Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the vary...Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the varying importance of each modality across different contexts,a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process.In response to these critical limitations,we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues.In our model,text is treated as the core modality,while speech and video features are selectively incorporated through a unique position-aware fusion process.The spatial position encoding strategy preserves the internal structural information of speech and visual modalities,enabling the model to capture localized intra-modal dependencies that are often overlooked.This design enhances the richness and discriminative power of the fused representation,enabling more accurate and context-aware sentiment prediction.Finally,we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model.The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks.展开更多
Polarization-based integrated navigation system(PINS)that combines the polarization sensor(PS)and the inertial navigation system(INS)has been widely recognized as an effective solution for acquiring attitude informati...Polarization-based integrated navigation system(PINS)that combines the polarization sensor(PS)and the inertial navigation system(INS)has been widely recognized as an effective solution for acquiring attitude information of unmanned aerial vehicles(UAVs).However,based on the PINS hardware configuration,the accurate acquisition of UAV position information remains a challenge.In this article,we propose an improved PS/INS integrated navigation scheme by incorporating an embedded UAV dynamic model(UDM).Compared with existing PS/INS fusion methods,the presented PINS enables the optimal estimation of the UDM thrust coefficient error along with other system state elements,thus significantly improving the UDM accuracy.On this basis,the UDM and PS are used to fuse with the INS,which improves the estimation accuracy of both the UAV attitude and position.Furthermore,we employ an adaptive fusion strategy to detect the reliability of PS data.Therefore,once the UDM is corrected using reliable PS data,it can further fuse with the INS,thereby improving the environmental adaptability of the PINS.The simulation and flight experiment results verified the effectiveness of the proposed PS/INS/UDM integrated navigation scheme.展开更多
基金supported by the Collaborative Tackling Project of the Yangtze River Delta SciTech Innovation Community(Nos.2024CSJGG01503,2024CSJGG01500)Guangxi Key Research and Development Program(No.AB24010317)Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics(Jiangxi Police College)(No.2025JXJYKFJJ002).
文摘Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the varying importance of each modality across different contexts,a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process.In response to these critical limitations,we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues.In our model,text is treated as the core modality,while speech and video features are selectively incorporated through a unique position-aware fusion process.The spatial position encoding strategy preserves the internal structural information of speech and visual modalities,enabling the model to capture localized intra-modal dependencies that are often overlooked.This design enhances the richness and discriminative power of the fused representation,enabling more accurate and context-aware sentiment prediction.Finally,we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model.The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks.
基金supported by the National Natural Science Foundation of China(Grant Nos.62388101,62425302,62227813,62373033,62403024)the National Key R&D Program of China(Grant No.2020YFA0711200)。
文摘Polarization-based integrated navigation system(PINS)that combines the polarization sensor(PS)and the inertial navigation system(INS)has been widely recognized as an effective solution for acquiring attitude information of unmanned aerial vehicles(UAVs).However,based on the PINS hardware configuration,the accurate acquisition of UAV position information remains a challenge.In this article,we propose an improved PS/INS integrated navigation scheme by incorporating an embedded UAV dynamic model(UDM).Compared with existing PS/INS fusion methods,the presented PINS enables the optimal estimation of the UDM thrust coefficient error along with other system state elements,thus significantly improving the UDM accuracy.On this basis,the UDM and PS are used to fuse with the INS,which improves the estimation accuracy of both the UAV attitude and position.Furthermore,we employ an adaptive fusion strategy to detect the reliability of PS data.Therefore,once the UDM is corrected using reliable PS data,it can further fuse with the INS,thereby improving the environmental adaptability of the PINS.The simulation and flight experiment results verified the effectiveness of the proposed PS/INS/UDM integrated navigation scheme.
