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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Prabhu, R. Priya, M. Sangeetha Dayana, R. Suresh, M. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Campus Chennai India Saveetha Engineering College Department of Computer Science and Engineering Thandalam Chennai India Jeppiaar Institute of Technology Department of Computer Science and Engineering Sriperumbudur Chennai India R.M.D. Engineering College Kavaraipettai Department of Mathematics Thiruvallur India

ISBN: (纸本)9798350361186

Slums (Informal Settlements) are the most dynamic areas whose geographical locations are inaccurate according to official statistics and maps because of the exponential expansion of urbanization strategies. As a result, a method has to be developed to distinguish between informal settlements and track their spatial patterns. This paper presents an integrated method for detecting slums from satellite images that employs homogeneous urban patches (HUP) and grey level co-occurrence matrix (GLCM)-based image segmentation. In order to extract meaningful spatial features, urban patches are segmented according to their homogeneity and GLCM analysis provides useful texture information about slum areas. The combination of both these GLCM and HUP features allows for the creation of a classification map that accurately identifies and maps slum areas. We utilized WorldView-2 (1.84m) satellite imagery of Madurai to test the effectiveness of proposed HUP approach. The validation process includes rigorous testing against ground truth data, with careful consideration given to the selection of training and testing datasets. The effectiveness of the proposed method is evaluated using metrics including overall accuracy, user accuracy, and producer accuracy Furthermore, the proposed algorithm outperformed existing GLCM-based approaches in terms of accuracy. © 2024 IEEE.

关键词： Image segmentation

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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Computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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Topological properties of Sb(111)surface:A first-principles study

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Chinese Physics B 2022年第4期31卷 603-607页

作者： Shuangxi Wang Ping Zhang Department of Materials Science and Engineering China University of PetroleumBeijing 102249China Institute of Applied Physics and Computational Mathematics Beijing 100088China Center for Applied Physics and Technology Peking UniversityBeijing 100871China

First-principles calculations based on the density functional theory were performed to systematically study the electronic properties of the thin film of antimony in(111)*** considering the spin-orbit interaction,for stoichiometric surface,the topological states keep robust for six-bilayer case,and can be recovered in the three-bilayer film,which are guaranteed by time-reversal symmetry and inverse *** reduced surface doped by non-magnetic Bi or magnetic Mn atom,localized three-fold symmetric features can be ***,band structures show that the non-trivial topological states stand for non-magnetic substitutional Bi atom,while can be eliminated by adsorbed or substitutional magnetic Mn atom.

关键词： first-principles study antimony topological states

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Exploration-Aided Downstream Graph Learning Tasks: A Survey on Exploratory Graph Learning

Exploration-Aided Downstream Graph Learning Tasks: A Survey ...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Yu Hou Won-Yong Shin School of Mathematics and Computing (Computational Science and Engineering) Yonsei University Seoul Republic of Korea

Graph learning is crucial for extracting meaningful information from graph-structured data, enabling effective solutions to various downstream tasks. However, existing methods for solving downstream graph learning tasks often rely on the availability of the graph structure, which may not always be accessible in real-world applications. To overcome this limitation, recent approaches have introduced exploratory learning techniques, which aim to tackle graph learning tasks on graphs with unknown topology. In this article, we provide a comprehensive overview of exploratory graph learning applied to two widely studied graph learning tasks: 1) influence maximization and 2) community detection. We delve into the problem formulation of both tasks concerning graphs with unknown topological information. Additionally, we explore the application of exploratory learning techniques to address these problems effectively.

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A study of geometry teaching mode based on Van Hiele theory and NetPad 23

A study of geometry teaching mode based on Van Hiele theory ...

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7th International Conference on Education and Multimedia Technology, ICEMT 2023

作者： Gao, Jinghui Wang, Ying Ding, Qianhui Xu, Zhangtao Guan, Hao Institute of Computing Science and Technology Guangzhou University China Software Engineering Institute of Guangzhou Guangzhou China School of Mathematics and Statistics of CCNU Central China Normal University China

ISBN: (纸本)9798400709142

It is one of the main goals of the mathematics curriculum reform to cultivate and develop geometric thinking in junior high school students. Geometric thinking skills have a very profound impact on middle school students learning of mathematics. Van Hiele's theory is dedicated to reducing the gap between the level of difficulty defined by the material and the thinking of the students. It provides strong theoretical support for analyzing the development process of students' geometric thinking and evaluating their level of geometric thinking. With the combination of dynamic geometry software (NetPad) and Van Hiele theory, this study proposes a geometry teaching mode for junior high school students' geometric thinking cultivation, which includes five teaching sessions of Interview, Direct Orientation, Explication, Activities, and Integration. An effective teacher-led, student inquiry-based teaching process, it helps students discover geometric patterns, understand geometric properties, and improve their geometric thinking. The effectiveness of the mode was verified by seventh graders using NetPad to explore Euler's theorem. The results verified the effectiveness of this geometry teaching model to a certain extent. On the other hand, it proves that this model can help improve teaching efficiency. The teaching practice provides a program reference for teachers to design a scientific and reasonable geometry lesson. © 2023 ACM.

关键词： Geometry

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Pareto smoothed importance sampling

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 3491-3548页

作者： Aki Vehtari Daniel Simpson Andrew Gelman Yuling Yao Jonah Gabry Department of Computer Science Aalto University Normal Computing Departments of Statistics and Political Science Columbia University Center for Computational Mathematics Flatiron Institute Department of Statistics Columbia University

Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail. This routinely occurs when there are aspects of the target distribution that are not well captured by the approximating distribution, in which case more stable estimates can be obtained by modifying extreme importance ratios. We present a new method for stabilizing importance weights using a generalized Pareto distribution fit to the upper tail of the distribution of the simulated importance ratios. The method, which empirically performs better than existing methods for stabilizing importance sampling estimates, includes stabilized effective sample size estimates, Monte Carlo error estimates, and convergence diagnostics. The presented Pareto $\hat{k}$ finite sample convergence rate diagnostic is useful for any Monte Carlo estimator.

关键词： importance sampling Monte Carlo Bayesian computation diagnostics

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Reinforcement Learning in Cognitive Robots for Autonomous Path Planning 5

Reinforcement Learning in Cognitive Robots for Autonomous Pa...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Das, Archana Poornima, R. Indu Deepa, G. Gokila Sheela, K. Selva Akshya, J. Choudhry, Mani Deepak New Horizon College of Engineering Department of Information Science and Engineering Bengaluru India KGiSL Institute of Technology Department of Information Technology Coimbatore India Ppg Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350389609

Cognitive robots are intelligent systems that learn from their environment, adapt to dynamic changes, and make decisions without a human's direct intervention. This research studies the integration of Reinforcement Learning in cognitive robots with Q-Learning for autonomous path planning. The study addresses the most critical issues in traditional A∗ and Dynamic Programming algorithms by devising path-planning techniques, mainly the lack of adaptability and computational efficiency in real-time and unpredictable environments. Our approach is based on Q-Learning, which is a model-free RL algorithm allowing the robots to find paths autonomously by optimizing their paths and avoiding obstacles. The Q-Learning algorithm provides the possibility of learning optimal policies for the robot through iterative interaction with its environment balanced between exploration and exploitation of the decision process. A reward system is utilized by the proposed model, encouraging the robot to explore shorter, collision-free paths and adjust based on feedback in real time from its surroundings. The model was found to be significantly better than its counterparts. In this context, as indicated, the Q-Learning model runs at 11.5 seconds, faster than A∗ at 18.3 seconds and Dynamic Programming at 16.7 seconds, yet with an accuracy of 94% and was found to have the highest collision avoidance rate at 98%. Additionally, the adaptability of the model about the environment presents a marked difference in terms of path length optimization, having done so with a mean path length of 12.3, compared to approaches or models. The robustness and scalability of the Q-Learning model make it highly applicable in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks

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npj computational Materials 2022年第1期8卷 1886-1898页

作者： Zhenze Yang Markus J.Buehler Laboratory for Atomistic and Molecular Mechanics(LAMM) Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA02139USA Department of Materials Science and Engineering Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA02139USA Center for Computational Science and Engineering Schwarzman College of ComputingMassachusetts Institute of Technology77 Massachusetts AveCambridgeMA02139USA Center for Materials Science and Engineering 77 Massachusetts Ave CambridgeMA02139USA

Structural defects are abundant in solids,and vital to the macroscopic materials ***,a defect-property linkage typically requires significant efforts from experiments or simulations,and often contains limited information due to the breadth of nanoscopic design *** we report a graph neural network(GNN)-based approach to achieve direct translation between mesoscale crystalline structures and atom-level properties,emphasizing the effects of structural *** end-to-end method offers great performance and generality in predicting both atomic stress and potential energy of multiple systems with different ***,the approach also precisely captures derivative properties which strictly observe physical laws and reproduces evolution of properties with varying boundary *** incorporating a genetic algorithm,we then design de novo atomic structures with optimum global properties and target local *** method would significantly enhance the efficiency of evaluating atomic behaviors given structural imperfections and accelerates the design process at the meso-level.

关键词： structural solids crystalline

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A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

A Scalable Real-Time Data Assimilation Framework for Predict...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Yin, Junqi Liang, Siming Liu, Siyan Bao, Feng Chipilski, Hristo G. Lu, Dan Zhang, Guannan Oak Ridge National Laboratory National Center for Computational Science Oak RidgeTN United States Florida State University Department of Mathematics TallahasseeFL United States Oak Ridge National Laboratory Computational Science and Engineering Division Oak RidgeTN United States Florida State University Department of Scientific Computing TallahasseeFL United States Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States

ISBN: (纸本)9798350355543

AI-based foundation models like FourCastNet, GraphCast, ClimaX, and Pangu-Weather are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, e.g., the Local Ensemble Transform Kalman Filter (LETKF);and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasi-geostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations. © 2024 IEEE.

关键词： data assimilation Diffusion models foundation models generative AI high-performance computing uncertainty quantification

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Martingale deep learning for very high dimensional quasi-linear partial differential equations and stochastic optimal controls

arXiv

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arXiv 2024年

作者： Cai, Wei Fang, Shuixin Zhang, Wenzhong Zhou, Tao Department of Mathematics Southern Methodist University United States Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences China Suzhou Institute for Advanced Research University of Science and Technology of China China

In this paper, a highly parallel and derivative-free martingale neural network learning method is proposed to solve Hamilton-Jacobi-Bellman (HJB) equations arising from stochastic optimal control problems (SOCPs), as well as general quasilinear parabolic partial differential equations (PDEs). In both cases, the PDEs are reformulated into a martingale formulation such that loss functions will not require the computation of the gradient or Hessian matrix of the PDE solution, while its implementation can be parallelized in both time and spatial domains. Moreover, the martingale conditions for the PDEs are enforced using a Galerkin method in conjunction with adversarial learning techniques, eliminating the need for direct computation of the conditional expectations associated with the martingale property. For SOCPs, a derivative-free implementation of the maximum principle for optimal controls is also introduced. The numerical results demonstrate the effectiveness and efficiency of the proposed method, which is capable of solving HJB and quasilinear parabolic PDEs accurately in dimensions as high as 10,000. © 2024, CC BY.

关键词： Galerkin methods

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