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An Improved Multi-objective Artificial Hummingbird Algorithm for Capacity Allocation of Supercapacitor Energy Storage Systems in Urban Rail Transit

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Journal of Bionic engineering 2025年第2期22卷 866-883页

作者： Xin Wang Jian Feng Yuxin Qin College of Electrical and Information Engineering Hunan University of TechnologyZhuzhou412007HunanChina Hunan Engineering Research Centre of Electrical Drive and Regenerative Energy Storage and Utilization Zhuzhou412007HunanChina School of Computer Science University of GlasgowGlasgowG128QQScotlandUK

To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.

关键词： Multi-objective artificial hummingbird algorithm Tent mapping based on random variables Urban rail transit Supercapacitor energy storage systems Capacity allocation

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Conditional Generative Adversarial Network Approach for Autism Prediction

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computer Systems science & engineering 2023年第1期44卷 741-755页

作者： K.Chola Raja S.Kannimuthu Department of Computer Science and Engineering Sri Eshwar College of EngineeringCoimbatore641202TamilnaduIndia Department of Information Technology Karpagam College of EngineeringCoimbatore641032TamilnaduIndia

Autism Spectrum Disorder(ASD)requires a precise diagnosis in order to be managed and ***-invasive neuroimaging methods are disease markers that can be used to help diagnose *** majority of available techniques in the literature use functional magnetic resonance imaging(fMRI)to detect ASD with a small dataset,resulting in high accuracy but low *** supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text,images,and videos,but their performance and robustness are restricted by the size of the accompanying training *** learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering *** takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural *** modelling,also known as Generative Adversarial Networks(GANs),is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original *** are an exciting and rapidly changingfield that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains,most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the *** this paper,we present a novel conditional generative adversarial network,or cGAN for short,which is a form of GAN that uses a generator model to conditionally generate *** terms of prediction and accuracy,they outperform the standard *** pro-posed model is 74%more accurate than the traditional methods and takes only around 10 min for training even with a huge dat

关键词： Autism classification attributes imaging adversarial fMRI functional graph neural networks

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Design and Analysis of Optimization Method for Ultra-Wideband PA Based on Improved MOEA/D Algorithm Using Mixed Objective Function

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第7期44卷 2641-2654页

作者： Ni, Zhongpeng Xia, Jing Zhou, Xinyu Kong, Wa Zhang, Heng Yu, Chao Zhu, Xiao-Wei Jiangsu University School of Computer Science and Communication Engineering Zhenjiang212004 China Hong Kong Polytechnic University Department of Electronic Information Engineering 999077 Hong Kong Southeast University School of Information Science and Engineering Nanjing210096 China

This paper proposes a design and optimization method for ultra-wideband power amplifiers (PAs) using improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) and mixed optimization objective function. In order to address the insufficient optimization capabilities of conventional MOEA/D algorithm when dealing with complex Pareto fronts, the optimization algorithm is improved by using adaptive weights, neighborhoods, and global replacement. Initially, based on the sparsity of the population and an external population, the weight vectors corresponding to invalid or crowded individuals in the population are replaced. And, the neighborhoods are adaptively adjusted based on the number of iterations and the sparsity of each individual. Then, a global replacement is employed to accelerate the convergence process. Moreover, a mixed optimization objective function using the load impedance, output power, efficiency and gain is conducted in the PA optimization design. To reduce the difficulty of impedance judgment, a construction method of impedance solution set based on Poisson disk sampling has been proposed and employed for optimization. For validation, an ultra-wideband PA operating at 100-4000 MHz (190.2% fractional bandwidth) was designed and optimized. Measured results show a drain efficiency ranging from 56.0% to 70.9% with an output power higher than 39 dBm at saturation within the whole operational frequency band. © 1982-2012 IEEE.

关键词： Ultra-wideband (UWB)

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Advanced Semantic Communication Techniques for IoT Using Disentangled information Bottleneck

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IEEE Internet of Things Journal 2025年第12期12卷 20122-20137页

作者： Xie, Wenwu Xiong, Ming Yang, Liang Wang, Ji Li, Xingwang Yang, Zhihe Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Central China Normal University College of Physical Science and Technology Department of Electronics and Information Engineering Wuhan430079 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China

This article explores the impact of source data compression on the performance of task execution at the receiver side of a communication system, and investigates the interference and impact of channel environment variations on semantic coding features. In order to further optimize the performance of the semantic communication model, a semantic communication framework (DIB-DeepSC) based on disentangled information bottleneck is proposed, which improves the inference accuracy of the model by separating and decoupling irrelevant information in the source data, thus compressing valid information related to downstream task execution to a greater extent, reduced communication overhead. Meanwhile, the influence of the Lagrange multiplier β in the classical information bottleneck (IB) framework is eliminated, which avoids the need to manually optimize β several times in the semantic communication model. And the dynamic coding method (DIB-DE) is further designed based on adaptive weights, which can dynamically adjust the coding features according to the channel conditions and enhance the robustness of the model. Numerous experiments show that the proposed DIB-DeepSC framework combined with the DIB-DE dynamic encoding communication scheme possesses better semantic extraction and task inference performance relative to the benchmark methods. This scheme is expected to realize more efficient and reliable semantic transmission in practical communication systems and provides new ideas for developing practical semantic communication systems. © 2014 IEEE.

关键词： Channel coding

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Thermoelectric infrared detectors:Design,fabrication,and performance assessment

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Journal of Semiconductors 2024年第12期45卷 95-110页

作者： Daryoosh Vashaee Department of Electrical and Computer Engineering North Carolina State UniversityRaleighNC 27606USA Department of Materials Science and Engineering North Carolina State UniversityRaleighNC 27606USA

This study presents a comprehensive optimization and comparative analysis of thermoelectric(TE)infrared(IR)detec-tors using Bi_(2)Te_(3) and Si *** theoretical modeling and numerical simulations,we explored the impact of TE mate-rial properties,device structure,and operating conditions on responsivity,detectivity,noise equivalent temperature difference(NETD),and noise equivalent power(NEP).Our study offers an optimally designed IR detector with responsivity and detectivity approaching 2×10^(5) V/W and 6×10^(9) cm∙Hz^(1/2)/W,*** enhancement is attributed to unique design features,includ-ing raised thermal collectors and long suspended thin thermoelectric wire sensing elements embedded in low thermal conductivity organic materials like ***,we demonstrate the compatibility of Bi_(2)Te_(3)-based detector fabrication pro-cesses with existing MEMS foundry processes,facilitating scalability and ***,for TE IR detectors,zT/κemerges as a critical parameter contrary to conventional TE material selection based solely on zT(where zT is the thermoelec-tric figure of merit andκis the thermal conductivity).

关键词： thermoelectric infrared focal plane arrays micromachining Bi_(2)Te_(3)

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A Computationally Effective SDP Solution for Target Localization Using DTD and DFS Measurements in Multistatic Systems With Unknown Signal Propagation Speed

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IEEE Sensors Journal 2025年第12期25卷 22264-22278页

作者： Qi, Qinke Li, Youming Wu, Yonghong Guo, Qiang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China China Research Institute of Radiowave Propagation Qingdao266107 China Harbin Engineering University College of Information and Communication Engineering Harbin150000 China

This paper investigates the moving target localization in multistatic systems for unknown signal propagation speed environments and presents a semi-definite programming (SDP) solution with reduced computational complexity by differential time delay (DTD) and differential frequency shift (DFS) measurements. Existing research on this problem defines numerous auxiliary variables to pseudo-linearize measurement models, proposing high requirement related to the number of transmitters/receivers for effective localization performance of the solution or causing the computationally demanding solution. To address the aforementioned shortcomings, the auxiliary variables introduced when constructing pseudo-linear equations from the measurement models are minimized as much as possible. After that, based on the weighted least squares (WLS) criterion, a constrained WLS formulation of reducing the number of constraints is proposed, as the constraints included are formulated according to the relationships among auxiliary variables. Considering the non-convexity of the formulation, semi-definite relaxation (SDR) is applied to transform the constrained WLS problem into its SDP form for the global optimum. Besides, the mean square error (MSE) and complexity analyses are conducted to validate the performance of the proposed method from two perspectives. Both analyses and simulation results demonstrate that the method has the ability to balance the localization accuracy and computational load. © 2001-2012 IEEE.

关键词： NP-hard

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Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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Parameters Estimation of Modified Triple Diode Model of PSCs Considering Charge Accumulations and Electric Field Effects Using Puma Optimizer

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computer Modeling in engineering & sciences 2025年第4期143卷 723-745页

作者： Amlak Abaza Ragab A.El-Sehiemy Mona Gafar Ahmed Bayoumi Electrical Engineering Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam Bin Abdulaziz UniversityKharj16278Saudi Arabia Machine Learning and Information Retrieval Department Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Electrical Department Faculty of Engineering and TechnologyBadr University in CairoBadr11829Egypt Physics and Mathematics Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt College of Engineering Al-Ayen UniversityThi-Qar64001Iraq

Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution *** study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on *** models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs *** PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis *** suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its *** results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the *** statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing *** convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive ***,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.

关键词： Dynamic model of PSCs puma optimizer parameter estimation triple diode model

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

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Journal of Ambient Intelligence and Humanized Computing 2024年第7期15卷 3009-3033页

作者： Inani, Hardik Mehta, Veerangi Bhavsar, Drashti Gupta, Rajeev Kumar Jain, Arti Akhtar, Zahid Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Network and Computer Security State University of New York Polytechnic Institute NY United States

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Chemical activation

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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