Substantial accuracy was observed in our approach: 99.32% in identifying targets, 96.14% in determining faults, and 99.54% in IoT applications for decision-making.
Bridge deck pavement damage substantially affects the safe operation of vehicles and the long-term structural soundness of the bridge. This study proposes a three-stage damage detection and localization method for bridge deck pavement, utilizing a YOLOv7 network and a revised LaneNet. The YOLOv7 model's training, in stage 1, utilizes the Road Damage Dataset 2022 (RDD2022) after preprocessing and adjustment, which produced five distinct damage classes. To achieve stage 2, the LaneNet network was trimmed down to the semantic segmentation part; the VGG16 network acted as the encoder, outputting binary images depicting lane lines. Employing a newly developed image processing algorithm, the lane area was derived from the lane line binary images in stage 3. Stage 1's damage coordinates yielded the final pavement damage classifications and lane locations. Employing the RDD2022 dataset, the proposed method was subjected to comparative and analytical scrutiny, preceding its use on the Fourth Nanjing Yangtze River Bridge in China. The preprocessed RDD2022 data indicates that YOLOv7 possesses a higher mean average precision (mAP) of 0.663 compared to other YOLO models. The revised LaneNet's lane localization accuracy of 0.933 is more accurate than instance segmentation's accuracy of 0.856. Concurrently, the inference speed of the revised LaneNet reaches 123 frames per second (FPS) on the NVIDIA GeForce RTX 3090, exceeding the significantly faster 653 FPS of instance segmentation. The suggested method serves as a guide for maintaining the pavement of a bridge's deck.
The fish industry's traditional supply chains are significantly impacted by illegal, unreported, and unregulated (IUU) fishing activities. A key aspect of transforming the fish supply chain (SC) lies in the convergence of blockchain technology and the Internet of Things (IoT), leveraging distributed ledger technology (DLT) to develop reliable, transparent, and decentralized traceability systems that promote safe data sharing and enhance IUU prevention and detection strategies. We have examined the current research on the application of Blockchain to enhance the efficiency of fish supply chains. Our conversations about traceability have spanned traditional and smart supply chain models, specifically utilizing Blockchain and IoT technologies. Traceability considerations, in conjunction with a quality model, were demonstrated as essential design elements in the creation of smart blockchain-based supply chain systems. We have also designed a new fish supply chain framework, incorporating intelligent blockchain and IoT technology, and using DLT to track and trace fish products from harvesting, processing, packaging, shipping, and distribution, ensuring full transparency to the final consumer. To be more exact, the framework under consideration should provide useful, immediate data for tracking fish products and verifying their authenticity from start to finish. Our research, contrasting with other work, investigates the advantages of incorporating machine learning (ML) into blockchain-enabled IoT supply chain systems, emphasizing the role of ML in analyzing fish quality, freshness, and detecting fraudulent practices.
A hybrid kernel support vector machine (SVM) and Bayesian optimization (BO) system is put forth for the novel fault diagnosis of rolling bearings. To pinpoint the specific fault type among four bearing failure scenarios, the model leverages discrete Fourier transforms (DFT) for extracting fifteen features from vibration signals in both the time and frequency domains. This approach remedies the ambiguity in fault identification caused by the non-linear and non-stationary characteristics of the vibrations. The input for SVM-based fault diagnosis is constructed by dividing the extracted feature vectors into a training and a testing dataset. To optimize the Support Vector Machine (SVM), we create a hybrid SVM using polynomial and radial basis kernels. The objective function's extreme values and their weight coefficients are determined using the BO method. To execute the Gaussian regression process of Bayesian optimization, we construct an objective function, utilizing training data as one input and test data as a separate input. A-485 cost The support vector machine (SVM) is re-engineered for network classification prediction using the optimized parameters. Employing the bearing dataset from Case Western Reserve University, we examined the performance of the proposed diagnostic model. The fault diagnosis accuracy has been improved from 85% to 100% according to the verification results, a considerable enhancement compared to the previous method of direct SVM input of vibration signals. Our Bayesian-optimized hybrid kernel SVM model boasts the highest accuracy rate when contrasted with other diagnostic models. Sixty sets of sample values for each of the four observed failure modes were collected in the laboratory's verification, and this process was repeated. Replicate tests of the Bayesian-optimized hybrid kernel SVM demonstrated a remarkable accuracy of 967%, exceeding the original 100% accuracy of the experimental results. Our proposed method for rolling bearing fault diagnosis demonstrates both its feasibility and superiority, as evidenced by these results.
The genetic improvement of pork's quality is inextricably linked to marbling's characteristics. To quantify these traits, accurate marbling segmentation is essential. Segmentation of the pork is complicated by the small, thin, and inconsistently sized and shaped marbling targets that are dispersed throughout the meat. A deep learning-based pipeline, featuring a shallow context encoder network (Marbling-Net), was constructed using patch-based training and image upsampling to precisely segment marbling regions within images of pork longissimus dorsi (LD) captured by smartphones. A pixel-wise annotation marbling dataset, the pork marbling dataset 2023 (PMD2023), comprises 173 images of pork LD, derived from a range of pigs. Regarding the PMD2023 dataset, the proposed pipeline's performance exceeded existing state-of-the-art models, achieving an IoU of 768%, precision of 878%, recall of 860%, and an F1-score of 869%. A strong correlation is observed between the marbling ratios from 100 pork LD images and both the marbling scores and intramuscular fat content, as measured using the spectrometer method (R² = 0.884 and 0.733, respectively), highlighting the accuracy of our method. Benefiting pork quality breeding and the meat industry, the trained model can precisely quantify pork marbling characteristics on mobile platforms.
The roadheader, a cornerstone piece of equipment, is fundamental to underground mining. The bearing within the roadheader, being a primary element, is often subjected to intricate working environments and significant radial and axial loads. The health of the system is paramount for secure and effective subterranean operations. The early failure of a roadheader bearing exhibits weak impact characteristics, frequently obscured by complex and potent background noise. In this paper, a fault diagnosis strategy incorporating variational mode decomposition with a domain-adaptive convolutional neural network is formulated. The gathered vibration signals are first decomposed into their constituent IMF sub-components using VMD. After the computation of the IMF's kurtosis index, the maximum index value is selected and used as input to the neural network. multi-strain probiotic A novel transfer learning approach is presented to address the discrepancy in vibration data distributions experienced by roadheader bearings operating under fluctuating working conditions. The method was successfully incorporated into the real-world task of diagnosing bearing faults in a roadheader. The experimental results unequivocally show the method's superiority in terms of diagnostic accuracy and its practical engineering application.
This article proposes STMP-Net, a video prediction network specifically designed to mitigate the inadequacy of Recurrent Neural Networks (RNNs) in extracting complete spatiotemporal information and motion changes during video prediction. The amalgamation of spatiotemporal memory and motion perception within STMP-Net results in more precise predictions. Within the prediction network architecture, the spatiotemporal attention fusion unit (STAFU) is established as a primary module, learning and transferring spatiotemporal features in both horizontal and vertical directions through the use of spatiotemporal feature information and a contextual attention mechanism. A contextual attention mechanism is also introduced into the hidden state, enabling the concentration on prominent details and enhancing the capture of intricate characteristics, resulting in a substantial decrease in the computational load of the network. Subsequently, a motion gradient highway unit (MGHU) is presented. It is constructed by incorporating motion perception modules between layers, thus enabling the adaptive learning of salient input features and the fusion of motion change characteristics. This combination leads to a substantial enhancement in the model's predictive accuracy. Lastly, a high-velocity channel is positioned between layers to facilitate the rapid exchange of crucial features and counteract the back-propagation-induced gradient vanishing issue. Experimental findings indicate that the proposed method outperforms mainstream video prediction networks, especially in long-term prediction of motion-rich videos.
This paper's focus is on a smart CMOS temperature sensor that incorporates a BJT. The analog front-end circuit's structure incorporates a bias circuit and a bipolar core; the data conversion interface is equipped with an incremental delta-sigma analog-to-digital converter. cutaneous immunotherapy To address process variations and non-ideal device characteristics, the circuit incorporates chopping, correlated double sampling, and dynamic element matching techniques, thereby improving measurement accuracy.