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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

computer systems science & engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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Intent-Guided Trajectory Prediction for E-Scooter Riders and Bicyclists 27

Intent-Guided Trajectory Prediction for E-Scooter Riders and...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Zhengming Ding, Zhengming Chen, Yaobin Chien, Stanley Li, Lingxi Sheroy, Rini Domeyer, Joshua Tian, Renran School of Industrial Engineering Purdue University United States Tulane University Department of Computer Science United States School of Electrical and Computer Engineering Purdue University United States United States North Carolina State University Department of Industrial and Systems Engineering United States

ISBN: (纸本)9798331505929

The rapidly increasing popularity of micromobility devices, such as e-scooters and bicycles, in urban environments underscores the critical need for advanced trajectory prediction models to enhance road safety and facilitate seamless integration into intelligent transportation systems. This study introduces a novel approach to predicting the movements of bicyclists and e-scooter riders by leveraging a unique dataset that combines bird's-eye and egocentric views, collected from diverse urban settings across the United States. Employing state-of-the-art deep learning techniques, our model significantly outperforms traditional linear and polynomial regression models in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE), demonstrating superior accuracy in forecasting the trajectories of vulnerable road users. Furthermore, our approach incorporates cognitive annotations to predict crossing intentions, enriching the model's predictive capabilities. The findings highlight the potential of our method to contribute to the development of proactive safety measures and collision avoidance systems, ultimately fostering safer urban mobility landscapes for all road users. © 2024 IEEE.

关键词： Prediction models

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Intelligent edge CDN with smart contract-aided local IoT sharing

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High-Confidence Computing 2024年第4期4卷 56-68页

作者： Jiamin Fan Daming Liu Guoming Tang Kui Wu Xun Shao Department of Computer Science University of VictoriaVictoria BC V8W 2Y2Canada College of Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China Peng Cheng Laboratory Shenzhen 518000China Department of Electrical and Electronic Information Engineering Toyohashi University of TechnologyToyohashi Aichi 441-8580Japan

A content delivery network(CDN)aims to reduce the content delivery latency to end-users by using distributed cache ***,deploying and maintaining cache servers on a large scale is very *** solve this problem,CDN providers have developed a new content delivery strategy:allowing end-users’s IoT edge devices to share their storage/bandwidth *** new edge CDN platform must address two core questions:(1)how can we incentivize end users to share IoT devices?(2)how can we facilitate a safe and transparent content transaction environment for end users?This paper introduces SmartSharing,a new content delivery network solution to address these *** smartSharing,the over-the-top(OTT)IoT devices belonging to end-users are used as mini-cache *** motivate end users to share the idle devices and storage/bandwidth resources,SmartSharing designs the content delivery schedule and the pricing scheme based on game theory and machine learning algorithms(specifically,a tailored expectation-maximization(EM)algorithm).To facilitate content trading among end users,SmartSharing creates a secure and transparent transaction platform based on smart contracts in *** addition,SmartSharing’s performance evaluation is through trace-driven simulations in the real world and a prototype using content metadata and the achieved pricing *** evaluation results show that CDN providers,end users and content providers can all benefit from our SmartSharing framework.

关键词： CDN over edge IoT Game theory EM algorithm Smart contracts

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Fluid-Driven Pattern Reconfigurable Antenna With Cavity-Backed Slot

Fluid-Driven Pattern Reconfigurable Antenna With Cavity-Back...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Khan, Muhammad U. Muhammad, Awab Nadeem, Adnan Arya, Ravi Kumar Chaudhary, Prashant Mittra, Raj Islamabad Pakistan Department of Electrical Engineering Frederick University Cyprus Xiangshan Laboratory Zhongshan Institute of Changchun University of Science and Technology China Department of Electrical and Computer Engineering University of Central Florida FL United States

ISBN: (纸本)9798350379693

This work introduces a novel cavity-backed slot antenna design operating at 10 GHz, employing a shorted monopole as the feed mechanism. The cavity structure is realized by integrating PLA (Polylactic Acid) posts filled with liquid mercury, enabling dynamic beam steering capabilities. By selectively emptying sets of eleven consecutive PLA posts, the antenna achieves 360-degree scan coverage without any degradation in scanning efficiency. Furthermore, to enhance the antenna's directivity, cone structures are integrated at the upper and lower extremities of the cavity. Simulation results demonstrate that the proposed antenna achieves complete 360-degree azimuth coverage with a directivity of 12 dBi. The proposed design is realizable and can be employed in various applications where beam steering is required such as communication and radar. © 2024 IEEE.

关键词： Scanning antennas

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Ultrafast magneto-optical dynamics in nickel(111)single crystal studied by the integration of ultrafast reflectivity and polarimetry probes

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Chinese Physics B 2024年第3期33卷 65-69页

作者：匡皓余军潇陈洁 H.E.Elsayed-Ali 李润泽 Peter M.Rentzepis School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Center for Transformative Science ShanghaiTech UniversityShanghai 201210China Center for Ultrafast Science and Technology Key Laboratory for Laser Plasmas (Ministry of Education)Collaborative Innovation Center of IFSA (CICIFSA)School of Physics and AstronomyShanghai Jiao Tong UniversityShanghai 200240China Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginia 23529USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTexas 77843USA

With the integration of ultrafast reflectivity and polarimetry probes,we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni(111)single *** carrier relaxation time within the linear excitation range reveals that electron-phonon coupling and dissipation of photon energy into the bulk of the crystal take tens of *** the other hand,the observed spin dynamics indicate a longer time of about 120 *** further understand how the lattice degree of freedom is coupled with these dynamics may require the integration of an ultrafast diffraction probe.

关键词： ultrafast spin dynamics non-equilibrium dynamics multi-probe

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Intelligent Control Based in Neural-Fuzzy of Adaptive Backstepping for The Double Stator IPMSM Powered by a Five-Level NPC Inverter 13

Intelligent Control Based in Neural-Fuzzy of Adaptive Backst...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Benaissa, Rabie Mansouri, Smail Ouledali, Omar University of Adrar Faculty of Sciences and Technology Department of Electrical Engineering Laboratory for Sustainable Development and Computer Science 01000 Algeria University of Adrar Faculty of Sciences and Technology Department of Hydrocarbons and Renewable Energies Laboratory for Energy Environment and Information Systems 01000 Algeria

ISBN: (纸本)9798350356199

This article supplies a proposed approach Neuro Fuzzy Controller (NFC)-Adaptive Backstepping Controller (ABC)-Space Vector Modulation (SVM) for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM). This paper's primary objective is to enhance DSIPMSM's performance by the use of strong control using the proposed approach. Given its benefits - including high torque density, high efficiency, high power density, and minimal maintenance - DSIPMSM, have recently been the focus of much research. Using the input-output linearization approach and Lyapunov stability theory, asymptotically stable trajectory-following dynamics are demonstrated using nonlinear adaptive backstepping control. The use of fuzzy logic and intelligence-based controllers are increasingly being used to enhance traditional control's performance and output. In addition, the fuzzy logic controller has a lot of flaws that are presently being fixed by the extremely effective NFC tool. The proposed method's worth was demonstrated by the simulation results in terms of durability, effective tracking dynamics, precision, and good disturbance rejection with little ripple. © 2024 IEEE.

关键词： Disturbance rejection

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Invertibility of Discrete-Time Linear systems with Sparse Inputs 63

Invertibility of Discrete-Time Linear Systems with Sparse In...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Poe, Kyle Mallada, Enrique Vidal, Rene University of Pennsylvania Applied Mathematics and Computational Science Group PA19104 United States Johns Hopkins University Mallada Is with the Department of Electrical and Computer Engineering MD21218 United States University of Pennsylvania Radiology Computer and Information Science Statistics and Data Science Departments of Electrical and Systems Engineering PA19104 United States

ISBN: (纸本)9798350316339

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024 IEEE.

关键词： State space methods

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A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters

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Nano Research 2024年第10期17卷 9181-9192页

作者： Negar Hosseinzadeh Kouchehbaghi Maryam Yousefzadeh Aliakbar Gharehaghaji Safoora Khosravi Danial Khorsandi Reihaneh Haghniaz Ke Cao Mehmet R.Dokmeci Mohammad Rostami Ali Khademhosseini Yangzhi Zhu Terasaki Institute for Biomedical Innovation Los AngelesCA 91367USA Department of Textile Engineering Amirkabir University of Technology(Tehran Polytechnic)Hafez Avenue*** TehranIran Department of Electrical and Computer Engineering University of British ColumbiaVancouverBC V6T1Z4Canada Chemical Sciences Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90007USA

Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive listening ***,their reallife efficacy is hampered by low power output,particularly in the low-frequency range(<1 kHz).This study introduces a novel approach to enhance the performance of NAEHs by applying machine learning(ML)techniques to guide the synthesis of electrospun polyvinylidene fluoride(PVDF)/polyurethane(PU)nanofibers,optimizing their application in wearable *** use a feed-forward neural network along with solving an optimization problem to find the optimal input values of the electrospinning(applied voltage,nozzle-collector distance,electrospinning time,and drum rotation speed)to generate maximum output performance(acoustic-to-electricity conversion efficiency).We first prepared a dataset to train the network to predict the output power given the input variables with high *** introducing the neural network,we fix the network and then solve an optimization problem using a genetic algorithm to search for the input values that lead to the maximum energy harvesting *** ML-guided wearable PVDF/PU NAEH platform can deliver a maximal acoustoelectric power density output of 829μW/cm^(3) within the surrounding noise *** addition,our system can function stably in a broad frequency(0.1-2 kHz)with a high energy conversion efficiency of 66%.Sound recognition analysis reveals a robust correlation exceeding 0.85 among lexically akin terms with varying sound intensities,contrasting with a diminished correlation below 0.27 for words with disparate semantic ***,this work provides a previously unexplored route to utilize ML in advancing wearable NAEHs with excellent practicability.

关键词： wearable electronics acoustic energy harvester machine learning piezoelectric nanogenerator electrospun nanofiber

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Hierarchical Codebook Design Using Scale-Changeable Reconfigurable Holographic Surfaces in Near-Far Field Communications

Hierarchical Codebook Design Using Scale-Changeable Reconfig...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Shupei Di, Boya Zhang, Hongliang Poor, H. Vincent Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks Beijing China Princeton University Department of Electrical and Computer Engineering Princeton United States

ISBN: (纸本)9798350351255

Reconfigurable holographic surfaces (RHSs) have been proposed as a cost-effective and power-efficient solution for extremely large-scale arrays, where the amplitude of electromagnetic waves radiated at each element is controlled to achieve high directive gain. However, the complexity of acquiring real-time channel state information (CSI) required for beamforming is prohibitively high, especially when the near-field expansion brought by the large-scale RHS is considered. In this paper, we propose a codebook-based beam training scheme for a large-scale RHS-enabled communication system to bypass CSI estimation. Unlike traditional phase-controlled arrays, the amplitude-controlled property of the RHS implies that each RHS element can be selectively activated. This motivates an array reconfiguration method where a scale-changeable RHS array is constructed to generate gain-flat beams with different coverage in the angle-range domain. A hierarchical RHS codebook is then proposed where the coverage of the codewords in each layer is progressively refined. To address the substantial beam search overhead in the near-far field, a two-stage beam training scheme is performed in the proposed codebook, thereby reducing the overhead to a logarithmic level of the element number. The simulation results show that the proposed scheme performs better than phased arrays given the same input power in terms of sum rate, and it also approaches the upper bound achieved by the exhaustive search at a significantly reduced overhead. © 2024 IEEE.

关键词： Antenna phased arrays

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Transferable Learning of GCN Sampling Graph Data Clusters from Different Power systems 60

Transferable Learning of GCN Sampling Graph Data Clusters fr...

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Wu, Tong Scaglione, Anna Arnold, Daniel Chen, Tianyi University of Central Florida The Department of Electrical and Computer Engi-neering OrlandoFL32816 United States Cornell University The Department of Electrical and Computer Engineering Cornell Tech New York CityNY10044 United States Lawrence Berkeley National Laboratory Rensselaer Polytechnic Institute The Department of Electrical TroyNY12180 United States Computer and Systems Engineering Rensselaer Polytechnic Institute The Department of Electrical TroyNY12180 United States

ISBN: (纸本)9798331541033

Contemporary neural network (NN) detectors for power systems face two primary challenges. First, each power system requires individual training of NN detectors to accommodate its unique configuration and base demands. Second, significant changes within the power system, such as the introduction of new substations or new generators, necessitate retraining. To overcome these issues, we introduce a novel architecture, the Nodal Graph Convolutional Neural Network (NGCN), which utilizes graph convolutions at each bus and its neighborhoods. This approach allows the training process to encompass multiple power systems and include all buses, thereby enhancing the transferability of the method across different power systems. The NGCN is particularly effective for detection tasks, such as cyber-attacks on smart inverters and false data injection attacks. Our tests demonstrate that the NGCN significantly improves performance over traditional NNs, boosting detection accuracy from approximately 85% to around 97% for the aforementioned task. Furthermore, the transferable NGCN, which is trained by samples from multiple power systems, performs considerably better in evaluations than the NGCN trained on a single power system. © 2024 IEEE.

关键词： Convolutional neural networks

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