The original monitoring data from aero-engines possess characteristics such as high dimen-sionality,strong noise,and imbalance,which present substantial challenges to traditional anomalydetection methods.In response,t...The original monitoring data from aero-engines possess characteristics such as high dimen-sionality,strong noise,and imbalance,which present substantial challenges to traditional anomalydetection methods.In response,this paper proposes a method based on Fuzzy Fusion of variablesand Discriminant mapping of features for Clustering(FFD-Clustering)to detect anomalies in originalmonitoring data from Aircraft Communication Addressing and Reporting System(ACARS).Firstly,associated variables are fuzzily grouped to extract the underlying distribution characteristics and trendsfrom the data.Secondly,a multi-layer contrastive denoising-based feature Fusion Encoding Network(FEN)is designed for each variable group,which can construct representative features for each variablegroup through eliminating strong noise and complex interrelations between variables.Thirdly,a featureDiscriminative Mapping Network(DMN)based on reconstruction difference re-clustering is designed,which can distinguish dissimilar feature vectors when mapping representative features to a unified fea-ture space.Finally,the K-means clustering is used to detect the abnormal feature vectors in the unifiedfeature space.Additionally,the algorithm is capable of reconstructing identified abnormal vectors,thereby locating the abnormal variable groups.The performance of this algorithm was tested ontwo public datasets and real original monitoring data from four aero-engines'ACARS,demonstratingits superiority and application potential in aero-engine anomaly detection.展开更多
A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism.The secondorder neighborhood shows the connection between the current pixel and the sur...A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism.The secondorder neighborhood shows the connection between the current pixel and the surroundings of this pixel.This connection is for each RGB component color of the input image.Once the image edges are detected for the three primary colors:red,green,and blue,these colors are merged using the combination rule.Then,the final decision is applied to obtain the segmentation.This process allows different data sources to be combined,which is essential to improve the image information quality and have an optimal image segmentation.Finally,the segmentation results of the proposed model are validated.Moreover,the classification accuracy of the tested data is assessed,and a comparison with other current models is conducted.The comparison results show that the proposed model outperforms the existing models in image segmentation.展开更多
Surface electromyography(sEMG)-based gesture recognition is a key technology in the field of human–computer interaction.However,existing gesture recognition methods face challenges in effectively integrating discrimi...Surface electromyography(sEMG)-based gesture recognition is a key technology in the field of human–computer interaction.However,existing gesture recognition methods face challenges in effectively integrating discriminative temporal feature representations from sEMG signals.In this paper,we propose a deep learning framework named TFN-FICFM comprises a Temporal Fusion Network(TFN)and Fuzzy Integral-Based Classifier Fusion method(FICFM)to improve the accuracy and robustness of gesture recognition.Firstly,we design a TFN module,which utilizes an attention-based recurrent multi-scale convolutional module to acquire multi-level temporal feature representations and achieves deep fusion of temporal features through a feature pyramid module.Secondly,the deep-fused temporal features are utilized to generate multiple sets of gesture category prediction confidences through a feedback loop.Finally,we employ FICFM to perform fuzzy fusion on prediction confidences,resulting in the ultimate decision.This study conducts extensive comparisons and ablation studies using the publicly available datasets Ninapro DB2 and DB5.Results demonstrate that the TFN-FICFM model outperforms state-of-the-art methods in classification performance.This research can serve as a benchmark for sEMG-based gesture recognition and related deep learning modeling.展开更多
Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing le...Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing learners’potential preferences by aggregating high-level semantic information from this network and accurately modeling their cognitive states is crucial for identifying similar learners.Combining similar learners’learning records helps recommend suitable exercises to improve the effectiveness of exercise recommendations.This article tackles the challenging problem of how to aggregate high-level semantic information in a huge graph and accurately model learners’cognitive states.Design/methodology/approach-Firstly,this approach constructs e-learning environments’knowledge graphs by integrating the difficulty of exercises and characteristics of answering behaviors,and the knowledge graph attention network(KGAT)is used to train the graph embedding model of the knowledge graph.Secondly,a score reevaluation method is designed based on the coefficient of completion quality to help accurately model learners’cognitive states.Then,the learners’actual cognitive states,obtained by the cognitive diagnosis model(CDM),are innovatively incorporated into graph matching for acquiring similar subgraphs.Finally,the personalized recommendation results are ranked according to learners’interaction probability on similar exercises.Findings-First,the proposed method has superior exercise recommendation performance.Experiments demonstrate that,compared to the existing approach,the proposed approach has an increase rate of 3.21%,3.32%,3.27%and 0.38%in precision,recall,F1 score and HR@10,respectively,in the large-scale graph data scenario.Second,aggregating high-level semantic information from the knowledge network helps explore learners’potential preferences.Finally,the fine-grained scoring mechanism based on learners’exercise completion quality can better reflect the actual mastery levels of learners,which improves the accuracy of modeling their cognitive states.Originality/value-First,an approach to personalized exercise recommendation is proposed via knowledge enhancement and fuzzy cognitive fusion.The experiments demonstrate the effectiveness and feasibility of this approach in a scenario with large-scale graph data.Second,this approach provides a flexible and adaptable framework.In it,the CDM can be replaced to explore for better accuracy of cognitive evaluation.Third,KGAT is employed to embed the knowledge graph in e-learning environments for aggregating high-level semantic information from the graph.Finally,a score reevaluation method is designed to analyze learners’learning behavior for accurately modeling their cognitive states.展开更多
The use of the multimodel approach in the modelling, analysis and control of non-linear complex and/or ill-defined systems was advocated by many researchers. This approach supposes the definition of a set of local mod...The use of the multimodel approach in the modelling, analysis and control of non-linear complex and/or ill-defined systems was advocated by many researchers. This approach supposes the definition of a set of local models valid in a given region or domain. Different strategies exist in the literature and are generally based on a partitioning of the non-linear system’s full range of operation into multiple smaller operating regimes each of which is associated with a locally valid model or controller. However, most of these strategies, which suppose the determination of these local models as well as their validity domain, remain arbitrary and are generally fixed thanks to a certain a priori knowledge of the system whatever its order. Recently, we have proposed a new approach to derive a multimodel basis which allows us to limit the number of models in the basis to almost four models. Meanwhile, the transition problem between the different models, which may use either a simple commutation or a fusion technique, remains still arise. In this plenary talk, a fuzzy fusion technique is presented and has the following main advantages: （1） use of a fuzzy partitioning in order to determine the validity of each model which enhances the robustness of the solution; 2 introduction, besides the four extreme models, of another model, called average model, determined as an average of the boundary models.展开更多
基金co-supported by the National Science and Technology Major Project,China(No.J2019-I-0001-0001)the National Natural Science Foundation of China(No.52105545)。
文摘The original monitoring data from aero-engines possess characteristics such as high dimen-sionality,strong noise,and imbalance,which present substantial challenges to traditional anomalydetection methods.In response,this paper proposes a method based on Fuzzy Fusion of variablesand Discriminant mapping of features for Clustering(FFD-Clustering)to detect anomalies in originalmonitoring data from Aircraft Communication Addressing and Reporting System(ACARS).Firstly,associated variables are fuzzily grouped to extract the underlying distribution characteristics and trendsfrom the data.Secondly,a multi-layer contrastive denoising-based feature Fusion Encoding Network(FEN)is designed for each variable group,which can construct representative features for each variablegroup through eliminating strong noise and complex interrelations between variables.Thirdly,a featureDiscriminative Mapping Network(DMN)based on reconstruction difference re-clustering is designed,which can distinguish dissimilar feature vectors when mapping representative features to a unified fea-ture space.Finally,the K-means clustering is used to detect the abnormal feature vectors in the unifiedfeature space.Additionally,the algorithm is capable of reconstructing identified abnormal vectors,thereby locating the abnormal variable groups.The performance of this algorithm was tested ontwo public datasets and real original monitoring data from four aero-engines'ACARS,demonstratingits superiority and application potential in aero-engine anomaly detection.
文摘A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism.The secondorder neighborhood shows the connection between the current pixel and the surroundings of this pixel.This connection is for each RGB component color of the input image.Once the image edges are detected for the three primary colors:red,green,and blue,these colors are merged using the combination rule.Then,the final decision is applied to obtain the segmentation.This process allows different data sources to be combined,which is essential to improve the image information quality and have an optimal image segmentation.Finally,the segmentation results of the proposed model are validated.Moreover,the classification accuracy of the tested data is assessed,and a comparison with other current models is conducted.The comparison results show that the proposed model outperforms the existing models in image segmentation.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LQ23F030015)the Key Laboratory of Intelligent Processing Technology for Digital Music(Zhejiang Conservatory of Music),Ministry of Culture and Tourism(2023DMKLC013).
文摘Surface electromyography(sEMG)-based gesture recognition is a key technology in the field of human–computer interaction.However,existing gesture recognition methods face challenges in effectively integrating discriminative temporal feature representations from sEMG signals.In this paper,we propose a deep learning framework named TFN-FICFM comprises a Temporal Fusion Network(TFN)and Fuzzy Integral-Based Classifier Fusion method(FICFM)to improve the accuracy and robustness of gesture recognition.Firstly,we design a TFN module,which utilizes an attention-based recurrent multi-scale convolutional module to acquire multi-level temporal feature representations and achieves deep fusion of temporal features through a feature pyramid module.Secondly,the deep-fused temporal features are utilized to generate multiple sets of gesture category prediction confidences through a feedback loop.Finally,we employ FICFM to perform fuzzy fusion on prediction confidences,resulting in the ultimate decision.This study conducts extensive comparisons and ablation studies using the publicly available datasets Ninapro DB2 and DB5.Results demonstrate that the TFN-FICFM model outperforms state-of-the-art methods in classification performance.This research can serve as a benchmark for sEMG-based gesture recognition and related deep learning modeling.
文摘Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing learners’potential preferences by aggregating high-level semantic information from this network and accurately modeling their cognitive states is crucial for identifying similar learners.Combining similar learners’learning records helps recommend suitable exercises to improve the effectiveness of exercise recommendations.This article tackles the challenging problem of how to aggregate high-level semantic information in a huge graph and accurately model learners’cognitive states.Design/methodology/approach-Firstly,this approach constructs e-learning environments’knowledge graphs by integrating the difficulty of exercises and characteristics of answering behaviors,and the knowledge graph attention network(KGAT)is used to train the graph embedding model of the knowledge graph.Secondly,a score reevaluation method is designed based on the coefficient of completion quality to help accurately model learners’cognitive states.Then,the learners’actual cognitive states,obtained by the cognitive diagnosis model(CDM),are innovatively incorporated into graph matching for acquiring similar subgraphs.Finally,the personalized recommendation results are ranked according to learners’interaction probability on similar exercises.Findings-First,the proposed method has superior exercise recommendation performance.Experiments demonstrate that,compared to the existing approach,the proposed approach has an increase rate of 3.21%,3.32%,3.27%and 0.38%in precision,recall,F1 score and HR@10,respectively,in the large-scale graph data scenario.Second,aggregating high-level semantic information from the knowledge network helps explore learners’potential preferences.Finally,the fine-grained scoring mechanism based on learners’exercise completion quality can better reflect the actual mastery levels of learners,which improves the accuracy of modeling their cognitive states.Originality/value-First,an approach to personalized exercise recommendation is proposed via knowledge enhancement and fuzzy cognitive fusion.The experiments demonstrate the effectiveness and feasibility of this approach in a scenario with large-scale graph data.Second,this approach provides a flexible and adaptable framework.In it,the CDM can be replaced to explore for better accuracy of cognitive evaluation.Third,KGAT is employed to embed the knowledge graph in e-learning environments for aggregating high-level semantic information from the graph.Finally,a score reevaluation method is designed to analyze learners’learning behavior for accurately modeling their cognitive states.
文摘The use of the multimodel approach in the modelling, analysis and control of non-linear complex and/or ill-defined systems was advocated by many researchers. This approach supposes the definition of a set of local models valid in a given region or domain. Different strategies exist in the literature and are generally based on a partitioning of the non-linear system’s full range of operation into multiple smaller operating regimes each of which is associated with a locally valid model or controller. However, most of these strategies, which suppose the determination of these local models as well as their validity domain, remain arbitrary and are generally fixed thanks to a certain a priori knowledge of the system whatever its order. Recently, we have proposed a new approach to derive a multimodel basis which allows us to limit the number of models in the basis to almost four models. Meanwhile, the transition problem between the different models, which may use either a simple commutation or a fusion technique, remains still arise. In this plenary talk, a fuzzy fusion technique is presented and has the following main advantages: （1） use of a fuzzy partitioning in order to determine the validity of each model which enhances the robustness of the solution; 2 introduction, besides the four extreme models, of another model, called average model, determined as an average of the boundary models.
