A road adhesion coefficient-tire cornering stiffness normalization method combining a fractional-order multi-variable gray model with a LSTM network and vehicle direct yaw-moment robust control

A normalization method of road adhesion coefficient and tire cornering stiffness is proposed to provide the significant information for vehicle direct yaw-moment control (DYC) system design. This method is carried out based on a fractional-order multi-variable gray model (FOMVGM) and a long short-term memory (LSTM) network. A FOMVGM is used to generate training data and testing data for LSTM network, and LSTM network is employed to predict tire cornering stiffness with road adhesion coefficient. In addition to that, tire cornering stiffness represented by road adhesion coefficient can be used to built vehicle lateral dynamic model and participate in DYC robust controller design. Simulations under different driving cycles are carried out to demonstrate the feasibility and effectiveness of the proposed normalization method of road adhesion coefficient and tire cornering stiffness and vehicle DYC robust control system, respectively

Occlusion facial expression recognition based on feature fusion residual attention network

Recognizing occluded facial expressions in the wild poses a significant challenge. However, most previous approaches rely solely on either global or local feature-based methods, leading to the loss of relevant expression features. To address these issues, a feature fusion residual attention network (FFRA-Net) is proposed. FFRA-Net consists of a multi-scale module, a local attention module, and a feature fusion module. The multi-scale module divides the intermediate feature map into several sub-feature maps in an equal manner along the channel dimension. Then, a convolution operation is applied to each of these feature maps to obtain diverse global features. The local attention module divides the intermediate feature map into several sub-feature maps along the spatial dimension. Subsequently, a convolution operation is applied to each of these feature maps, resulting in the extraction of local key features through the attention mechanism. The feature fusion module plays a crucial role in integrating global and local expression features while also establishing residual links between inputs and outputs to compensate for the loss of fine-grained features. Last, two occlusion expression datasets (FM_RAF-DB and SG_RAF-DB) were constructed based on the RAF-DB dataset. Extensive experiments demonstrate that the proposed FFRA-Net achieves excellent results on four datasets: FM_RAF-DB, SG_RAF-DB, RAF-DB, and FERPLUS, with accuracies of 77.87%, 79.50%, 88.66%, and 88.97%, respectively. Thus, the approach presented in this paper demonstrates strong applicability in the context of occluded facial expression recognition (FER)

Adaptive Label Allocation for Unsupervised Person Re-Identification

Most unsupervised methods of person re-identification (Re-ID) obtain pseudo-labels through clustering. However, in the process of clustering, the hard quantization loss caused by clustering errors will make the model produce false pseudo-labels. In order to solve this problem, an unsupervised model based on softened labels training method is proposed. The innovation of this method is that the correlation among image features is used to find the reliable positive samples and train them in a smooth manner. To further explore the correlation among image features, some modules are carefully designed in this article. The dynamic adaptive label allocation (DALA) method which generates pseudo-labels of adaptive size according to different metric relationships among features is proposed. The channel attention and transformer architecture (CATA) auxiliary module is designed, which, associated with convolutional neural network (CNN), functioned as the feature extractor of the model aimed to capture long range dependencies and acquire more distinguishable features. The proposed model is evaluated on the Market-1501 and the DukeMTMC-reID. The experimental results of the proposed method achieve 60.8 mAP on Market-1501 and 49.6 mAP on DukeMTMC-reID respectively, which outperform most state-of-the-art models in fully unsupervised Re-ID task

Adaptive Label Allocation for Unsupervised Person Re-Identification

Most unsupervised methods of person re-identification (Re-ID) obtain pseudo-labels through clustering. However, in the process of clustering, the hard quantization loss caused by clustering errors will make the model produce false pseudo-labels. In order to solve this problem, an unsupervised model based on softened labels training method is proposed. The innovation of this method is that the correlation among image features is used to find the reliable positive samples and train them in a smooth manner. To further explore the correlation among image features, some modules are carefully designed in this article. The dynamic adaptive label allocation (DALA) method which generates pseudo-labels of adaptive size according to different metric relationships among features is proposed. The channel attention and transformer architecture (CATA) auxiliary module is designed, which, associated with convolutional neural network (CNN), functioned as the feature extractor of the model aimed to capture long range dependencies and acquire more distinguishable features. The proposed model is evaluated on the Market-1501 and the DukeMTMC-reID. The experimental results of the proposed method achieve 60.8 mAP on Market-1501 and 49.6 mAP on DukeMTMC-reID respectively, which outperform most state-of-the-art models in fully unsupervised Re-ID task

Influence of Stress Sensitivity on Water-Gas Flow in Carbonate Rocks

Carbonate reservoirs significantly contribute to exploitation. Due to their strong heterogeneity, it is of great significance to study core seepage capacity and gas-water two-phase flow of reservoirs with various pore structures under different stresses for productivity prediction, gas reservoir development, and reservoir protection. We utilize micrometer-resolution X-ray tomography to obtain the digital rocks of porous, fractured-porous, and fractured-vuggy carbonate rocks during pressurized process and depressurization. The Lattice Boltzmann method and pore network model are used to simulate the permeability and gas-water two-phase flow under different confining pressures. We show that at the early stage of pressure increase, fractures, vugs, or large pores as the main flow channels first undergo compaction deformation, and the permeability decreases obviously. Then, manyisolated small pores are extruded and deformed; thus, the permeability reduction is relatively slow. As the confining pressure increases, the equal-permeability point of fractured-porous sample moves to right. At the same confining pressure, the water saturation corresponding to equal-permeability point during depressurization is greater than that of pressurized process. It is also proved that the pore size decreases irreversibly, and the capillary force increases, which is equivalent to the enhancement of water wettability. Therefore, the irreversible closure of pores leads to the decrease of permeability and the increase of gas-phase seepage resistance, especially in carbonate rockswith fractures, vugs, and large pores. The findings of this study are helpful to better understand the gas production law of depletion development of carbonate gas reservoirs and provide support forefficient development