Search Results (1,461)

Background/Objectives: Understanding the relationship between DNA sequences and gene expression levels is of significant biological importance. Recent advancements have demonstrated the ability of deep learning to predict gene expression levels directly from genomic data. However, traditional methods are limited by basic word encoding techniques, which fail to capture the inherent features and patterns of DNA sequences. Methods: We introduce TExCNN, a novel framework that integrates the pre-trained models DNABERT and DNABERT-2 to generate word embeddings for DNA sequences. We partitioned the DNA sequences into manageable segments and computed their respective embeddings using the pre-trained models. These embeddings were then utilized as inputs to our deep learning framework, which was based on convolutional neural network. Results: TExCNN outperformed current state-of-the-art models, achieving an average R² score of 0.622, compared to the 0.596 score achieved by the DeepLncLoc model, which is based on the Word2Vec model and a text convolutional neural network. Furthermore, when the sequence length was extended from 10,500 bp to 50,000 bp, TExCNN achieved an even higher average R² score of 0.639. The prediction accuracy improved further when additional biological features were incorporated. Conclusions: Our experimental results demonstrate that the use of pre-trained models for word embedding generation significantly improves the accuracy of predicting gene expression. The proposed TExCNN pipeline performes optimally with longer DNA sequences and is adaptable for both cell-type-independent and cell-type-dependent predictions. Full article

Although approaches for the online surface detection of automotive pipelines exist, low defect area rates, small-sample and long-tailed data, and the difficulty of detection due to the variable morphology of defects are three major problems faced when using such methods. In order to solve these problems, this study combines traditional visual detection methods and deep neural network technology to propose a transfer learning multi-channel fusion decision network without significantly increasing the number of network layers or the structural complexity. Each channel of the network is designed according to the characteristics of different types of defects. Dynamic weights are assigned to achieve decision-level fusion through the use of a matrix of indicators to evaluate the performance of each channel’s recognition ability. In order to improve the detection efficiency and reduce the amount of data transmission and processing, an improved ROI detection algorithm for surface defects is proposed. It can enable the rapid screening of target surfaces for the high-quality and rapid acquisition of surface defect images. On an automotive pipeline surface defect dataset, the detection accuracy of the multi-channel fusion decision network with transfer learning was 97.78% and its detection speed was 153.8 FPS. The experimental results indicate that the multi-channel fusion decision network could simultaneously take into account the needs for real-time detection and accuracy, synthesize the advantages of different network structures, and avoid the limitations of single-channel networks. Full article

Background: The advent of artificial intelligence has significantly impacted radiology, with radiomics emerging as a transformative approach that extracts quantitative data from medical images to improve diagnostic and therapeutic accuracy. This study aimed to enhance the radiomic workflow by applying deep learning, through transfer learning, for the automatic segmentation of lung regions in computed tomography scans as a preprocessing step. Methods: Leveraging a pipeline articulated in (i) patient-based data splitting, (ii) intensity normalization, (iii) voxel resampling, (iv) bed removal, (v) contrast enhancement and (vi) model training, a DeepLabV3+ convolutional neural network (CNN) was fine tuned to perform whole-lung-region segmentation. Results: The trained model achieved high accuracy, Dice coefficient (0.97) and BF (93.06%) scores, and it effectively preserved lung region areas and removed confounding anatomical regions such as the heart and the spine. Conclusions: This study introduces a deep learning framework for the automatic segmentation of lung regions in CT images, leveraging an articulated pipeline and demonstrating excellent performance of the model, effectively isolating lung regions while excluding confounding anatomical structures. Ultimately, this work paves the way for more efficient, automated preprocessing tools in lung cancer detection, with potential to significantly improve clinical decision making and patient outcomes. Full article

In this study, an innovative leak detection model based on Convolutional Graph Neural Networks (CGNNs) is proposed to enhance response speed during pipeline bursts and to improve detection accuracy. By integrating node features into pipe segment features, the model effectively combines CGNN with water distribution networks, achieving leak detection at the pipe segment level. Optimizing the receptive field and convolutional layers ensures high detection performance even with sparse monitoring device density. Applied to two representative water distribution networks in City H, China, the model was trained on synthetic leak data generated by EPANET simulations and validated using real-world leak events. The experimental results show that the model achieves 90.28% accuracy in high-density monitoring areas, and over 85% accuracy within three pipe segments of actual leaks in low-density areas (10%–20%). The impact of feature engineering on model performance is also analyzed and strategies are suggested for optimizing monitoring point placement, further improving detection efficiency. This research provides valuable technical support for the intelligent management of water distribution networks under resource-limited conditions. Full article

With the popularity of High Dynamic Range (HDR) display technology, consumer demand for HDR images is increasing. Since HDR cameras are expensive, reconstructing High Dynamic Range (HDR) images from traditional Low Dynamic Range (LDR) images is crucial. However, existing HDR image reconstruction algorithms often fail to recover fine details and do not adequately address the fundamental principles of the LDR imaging pipeline. To overcome these limitations, the Reversing Lossy UNet (RLUNet) has been proposed, aiming to effectively balance dynamic range expansion and recover overexposed areas through a deeper understanding of LDR image pipeline principles. The RLUNet model comprises the Reverse Lossy Network, which is designed according to the LDR–HDR framework and focuses on reconstructing HDR images by recovering overexposed regions, dequantizing, linearizing the mapping, and suppressing compression artifacts. This framework, grounded in the principles of the LDR imaging pipeline, is designed to reverse the operations involved in lossy image operations. Furthermore, the integration of the Texture Filling Module (TFM) block with the Recovery of Overexposed Regions (ROR) module in the RLUNet model enhances the visual performance and detail texture of the overexposed areas in the reconstructed HDR image. The experiments demonstrate that the proposed RLUNet model outperforms various state-of-the-art methods on different testsets. Full article

In order to identify the leak source in complex heating pipeline networks, a timely and effective simulation of the leakage process was conducted. The open-source computational fluid dynamics software OpenFOAM 5.0 was combined with the PISO algorithm to simulate the pressure during the leakage in water supply networks, transforming the reverse source tracing problem into the solution of an adjoint equation. The validation of the transient adjoint equation for single-phase flow was completed through simulation, and the pressure wave change graph at the moment of the network leakage was solved, which was consistent with the experimental results. Using the open-source finite element analysis software OpenFOAM 5.0, the positioning accuracy of pipeline leak points can be controlled within the range from 92% to 96%. Based on the pressure wave change graph, the position of the leak source in the complex network was determined using the reverse source tracing method combined with the second correlation theory. The results show that the calculation speed of the PISO algorithm combined with the adjoint equation is significantly better than that of the individual SIMPLE and PISO algorithms, thereby proving the superiority of the adjoint method. Full article

Computer vision technology for the automatic recognition and geometric characterization of carbon and glass fibers in scanning electron microscopy images is proposed. The proposed pipeline, combining the SAM model and DeepLabV3+, provides the generalizability and accuracy of the foundational SAM model and the ability to quickly train on a small amount of data via the DeepLabV3+ model. The pipeline was trained several times more rapidly with lower requirements for computing resources than fine-tuning the SAM model, with comparable inference time. On the basis of the pipeline, an end-to-end technology for processing images of electron microscopic fibers was developed, the input of which is images with metadata and the output of which is statistics on the distribution of the geometric characteristics of the fibers. This innovation is of great practical importance for modeling the physical characteristics of materials. This paper proposes a few-shot training procedure for the DeepLabV3+/SAM pipeline, combining the training of the DeepLabV3+ model weights and the SAM model parameters. It allows effective training of the pipeline using only 37 real labeled images. The pipeline was then adapted to a new type of fiber and background using 15 additional real labeled images. This article also proposes a method for generating synthetic data for training neural network models, which improves the quality of segmentation by the IoU and PixAcc metrics from 0.943 and 0.949 to 0.953 and 0.959, i.e., by 1% on average. The developed pipeline significantly reduces the time required to evaluate fiber length in scanning electron microscope images. Full article

Detecting defects in complex urban sewer scenes is crucial for urban underground structure health monitoring. However, most image-based sewer defect detection models are complex, have high resource consumption, and fail to provide detailed damage information. To increase defect detection efficiency, visualize pipelines, and enable deployment on edge devices, this paper proposes a computer vision-based robotic defect detection framework for sewers. The framework encompasses positioning, defect detection, model deployment, 3D reconstruction, and the measurement of realistic pipelines. A lightweight Sewer-YOLO-Slim model is introduced, which reconstructs the YOLOv7-tiny network by adjusting its backbone, neck, and head. Channel pruning is applied to further reduce the model’s complexity. Additionally, a multiview reconstruction technique is employed to build a 3D model of the pipeline from images captured by the sewer robot, allowing for accurate measurements. The Sewer-YOLO-Slim model achieves reductions of 60.2%, 60.0%, and 65.9% in model size, parameters, and floating-point operations (FLOPs), respectively, while improving the mean average precision (mAP) by 1.5%, reaching 93.5%. Notably, the pruned model is only 4.9 MB in size. Comprehensive comparisons and analyses are conducted with 12 mainstream detection algorithms to validate the superiority of the proposed model. The model is deployed on edge devices with the aid of TensorRT for acceleration, and the detection speed reaches 15.3 ms per image. For a real section of the pipeline, the maximum measurement error of the 3D reconstruction model is 0.57 m. These results indicate that the proposed sewer inspection framework is effective, with the detection model exhibiting advanced performance in terms of accuracy, low computational demand, and real-time capability. The 3D modeling approach offers valuable insights for underground pipeline data visualization and defect measurement. Full article

This study explored the optimization of control systems for atmospheric pipeline air-floating vehicles traveling at ground level by introducing a novel composite wheel-fan system that integrates both wheels and fans. To evaluate the control impedance, the system simulates road conditions like inclines, uneven surfaces, and obstacles by using fixed, random, and high torque settings. The hub motor of the wheel fan is managed through three distinct algorithms: PID, fuzzy PID, and the backpropagation neural network (BP). Each algorithm’s control strategy is outlined, and tracking experiments were conducted across straight, circular, and curved trajectories. Analysis of these experiments supports a hybrid control approach: initiating with fuzzy PID, employing the PID algorithm on straight paths, and utilizing the BP neural network for sinusoidal and circular paths. The adaptive capacity of the BP neural network suggests its potential to eventually supplant the PID algorithm in straight path scenarios over extended testing and operation, ensuring improved control performance. Full article

In order to ensure the safe operation of adjacent buried pipelines under blast vibration, it is of great practical engineering significance to accurately predict the peak vibration velocity ofburied pipelines under blasting loads. Relying on the test results of the buried steel pipe blast model test, a sensitivity analysis of relevant influencing factors was carried out by using the gray correlation analysis method. A least squares support vector machine (LS-SVM) model was established to predict the peak vibration velocity of the pipeline and determine the best parameter combination in the LS-SVM model through a local particle swarm optimization (PSO), and the results of the PSO-LSSVM model were predicted. These were compared with BP neural network model and Sa’s empirical formula. The results show that the fitting correlation coefficient (R2), root mean square error (RMSE), average relative error (MRE), and Nash coefficient (NSE) of the PSO-LSSVM model for the prediction of pipeline peak vibration velocity are 91.51%, 2.95%, 8.69%, and 99.03%, showing that the PSO-LSSVM model has a higher prediction accuracy and better generalization ability, which provides a new idea for the vibration velocity prediction of buried pipelines under complex blasting conditions. Full article

The structural integrity of buried pipelines is threatened by the effects of Permanent Ground Deformation (PGD), resulting from seismic-induced landslides and lateral spreading due to liquefaction, requiring accurate analysis of the system performance. Analytical fragility functions allow us to estimate the likelihood of seismic damage along the pipeline, supporting design engineers and network operators in prioritizing resource allocation for mitigative or remedial measures in spatially distributed lifeline systems. To efficiently and accurately evaluate the seismic fragility of a buried operating steel pipeline under longitudinal PGD, this study develops a new analytical model, accounting for the asymmetric pipeline behavior in tension and compression under varying operational loads. This validated model is further implemented within a fragility function calculation framework based on the Monte Carlo Simulation (MCS), allowing us to efficiently assess the probability of the pipeline exceeding the performance limit states, conditioned to the PGD demand. The evaluated fragility surfaces showed that the probability of the pipeline exceeding the performance criteria increases for larger soil displacements and lengths, as well as cover depths, because of the greater mobilized soil reaction counteracting the pipeline deformation. The performed Global Sensitivity Analysis (GSA) highlighted the influence of the PGD and soil–pipeline interaction parameters, as well as the effect of the service loads on structural performance, requiring proper consideration in pipeline system modeling and design. Overall, the proposed analytical fragility function calculation framework provides a useful methodology for effectively assessing the performance of operating pipelines under longitudinal PGD, quantifying the effect of the uncertain parameters impacting system response. Full article

To address the challenge of rapid geometric model development in the digital twin industry, this paper presents a comprehensive pipeline for constructing 3D models from images using monocular vision imaging principles. Firstly, a structure-from-motion (SFM) algorithm generates a 3D point cloud from photographs. The feature detection methods scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE are compared across six datasets, with SIFT proving the most effective (matching rate higher than 0.12). Using K-nearest-neighbor matching and random sample consensus (RANSAC), refined feature point matching and 3D spatial representation are achieved via antipodal geometry. Then, the Poisson surface reconstruction algorithm converts the point cloud into a mesh model. Additionally, texture images are enhanced by leveraging a visual geometry group (VGG) network-based deep learning approach. Content images from a dataset provide geometric contours via higher-level VGG layers, while textures from style images are extracted using the lower-level layers. These are fused to create texture-transferred images, where the image quality assessment (IQA) metrics SSIM and PSNR are used to evaluate texture-enhanced images. Finally, texture mapping integrates the enhanced textures with the mesh model, improving the scene representation with enhanced texture. The method presented in this paper surpassed a LiDAR-based reconstruction approach by $20\%$ in terms of point cloud density and number of model facets, while the hardware cost was only $1\%$ of that associated with LiDAR. Full article

Assessing leakage risks in water distribution networks (WDNs) and implementing preventive monitoring for high-risk pipelines has become a widely accepted approach for leakage control. However, existing methods face significant data barriers between Geographic Information System (GIS) and leakage prediction systems. These barriers hinder traditional pipeline risk assessment methods, particularly when addressing challenges such as data imbalance, poor model interpretability, and lack of intuitive prediction results. To overcome these limitations, this study proposes a leakage assessment framework for water distribution networks based on multiple attention mechanisms and a generative model-based data balancing method. Extensive comparative experiments were conducted using water distribution network data from B2 and B3 District Metered Areas in Zhengzhou. The results show that the proposed model, optimized with a balanced data method, achieved a 40.76% improvement in the recall rate for leakage segment assessments, outperforming the second-best model using the same strategy by 1.7%. Furthermore, the strategy effectively enhanced the performance of all models, further proving that incorporating more valid data contributes to improved assessment results. This study comprehensively demonstrates the application of data-driven models in the field of “smart water management”, providing practical guidance and reference cases for advancing the development of intelligent water infrastructure. Full article

Ensuring the safety of oil tank farms is essential to maintaining energy security and minimizing the impact of potential accidents. This paper develops a quantitative regional risk model designed to assess both individual and societal risks in oil tank farms, with particular attention to energy-related risks such as leaks, fires, and explosions. The model integrates factors like day–night operational variations, weather conditions, and risk superposition to provide a comprehensive and accurate evaluation of regional risks. By considering the cumulative effects of multiple hazards, including those tied to energy dynamics, and the stability and validity of the model are researched through Monte Carlo simulations and case application. The results show that the model enhances the reliability of traditional risk assessment methods, making it more applicable to oil tank farm safety concerns. Furthermore, this study introduces a practical tool that simplifies the risk assessment process, allowing operators and decision-makers to evaluate risks without requiring in-depth technical expertise. The methodology improves the ability to safeguard oil tank farms, ensuring the stability of energy supply chains and contributing to broader energy security efforts. This study provides a valuable method for researchers and engineers seeking to enhance regional risk calculation efficiency, with a specific focus on energy risks. Full article

This research investigates the use of a 1D Convolutional Neural Network (CNN) to classify electroencephalography (EEG) signals into four categories of ischemia severity: normal, mild, moderate, and severe. The model’s accuracy was lower in moderate instances (75%) and severe cases (65%) compared to normal cases (95%) and mild cases (85%). The preprocessing pipeline now incorporates Power Spectral Density (PSD) analysis, and segment lengths of 32, 64, and 128 s are thoroughly examined. The work highlights the potential of the model to identify ischemia in real time during carotid endarterectomy (CEA) to prevent perioperative stroke. The 1D-CNN effectively captures both temporal and spatial EEG signals, providing a combination of processing efficiency and accuracy when compared to existing approaches. In order to enhance the identification of moderate and severe instances of ischemia, future studies should prioritize the integration of more complex datasets, specifically for severe ischemia, as well as increasing the current dataset. Our contributions in this study are implementing a novel 1D-CNN model to achieve a classification accuracy of over 93%, improving feature extraction by utilizing Power Spectral Density (PSD), automating the ischemia detection procedure, and enhancing model performance using a well-balanced dataset. Full article