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Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

computers, Materials & Continua 2022年第8期72卷 2467-2482页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Shady Y.El-Mashad Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Mechanical Engineering Department Qassim UniversityBuraidah51452Saudi Arabia Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt

The sample’s hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing ***(HGB)is a critical component of the human body because it transports oxygen from the lungs to the body’s tissues and returns carbon dioxide from the tissues to the *** the HGB level is a critical step in any blood analysis *** often indicate whether a person is anemic or polycythemia *** ensemble models by combining two or more base machine learning(ML)models can help create a more improved *** purpose of this work is to present a weighted average ensemble model for predicting hemoglobin *** optimization method is utilized to get the ensemble’s optimum *** optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search(SCSFS).The proposed SCSFS ensemble is compared toDecision Tree,Multilayer perceptron(MLP),Support Vector Regression(SVR)and Random Forest Regressors as model-based approaches and the average ensemble *** SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.

关键词： Sine cosine optimization metaheuristics optimization hemoglobin estimation weight average ensemble

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Hamiltonian quantum generative adversarial networks

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Physical Review Research 2024年第3期6卷 033019-033019页

作者： Leeseok Kim Seth Lloyd Milad Marvian Department of Electrical & Computer Engineering and Center for Quantum Information and Control Department of Mechanical Engineering

We propose Hamiltonian quantum generative adversarial networks (HQuGANs) to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the success of classical generative adversarial networks in learning high-dimensional distributions. The quantum optimal control approach not only makes the algorithm naturally adaptable to the experimental constraints of near-term hardware, but also offers a more natural characterization of overparameterization compared to the circuit model. We numerically demonstrate the capabilities of the proposed framework to learn various highly entangled many-body quantum states, using simple two-body Hamiltonians and under experimentally relevant constraints such as low-bandwidth controls. We analyze the computational cost of implementing HQuGANs on quantum computers and show how the framework can be extended to learn quantum dynamics. Furthermore, we introduce a cost function that circumvents the problem of mode collapse that prevents convergence of HQuGANs and demonstrate how to accelerate the convergence of them when generating a pure state.

关键词： Machine learning Quantum algorithms Quantum computation Quantum control

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Circularity Reinforcement of Critical Raw Materials in Europe: A Case of Niobium

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Circular Economy and Sustainability 2024年第4期4卷 2449-2472页

作者： von Rennenberg, Theresa Yu, Yifei Fraccascia, Luca Yazan, Devrim Murat Department of Industrial Engineering and Business Information Systems University of Twente Drienerlolaan 5 Enschede 7522 NB Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy

Critical Raw Materials attract increasing attention due to their depleting reserves and low recyclability. Niobium, one of the most rare and vital elements, is primarily found in Brazil. This research explores the potential impact of Circular Economy (CE) strategies on mitigating niobium's criticality within Europe. First, a niobium supply chain is designed and analysed by Enterprise Input–Output modelling. Second, the supply risk is calculated based on the criticality matrix proposed by the European Commission under three scenarios associated with resources, technologies, and policies. The results show that urban mining is a potential solution to reduce niobium’s criticality and mitigate its environmental impacts. A higher recycling input rate and/or a mix of recycling and substitution strategies is necessary to offset niobium’s criticality. Aligned with the CE action plan, the research offers a scientific foundation to strategically prevent the risk of niobium supply shortages. © The Author(s) 2024.

关键词： Circular economy Critical raw materials Enterprise input–output Niobium supply chain Scenario analysis

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Correction of Time-varying PMU Phase Angle Deviation with Unknown Transmission Line Parameters

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 315-325页

作者： Ancheng Xue He Kong Feiyang Xu Junbo Zhao Naichao Chang Joe H.Chow Haiyan Hong State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering University of ConnecticutStorrs06268USA National Electric Power Dispatching and Control Center Beijing 100031China Department of Electrical Computerand Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNY 12180USA

Phasor measurement units(PMUs)provide useful data for real-time monitoring of the smart ***,there may be time-varying deviation in phase angle differences(PADs)between both ends of the transmission line(TL),which may deteriorate application performance based on *** address that,this paper proposes two robust methods of correcting time-varying PAD deviation with unknown parameters of TL(ParTL).First,the phenomena of time-varying PAD deviation observed from field PMU data are *** general formulations for PAD estimation are then *** simplify the formulations,estimation of PADs is converted into the optimal problem with a single ParTL as the variable,yielding a linear estimation of *** latter is used by second-order Taylor series expansion to estimate PADs *** reduce the impact of possible abnormal amplitude data in field data,the IGG(Institute of Geodesy&Geophysics,Chinese Academy of Sciences)weighting function is *** using both simulated and field data verify the effectiveness and robustness of the proposed methods.

关键词： Correction line parameters parameter identification phasor measurement time-varying phase angle difference deviation

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Interpretation of ADRC in Q-Structure 6

Interpretation of ADRC in Q-Structure

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Xu, Jiapeng Chen, Xiang Tan, Ying Zhou, Kemin University of Windsor Department of Electrical and Computer Engineering Windsor Canada The University of Melbourne Department of Mechanical Engineering Melbourne Australia Center for Advanced Control and Smart Operations Nanjing University Suzhou China

ISBN: (纸本)9798350356618

This paper presents a control structure featuring an operator Q driven by the residual signal, which indicates the difference between the measurement output and the estimated output from an observer. The form of this observer is very general, as long as it can generate the estimated state or relevant information that will be incorporated into this control structure. The operator Q introduces an extra design freedom to address uncertainties, such that linear active disturbance rejection control (LADRC) can be interpreted as a special case in the Q-structure when a linear extended state observer is used. © 2024 IEEE.

关键词： Disturbance rejection

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Unsupervised pedestrian intention estimation through deep neural embeddings and spatio-temporal graph convolutional networks

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Pattern Analysis and Applications 2025年第2期28卷 1-14页

作者： Scaccia, Simone Pro, Francesco Amerini, Irene Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

A deep understanding of pedestrian intention and crossing behaviors is crucial in applications like pedestrian attribute recognition and autonomous driving. While vehicles need to predict the movements of pedestrians accurately for safety, the recognition and re-identification systems rely on behavioral cues that help them enhance identity tracking and attribute analysis. Traditional trajectory-based methods for pedestrian intention estimation evaluate the future positions of pedestrians based on their past movements but may fail to capture their true intentions. A more effective approach will anticipate actions by analyzing underlying intent, improving the precision of pedestrian recognition and the motion prediction. Current research on estimating pedestrian intentions primarily depends on supervised learning methods. In contrast, this work introduces an unsupervised learning approach to learn intention representations. This method is based on the idea that similar intentions lead to comparable behaviors among pedestrians, and, therefore, they can be clustered. To achieve this, this paper introduces UnPIE, an unsupervised method for predicting pedestrian intentions. It utilizes Spatio-Temporal Graph Convolutional Networks to encode intentions from videos and map them into a D-dimensional latent space. The training phase incorporates Instance Recognition to increase separation between embeddings from different classes and Local Aggregation to form soft clusters of related embeddings. A supervised non-parametric classifier is used to evaluate the performance of the method. The results demonstrate that UnPIE has comparable performance with respect to supervised approaches and even surpasses them, achieving a higher Precision by about 7% on the Pedestrian Intention Estimation dataset.

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A Combination of Soft Attention-aided CNN Models using Dempster-Shafer Theory for Skin Cancer Classification

A Combination of Soft Attention-aided CNN Models using Demp...

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International Conference on Current Problems of Applied Mathematics and computer Systems, CPAMCS 2023

作者： Sarkar, Sujan Ray, Amartya Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031640094

Skin cancer is one of the most deadly forms of cancer in the world, causing hundreds of deaths every year. Researchers have been developing various computer-aided diagnosis (CAD) systems to help medical professionals classify the types of skin cancer using different image modalities. In this paper, we propose a model, which combines the outcomes of three separately trained attention-based convolutional neural networks (CNNs) using the Dempster-Shafer theory. The three base learners used in this paper are ResNet50, InceptionResNetV2 and Dense-Net201, each of which is aided by a soft attention module. For evaluating the proposed model, we have considered a publicly accessible skin cancer dataset, called HAM10000. This is a challenging dataset to work with as it is a class imbalanced dataset. Our proposed approach yields an accuracy of 0.932, which is better than many recently published methods found in the literature. The source code is available at: https://***/Cmatermedicalimageanalysis/DST_Combination © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Memristive effects on an improved discrete Rulkov neuron model

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Science China(Technological Sciences) 2023年第11期66卷 3153-3163页

作者： BAO Han LI KeXin MA Jun HUA ZhongYun XU Quan BAO BoCheng School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213159China Department of Physics Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China

A change in neuronal-action potential can generate a magnetically induced current during the release and propagation of intracellular *** better characterize the electromagnetic-induction effect,this paper presents an improved discrete Rulkov(ID-Rulkov)neuron model by coupling a discrete model of a memristor with sine memductance into a discrete Rulkov neuron *** ID-Rulkov neuron model possesses infinite invariant points,and its memristor-induced stability effect is evaluated by detecting the routes of period-doubling and Neimark-Sacker *** investigated the memristor-induced dynamic effects on the neuron model using bifurcation plots and firing ***,we theoretically expounded the memristor initial-boosting mechanism of infinite coexisting *** results show that the ID-Rulkov neuron model can realize diverse neuron firing patterns and produce hyperchaotic attractors that are nondestructively boosted by the initial value of the memristor,indicating that the introduced memristor greatly benefits the original neuron *** hyperchaotic attractors initially boosted by the memristor were verified by hardware experiments based on a hardware *** addition,pseudorandom number generators are designed using the ID-Rulkov neuron model,and their high randomness is demonstrated based onstrict test results.

关键词： neuron model invariant point memristive effect complex dynamics pseudorandom number generators

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LLCoach: Generating Robot Soccer Plans Using Multi-role Large Language Models

LLCoach: Generating Robot Soccer Plans Using Multi-role Larg...

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27th RoboCup International Symposium, 2024

作者： Brienza, M. Musumeci, E. Suriani, V. Affinita, D. Pennisi, A. Nardi, D. Bloisi, D.D. Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy School of Engineering University of Basilicata Potenza Italy Department of International Humanities and Social Sciences International University of Rome Rome Italy

ISBN: (纸本)9783031858581

The deployment of robots into human scenarios necessitates advanced planning strategies, particularly when we ask robots to operate in dynamic, unstructured environments. RoboCup offers the chance to deploy robots in one of those scenarios, a human-shaped game represented by a soccer match. In such scenarios, robots must operate using predefined behaviors that can fail in unpredictable conditions. This paper introduces a novel application of Large Language Models (LLMs) to address the challenge of generating actionable plans in such settings, specifically within the context of the RoboCup Standard Platform League (SPL) competitions where robots are required to autonomously execute soccer strategies that emerge from the interactions of individual agents. In particular, we propose a multi-role approach leveraging the capabilities of LLMs to generate and refine plans for a robotic soccer team. The potential of the proposed method is demonstrated through an experimental evaluation, carried out simulating multiple matches where robots with AI-generated plans play against robots running human-built code. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anthropomorphic robots

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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

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computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer Science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

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