Search Results (805)

This paper presents a novel image fusion method designed to enhance the integration of infrared and visible images through the use of a residual attention mechanism. The primary objective is to generate a fused image that effectively combines the thermal radiation information from infrared images with the detailed texture and background information from visible images. To achieve this, we propose a multi-level feature extraction and fusion framework that encodes both shallow and deep image features. In this framework, deep features are utilized as queries, while shallow features function as keys and values within a residual cross-attention module. This architecture enables a more refined fusion process by selectively attending to and integrating relevant information from different feature levels. Additionally, we introduce a dynamic feature preservation loss function to optimize the fusion process, ensuring the retention of critical details from both source images. Experimental results demonstrate that the proposed method outperforms existing fusion techniques across various quantitative metrics and delivers superior visual quality. Full article

The robotic arm frequently performs grasping tasks in unstructured environments. However, due to the complex network architecture and constantly changing operational environments, balancing between grasping accuracy and speed poses significant challenges. Unlike fixed robotic arms, mobile robotic arms offer flexibility but suffer from relatively unstable bases, necessitating improvements in disturbance resistance for grasping tasks. To address these issues, this paper proposes a light-weight grasping pose estimation method called Grasp-DSC, specifically tailored for mobile robotic arms. This method integrates the deep residual shrinkage network and depthwise separable convolution. Attention mechanisms and soft thresholding are employed to improve the arm’s ability to filter out interference, while parallel convolutions enhance computational efficiency. These innovations collectively enhance the grasping decision accuracy and efficiency of mobile robotic arms in complex environments. Grasp-DSC is evaluated using the Cornell Grasp Dataset and Jacquard Grasp Dataset, achieving 96.6% accuracy and a speed of 14.4 ms on the former one. Finally, grasping experiments conducted on the MR2000-UR5 validate the practical applicability of Grasp-DSC in practical scenarios, achieving an average grasping success rate of 96%. Full article

Background/Objectives: Minimizing interruptions in chest compressions is very important when resuscitating patients with cardiac arrest. Recently, research has analyzed electrocardiograms (ECGs) during chest compressions using convolutional neural networks (CNNs). This study aimed to compare the accuracy of deeper neural networks and more advanced structures. Methods: ECGs with chest compression artifacts were obtained from patients who visited the emergency department of Korea University Ansan Hospital from September 2019 to February 2024. We compared the accuracy of a deeper CNN, long short-term memory (LSTM), and a CNN with an attention mechanism and residual block against a reference model. The input of the model was 5 s ECG segments with compression artifacts, and the output was the probability that the ECG with the artifacts was a shockable rhythm. Results: A total of 1889 ECGs with compression artifacts from 172 patients were included in this study. There were 969 ECGs annotated as shockable and 920 as non-shockable. The area under the receiver operating characteristic curve (AUROC) of the reference model was 0.8672. The AUROCs of the deeper CNN for five and seven layers were 0.7374 and 0.6950, respectively. The AUROCs of LSTM and bidirectional LSTM were 0.7719 and 0.8287, respectively. The AUROC of the CNN with the attention mechanism and residual block was 0.7759. Conclusions: CNNs with deeper layers or those incorporating attention mechanisms, residual blocks, and LSTM architectures did not exhibit better accuracy. To improve the model accuracy, a larger dataset or advanced augmentation techniques may be required, rather than complicating the structure of the model. Full article

Traditional fault diagnosis methods for rolling bearings rely on nemerous labeled samples, which are difficult to obtain in engineering applications. Moreover, when unseen fault categories appear in the test set, these models fail to achieve accurate diagnoses, as the fault categories are not represented in the training data. To address these challenges, a zero-shot fault diagnosis model for rolling bearings is proposed, which realizes knowledge transfer from seen to unseen categories by constructing attribute information, thereby reducing the dependence on labeled samples. First, an attribute method Discrete Label Embedding Method (DLEM) based on word embedding and envelope analysis is designed to generate fault attributes. Fault features are extracted using the Swin Transformer model. Then, the attributes and features are input into the constructed model Distribution Consistency and Multi-modal Cross Alignment Variational Autoencoder (DCMCA-VAE), which is built on Convolutional Residual SE-Attention Variational Autoencoder (CRS-VAE). CRS-VAE replaces fully connected layers with convolutional layers and incorporates residual connections with the Squeeze-and-Excitation Joint Attention Mechanism (SE-JAM) for improved feature extraction. The DCMCA-VAE also incorporates a reconstruction alignment module with the proposed distribution consistency loss LWT and multi-modal cross alignment loss function LMCA. The reconstruction alignment module is used to generate high-quality features with distinguishing information between different categories for classification. In the face of multiple noisy datasets, this model can effectively distinguish unseen categories and has stronger robustness than other models. The model can achieve 100% classification accuracy on the SQ dataset, and more than 85% on the CWRU dataset when unseen and seen categories appear simultaneously with noise interference. Full article

Atmospheric jets are pivotal components of atmospheric circulation, profoundly influencing surface weather patterns and the development of extreme weather events such as storms and cold waves. Accurate detection of the jet stream axis is indispensable for enhancing weather forecasting, monitoring climate change, and mitigating disasters. However, traditional methods for delineating atmospheric jets are plagued by inefficiency, substantial errors, and pronounced subjectivity, limiting their applicability in complex atmospheric scenarios. Current research on semi-supervised methods for extracting atmospheric jets remains scarce, with most approaches dependent on traditional techniques that struggle with stability and generalization. To address these limitations, this study proposes a semi-supervised jet stream axis extraction method leveraging an enhanced U-Net++ model. The approach incorporates improved residual blocks and enhanced attention gate mechanisms, seamlessly integrating these enhanced attention gates into the dense skip connections of U-Net++. Furthermore, it optimizes the consistency learning phase within semi-supervised frameworks, effectively addressing data scarcity challenges while significantly enhancing the precision of jet stream axis detection. Experimental results reveal the following: (1) With only 30% of labeled data, the proposed method achieves a precision exceeding 80% on the test set, surpassing state-of-the-art (SOTA) baselines. Compared to fully supervised U-Net and U-Net++ methods, the precision improves by 17.02% and 9.91%. (2) With labeled data proportions of 10%, 20%, and 30%, the proposed method outperforms the MT semi-supervised method, achieving precision gains of 9.44%, 15.58%, and 19.50%, while surpassing the DCT semi-supervised method with improvements of 10.24%, 16.64%, and 14.15%, respectively. Ablation studies further validate the effectiveness of the proposed method in accurately identifying the jet stream axis. The proposed method exhibits remarkable consistency, stability, and generalization capabilities, producing jet stream axis extractions closely aligned with wind field data. Full article

As the core component of an airplane, the health status of the aviation engine is crucial for the safe operation of the aircraft. Therefore, predicting the remaining service life of the engine is of great significance for ensuring its safety and reliability. In this paper, a multichannel hybrid network is proposed; this network is a combination of the one-dimensional convolutional neural network (1D-CNN), the bidirectional long short-term memory network (BiLSTM), and the self-attention mechanism. For each sensor of the engine, an SA-CNN-BiLSTM network is established. The one-dimensional convolutional neural network and the bidirectional long short-term memory network are used to extract the spatial features and temporal features of the input data, respectively. Moreover, multichannel modeling is utilized to achieve the parallel processing of different sensors. Subsequently, the results are stitched together to establish a mapping relationship with the engine’s remaining useful life (RUL). Experimental validation was conducted on the aero-engine C-MAPSS dataset. The prediction results were compared with those of the other seven models to verify the effectiveness of this method in predicting the remaining service life. The results indicate that the proposed method significantly reduces the prediction error compared to other models. Specifically, for the two datasets, their mean absolute errors were only 11.47 and 11.76, the root-mean-square error values were only 12.26 and 12.78, and the scoring function values were only 195 and 227. Full article

Thermal barrier coatings (TBCs) and air film-cooling technology have been extensively utilized in nickel-based, single-crystal turbine blades to enhance their heat resistance. However, structural complexity and material property mismatches between layers can affect residual stresses and potentially lead to coating failure. In this study, a three-dimensional finite element model with atmospheric plasma-spraying thermal barrier coatings (APS-TBCs) deposited on air-cooled, nickel-based, single-crystal blades was established to investigate residual stress character under centrifugal load, considering the effect of temperature, crystal orientation deviation angle, oxide layer thickness, and the number of cycles. The results show that when the centrifugal load is increased from 300 MPa to 700 MPa, the absolute value of the residual stress at the crest of the interface between Top Coat (TC) and Thermally Grown Oxide (TGO) increases by only 8.5%, whereas in the region of compressive to tensile stress conversion, residual stress decreases by 100.9%. As the crystal orientation deviation angle increases, the absolute value of the residual compressive stress increases and the absolute value of the residual tensile stress decreases, but the performance is more special in the valley region, where the absolute value of the residual stress increases with the increase in the deviation angle. Special attention is required, as the increase in temperature leads to a rise in the absolute value of residual stress. For example, at the trough of the TC–TGO interface, when the temperature increases from 910 °C to 1100 °C, the residual stress increases by 9.8%. The effect of the number of cycles on residual stress is relatively weak. For instance, at the wave crest of the TC–TGO interface, the residual stress differs by only 0.6 MPa between one cycle and three cycles. The effect of oxide layer thickness on residual stress in the TBCs after a single cycle is nonlinear. When the oxide layer thickness is 0, 4, and 7 μm, the residual stress undergoes a transition between tensile and compressive directions at different locations. The exploration of these results has yielded some valuable laws that can provide a reference for the study of the damage mechanism of TBCs, as well as a guide for the optimization of nickel-based turbine blades in the manufacturing and use processes. Full article

Air pollution concerns have led to the widespread deployment of air quality monitoring stations. While high-cost government stations provide accurate data, their deployment is limited, whereas low-cost sensors offer widespread coverage but with lower accuracy. To enhance the accuracy of measurement data from low-cost air monitoring sensors, this study proposes a Multi-Scale Convolutional Residual Time-Frequency Calibration Method (MCRTF-CM), focusing on the PM_2.5 sensor as an example. This method leverages multi-scale convolution in the feature extractor to capture diverse features at various scales using parallel convolutional kernels. Residual connections merge the original and multi-scale features, preserving the initial input for enhanced stability. The calibration module employs Gated Recurrent Units (GRUs) to capture long-term dependencies in time-series data through reset and update gates. Additionally, the Frequency Enhanced Channel Attention Mechanism (FECAM) uses Discrete Cosine Transform (DCT) to convert time-domain data to frequency-domain, assigning weights to different frequency components to enhance key features and suppress irrelevant ones. Experimental results demonstrate that MCRTF-CM outperforms optimal Long Short-Term Memory (LSTM) networks, reducing RMSE, MAE, MSE, and MAPE by 13.59%, 14.04%, 25.33%, and 8.22%, respectively, indicating its better performance. Full article

Endogenous peptides in Baijiu have primarily focused on finished liquor research, with limited attention given to the peptides in base liquor prior to blending. Liquid chromatography–tandem mass spectrometry (LC-MS) was employed to identify endogenous peptides in the distillates from the first to seventh rounds of soy sauce-flavored Baijiu. Two hundred and five oligopeptides were identified from these distillates, all of which had molecular weights below 1000 Da and were composed of amino acid residues associated with flavor (sweet, sour, and bitter) and biological activity. Furthermore, full-wavelength scanning, content determination of the main compounds, and molecular docking were performed to analyze these oligopeptides’ effect on the ester–acid–alcohol profile in Baijiu. This determination revealed a negative correlation between the peptide content and total ester content (r = −0.691), as well as the total acid content (r = −0.323), and a highly significant negative correlation with ethanol content (r = −0.916). Notably, the screened peptides (TRH, YHY, RQTQ, PLDLTSFVLHEAI, KHVS, LPQRHRMVYSLL, and NEWH) had specific interactions with the major flavor substances via hydrogen bonds, including esters (ethyl acetate, ethyl butanoate, ethyl hexanoate, and ethyl lactate), acids (acetate acid, butanoate acid, hexanoate acid, lactate acid), and alcohols (ethanol, 1-propanol, 1-butanol, and 1-hexanol). These findings elucidate the distribution and dynamic changes of endogenous peptides in the distillates from various rounds of soy sauce-flavored Baijiu, providing a theoretical foundation for further investigation into their interaction mechanisms associated with flavor compounds. Full article

The potential health risks posed by the coexistence of nanoplastics (NPs) and triclosan (TCS) have garnered significant attention. However, the effects and underlying mechanisms of NPs and TCS on key functional proteins at the molecular level remain poorly understood. This study reports the effect of polystyrene nanoplastics (PSNPs) on the binding of TCS to human serum albumin (HSA) using multispectral methods and molecular simulation systems. The experimental results show that TCS significantly inhibits HSA esterase activity, with exacerbating inhibition in the presence of PSNPs, which is attributed to the alteration of HSA conformation and microenvironment of the amino acid residues induced by PSNPs. Molecular docking and site marker competitive studies indicate that TCS predominantly binds to site I of subdomain Sudlow II and the presence of PSNPs does not affect the binding sites. Spectra analyses indicate that the quenching mechanism between TCS and HSA belongs to the static quenching type and the presence of PSNPs does not change the fluorescence quenching type. The HSA fluorescence quenching and the conformational alterations induced by TCS are further enhanced in the presence of PSNPs, indicating that PSNPs enhance the binding of TCS to HSA by making TCS more accessible to the binding sites. This study provides valuable information about the toxicity of PSNPs and TCS in case of co-exposure. Full article

Infrared target detection has a wide range of application value, but due to the characteristics of infrared images, infrared targets are easily submerged in the complex background. Therefore, in complex scenes, it is difficult to effectively and accurately detect infrared dim small targets. For this reason, we design an infrared dim small target (IDST) detection algorithm containing Large-size Receptive Fields (LRFNet). It uses the Residual network with an Inverted Pyramid Structure (RIPS), which consists of convolutional layers that become progressively smaller, so it can have a larger effective receptive field and can improve the robustness of the model. In addition, through the Attention Mechanisms with Large Receptive Fields and Inverse Bottlenecks (LRIB), it can make the network better localize the region where the target is located and improve the detection effect of the model. The experimental results show that our proposed algorithm outperforms other state-of-the-art algorithms in all evaluation metrics. Full article

The aquatic environment in aquaculture serves as the foundation for the survival and growth of aquatic animals, while a high-quality water environment is a necessary condition for promoting efficient and healthy aquaculture development. To effectively guide early warnings and the regulation of water quality in aquaculture, this study proposes a predictive model based on a dual-channel and dual-attention mechanism, namely, the DAM-ResNet-LSTM model. This model encompasses two parallel feature extraction channels: a residual network (ResNet) and long short-term memory (LSTM), with dual-attention mechanisms integrated into each channel to enhance the model’s feature representation capabilities. Then, the proposed model is trained, validated, and tested using water quality and meteorological parameter data collected by an offshore farm environmental monitoring system. The results demonstrate that the proposed dual-channel structure and dual-attention mechanism can significantly improve the predictive performance of the model. The prediction accuracy for pH, dissolved oxygen (DO), and salinity (SAL) (with Nash coefficients of 0.9361, 0.9396, and 0.9342, respectively) is higher than that for chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), nitrite (NO₂⁻), and active phosphate (AP) (with Nash coefficients of 0.8578, 0.8542, 0.8372, and 0.8294, respectively). Compared to the single-channel model DA-ResNet (ResNet integrated with the proposed dual-attention mechanism), the Nash coefficients for predicting pH, DO, SAL, COD, NH₃-N, NO₂⁻, and AP increase by 12.76%, 12.58%, 11.68%, 18.350%, 19.32%, 16%, and 14.99%, respectively. Compared to the single-channel DA-LSTM model (LSTM integrated with the proposed dual-attention mechanism), the corresponding increases in Nash coefficients are 9.15%, 9.93%, 9.11%, 10.91%, 10.11%, 10.39%, and 10.2%, respectively. Compared to the ResNet-LSTM (ResNet and LSTM in parallel) model without the attention mechanism, the improvements in Nash coefficients are 1.91%, 2.4%, 0.74%, 3.41%, 2.71%, 3.55%, and 4.13%, respectively. The predictive performance of the model fulfills the practical requirements for accurate forecasting of water quality in nearshore aquaculture. Full article

Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on ResNet Long Short-Term Memory with an Attention Mechanism (RLA). In the aspect of signal processing in RLA, a range–Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal. Regarding the network architecture in RLA, the relevant features of the residual network with channel and spatial attention modules are combined to prevent some useful information from being neglected. We introduce a residual attention mechanism to enhance the network’s focus on gesture features and avoid the impact of irrelevant features on recognition accuracy. Additionally, we use a long short-term memory network to process temporal features, ensuring high recognition accuracy even with single-feature inputs. A series of experimental results show that the algorithm proposed in this paper has higher recognition performance. Full article

Autonomous driving vehicles have strong path planning and obstacle avoidance capabilities, which provide great support to avoid traffic accidents. Autonomous driving has become a research hotspot worldwide. Depth estimation is a key technology in autonomous driving as it provides an important basis for accurately detecting traffic objects and avoiding collisions in advance. However, the current difficulties in depth estimation include insufficient estimation accuracy, difficulty in acquiring depth information using monocular vision, and an important challenge of fusing multiple sensors for depth estimation. To enhance depth estimation performance in complex traffic environments, this study proposes a depth estimation method in which point clouds and images obtained from MMwave radar and cameras are fused. Firstly, a residual network is established to extract the multi-scale features of the MMwave radar point clouds and the corresponding image obtained simultaneously from the same location. Correlations between the radar points and the image are established by fusing the extracted multi-scale features. A semi-dense depth estimation is achieved by assigning the depth value of the radar point to the most relevant image region. Secondly, a bidirectional feature fusion structure with additional fusion branches is designed to enhance the richness of the feature information. The information loss during the feature fusion process is reduced, and the robustness of the model is enhanced. Finally, parallel channel and position attention mechanisms are used to enhance the feature representation of the key areas in the fused feature map, the interference of irrelevant areas is suppressed, and the depth estimation accuracy is enhanced. The experimental results on the public dataset nuScenes show that, compared with the baseline model, the proposed method reduces the average absolute error (MAE) by 4.7–6.3% and the root mean square error (RMSE) by 4.2–5.2%. Full article

Multistage text-to-image generation algorithms have shown remarkable success. However, the images produced often lack detail and suffer from feature loss. This is because these methods mainly focus on extracting features from images and text, using only conventional residual blocks for post-extraction feature processing. This results in the loss of features, greatly reducing the quality of the generated images and necessitating more resources for feature calculation, which will severely limit the use and application of optical devices such as cameras and smartphones. To address these issues, the novel High-Detail Feature-Preserving Network (HDFpNet) is proposed to effectively generate high-quality, near-realistic images from text descriptions. The initial text-to-image generation (iT2IG) module is used to generate initial feature maps to avoid feature loss. Next, the fast excitation-and-squeeze feature extraction (FESFE) module is proposed to recursively generate high-detail and feature-preserving images with lower computational costs through three steps: channel excitation (CE), fast feature extraction (FFE), and channel squeeze (CS). Finally, the channel attention (CA) mechanism further enriches the feature details. Compared with the state of the art, experimental results obtained on the CUB-Bird and MS-COCO datasets demonstrate that the proposed HDFpNet achieves better performance and visual presentation, especially regarding high-detail images and feature preservation. Full article