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capsule networks embedded with prior known support information for image reconstruction

High-Confidence Computing 2023年第4期3卷 1-6页

作者： Meng Wang Ping Yang Yahao Zhang Department of Mathematics and Physics Beijing Information Science and Technology UniversityBeijing 100192China Cybersecurity Monitoring Center State Grid Cooperation of China Information&Telecommunication BranchBeijing 100053China

Compressed sensing(CS)has been successfully applied to realize image *** networks have been introduced to the CS of images to exploit the prior known support information,which can improve the reconstruction *** Network(Caps Net)is the latest achievement in neural networks,and can well represent the instantiation parameters of a specific type of entity or part of an *** study aims to propose a Caps Net with a novel dynamic routing to embed the information within the CS *** output of the network represents the probability that the index of the nonzero entry exists on the support of the signal of *** lead the dynamic routing to the most likely index,a group of prediction vectors is designed determined by the ***,the results of experiments on imaging signals are taken for a comparation of the performances among different *** is concluded that the proposed capsule network(Caps Net)creates higher reconstruction quality at nearly the same time with traditional Caps Net.

关键词： Image reconstruction capsule networks Signal processing

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Data-driven Reactive Power Optimization for Distribution networks Using capsule networks

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Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1274-1287页

作者： Wenlong Liao Jiejing Chen Qi Liu Ruijin Zhu Like Song Zhe Yang AAU Energy Aalborg UniversityAalborgDenmark School of Softwareand Microelectronics Peking UniversityBeijingChina Key Laboratory of Smart Gridof Ministry of Education Tianjin UniversityTianjinChina School of Electrical Engineering Tibet Agriculture and Animal Husbandry UniversityLinzhiChina Maintenance Branch of State Grid Jibei Electric Power Co. Ltd.BeijingChina

The construction of advanced metering infrastructure and the rapid evolution of artificial intelligence bring opportunities to quickly searching for the optimal dispatching strategy for reactive power optimization. This can be realized by mining existing prior knowledge and massive data without explicitly constructing physical models. Therefore, a novel datadriven approach is proposed for reactive power optimization of distribution networks using capsule networks(CapsNet). The convolutional layers with strong feature extraction ability are used to project the power loads to the feature space to realize the automatic extraction of key features. Furthermore, the complex relationship between input features and dispatching strategies is captured accurately by capsule layers. The back propagation algorithm is utilized to complete the training process of the CapsNet. Case studies show that the accuracy and robustness of the CapsNet are better than those of popular baselines(e.g.,convolutional neural network, multi-layer perceptron, and casebased reasoning). Besides, the computing time is much lower than that of traditional heuristic methods such as genetic algorithm, which can meet the real-time demand of reactive power optimization in distribution networks.

关键词： Data-driven reactive power optimization distribution networks deep learning capsule networks

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Using capsule networks for Android Malware Detection Through Orientation-Based Features

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Computers, Materials & Continua 2022年第3期70卷 5345-5362页

作者： Sohail Khan Mohammad Nauman Suleiman Ali Alsaif Toqeer Ali Syed Hassan Ahmad Eleraky Deanship of Preparatory Year and Supporting Studies Computer Science DepartmentImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia National University of Computer and Emerging Sciences Pakistan Department of Computer Science Islamic University of MedinaMedinaSaudi Arabia

Mobile phones are an essential part of modern *** two popular mobile phone platforms,Android and iPhone Operating System(iOS),have an immense impact on the lives of millions of *** these two,Android currently boasts more than 84%market ***,any personal data put on it are at great risk if not properly *** the other hand,more than a million pieces of malware have been reported on Android in just 2021 till *** and mitigating all this malware is extremely difficult for any set of human *** to this reason,machine learning-and specifically deep learning-has been utilized in the recent past to resolve this ***,deep learning models have primarily been designed for image *** this line of research has shown promising results,it has been difficult to really understand what the features extracted by deep learning models are in the domain of ***,due to the translation invariance property of popular models based on ConvolutionalNeural Network(CNN),the true potential of deep learning for malware analysis is yet to be *** resolve this issue,we envision the use of capsule networks(CapsNets),a state-of-the-art model in deep *** argue that since CapsNets are orientation-based in terms of images,they can potentially be used to capture spatial relationships between different features at different locations within a sequence of *** design a deep learning-based architecture that efficiently and effectively handles very large scale malware datasets to detect Androidmalware without resorting to very deep *** leads tomuch faster detection as well as increased *** achieve state-of-the-art F1 score of 0.987 with an FPR of just 0.002 for three very large,real-world malware *** code is made available as open source and can be used to further enhance our work with minimal effort.

关键词： Malware security Android deep learning capsule networks

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Deep capsule Residual networks for Better Diagnosis Rate in Medical Noisy Images

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Intelligent Automation & Soft Computing 2023年第5期36卷 2959-2971页

作者： P.S.Arthy A.Kavitha Department of Electronics and Communication Engineering Anna UniversityChennai600025India Department of Electronics and Communication Engineering M.Kumarasamy College of EngineeringKarur639113India

With the advent of Machine and Deep Learning algorithms,medical image diagnosis has a new perception of diagnosis and clinical ***-tably,medical images are more susceptible to capturing noises despite the peak in intelligent imaging ***,the presence of noise images degrades both the diagnosis and clinical treatment *** existing intelligent meth-ods suffer from the deficiency in handling the diverse range of noise in the ver-satile medical *** paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alle-viate this *** proposed deep learning architecture exploits the advan-tages of the capsule network,which is used to extract correlation features and combine them with redefined residual ***,the final stage of dense learning is replaced with powerful extreme learning machines to achieve a better diagnosis rate,even for noisy and complex *** experimen-tation has been conducted using different medical *** performances such as Peak-Signal-To-Noise Ratio(PSNR)and Structural-Similarity-Index-Metrics(SSIM)are compared with the existing deep learning ***-tionally,a comprehensive analysis of individual algorithms is *** experimental results prove that the proposed model has outperformed the other existing algorithms by a substantial margin and proved its supremacy over the other learning models.

关键词： Machine and deep learning algorithm capsule networks residual networks extreme learning machines correlation features

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