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18th International Conference on Intelligent Computing (ICIC)

作者： Wang, Shaoshao Zhang, Aihua Wang, Han Bohai Univ Colleag Control Sci & Engn Jinzhou 121013 Liaoning Peoples R China

ISBN: (纸本)9783031138706;9783031138690

In order to solve the problem that the ORB algorithm increases the probability of feature point loss and mis-matching in some cases such as insufficient light intensity, low texture, large camera rotation, etc. This paper introduces an enhanced graphical local adaptive thresholding (EGLAT) feature extraction algorithm, which enhances the front-end real-time input image to make the blurred texture and corners clearer, replacing the existing ORB extraction method based on static thresholding, the local adaptive thresholding algorithm makes the extraction of feature points more uniform and good quality, avoiding the problems of over-concentration of feature points and partial information loss. Comparing the proposed algorithm with ORB-SLAM2 in a public dataset and a real environment, the results show that our proposed method outperforms the ORB-SLAM2 algorithm in terms of the number of extracted feature points, the correct matching rate and the matching time, especially the matching rate of feature points is improved by 18.7% and the trajectory error of the camera is reduced by 16.5%.

关键词： SLAM adaptive algorithm Image matching Feature extraction Image processing

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Randomized block-coordinate adaptive algorithms for nonconvex optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期121卷

作者： Zhou, Yangfan Huang, Kaizhu Li, Jiang Cheng, Cheng Wang, Xuguang Hussian, Amir Liu, Xin Univ Sci & Technol China Sch Nanotech & Nanob Hefei 230026 Peoples R China Chinese Acad Sci Suzhou Inst Nanotech & Nanob SINANO Suzhou 215123 Peoples R China Duke Kunshan Univ Data Sci Res Ctr 8 Duke Ave Kunshan 215316 Peoples R China Gusu Lab Mat Sci 388 Ruoshui Rd Suzhou 215123 Jiangsu Peoples R China Edinburgh Napier Univ Sch Comp Edinburgh EH11 4BN Scotland

Nonconvex optimization problems have always been one focus in deep learning, in which many fast adaptive algorithms based on momentum are applied. However, the full gradient computation of high-dimensional feature vector in the above tasks become prohibitive. To reduce the computation cost for optimizers on nonconvex optimization problems typically seen in deep learning, this work proposes a randomized block -coordinate adaptive optimization algorithm, named RAda, which randomly picks a block from the full coordinates of the parameter vector and then sparsely computes its gradient. We prove that RAda converges to a 6-accurate solution with the stochastic first-order complexity of O(1/62), where 6 is the upper bound of the gradient's square, under nonconvex cases. Experiments on public datasets including CIFAR-10, CIFAR-100, and Penn TreeBank, verify that RAda outperforms the other compared algorithms in terms of the computational cost.

关键词： adaptive algorithm Deep model training Nonconvex optimization Randomized block-coordinate

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CALMS: Constrained adaptive Lasso with Multi-directional Signals for latent networks reconstruction

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NEUROCOMPUTING 2025年 630卷

作者： Xing, Zhaoyu Tan, Huaiyu Zhong, Wei Shi, Lei Xiamen Univ Paula & Gregory Chow Inst Studies Econ 422 Siming South Rd Xiamen 361005 Fujian Peoples R China Yunnan Univ Finance & Econ Sch Stat & Math 237 Longquan Rd Kunming 650224 Yunnan Peoples R China Xiamen Univ Dept Stat & Data Sci MOE Key Lab Econometr WISESOE 422 Siming South Rd Xiamen 361005 Fujian Peoples R China

The ubiquity of networks in the real world significantly influences dynamic behaviors, with empirical analysis often relying on known network structures to identify network effects. However, inferring latent network structures based solely on observed data remains a challenging inverse problem. To this end, we introduce a new method, adaptive Lasso with Multi-directional Signals (ALMS), which transforms the network reconstruction problem into a high-dimensional estimation optimization process. ALMS shrinks the coefficients effectively to different directions by adaptive multi-directional penalties, which is able to reduce the estimation bias. Furthermore, we propose a constrained modification, Constrained adaptive Lasso with Multi-directional Signals (CALMS) to improve both the accuracy and the interpretability of the estimated results. Theoretically, the oracle properties of both the ALMS estimator and the CALMS estimator are established. We demonstrate the excellent finite sample performances of both ALMS and CALMS via comprehensive simulation studies and empirical analysis involving the evolutionary ultimatum game, synchronization model and real experimental data.

关键词： Network inferring Compressive sensing Multi-directional penalty Evolutionary game adaptive algorithm

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Condition Assessment of Power Transformers through DGA Measurements Evaluation Using adaptive algorithms and Deep Learning

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ENERGIES 2023年第1期16卷 54-54页

作者： Barkas, Dimitris A. A. Kaminaris, Stavros D. D. Kalkanis, Konstantinos K. K. Ioannidis, George Ch. Psomopoulos, Constantinos S. S. Univ West Attica Dept Elect & Elect Engn GR-12244 Egaleo Greece

Condition assessment for critical infrastructure is a key factor for the wellbeing of the modern human. Especially for the electricity network, specific components such as oil-immersed power transformers need to be monitored for their operating condition. Classic approaches for the condition assessment of oil-immersed power transformers have been proposed in the past, such as the dissolved gases analysis and their respective concentration measurements for insulating oils. However, these approaches cannot always correctly (and in many cases not at all) classify the problems in power transformers. In the last two decades, novel approaches are implemented so as to address this problem, including artificial intelligence with neural networks being one form of algorithm. This paper focuses on the implementation of an adaptive number of layers and neural networks, aiming to increase the accuracy of the operating condition of oil-immersed power transformers. This paper also compares the use of various activation functions and different transfer functions other than the neural network implemented. The comparison incorporates the accuracy and total structure size of the neural network.

关键词： Dissolved Gas Analysis neural networks adaptive algorithm

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Sine and cosine based learning rate for gradient descent method

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APPLIED INTELLIGENCE 2025年第5期55卷 1-28页

作者： Verma, Krutika Maiti, Abyayananda Indian Inst Technol Patna Dept Comp Sci & Engn Bihta 801106 Bihar India

Deep learning networks have been trained using first-order-based methods. These methods often converge more quickly when combined with an adaptive step size, but they tend to settle at suboptimal points, especially when learning occurs in a large output space. When first-order-based methods are used with a constant step size, they oscillate near the zero-gradient region, which leads to slow convergence. However, these issues are exacerbated under nonconvexity, which can significantly diminish the performance of first-order methods. In this work, we propose a novel Boltzmann Probability Weighted Sine with a Cosine distance-based adaptive Gradient (BSCAGrad) method. The step size in this method is carefully designed to mitigate the issue of slow convergence. Furthermore, it facilitates escape from suboptimal points, enabling the optimization process to progress more efficiently toward local minima. This is achieved by combining a Boltzmann probability-weighted sine function and cosine distance to calculate the step size. The Boltzmann probability-weighted sine function acts when the gradient vanishes and the cooling parameter remains moderate, a condition typically observed near suboptimal points. Moreover, using the sine function on the exponential moving average of the weight parameters leverages geometric information from the data. The cosine distance prevents zero in the step size. Together, these components accelerate convergence, improve stability, and guide the algorithm toward a better optimal solution. A theoretical analysis of the convergence rate under both convexity and nonconvexity is provided to substantiate the findings. The experimental results from language modeling, object detection, machine translation, and image classification tasks on a real-world benchmark dataset, including CIFAR10, CIFAR100, PennTreeBank, PASCALVOC and WMT2014, demonstrate that the proposed step size outperforms traditional baseline methods.

关键词： Vanishing gradient Boltzmann probability Local minima adaptive algorithm Neural network Stochastic gradient descent

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Anisotropic adaptive Finite Elements for a p-Laplacian Problem

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COMPUTATIONAL METHODS IN APPLIED MATHEMATICS 2025年第2期25卷 487-509页

作者： Passelli, Paride Picasso, Marco Ecole Polytech Fed Lausanne Inst Math CH-1015 Lausanne Switzerland

The p-Laplacian problem -del & sdot;((mu + |del u|(p-2))del u) = f is considered, where mu is a given positive number. An anisotropic a posteriori residual-based error estimator is presented. The error estimator is shown to be equivalent, up to higher order terms, to the error in a quasi-norm. The involved constants being independent of mu, the solution, the mesh size and aspect ratio. An adaptive algorithm is proposed and numerical results are presented when p=3 . From this model problem, we propose a simplified error estimator and use it in the framework of an industrial application, namely a nonlinear Navier-Stokes problem arising from aluminium electrolysis.

关键词： A Posteriori Error Estimates adaptive algorithm Anisotropic Finite Elements Nonlinear Equation Aluminium Electrolysis

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Comparison of guaranteed lower eigenvalue bounds with three

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2025年 433卷

作者： Carstensen, Carsten Graessle, Benedikt Pirch, Emilie Humboldt Univ Inst Math D-10117 Berlin Germany Univ Zurich Inst Math CH-8057 Zurich Switzerland Friedrich Schiller Univ Jena Inst Math D-07743 Jena Germany

Specially tailored skeletal schemes enable cell and face variables linked with a stabilisation and a fine-tuned parameter can provide guaranteed lower eigenvalue bounds for the Laplacian. This paper briefly presents a unified derivation of skeletal higher-order methods from Carstensen, Zhai, and Zhang (2020), Carstensen, Ern, and Puttkammer (2021), and Carstensen, Gr & auml;ss le, and Tran (2024). It suggests a paradigm shift from conditional to unconditional lower eigenvalue bounds. adaptive mesh-refining leads to optimal convergence rates in computational benchmark examples and underlines the superiority of higher-order methods.

关键词： A posteriori error control Eigenvalues Guaranteed lower bounds adaptive algorithm Competition Higher order HHO Weak Galerkin HDG

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adaptive space-time finite element method for time fractional-order reaction-diffusion equations

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JOURNAL OF APPLIED MATHEMATICS AND COMPUTING 2025年第3期71卷 3153-3178页

作者： Li, Man Ge, Liang Univ Jinan Sch Math Sci Jinan 250022 Peoples R China

In this article, a space-time finite element method (STFEM) for time fractional-order reaction-diffusion equations (TFORDEs) containing fractional derivatives of order alpha is an element of(0,1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha \in (0,1)$$\end{document} with initial data and boundary value. Utilizing the variational formulation, we establish a space-time finite element framework for addressing the problem. Additionally, we derive a posterior error estimators for both spatial and temporal components, and based on these estimators, we develop an adaptive algorithm. The effectiveness of this adaptive algorithm is ultimately demonstrated through a series of numerical experiments.

关键词： Fractional reaction-diffusion Space-time finite element method A posterior error estimators adaptive algorithm

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adaptive fast L 1-2 scheme for solving time fractional parabolic problems

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2025年 179卷 59-76页

作者： Cao, Jiliang Wang, Wansheng Xiao, Aiguo Hunan Univ Sci & Technol Sch Math & Computat Sci Xiangtan 411201 Hunan Peoples R China Shanghai Normal Univ Dept Math Shanghai 200234 Peoples R China Xiangtan Univ Hunan Key Lab Computat & Simulat Sci & Engn Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Hunan Peoples R China

In this paper, we study a posteriori error estimates of the fast L1 -2 scheme for time discretization of time fractional parabolic differential equations. To overcome the huge workload caused by the nonlocality of fractional derivative, a fast algorithm is applied to the construction of the L1 - 2 scheme. Employing the numerical solution obtained by the fast L1 - 2 scheme, a piecewise continuous function approximating the exact solution is constructed. Then, by exploring the error equations, a posteriori error estimates are obtained in different norms, which depend only on the discretization parameters and the data of the problems. Various numerical experiments for the fractional parabolic equations with smooth or nonsmooth exact solutions on different time meshes, including the frequently-used graded mesh, are carried out to verify and complement our theoretical results. Based on the obtained a posteriori error estimates, a time adaptive algorithm is proposed to reduce the computational cost substantially and provides efficient error control for the solution.

关键词： Time fractional parabolic differential equations A posteriori error estimates L1-2 scheme Fast algorithm adaptive algorithm

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Vibration Suppression Based on Improved adaptive Optimal Arbitrary-Time-Delay Input Shaping

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2025年第3期147卷 031009页

作者： Jiang, Ruoyu She, Jinhua Yin, Xiang Wu, Lulu Wang, Feng Kawata, Seiichi China Univ Geosci Res Ctr Intelligent Technol Geoexplorat Hubei Key Lab Adv Control & Intelligent Automat Co Sch AutomatMinist Educ Wuhan 430074 Peoples R China Tokyo Univ Technol Sch Engn Hachioji Tokyo 1920982 Japan North China Univ Technol Sch Elect & Control Engn Beijing 100114 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China North China Univ Technol Sch Elect & Control Engn Beijing 100114 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China

Vibration suppression is important for high-precision motion control because positioning accuracy is an essential figure of merit for a servosystem. The paper presents a vibration-suppression method based on improved adaptive optimal arbitrary-time-delay (IAOAT) input shaping. First, analyzing a conventional optimal-arbitrary-time-delay (OAT) input shaper yields an OAT input shaper with five pulses that completely suppresses two-mode vibrations when the natural frequency and damping ratio are exactly known. Next, embedding an adaptive algorithm in the shaper gives a conventional adaptive optimal arbitrary-time-delay (AOAT) input shaper that handles unknown natural frequencies and damping ratios. Note that the conventional AOAT input shaper contains five pulses that have to be updated, but the delay times of the pulses are prescribed. Then, the IAOAT method extends the symmetry of pulse amplitudes for zero damping ratio to a nonzero case and reduces the number of pulses to three. A recursive-least-squares (RLS) algorithm is devised to update these parameters. This shaper features the smallest number of parameters and high robust performance. Finally, comparisons with other input-shaping methods show the effectiveness and superiority of the developed method over others.

关键词： adaptive algorithm input shaper optimal-arbitrary-time-delay (OAT) input shaping symmetry of pulse amplitudes vibration suppression

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