dynamic credibility of temporal evidence.Finally,the sequential combination of temporal-spatial evidences is achieved by Shafer’s discount criterion and Dempster’s combination rule.The simulation results show that the proposed method not only considers the dynamic and sequential characteristics of the temporal-spatial evidences com-bination,but also has a strong conflict information processing capability,which provides a new refer-ence for the field of temporal-spatial information fusion.展开更多
Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However,...Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However, in practical applications, the information obtained by a single device is limited, and it is necessary to integrate the information of maritime radar and AIS messages to achieve better recognition effects. In this study, the D-S evidence theory is used to fusion the two kinds of heterogeneous information: maritime radar images and AIS messages. Firstly, the radar image and AIS message are processed to get the targets of interest in the same coordinate system. Then, the coordinate position and heading of targets are chosen as the indicators for judging target similarity. Finally, a piece of D-S evidence theory based on the information fusion method is proposed to match the radar target and the AIS target of the same ship. Particularly, the effectiveness of the proposed method has been validated and evaluated through several experiments, which proves that such a method is practical in maritime safety supervision.展开更多
The ever-increasing complexity of industry facilities has made the reliability analysis and assessment an imperative yet tough work. Motivated by practical engineering requirement, this paper develops a Bayesian-based...The ever-increasing complexity of industry facilities has made the reliability analysis and assessment an imperative yet tough work. Motivated by practical engineering requirement, this paper develops a Bayesian-based information extraction and aggregation(BIEA) approach for system level reliability estimation of a complex system. It takes both subjective judgments and objective field outputs into consideration. Novel features of this approach is a unique information content based aggregation process, which allows a flexible application of this framework in separated modules on account for purpose. The coherency of which is guaranteed by the objective information content calculation. This work goes beyond the alternatives that deal with solely attributed data under ideal information circumstance, and investigates a more generic tool for real engineering application. Limitations embedded in traditional statistical modeling methods have been eliminated in a nature manner by information transition and integration. In addition, a double axis driving mechanism(DADM) for erecting the antenna of a satellite is demonstrated as case study for benefit illustration and effectiveness verification.展开更多
For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for...For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.展开更多
In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for th...In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.展开更多
In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese...In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.展开更多
For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intell...For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy.First,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure.A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features.Finally,the advantage of the deep belief network probability model is used as the fault classifier to identify the faults.The effectiveness of the proposed method was verified by a gearbox test-bed.Experimental results show that,compared with traditional and existing intelligent fault diagnosis methods,the proposed method can obtain representative information and features from the raw data with higher classification accuracy.展开更多
This paper derives a square-root information-type filtering algorithm for nonlinear multi-sensor fusion problems using the cubature Kalman filter theory. The resulting filter is called the square-root cubature Informa...This paper derives a square-root information-type filtering algorithm for nonlinear multi-sensor fusion problems using the cubature Kalman filter theory. The resulting filter is called the square-root cubature Information filter (SCIF). The SCIF propagates the square-root information matrices derived from numerically stable matrix operations and is therefore numerically robust. The SCIF is applied to a highly maneuvering target tracking problem in a distributed sensor network with feedback. The SCIF’s performance is finally compared with the regular cubature information filter and the traditional extended information filter. The results, presented herein, indicate that the SCIF is the most reliable of all three filters and yields a more accurate estimate than the extended information filter.展开更多
The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communica...The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.展开更多
Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to u...Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.展开更多
When the Transformer proposed by Google in 2017,it was first used for machine translation tasks and achieved the state of the art at that time.Although the current neural machine translation model can generate high qu...When the Transformer proposed by Google in 2017,it was first used for machine translation tasks and achieved the state of the art at that time.Although the current neural machine translation model can generate high quality translation results,there are still mistranslations and omissions in the translation of key information of long sentences.On the other hand,the most important part in traditional translation tasks is the translation of key information.In the translation results,as long as the key information is translated accurately and completely,even if other parts of the results are translated incorrect,the final translation results’quality can still be guaranteed.In order to solve the problem of mistranslation and missed translation effectively,and improve the accuracy and completeness of long sentence translation in machine translation,this paper proposes a key information fused neural machine translation model based on Transformer.The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder.After the same encoding as the source language text,it is fused with the output of the source language text encoded by the encoder,then the key information is processed and input into the decoder.With incorporating keyword information from the source language sentence,the model’s performance in the task of translating long sentences is very reliable.In order to verify the effectiveness of the method of fusion of key information proposed in this paper,a series of experiments were carried out on the verification set.The experimental results show that the Bilingual Evaluation Understudy(BLEU)score of the model proposed in this paper on theWorkshop on Machine Translation(WMT)2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset.The experimental results show the advantages of the model proposed in this paper.展开更多
Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data mu...Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.展开更多
Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification(SMI) and ...Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification(SMI) and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence(PoDE) in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.展开更多
Altitude regulation is a fundamental problem in UAV(unmanned aerial vehicles) control to ensure hovering and autonomous navigation performance.However,data from altitude sensors may be unstable by interference.A digit...Altitude regulation is a fundamental problem in UAV(unmanned aerial vehicles) control to ensure hovering and autonomous navigation performance.However,data from altitude sensors may be unstable by interference.A digital-filter-based improved adaptive Kalman method is proposed to improve accuracy and reliability of the altitude measurement information.A unique sensor data fusion structure is designed to make different sensors switch automatically in different environment.Simulation and experimental results show that an improved Sage-Husa adaptive extended Kalman filter(SHAEKF) is adopted in altitude data fusion which means that altitude error is limited to 1.5m in high altitude and 1.2m near the ground.This method is proved feasible and effective through hovering flight test and three-dimensional track flight experiment.展开更多
文摘When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.
基金financed with the means of Basic Scientific Research Youth Program of Education Department of Liaoning Province,No.LJKQZ2021185Yingkou Enterprise and Doctor Innovation Program (QB-2021-05).
文摘For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.
基金supported by the National Natural Science Foundation of China(Nos.61925302,62273027)the Beijing Natural Science Foundation(L211021).
文摘This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.
基金the National Natural Science Foundation of China(Nos.11774073 and 51279033).
文摘Efficiently performing high-resolution direction of arrival(DOA)estimation under low signal-to-noise ratio(SNR)conditions has always been a challenge task in the literatures.Obvi-ously,in order to address this problem,the key is how to mine or reveal as much DOA related in-formation as possible from the degraded array outputs.However,it is certain that there is no per-fect solution for low SNR DOA estimation designed in the way of winner-takes-all.Therefore,this paper proposes to explore in depth the complementary DOA related information that exists in spa-tial spectrums acquired by different basic DOA estimators.Specifically,these basic spatial spec-trums are employed as the input of multi-source information fusion model.And the multi-source in-formation fusion model is composed of three heterogeneous meta learning machines,namely neural networks(NN),support vector machine(SVM),and random forests(RF).The final meta-spec-trum can be obtained by performing a final decision-making method.Experimental results illus-trate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estim-ators.Even under low SNR conditions,promising DOA estimation performance can be achieved.
文摘To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.
基金supported by the Chongqing Normal University Graduate Scientific Research Innovation Project (Grants YZH21014 and YZH21010).
文摘The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile teaching.Therefore,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,respectively.By integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is constructed.According to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple perspectives.Finally,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.
基金the National Natural Science Foundation of China(No.61976080)the Key Project on Research and Practice of Henan University Graduate Education and Teaching Reform(YJSJG2023XJ006)+1 种基金the Key Research and Development Projects of Henan Province(231111212500)the Henan University Graduate Education Innovation and Quality Improvement Program(SYLKC2023016).
文摘In the field of target recognition based on the temporal-spatial information fusion,evidence the-ory has received extensive attention.To achieve accurate and efficient target recognition by the evi-dence theory,an adaptive temporal-spatial information fusion model is proposed.Firstly,an adaptive evaluation correction mechanism is constructed by the evidence distance and Deng entropy,which realizes the credibility discrimination and adaptive correction of the spatial evidence.Secondly,the credibility decay operator is introduced to obtain the dynamic credibility of temporal evidence.Finally,the sequential combination of temporal-spatial evidences is achieved by Shafer’s discount criterion and Dempster’s combination rule.The simulation results show that the proposed method not only considers the dynamic and sequential characteristics of the temporal-spatial evidences com-bination,but also has a strong conflict information processing capability,which provides a new refer-ence for the field of temporal-spatial information fusion.
文摘Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However, in practical applications, the information obtained by a single device is limited, and it is necessary to integrate the information of maritime radar and AIS messages to achieve better recognition effects. In this study, the D-S evidence theory is used to fusion the two kinds of heterogeneous information: maritime radar images and AIS messages. Firstly, the radar image and AIS message are processed to get the targets of interest in the same coordinate system. Then, the coordinate position and heading of targets are chosen as the indicators for judging target similarity. Finally, a piece of D-S evidence theory based on the information fusion method is proposed to match the radar target and the AIS target of the same ship. Particularly, the effectiveness of the proposed method has been validated and evaluated through several experiments, which proves that such a method is practical in maritime safety supervision.
基金supported by the National Basic Research Program of China(973 Program)(2013CB733000)
文摘The ever-increasing complexity of industry facilities has made the reliability analysis and assessment an imperative yet tough work. Motivated by practical engineering requirement, this paper develops a Bayesian-based information extraction and aggregation(BIEA) approach for system level reliability estimation of a complex system. It takes both subjective judgments and objective field outputs into consideration. Novel features of this approach is a unique information content based aggregation process, which allows a flexible application of this framework in separated modules on account for purpose. The coherency of which is guaranteed by the objective information content calculation. This work goes beyond the alternatives that deal with solely attributed data under ideal information circumstance, and investigates a more generic tool for real engineering application. Limitations embedded in traditional statistical modeling methods have been eliminated in a nature manner by information transition and integration. In addition, a double axis driving mechanism(DADM) for erecting the antenna of a satellite is demonstrated as case study for benefit illustration and effectiveness verification.
基金supported by the National Natural Science Foundation of China under Grant 51722406,52074340,and 51874335the Shandong Provincial Natural Science Foundation under Grant JQ201808+5 种基金The Fundamental Research Funds for the Central Universities under Grant 18CX02097Athe Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008the Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002the National Research Council of Science and Technology Major Project of China under Grant 2016ZX05025001-006111 Project under Grant B08028Sinopec Science and Technology Project under Grant P20050-1
文摘For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.
基金supported by the Science and Technology Project of State Grid Shandong Electric Power Company?“Research on the Data-Driven Method for Energy Internet”?(Project No.2018A-100)。
文摘In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.
基金supported by the National Natural Science Foundation of China(Grant No.:U2202213)the Special Program for the Major Science and Technology Projects of Yunnan Province,China(Grant Nos.:202102AE090051-1-01,and 202202AE090001).
文摘In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.
基金Supported by National Natural Science Foundation of China and Civil Aviation Administration of China Joint Funded Project(Grant No.U1733108)Key Project of Tianjin Science and Technology Support Program(Grant No.16YFZCSY00860).
文摘For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy.First,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure.A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features.Finally,the advantage of the deep belief network probability model is used as the fault classifier to identify the faults.The effectiveness of the proposed method was verified by a gearbox test-bed.Experimental results show that,compared with traditional and existing intelligent fault diagnosis methods,the proposed method can obtain representative information and features from the raw data with higher classification accuracy.
文摘This paper derives a square-root information-type filtering algorithm for nonlinear multi-sensor fusion problems using the cubature Kalman filter theory. The resulting filter is called the square-root cubature Information filter (SCIF). The SCIF propagates the square-root information matrices derived from numerically stable matrix operations and is therefore numerically robust. The SCIF is applied to a highly maneuvering target tracking problem in a distributed sensor network with feedback. The SCIF’s performance is finally compared with the regular cubature information filter and the traditional extended information filter. The results, presented herein, indicate that the SCIF is the most reliable of all three filters and yields a more accurate estimate than the extended information filter.
基金sponsored by the National Natural Science Foundation of China under Grant 61901066,Grant 61971077sponsored by Natural Science Foundation of Chongqing,China under Grant cstc2019jcyjmsxmX0575,Grant cstc2021jcyj-msxmX0458+2 种基金in part by the Entrepreneurship and Innovation Support Plan of Chongqing for Returned Overseas Scholars under Grant cx2021092supported by the open research fund of National Mobile Communications Research Laboratory,Southeast University(No.2021D13,No.2022D06)the Industrial Internet innovation and development project(No.TC200A00M).
文摘The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.
文摘Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.
基金Major Science and Technology Project of Sichuan Province[No.2022YFG0315,2022YFG0174]Sichuan Gas Turbine Research Institute stability support project of China Aero Engine Group Co.,Ltd.[No.GJCZ-2019-71].
文摘When the Transformer proposed by Google in 2017,it was first used for machine translation tasks and achieved the state of the art at that time.Although the current neural machine translation model can generate high quality translation results,there are still mistranslations and omissions in the translation of key information of long sentences.On the other hand,the most important part in traditional translation tasks is the translation of key information.In the translation results,as long as the key information is translated accurately and completely,even if other parts of the results are translated incorrect,the final translation results’quality can still be guaranteed.In order to solve the problem of mistranslation and missed translation effectively,and improve the accuracy and completeness of long sentence translation in machine translation,this paper proposes a key information fused neural machine translation model based on Transformer.The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder.After the same encoding as the source language text,it is fused with the output of the source language text encoded by the encoder,then the key information is processed and input into the decoder.With incorporating keyword information from the source language sentence,the model’s performance in the task of translating long sentences is very reliable.In order to verify the effectiveness of the method of fusion of key information proposed in this paper,a series of experiments were carried out on the verification set.The experimental results show that the Bilingual Evaluation Understudy(BLEU)score of the model proposed in this paper on theWorkshop on Machine Translation(WMT)2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset.The experimental results show the advantages of the model proposed in this paper.
基金This study was supported by National Key Research and Development Project(Project No.2017YFD0301506)National Social Science Foundation(Project No.71774052)+1 种基金Hunan Education Department Scientific Research Project(Project No.17K04417A092).
文摘Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education (20091102120023)the Aeronautical Science Foundation of China (2012ZA51010)+1 种基金the National Natural Science Foundation of China (11002013)Defense Industrial Technology Development Program (A2120110001 and B2120110011)
文摘Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification(SMI) and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence(PoDE) in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.
基金Supported by the National Natural Science Foundation of China(No.61304017,11372309)Key Technology Development Project of Jilin Province(No.20150204074GX)+1 种基金the Project Development Plan of Science and Technology(No.20150520111zh)the Provincial Special Funds Project of Science and Technology Cooperation(No.2014SYHZ0004)
文摘Altitude regulation is a fundamental problem in UAV(unmanned aerial vehicles) control to ensure hovering and autonomous navigation performance.However,data from altitude sensors may be unstable by interference.A digital-filter-based improved adaptive Kalman method is proposed to improve accuracy and reliability of the altitude measurement information.A unique sensor data fusion structure is designed to make different sensors switch automatically in different environment.Simulation and experimental results show that an improved Sage-Husa adaptive extended Kalman filter(SHAEKF) is adopted in altitude data fusion which means that altitude error is limited to 1.5m in high altitude and 1.2m near the ground.This method is proved feasible and effective through hovering flight test and three-dimensional track flight experiment.
