In the electric power equipment industry,various insulating materials and accessories are manufactured using petroleum-based epoxy ***,petrochemical resources are gradually becoming *** addition,the global surge in pl...
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In the electric power equipment industry,various insulating materials and accessories are manufactured using petroleum-based epoxy ***,petrochemical resources are gradually becoming *** addition,the global surge in plastic usage has consistently raised concerns regarding greenhouse gas emissions,leading to worsening global ***,to facilitate eco-friendly policies,industrialising epoxy systems applicable to high-pressure components using bio-based epoxy composites is *** results of the characterisation conducted in this research regarding bio-content were confirmed through thermogravimetric analysis and differential scanning calorimetry,which showed that as the bio-content increased,the thermal stability *** the operating temperature of 105℃ for the insulation spacer,structurally,no issues would be encountered if the spacer was manufactured with a bio-content of 20%(bio 20%).Subsequent tensile and flexural strength measurements revealed mechanical properties equivalent to or better than those of their petroleum-based *** impact strength tended to decrease with increasing *** the dielectric properties confirmed that the epoxy composite containing 20%biomaterial is suitable for manufacturing insulation ***,a series of tests conducted after spacer fabrication confirmed the absence of internal metals and bubbles with no external discolouration or cracks observed.
Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal **...
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Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal *** at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is ***,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample *** the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original *** algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.
Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions a...
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Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-toend trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps.
Schizophrenia (SC) is a complex mental disorder with diverse symptoms that make diagnosis challenging. This study aims to enhance diagnostic accuracy for SC using deep learning techniques applied to resting-state func...
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Schizophrenia (SC) is a complex mental disorder with diverse symptoms that make diagnosis challenging. This study aims to enhance diagnostic accuracy for SC using deep learning techniques applied to resting-state functional MRI (rsfMRI) data, capturing both spatial and temporal features of brain activity. We introduced a novel slice-wise classification approach using convolutional neural networks (CNNs) to analyze brain activity from rsfMRI images. Preprocessing included normalization, noise reduction, and contrast enhancement. The study utilized data from 158 subjects, including 83 schizophrenia patients and 75 healthy controls. We combined CNN-extracted features with traditional machine learning models such as support vector machines (SVM), random forest (RF), and gradient boosting (GB) to boost classification performance. Transfer learning using pre-trained models like VGG16, ResNet50, and Xception was applied to leverage advanced feature extraction. Model performance was evaluated based on precision, accuracy, recall, and F1 score. The CNN-based approach achieved significant improvements in classification accuracy, with a peak accuracy of 98.67%. The hybrid models combining CNN features with SVM, RF, and GB achieved accuracies of 97.01%, 98.44%, and 98.84%, respectively. The CNN model alone achieved a precision of 0.9826, recall of 1.0, and F1 score of 0.9912. Pre-trained models also demonstrated high performance, with ResNet50 achieving an accuracy of 98.71%. Brain regions such as the frontal lobe (slices 5 and 9) and temporal lobe (slices 15 and 25) were identified as key areas with significant differences between schizophrenia patients and healthy controls. This study demonstrates the effectiveness of deep learning models, particularly CNNs and hybrid approaches with traditional machine learning models, in enhancing schizophrenia diagnosis using rsfMRI data. Identifying key brain regions provides insights into schizophrenia’s neurobiological underpinnings. Fu
This paper presents a novel integrated sensing and communication (ISAC) framework that leverages recent advancements in reconfigurable distributed antennas and reflecting surfaces (RDARS). RDARS is a programmable stru...
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Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless *** this paper,a robust transmission scheme for ...
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Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless *** this paper,a robust transmission scheme for an AirCompbased FL system with imperfect channel state information(CSI)is *** model CSI uncertainty,an expectation-based error model is *** main objective is to maximize the number of selected devices that meet mean-squared error(MSE)requirements for model broadcast and model *** problem is formulated as a combinatorial optimization problem and is solved in two ***,the priority order of devices is determined by a sparsity-inducing ***,a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are *** alternating optimization(AO)scheme is used to transform the resulting nonconvex problem into two convex *** results illustrate the effectiveness and robustness of the proposed scheme.
This article introduces a novel approach to bolster the robustness of Deep Neural Network (DNN) models against adversarial attacks named "Targeted Adversarial Resilience Learning (TARL)". The initial ev...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
The substring edit error replaces a substring u of x with another string v, where the lengths of u and v are bounded by a given constant k. It encompasses localized insertions, deletions, and substitutions within a wi...
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The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things(IoT)*** article addresses the privacy and security issues brought up by data sharing in the context ...
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The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things(IoT)*** article addresses the privacy and security issues brought up by data sharing in the context of IoT fog *** suggested framework,called"BlocFogSec",secures key management and data sharing through blockchain consensus and smart *** existing solutions,BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing,while employing a consensus protocol to validate transactions and maintain blockchain *** process and store data effectively at the network edge,the framework makes use of fog computing,notably reducing latency and raising *** successfully blocks unauthorized access and data breaches by restricting transactions to authorized *** addition,the framework uses a consensus protocol to validate and add transactions to the blockchain,guaranteeing data accuracy and *** compare BlocFogSec's performance to that of other models,a number of simulations are *** simulation results indicate that BlocFogSec consistently outperforms existing models,such as Security Services for Fog Computing(SSFC)and Blockchain-based Key Management Scheme(BKMS),in terms of throughput(up to 5135 bytes per second),latency(as low as 7 ms),and resource utilization(70％to 92％).The evaluation also takes into account attack defending accuracy(up to 100％),precision(up to 100％),and recall(up to 99.6％),demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.
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