This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear featu...This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.展开更多
To address the challenges of high model complexity and low accuracy in insulator component defect detection from drone-captured images,this paper presents adaptive downsampling and frequency-position fusion(ADFP),a li...To address the challenges of high model complexity and low accuracy in insulator component defect detection from drone-captured images,this paper presents adaptive downsampling and frequency-position fusion(ADFP),a lightweight algorithm based on YOLOv11(You Only Look Once version 11).The algorithm presents efficient downsampling module,new feature extraction module and innovative neck structure.By integrating the spatial channel attention module of frequency-aware cascade attention(FCA)and the ADown module,the number of parameters is reduced while accuracy is significantly improved.Additionally,the neck module is redesigned,and the position-aware key feature fusion network(PKFN)module is introduced to further improve feature fusion capabilities.Experiments were conducted on the SAID dataset using the improved model.Compared to the original model,the m AP(0.5)of ADFP shows a 5.3%improvement,while the model parameters are reduced by 12.0%.On other public insulator component defect datasets,these improvements still have better results.Multiple experiments have confirmed the effectiveness of the model and its strong generalization ability.展开更多
Signal processing in phase space based on nonlinear dynamics theory is a new method for underwater acoustic signal processing. One key problem when analyzing actual acoustic signal in phase space is how to reduce the ...Signal processing in phase space based on nonlinear dynamics theory is a new method for underwater acoustic signal processing. One key problem when analyzing actual acoustic signal in phase space is how to reduce the noise and lower the embedding dimen- sion. In this paper, local-geometric-projection method is applied to obtain fow dimensional element from various target radiating noise and the derived phase portraits show obviously low dimensional attractors. Furthermore, attractor dimension and cross prediction error are used for classification. It concludes that combining these features representing the geometric and dynamical properties respectively shows effects in target classification.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No. 61033012, No. 611003177, and No. 61070181Fundamental Research Funds for the Central Universities under Grant No.1600-852016 and No. DUT12JR07
文摘This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.
基金Supported by the Natural Science Foundution of Heilongjiang Province(LH2024E109)。
文摘To address the challenges of high model complexity and low accuracy in insulator component defect detection from drone-captured images,this paper presents adaptive downsampling and frequency-position fusion(ADFP),a lightweight algorithm based on YOLOv11(You Only Look Once version 11).The algorithm presents efficient downsampling module,new feature extraction module and innovative neck structure.By integrating the spatial channel attention module of frequency-aware cascade attention(FCA)and the ADown module,the number of parameters is reduced while accuracy is significantly improved.Additionally,the neck module is redesigned,and the position-aware key feature fusion network(PKFN)module is introduced to further improve feature fusion capabilities.Experiments were conducted on the SAID dataset using the improved model.Compared to the original model,the m AP(0.5)of ADFP shows a 5.3%improvement,while the model parameters are reduced by 12.0%.On other public insulator component defect datasets,these improvements still have better results.Multiple experiments have confirmed the effectiveness of the model and its strong generalization ability.
文摘Signal processing in phase space based on nonlinear dynamics theory is a new method for underwater acoustic signal processing. One key problem when analyzing actual acoustic signal in phase space is how to reduce the noise and lower the embedding dimen- sion. In this paper, local-geometric-projection method is applied to obtain fow dimensional element from various target radiating noise and the derived phase portraits show obviously low dimensional attractors. Furthermore, attractor dimension and cross prediction error are used for classification. It concludes that combining these features representing the geometric and dynamical properties respectively shows effects in target classification.
