67 research outputs found
Collision-induced Hopf-type bifurcation reversible transitions in a dual-wavelength femtosecond fiber laser
Collision refers to a striking nonlinear interaction in dissipative systems,
revealing the particle-like properties of solitons. In dual-wavelength
mode-locked fiber lasers, collisions are inherent and periodic. However, how
collisions influence the dynamical transitions in the dual-wavelength
mode-locked state has still not been explored. In our research, dispersion
management triggers the complex interactions between solitons in the cavity. We
reveal the smooth or reversible Hopf-type bifurcation transitions of dual-color
soliton molecules (SMs) during collision by real-time spectral measurement
technique of TS-DFT. The reversible transitions from stationary SM to vibrating
SM, revealing that cavity parameters pass through a bifurcation point in the
collision process without active external intervention. The numerical results
confirm the universality of collision-induced bifurcation behavior. These
findings provide new insights into collision dynamics in dual-wavelength
ultrafast fiber lasers. Furthermore, the study of intermolecular collisions is
of great significance for other branches of nonlinear science.Comment: 11 pages, 4 figure
MPC-STANet: Alzheimer’s Disease Recognition Method based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism
Alzheimer\u27s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer\u27s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50
MPC-STANet: Alzheimer’s Disease Recognition Method based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism
Alzheimer\u27s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer\u27s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50
Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection
By integrating complementary information from RGB image and depth map, the
ability of salient object detection (SOD) for complex and challenging scenes
can be improved. In recent years, the important role of Convolutional Neural
Networks (CNNs) in feature extraction and cross-modality interaction has been
fully explored, but it is still insufficient in modeling global long-range
dependencies of self-modality and cross-modality. To this end, we introduce
CNNs-assisted Transformer architecture and propose a novel RGB-D SOD network
with Point-aware Interaction and CNN-induced Refinement (PICR-Net). On the one
hand, considering the prior correlation between RGB modality and depth
modality, an attention-triggered cross-modality point-aware interaction (CmPI)
module is designed to explore the feature interaction of different modalities
with positional constraints. On the other hand, in order to alleviate the block
effect and detail destruction problems brought by the Transformer naturally, we
design a CNN-induced refinement (CNNR) unit for content refinement and
supplementation. Extensive experiments on five RGB-D SOD datasets show that the
proposed network achieves competitive results in both quantitative and
qualitative comparisons.Comment: Accepted by ACM MM 202
Association of miR-196a2 and miR-27a polymorphisms with gestational diabetes mellitus susceptibility in a Chinese population
IntroductionMiR-196a2 and miR-27a play a key role in the regulation of the insulin signaling pathway. Previous studies have indicated that miR-27a rs895819 and miR-196a2 rs11614913 have a strong association with type 2 diabetes (T2DM), but very few studies have investigated their role in gestational diabetes mellitus (GDM).MethodsA total of 500 GDM patients and 502 control subjects were enrolled in this study. Using the SNPscan™ genotyping assay, rs11614913 and rs895819 were genotyped. In the data treatment process, the independent sample t test, logistic regression and chi-square test were used to evaluate the differences in genotype, allele, and haplotype distributions and their associations with GDM risk. One-way ANOVA was conducted to determine the differences in genotype and blood glucose level.ResultsThere were obvious differences in prepregnancy body mass index (pre-BMI), age, systolic blood pressure (SBP), diastolic blood pressure (DBP) and parity between GDM and healthy subjects (P < 0.05). After adjusting for the above factors, the miR-27a rs895819 C allele was still associated with an increased risk of GDM (C vs. T: OR=1.245; 95% CI: 1.011-1.533; P = 0.039) and the TT-CC genotype of rs11614913-rs895819 was related to an increased GDM risk (OR=3.989; 95% CI: 1.309-12.16; P = 0.015). In addition, the haplotype T-C had a positive interaction with GDM (OR=1.376; 95% CI: 1.075-1.790; P=0.018), especially in the 18.5 ≤ pre-BMI < 24 group (OR=1.403; 95% CI: 1.026-1.921; P=0.034). Moreover, the blood glucose level of the rs895819 CC genotype was significantly higher than that of the TT and TC genotypes (P < 0.05). The TT-CC genotype of rs11614913-rs895819 showed that the blood glucose level was significantly higher than that of the other genotypes.DiscussionOur findings suggest that miR-27a rs895819 is associated with increased GDM susceptibility and higher blood glucose levels
Robust LFC Strategy for Wind Integrated Time-Delay Power System Using EID Compensation
This paper presents an active disturbance rejection control (ADRC) technique for load frequency control of a wind integrated power system when communication delays are considered. To improve the stability of frequency control, equivalent input disturbances (EID) compensation is used to eliminate the influence of the load variation. In wind integrated power systems, two area controllers are designed to guarantee the stability of the overall closed-loop system. First, a simplified frequency response model of the wind integrated time-delay power system was established. Then the state-space model of the closed-loop system was built by employing state observers. The system stability conditions and controller parameters can be solved by some linear matrix inequalities (LMIs) forms. Finally, the case studies were tested using MATLAB/SIMULINK software and the simulation results show its robustness and effectiveness to maintain power-system stability
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