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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of Systems science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach

Multidimensional Voronoi Constellations vs. Short Blocklengt...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Sheng, Yajie Chen, Bin Lei, Yi Deng, Jingxin Xu, Jiwei Fu, Mengfan Zhuge, Qunbi Li, Shen Department of Computer Science and Information Engineering Hefei University of Technology Hefei China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

ISBN: (纸本)9798350379266

关键词： Probability distributions

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks 23

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Poole, Benjamin Ratnakumar, Rajan Madurasinghe, Dulip Tharaka Kummerle, Christian Venayagamoorthy, Ganesh Kumar Lee, Minwoo University of North Carolina Department of Computer Science Charlotte United States University of Pretoria Department of Electrical Electronic and Computer Engineering South Africa Clemson University Real-Time Power and Intelligent Systems Laboratory United States

ISBN: (纸本)9798350374889

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases. © 2024 IEEE.

关键词： Undirected graphs

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Spatio-temporal Attention Graph Convolutions for Skeleton-based Action Recognition 23rd

Spatio-temporal Attention Graph Convolutions for Skeleton-b...

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22nd Scandinavian Conference on Image Analysis, SCIA 2023

作者： Le, Cuong Liu, Xin Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden

ISBN: (纸本)9783031314346

In skeleton-based action recognition, graph convolutional networks (GCN) have been applied to extract features based on the dynamic of the human body and the method has achieved excellent results recently. However, GCN-based techniques only focus on the spatial correlations between human joints and often overlook the temporal relationships. In an action sequence, the consecutive frames in a neighborhood contain similar poses and using only temporal convolutions for extracting local features limits the flow of useful information into the calculations. In many cases, the discriminative features can present in long-range time steps and it is important to also consider them in the calculations to create stronger representations. We propose an attentional graph convolutional network, which adapts self-attention mechanisms to respectively model the correlations between human joints and between every time steps for skeleton-based action recognition. On two common datasets, the NTU-RGB+D60 and the NTU-RGB+D120, the proposed method achieved competitive classification results compared to state-of-the-art methods. The project’s GitHub page: STA-GCN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Using Deep Reinforcement Learning in Optimal Energy Management for Residential House Aggregators with Uncertain User Behaviors 7

Using Deep Reinforcement Learning in Optimal Energy Manageme...

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7th IEEE Student Conference on Electric Machines and Systems, SCEMS 2024

作者： Lin, Yujun Yan, Linfang Hui, Hongxun Chen, Yin Chen, Xia Wen, Jinyu School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology Wuhan China City & Energy Research Institute Suzhou China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China University of Strathclyde Department of Electronic and Electrical Engineering Glasgow United Kingdom

ISBN: (纸本)9798350366945

In this study, the home energy management problem, which can be regarded as a high-dimensional optimization problem, for numerous residential houses, is addressed. The concept of the aggregator is utilized to reduce the state and action space and to handle the high dimensionality. A two-stage deep reinforcement learning (DRL)-based approach is proposed for the aggregators to track the schedule from a superior grid and guarantee the operation constraints. In the first stage, a DRL control agent is set to learn the optimal scheduling strategy interacting with the environment based on the soft-actor-critic framework and generate the aggregate control actions. In the second stage, the aggregate control actions are disaggregated to individual appliances considering the users' behaviors. The uncertainty of an electric vehicle's charging demand is quantitatively expressed based on the driver's experience. An aggregate anxiety concept is introduced to characterize the driver's anxiety on the electric vehicle's range and uncertain events. Finally, simulations are conducted to verify the effectiveness of the proposed approach under dynamic user behaviors, and comparisons show the superiority of the proposed approach over other benchmark methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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VLC-enabled UAV Network for IoT with Co-channel Interference: Joint Spatial Deployment and Resource Allocation

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IEEE Internet of Things Journal 2025年第12期12卷 20707-20721页

作者： Liu, Jiaji Yang, Fang An, Nan Song, Jian Han, Zhu State Key Laboratory of Widegap Semiconductor Optoelectronic Materials and Technologies Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Research Institute of Tsinghua University Shenzhen518057 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Unmanned aerial vehicles (UAVs) have gained significant attention due to their low cost and efficient deployment. Specifically, UAV-based communications hold promise to supplement the coverage limitations of terrestrial networks and achieve superior connectivity for the Internet of Things (IoT). Moreover, UAVs with visible light communication (VLC) capability do not interfere with existing radio frequency (RF) communication infrastructure, thereby providing flexible, high-throughput on-demand wireless communications. In this paper, considering the limited battery capacity of UAVs, we aim to maximize the energy efficiency (EE) of a multi-UAV downlink VLC system for IoT. To this end, the system model with co-channel interference is first established, followed by the formulation of the joint spatial deployment and resource allocation problem. Besides, the original problem is decomposed into three subproblems, focusing on spatial deployment, subchannel assignment, and power allocation. By employing the block coordinate descent method, the three subproblems are solved alternately. Moreover, simulation results demonstrate that the proposed method achieves superior improvements in EE compared to other baselines. In addition, the convergence and computational complexity of the proposed algorithm, as well as the impacts of key parameters on the overall system performance are also investigated. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Snowball Effect in Federated Learning: An Approach of Exponentially Expanding Structures for Optimizing the Training Efficiency

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1803-1817页

作者： Cheng, Guoliang Li, Peichun Tan, Beihai Yu, Rong Wu, Yuan Pan, Miao Guangdong University of Technology School of Automation Guangzhou China Guangdong Key Laboratory of IoT Information Technology Guangzhou China University of Macau State Key Laboratory of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China Guangdong University of Technology School of Integrated Circuits Guangzhou China University of Houston Department of Electrical and Computer Engineering Houston United States

Efficient federated learning (FL) in mobile edge networks faces challenges due to energy-consuming on-device training and wireless transmission. Optimizing the neural network structures is an effective approach to achieving energy savings. In this paper, we present a Snowball FL training with expanding neural network structure, which starts with a small-sized submodel and gradually progresses to a full-sized model. To achieve this, we first design the submodel and embedding extraction schemes for fine-grained model structure expansion. We then investigate the joint minimization problem of the global training loss and system-wise energy consumption. After that, we decompose the optimization problem into a long-term model structure expansion subproblem and a single-round local resource allocation subproblem. Specifically, the former subproblem is transformed into a variational calculus problem by leveraging theoretical analysis of the convergence bound. The Euler-Lagrange method is used to derive the solution, where the optimal evolution strategy for the model structure exponentially increases with the global round (i.e., the Snowball effect). Meanwhile, the latter subproblem is solved by convex optimization to acquire the optimal computing frequency and transmission power. Experiments indicate that the proposed framework can save about 50% of energy consumption to achieve on-par accuracy against state-of-the-art algorithms. © 2015 IEEE.

关键词： Resource allocation

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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GFlowNet Training by Policy Gradients 41

GFlowNet Training by Policy Gradients

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41st International Conference on Machine Learning, ICML 2024

作者： Niu, Puhua Wu, Shili Fan, Mingzhou Qian, Xiaoning Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Computer Science and Engineering Texas A&M University College StationTX United States Computational Science Intiative Brookhaven National Laboratory UptonNY United States

Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties. We here propose a new GFlowNet training framework, with policy-dependent rewards, that bridges keeping flow balance of GFlowNets to optimizing the expected accumulated reward in traditional Reinforcement-Learning (RL). This enables the derivation of new policy-based GFlowNet training methods, in contrast to existing ones resembling value-based RL. It is known that the design of backward policies in GFlowNet training affects efficiency. We further develop a coupled training strategy that jointly solves GFlowNet forward policy training and backward policy design. Performance analysis is provided with a theoretical guarantee of our policy-based GFlowNet training. Experiments on both simulated and real-world datasets verify that our policy-based strategies provide advanced RL perspectives for robust gradient estimation to improve GFlowNet performance. Our code is available at: ***/niupuhua1234/GFN-PG. Copyright 2024 by the author(s)

关键词： Generative adversarial networks

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