Differential evolution (DE) is a widely recognized method to solve complex optimization problems as shown by many researchers. Yet, non-adaptive versions of DE suffer from insufficient exploration ability and uses no ...
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Differential evolution (DE) is a widely recognized method to solve complex optimization problems as shown by many researchers. Yet, non-adaptive versions of DE suffer from insufficient exploration ability and uses no historical information for its performance enhancement. This work proposes Fractional Order Differential Evolution (FODE) to enhance DE performance from two aspects. Firstly, a bi-strategy co-deployment framework is proposed. The population-based and parameter-based strategies are combined to leverage their respective advantages. Secondly, the fractional order calculus is first applied to the differential vector to enhance DE’s exploration ability by using the historical information of populations, and ensures the diversity of population in an evolutionary process. We use the 2017 IEEE Congress on Evolutionary Computation (CEC) test functions, and CEC2011 real-world problems to evaluate FODE’s performance. Its sensitivity to parameter changes is discussed and an ablation study of multi-strategies is systematically performed. Furthermore, the variations of exploration and exploitation in FODE are visualized and analyzed. Experimental results show that FODE is superior to other state-of-the-art DE variants, the winners of CEC competitions, other fractional order calculus-based algorithms, and some powerful variants of classic algorithms. IEEE
With the fast development of artificial intelligence(AI) and industrial Internet of Things(IIoT)technologies, it is challenging to deal with the problems of data privacy protection and secure *** recent years, federat...
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With the fast development of artificial intelligence(AI) and industrial Internet of Things(IIoT)technologies, it is challenging to deal with the problems of data privacy protection and secure *** recent years, federated learning(FL) is proposed to attack the challenges of learning shared models collaboratively while protecting security based on the data from cross-domain clients. However, data in the real environment is usually not independent and identically distributed(Non-IID) due to the differences in business, working environments, and data acquisition, and thus classic federated methods suffer from significant performance degradation. In this paper, a novel federated framework is proposed for secure textile fiber identification(Fed TFI) via cross-domain texture representation based on high-definition fabric images. In addition to sharing the gradient of Fed TFI, the local patch of feature maps between crossdomain clients is randomly exchanged to build a richer image texture feature distribution while protecting data security simultaneously for secure computing. Furthermore, a texture embedding layer is designed to provide a joint representation through similarity measure between triplet samples in low-dimensional *** verify the effectiveness of the proposed framework, two textile image datasets, i.e., one public and the other we collected, are utilized to construct four Non-IID scenarios, including label skew, feature skew, and two combined skew scenarios. The experimental results confirm the effectiveness of our model to obtain better detection accuracies than benchmarks in four Non-IID scenarios by keeping data privacy for secure computing in fabric IIoT.
Tactile sensing provides robots the ability of object recognition,fine operation,natural interaction,***,in the actual scenario,robotic tactile recognition of similar objects still faces difficulties such as low effic...
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Tactile sensing provides robots the ability of object recognition,fine operation,natural interaction,***,in the actual scenario,robotic tactile recognition of similar objects still faces difficulties such as low efficiency and accuracy,resulting from a lack of high-performance sensors and intelligent recognition *** this paper,a flexible sensor combining a pyramidal microstructure with a gradient conformal ionic gel coating was demonstrated,exhibiting excellent signal-to-noise ratio(48 dB),low detection limit(1 Pa),high sensitivity(92.96 kPa^(-1)),fast response time(55 ms),and outstanding stability over 15,000 compression-release ***,a Pressure-Slip Dual-Branch Convolutional Neural Network(PSNet)architecture was proposed to separately extract hardness and texture features and perform feature *** tactile experiments on different kinds of leaves,a recognition rate of 97.16%was achieved,and surpassed that of human hands recognition(72.5%).These researches showed the great potential in a broad application in bionic robots,intelligent prostheses,and precise human–computer interaction.
Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these adva...
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Video matting aims at accurately separating foreground from videos. Recent video matting researches pursue to eliminate auxiliary inputs. However, due to the limited ability of extracting global correlation features, ...
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Image matting is a widely-used image processing technique that aims at accurately separating foreground from an image. However, this is a challenging and ill-posed problem that demands additional input, such as trimap...
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Airway segmentation serves as an essential foundational process for both the diagnosis of lung conditions and the navigation of surgical interventions. Although numerous attempts have been proposed to address airway s...
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Given the characteristics of road defect detection, such as the small proportion of defective targets in defective images, the large scale difference of different types of defects, and the complex background environme...
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ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
Given the characteristics of road defect detection, such as the small proportion of defective targets in defective images, the large scale difference of different types of defects, and the complex background environment, as well as the limitations of current methods, which are unsuitable for edge deployment and real-time detection, we propose a road defect detection model with focused feature enhancement and multi-scale feature fusion. Firstly, we create a Global-Local Feature Extractor (GLFE) that extracts global and local feature information from road defect images, allowing us to adapt to the vast span and slender features of crack objects while also improving feature extraction capacity for low resolution images. Secondly, we propose an Adaptive-Generalized Feature Pyramid Network (Adaptive-GFPN) based on contextual information as the model's feature fusion network, as well as design and introduce a Context Aware Fusion Module (CAFM) and a dynamic up-sampling operation (DYsample) that can be guided and adapted based on the contextual information of features at different scales to improve defect detection accuracy in complex backgrounds. Finally, to improve detection efficiency, we design a Lightweight Detection Head (LDH) that reduces the number of network parameters and computations. The experimental results reveal that, when compared to the baseline model YOLOv8, our model improved mAP@0.5 by 5.2%, Precision by 3.5%, and Recall by 4.5% on the RDD2022 dataset.
Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts t...
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Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.
In complex and ever-changing traffic environments, target detection tasks require extremely high accuracy and efficiency. However, current methods still have obvious limitations in their ability to focus on key areas ...
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ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
In complex and ever-changing traffic environments, target detection tasks require extremely high accuracy and efficiency. However, current methods still have obvious limitations in their ability to focus on key areas and the detection performance of multi-scale targets. In view of this, this paper proposes an innovative target detection model for complex traffic scenes. Specifically, the two-layer routing attention mechanism significantly enhances the model's ability to identify and detect targets by accurately focusing on key areas and effectively suppressing the interference of background noise. At the same time, the context information module combined with feature fusion integrates multi-level features, especially the fusion of low-level and high-level features, and introduces environmental context information, which significantly improves the detection accuracy of multi-scale targets in complex traffic scenes. In order to solve the problem that tiny targets are easily missed, this paper further increases the use of shallow features to improve the accuracy of tiny target detection, which significantly improves the detection accuracy of small targets. In order to verify the effectiveness of the proposed model, we conducted detailed experimental verification on the BDD100K dataset. Experimental results show that the mAP@50 index of the model has increased by 10.8%, and the mAP@50:95 index has also increased by 6.3%, while achieving high-speed real-time detection at 88.4 frames per second. This achievement fully proves that the model has successfully achieved dual optimization of accuracy and speed in complex traffic scenes, and its performance is better than other existing methods. It has broad practical application prospects, especially in the fields of intelligent transportation and autonomous driving.
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