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检索条件"任意字段=3rd International Conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2023"

共 90 条记录，以下是71-80 订阅

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Spatial Optimization Information Security Algorithm for UAV Cluster Oriented to RSA 3

Spatial Optimization Information Security Algorithm for UAV ...

引用

2021 3rd international conference on computer modeling, simulation and Algorithm, CMSA 2021

作者： Luo, Guilan Zhu, Yuanbo Wang, Shuailin Li, Zhongmu Zhang, Mei Wang, Xi School of Mathematics and Computer Science Dali University Dali671003 China Institute for Astronomy and History of Science and Technology Dali University Dali671003 China

In the face of the limited communication and computing capacity of UAV network in complex environment, in order to improve the operating efficiency of UAV cluster information security algorithm, this paper proposes a spatial optimization information security algorithm for Public Key Cryptography (RSA). Firstly, the UAV location and cluster space optimization algorithm (CA-PSO) is used to obtain the UAV location. Secondly, the optimal particle of UAV swarm is found through spatial optimization. Then the optimal particle is loaded with the UAV Cluster Information Security Optimization Algorithm (RSA-UAV). Finally, the UAV network simulation process is given. Through algorithm simulation and performance analysis, it is found that the positioning accuracy and convergence of the loaded information security algorithm are better after the improved algorithm space optimization. © Published under licence by IOP Publishing Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Multi-factor network modeling and simulation analysis of time-series dynamic and static climate 3

Multi-factor network modeling and simulation analysis of tim...

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3rd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Luo, Guilan Hu, Anshun Wang, Caikui Ma, Xin Fang, Lianbiao Liu, Xuan School of Mathematics and Computer Dali University Yunnan Dali671003 China Academician Wang Jingxiu Workstation Dali University Yunnan Dali671003 China

With the development of complex networks and big data disciplines, complex networks and big data are widely used in various fields to reveal the changing characteristics of things. This paper applies the method of complex network to climatology, explores the complex interaction of various climatic factors in China's regional climate system with the method of complex network, selects the meteorological big data of 288 meteorological stations in China from 1984 to 2016, defines the meteorological stations and connecting network under various climatic factors as a single factor climate network, and adds the connecting edge between meteorological stations with different climatic factors on this basis, Then the multi-factor climate network is constructed by the network characteristic parameters. The analysis of correlation and stability shows that the correlation of climate network from strong to weak is spring, autumn, winter and summer;In the process of 33 years of time series change, the correlation of climate network shows a weak downward trend, with strong stability and good viability. © Published under licence by IOP Publishing Ltd.

关键词： Complex networks

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JUSMAR-MANS: A platform software for compressible multimedium flow large-scale numerical simulation on JASMIN framework 3

JUSMAR-MANS: A platform software for compressible multimediu...

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3rd international conference on Defence Technology, ICDT 2022

作者： Yu, Y.Q. Liu, N. Zhang, P. Zhang, A.Q. CAEP Software Center for High Performance Numerical Simulation Beijing China Institute of Applied Physics and Computational Mathematics Beijing China

A new platform software named JUSMAR-MANS based on JASMIN was developed for the massively parallel numerical simulation on compressible multimedia multiphase flow under complex conditions. Different from the traditional "chimney"program research and development (r & d) pattern, the MANS who adopted the "platform"r & d pattern provides a way by which the users can rapidly and conveniently customize personalized high-credibility program of multimedium without considering in numerical discrete and parallel computing. MANS has the following advantages: 1) Decoupling of disciplines: In architecture design, physical modeling, numerical discreting, parallel computing are separated from each other and concept shielding so that the high barriers of program r & d caused by interdisciplinary are broken down. 2) Expandability: enables to easily extend new physical models and numerical schemes based on single point/element interfaces. 3) Rapid customization: enable to quickly assemble personalized program based on various models and algorithms libraries integrated by MANS. Based on MANS, it only takes serial code within a few days to obtain a well-posed large - scale multimedium simulation, which greatly reduce the complexity of software r & d for compressible multimedium flow. © Published under licence by IOP Publishing Ltd.

关键词： Numerical models

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Formation of Probabilistic Thinking of Students Through computer simulation on the Example of Studying Distributions with Heavy Tails

Formation of Probabilistic Thinking of Students Through Comp...

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Technology Enhanced Learning in Higher Education (TELE), international conference on

作者： Elena Kuznetsova Natalia Zhbanova Tatiana Fomina Margarita Karlova Faculty of Automation and Informatics Lipetsk State Technical University Lipetsk Russia Institute of Natural Mathematical and Technical Sciences Lipetsk State Pedagogical University Lipetsk Russia

The article considers the possibilities of integrating computer modeling into the process of the formation of probabilistic thinking in teaching mathematics students. We give an example of studying the features of hea...

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Construction and research of industrial production line simulation platform based on digital twin 3

Construction and research of industrial production line simu...

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3rd international conference on Advanced Manufacturing Technology and Manufacturing Systems, ICAMTMS 2024

作者： Li, Siying Zhu, Ling Li, Jianle Qian, Zhehao Liu, Guangyu Fan, Weiguo School of Humanities and Communication Zhejiang University of Finance and Economics Hangzhou China School of Information Management and Artificial Intelligence Zhejiang University of Finance and Economics Hangzhou China Key Laboratory of IoT and Information Fusion Technology School of Automation Hangzhou Dianzi University Hangzhou China Tippie College of Business University of Iowa Iowa United States

ISBN: (纸本)9781510681804

To address the problems such as poor real-time performance, difficulty in supervision, and low accuracy in the production process of industrial electronic equipment, an industrial production line simulation platform based on digital twin is proposed in this paper. It studies the timeliness problem of transforming the physical space into the virtual space. It uses a 2D simulation algorithm based on matrix transformation to construct online simulation quickly, configuration and other production monitoring and large screen visualization scenarios of automated production workshop, so as to improve production efficiency. At present, the simulation platform has been applied in the industrial production lines of the instrument and meter industry with good performance. © 2024 SPIE.

关键词： Matrices computer simulations Visualization Design Virtual reality Data acquisition Industry Mathematical optimization Systems modeling Visual process modeling

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Intelligent RGV Dynamic Scheduling Virtual simulation Technology Based on Machine Learning 3

Intelligent RGV Dynamic Scheduling Virtual Simulation Techno...

引用

3rd international conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Wang, Jianghan Qi, Xiaojing School of Communication Engineering and Internet of Things Chongqing Telecommunication Polytechnic College Chongqing402247 China

With the development of workshop automation, the complexity of RGV (Rail Guided Vehicle) dynamic scheduling schemes using virtual simulation technology is increasing. For the widely valued intelligent machining systems, machine learning based optimization algorithms can effectively respond to the increasingly complex RGV dynamic intelligent scheduling. In the whole model construction, how to complete the modular design of the intelligent processing system and optimize the solution is the key problem that needs to be solved urgently at present. This paper studied the use of particle swarm optimization to design the RGV dynamic scheduling model, aiming to improve the material processing production efficiency of RGV dynamic scheduling and reduce the system failure rate. Through problem modeling, solution and simulation experiment analysis, this paper applied particle swarm optimization based on machine learning, combined with RGV structure modular design and task parameter test set samples. According to the data results, the following conclusions can be drawn from the discussion. Under the background of intelligent logistics system, the RGV dynamic scheduling model using particle swarm optimization had higher material processing production efficiency than the traditional scheduling method in all job test samples, and the average increase was 13.25%. Meanwhile, in terms of system failures, optimization algorithms were better than traditional scheduling methods, with an average reduction of 4.6%. This shows that the RGV dynamic scheduling model based on particle swarm optimization has a better practical application effect. © 2023 Elsevier B.V.. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Asymptotic properties of solution and difference scheme for one nonlinear integro-differential modexsl 1

引用

3rd international conference on Applications of mathematics and Informatics in Natural Sciences and Engineering, AMINSE 2017

作者： Jangveladze, Temur Kiguradze, Zurab I. Vekua Institute of Applied Mathematics of I. Javakhishvili Tbilisi State University Georgian Technical University 2 University street Tbilisi0186 Georgia Department of Electrical and Computer Engineering Electromagnetic Compatibility Laboratory Missouri University of Science and Technology 4000 Enterprise Drive RollaMO65409 United States

ISBN: (数字)9783030104191

ISBN: (纸本)9783030104184

One type of integro-differential systems arising in mathematical modeling of the process of penetration of the magnetic field into a substance is studied. The model is based on the system of Maxwell equations. Uniqueness and large time behavior of solution of the corresponding initial-boundary value problem for the aforementioned model are given. Convergence of the fully discrete scheme is proved. A wide class of nonlinearity is studied. © Springer Nature Switzerland AG 2019.

关键词： Maxwell equations

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modeling and simulation of High Frequency and High Precision Multi Axis Cooperative Control Based on Reinforcement Learning

Modeling and Simulation of High Frequency and High Precision...

引用

computer, Control and Robotics (ICCCR), international conference on

作者： Haiyu Wang Zhuang Fu Jun Yan Zuohao Hua Yisheng Guan School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China School of Electromechanical Engineering Guangdong University of Technology Guangzhou China

Reinforcement learning has attracted much attention in the field of mechanical control, but it has rarely been applied in projects requiring high precision and short control cycle. In order to realize the high frequency and high precision control of multi axis cooperative complex mechanism, this paper decouples and virtualizes the control system of a structure consisting of four valves, infers commands and send control signals at 500Hz, and controls the displacement precisely. The author modified the Proximal Policy Optimization algorithm and made some improvements in the updating rules and training environment, such as batch advantage normalization, attenuation of learning rate, strategy entropy reward, speed control reward, and addition of noise. Finally, the performance of the algorithm exceeded that of PID, ray-PPO in the simulation.

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Dynamic iterative imputation algorithm based on rough fuzzy C-Means clustering 3

Dynamic iterative imputation algorithm based on rough fuzzy ...

引用

2023 3rd international conference on Computational modeling, simulation and Data Analysis, CMSDA 2023

作者： Gong, Zheng Yan, Chun Liu, Wei Mathematics and System Science Institute Shandong University of Science and Technology Qingdao Shandong266590 China Computer Science Institute Shandong University of Science and Technology Qingdao Shandong266590 China

Missing data is a very common phenomenon in daily life. To mitigate its adverse impact on data analysis, this paper proposes a dynamic iterative imputation algorithm based on the rough fuzzy C-means algorithm. The algorithm incorporates dynamic approximations to partition and iterate the cluster assignment of missing sample points, utilizing information from complete sample points within the same cluster for iterative imputation. The imputation results are evaluated by using MAE and RMSE metrics, and the optimal number of iterations and imputation values are determined by minimizing these metrics. To verify the effectiveness of the proposed algorithm, 6 UCI public datasets are selected for imputation tests, and the change curves of metric value with iterations are generated under different missing proportions in each dataset. The optimal imputation result is obtained based on the lowest point of the curve. Subsequently, the performance of the proposed algorithm is compared with three cluster-based and five machine learning-based imputation algorithms. The results demonstrate the significant superiority of the proposed algorithm over the other methods, achieving a more approximate imputation of missing values in the dataset. © Published under licence by IOP Publishing Ltd.

关键词： Statistical tests

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A Review on 3D Convolutional Neural Network

A Review on 3D Convolutional Neural Network

引用

Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Chuqi Wang Department of Mathematics and Statistics McGill University Montréal Canada

The convolutional neural network (CNN) is a potent and popular neural network types and has been crucial to deep learning in recent years. A standard CNN which is known as 2-dimensions CNN was first proposed to solve image classification problems at first. With advancements in science and technology, as well as the growth of the internet, tasks involving video analysis have received increased attention in recent years. As a result, it was suggested that a 3-dimensions convolutional neural network (3D CNN) be applied to tackle 3D image processing or video analysis. 3D CNN is primarily used in the field of human activity recognition, video classification, medical imaging detection, and so on. This paper offers a succinct introduction to the general architecture and representative models of 3D CNN, contrasts the variations between 2D CNN and 3D CNN, explores the widely used models derived from 3D CNN, and displays the application outcomes of 3D CNN.

关键词： Solid modeling Analytical models Three-dimensional displays Two dimensional displays Neural networks Power electronics Convolutional neural networks

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