Blockchain technology has gained significant attention for its ability to provide a decentralized and immutable platform for various applications. In this paper, we propose a Blockchain based Decentralized Case Manage...
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This article considers a topology construction problem involving a fixed-wing autonomous aerial vehicle (AAV), a set of quad-rotor AAVs and mobile ground users. The constructed topology must maximize the max-min flow ...
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Wind power plants(WPPs)are increasingly mandated to provide temporary frequency support to power systems during contingencies involving significant power ***,the frequency support capabilities of WPPs under derated op...
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Wind power plants(WPPs)are increasingly mandated to provide temporary frequency support to power systems during contingencies involving significant power ***,the frequency support capabilities of WPPs under derated operations remain insufficiently investigated,highlighting the potential for further improvement of the frequency *** paper proposes a bi-level optimized temporary frequency support(OTFS)strategy for a *** implementation of the OTFS strategy is collaboratively accomplished by individual wind turbine(WT)controllers and the central WPP ***,to exploit the frequency support capability of WTs,the stable operational region of WTs is expanded by developing a novel dynamic power control approach in WT *** approach synergizes the WTs'temporary frequency support with the secondary frequency control of synchronous generators,enabling WTs to release more kinetic energy without causing a secondary frequency ***,a model predictive control strategy is developed for the WPP *** strategy ensures that multiple WTs operating within the expanded stable region are coordinated to minimize the magnitude of the frequency drop through efficient kinetic energy ***,comprehensive case studies are conducted on a real-time simulation platform to validate the effectiveness of the proposed strategy.
Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...
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Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
There have been several developments in renewable resources, standby sources of energy, and storage technologies. Because renewable sources are inconsistent, the best method to ensure supply continuity is to combine t...
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Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by e...
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Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.
The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Rec...
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The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Recognition(HAR)***,the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained *** paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain,which reduces the model’s depth and accelerates activity *** traditional pruning methods that focus on the spatial domain and the importance of filters,this method converts sensor data,such as HAR data,to the frequency domain for *** emphasizes the low-frequency components by calculating their energy spectral density ***,filters that meet the predefined thresholds are retained,and redundant filters are removed,leading to a significant reduction in model size without compromising performance or incurring additional computational ***,the proposed algorithm’s effectiveness is empirically validated on a standard five-layer CNNs backbone *** computational feasibility and data sensitivity of the proposed scheme are thoroughly ***,the classification accuracy on three benchmark HAR datasets UCI-HAR,WISDM,and PAMAP2 reaches 96.20%,98.40%,and 92.38%,***,our strategy achieves a reduction in Floating Point Operations(FLOPs)by 90.73%,93.70%,and 90.74%,respectively,along with a corresponding decrease in memory consumption by 90.53%,93.43%,and 90.05%.
In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh env...
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In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.
— In recent years, significant efforts have been dedicated to detect human emotions. This interest primarily stems from the fact that emotions influence individuals' reactions and behaviors. Understanding these i...
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We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures...
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We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures(250℃)using these *** synthesis reduces the energy required for oxide conversion,thus accelerating the decomposition of organic ligands in the UV-exposed area,and promoting the formation of metal-oxygen networks,contributing to *** analysis confirmed a reduction in the conversion temperature of combustion precursors,and the prepared combustion ZrO_(2)films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice,along with an amorphous ***,the synergistic effect of combustion synthesis and UV/O_(3)-assisted photochemical activation resulted in patterned ZrO_(2)films forming even more complete metal-oxygen *** devices fabricated with patterned ZrO_(2)films using combustion synthesis exhibited excellent switching characteristics,including a narrow resistance distribution,endurance of 103 cycles,and retention for 105 s at 85℃,despite low-temperature *** synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.
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