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Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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Computer Modeling in engineering & Sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Multiagent Consensus over Time-Invariant and Time-Varying Signed Digraphs via Eventual Positivity

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IEEE Transactions on Automatic control 2023年第9期68卷 5429-5444页

作者： Fontan, Angela Wang, Lingfei Hong, Yiguang Shi, Guodong Altafini, Claudio Linköping University Division of Automatic Control Department of Electrical Engineering LinköpingSE-58183 Sweden KTH Royal Institute of Technology Division of Decision and Control Systems StockholmSE-10044 Sweden Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Tongji University Department of Control Science and Engineering Shanghai201804 China University of Sydney Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering SydneyNSW2008 Australia

Laplacian dynamics on signed digraphs have a richer behavior than those on nonnegative digraphs. In particular, for the so-called 'repelling' signed Laplacians, the marginal stability property (needed to achieve consensus) is not guaranteed a priori and, even when it holds, it does not automatically lead to consensus, as these signed Laplacians may lose rank even in strongly connected digraphs. Furthermore, in the time-varying case, instability can occur even when switching in a family of systems each of which corresponds to a marginally stable signed Laplacian with the correct corank. In this article, we present novel conditions for achieving consensus on signed digraphs based on the property of eventual positivity, a Perron-Frobenius (PF) type of property for signed matrices. The conditions we develop cover both time-invariant and time-varying cases. A particularly simple sufficient condition, valid in both cases, is that the Laplacians are normal matrices. Such condition can be relaxed in several ways. For instance, in the time-invariant case it is enough that the Laplacian has this PF property on the right side, but not on the left side (i.e., on the transpose). For the time-varying case, convergence to consensus can be guaranteed by the existence of a common Lyapunov function for all the signed Laplacians. All conditions can be easily extended to bipartite consensus. © 1963-2012 IEEE.

关键词： Lyapunov functions

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An Improved Machine Learning Model for Pure Component Property Estimation

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engineering 2024年第8期39卷 61-73页

作者： Xinyu Cao Ming Gong Anjan Tula Xi Chen Rafiqul Gani Venkat Venkatasubramanian State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Physics Bard College at Simon’s RockGreat BarringtonMA 01230USA PSE for SPEED Company Charlottenlund DK-2920Denmark Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 510000China Department of Applied Sustainability Széchenyi István UniversityGyör 9026Hungary Complex Resilient Intelligent Systems Laboratory Department of Chemical EngineeringColumbia UniversityNew YorkNJ 10027USA

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product ***,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these ***,accuracy and predictive capabilities still limit the scope and applicability of most property estimation *** paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different *** selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research *** framework is assessed using datasets of varying sizes for 20 pure component *** 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.

关键词： Group contribution Gaussian process Warping function Prior predictive checking

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MATD3-Based End-Edge Collaborative Resource Optimization for NOMA-Assisted Industrial Wireless Networks

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Computers, Materials & Continua 2025年第2期82卷 3203-3222页

作者： Ru Hao Chi Xu Jing Liu College of Information Engineering Shenyang University of Chemical TechnologyShenyang110142China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China

Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resources. This paper minimizes the system overhead regarding task processing delay and energy consumption for the IWN with hybrid NOMA and orthogonal multiple access (OMA) schemes. Specifically, we formulate the system overhead minimization (SOM) problem by considering the limited computation and communication resources and NOMA efficiency. To solve the complex mixed-integer nonconvex problem, we combine the multi-agent twin delayed deep deterministic policy gradient (MATD3) and convex optimization, namely MATD3-CO, for iterative optimization. Specifically, we first decouple SOM into two sub-problems, i.e., joint sub-channel allocation and task offloading sub-problem, and computation resource allocation sub-problem. Then, we propose MATD3 to optimize the sub-channel allocation and task offloading ratio, and employ the convex optimization to allocate the computation resource with a closed-form expression derived by the Karush-Kuhn-Tucker (KKT) conditions. The solution is obtained by iteratively solving these two sub-problems. The experimental results indicate that the MATD3-CO scheme, when compared to the benchmark schemes, significantly decreases system overhead with respect to both delay and energy consumption.

关键词： Industrial wireless networks(IWNs) multi-access edge computing(MEC) non-orthogonal multiple access(NOMA) task offloading resource allocation

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Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example

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Physical Review E 2024年第1期110卷 014121-014121页

作者： Nikolaos Evangelou Dimitris G. Giovanis George A. Kevrekidis Grigorios A. Pavliotis Ioannis G. Kevrekidis Department of Chemical and Biomolecular Engineering Department of Applied Mathematics and Statistics Department of Civil and Systems Engineering Department of Mathematics

Deriving closed-form analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM—the Desai-Zwanzig model—in its mean-field limit, using a smaller number of variables than traditional closed-form models. To this end, we use the manifold learning algorithm Diffusion Maps to identify a parsimonious set of data-driven latent variables, and we show that they are in one-to-one correspondence with the expected theoretical order parameter of the ABM. We then utilize a deep learning framework to obtain a conformal reparametrization of the data-driven coordinates that facilitates, in our example, the identification of a single parameter-dependent ordinary differential equation (ODE) in these coordinates. We identify this ODE through a residual neural network inspired by a numerical integration scheme (forward Euler). We then use the identified ODE—enabled through an odd symmetry transformation—to construct the bifurcation diagram exhibiting the phase transition.

关键词： Bifurcations Collective dynamics Dynamical systems High dimensional systems Multiple time scale dynamics Data mining Machine learning

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Feedback-Based Quantum Algorithm for Constrained Optimization Problems 15th

Feedback-Based Quantum Algorithm for Constrained Optimizati...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Abdul Rahman, Salahuddin Karabacak, Özkan Wisniewski, Rafal Automation and Control Section Department of Electronic Systems Aalborg University Aalborg Denmark Department of Mechatronics Engineering Kadir Has University Istanbul Turkey

ISBN: (纸本)9783031856990

The feedback-based algorithm for quantum optimization (FALQON) has recently been proposed to find ground states of Hamiltonians and solve quadratic unconstrained binary optimization problems. This paper efficiently generalizes FALQON to tackle quadratic constrained binary optimization (QCBO) problems. For this purpose, we introduce a new operator that encodes the problem’s solution as its ground state. Using control theory, we design a quantum control system such that the state converges to the ground state of this operator. When applied to the QCBO problem, we show that our proposed algorithm saves computational resources by reducing the depth of the quantum circuit and can perform better than FALQON. The effectiveness of our proposed algorithm is further illustrated through numerical simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Constrained optimization

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Reinforcement Learning Environment to Realistic Simulations for Multi-Agent System Validation and Deployment

Reinforcement Learning Environment to Realistic Simulations ...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Johnson, Seth Escamilla, Laura Lehman, Hannah Valasek, John Vehicle Systems and Control Laboratory Aerospace Engineering Department Texas AandM University College StationTX77843 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Reinforcement learning has been used to train autonomous single-agent and multi-agent systems to solve complex problems in the aerospace domain. This type of machine learning is a long process, often requiring training to run millions of time steps to achieve practical solutions. Training environments are often simplified to expedite training, such as using reduced vehicle dynamics and environmental abstractions to remove dependency on visual-based graphics. However, these complexities may be favorable when realism and accuracy are prioritized over run-time during full autonomous system validation. This paper introduces and develops an architecture that facilitates the transition from a reduced training environment to more realistic simulations. Modularity is emphasized to reduce implementation time and scalability to support large multi-agent systems. The proposed system leverages Unreal Engine 5 for high-fidelity graphics, optimized performance, and developer-oriented workflow to create a simulation for demonstrating and validating multi-agent systems for real-world deployment. The integration of Software-in-the-Loop (SITL) and Hardware-in-the-Loop (HITL) further validate the simulation framework, allowing for detailed testing and adjustment phases. Results presented in the paper investigate case studies on the validation for deployment of a single-agent autonomous tracking UAS and extend the proposed framework to an auction-based multi-agent coordination scenario. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reinforcement learning

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Machine learning driven rational design of dual atom catalysts on graphene for carbon dioxide electroreduction

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Nano Research 2025年第1期18卷 186-194页

作者： Dongxu Jiao Xinyi Li Mingzi Sun Lin Liu Jinchang Fan Jingxiang Zhao Bolong Huang Xiaoqiang Cui State Key Laboratory of Automotive Simulation and Control School of Materials Science and Engineeringand Key Laboratory of Automobile Materials of MOEJilin UniversityChangchun 130012China Department of Applied Biology and Chemical Technology The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina College of Chemistry and Chemical Engineering and Key Laboratory of Photonic and Electronic Bandgap MaterialsMinistry of EducationHarbin Normal UniversityHarbin 150025China

The development of high-performance atomiccatalysts for the carbon dioxide reduction reaction(CO_(2)RR)is atime-consuming process due to the complexity of the reactionmechanism and the uncertainty of the active ***,wehave proposed combining density functional theory(DFT)andmachine learning(ML)to investigate the potential of topologicalgraphene-based dual-atom catalysts(DACs)as CO_(2)*** analyzing the ML results,we identify thenumber of d-orbital electrons in the active site as a key factorinfluencing the CO_(2)RR catalytic ***,wepropose a simple descriptor to measure the CO_(2)RR activity ofthese *** findings provide plausible explanations for thesynergistic interactions between bimetallic atoms in CO_(2)RR andallow us to screen the homogeneous Ni-Ni pair as the mostpromising dual-atom *** work offers a fast MLapproach based on limited DFT calculations to predict the mostelectroactive and stable DACs on carbon support for CO_(2)RR,facilitating rapid screening of high-performance dual-atomcatalysts.

关键词： CO_(2)reaction reduction dual-atom catalysts density functional theory machine learning

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In-situ constructed polymer/alloy composite with high ionic conductivity as an artificial solid electrolyte interphase to stabilize lithium metal anode

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Nano Research 2023年第3期16卷 3888-3894页

作者： Ai-Long Chen Yushan Qian Shujun Zheng Yuyang Chen Yue Ouyang Lulu Mo Zheng-Long Xu Yue-E Miao Tianxi Liu State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai 201620China Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong HumKowloonHong Kong 999077China Key Laboratory of Synthetic and Biological Colloids Ministry of EducationSchool of Chemical and Material EngineeringJiangnan UniversityWuxi 214122China

Lithium(Li)metal is regarded as the best anode material for lithium metal batteries(LMBs)due to its high theoretical specific capacity and low redox ***,the notorious dendrites growth and extreme instability of the solid electrolyte interphase(SEI)layers have severely retarded the commercialization process of ***,a double-layered polymer/alloy composite artificial SEI composed of a robust poly(1,3-dioxolane)(PDOL)protective layer,Sn and LiCl nanoparticles,denoted as PDOL@Sn-LiCl,is fabricated by the combination of in-situ substitution and polymerization processes on the surface of Li metal *** lithiophilic Sn-LiCl multiphase can supply plenty of Li-ion transport channels,contributing to the homogeneous nucleation and dense accumulation of Li *** mechanically tough PDOL layer can maintain the stability and compact structure of the inorganic layer in the long-term cycling,and suppress the volume fluctuation and dendrites formation of the Li metal *** a result,the symmetrical cell under the double-layered artificial SEI protection shows excellent cycling stability of 300 h at 5.0 mA·cm^(−2)for 1 mAh·cm^(−2).Notably,the Li||LiFePO_(4)full cell also exhibits enhanced capacity retention of 150.1 mAh·g^(−1)after 600 cycles at 1.0 ***,the protected Li foil can effectively resist the air and water corrosion,signifying the safe operation of Li metal in practical *** present finding proposed a different tactic to achieve safe and dendrite-free Li metal anodes with excellent cycling stability.

关键词： polymer/alloy composite in-situ polymerization artificial solid electrolyte interphase(SEI) double-layered structure lithium metal battery.

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In situ investigation of lysozyme adsorption into polyelectrolyte brushes by quartz crystal microbalance with dissipation

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Chinese Journal of chemical engineering 2022年第8期35卷 106-115页

作者： Fenfen You Qing-Hong Shi Department of Biochemical Engineering School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300350China Key Laboratory of Systems Bioengineering(Ministry of Education) Tianjin UniversityTianjin 300072China

A well understanding about protein adsorption into charged polymer brushes is of importance in the elucidation of mechanism and important phenomena(such as “chain delivery”effect) in protein adsorption on polymer-grafted ion exchange adsorbents. In this work, quartz crystal microbalance with dissipation(QCM-D) was introduced to in situ investigate lysozyme adsorption on QCM sensors grafted with poly(3-sulfopropyl methacrylate)(pSPM) via atom transfer radical polymerization. It was achieved by analyzing frequency(f) and energy dissipation(D) shift simultaneously on pSPM-grafted sensors. The result showed that an initial decrease in ΔD was typical of lysozyme adsorption on pSPM-grafted sensor and more significant with an increase of chain length and grafting density. It was attributed to significant water release in the hydration layer of protein and polymer chains in lysozyme adsorption into pSPM *** pSPM-grafted sensors with long and dense chains, furthermore, lysozyme transitioned from monolayer to multilayer adsorption and the maximum adsorbed amount was obtained to be 374.0 ng·cmamong all pSPM-grafted sensors in this work. The results in D-f plot further revealed that lysozyme adsorption into pSPM brushes increased the rigidity of adsorbed layer and little structure adjustment of adsorbed lysozyme. It was unfavorable for “chain delivery” effect for facilitated transport of adsorbed protein. This work provided valuable insight into protein adsorption in pSPM brushes and outlined a feasible approach to increasing mass transport in polymer-grafted ion exchange adsorbents.

关键词： Adsorption Polymers Protein Water release Graft density Chain length

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