检索结果-内蒙古大学图书馆

Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network

Journal of Communications and Information Networks 2024年第3期9卷 296-308页

作者： Akshita Gupta Saurabh Jaiswal Martin Maier Vivek Ashok Bohara Anand Srivastava Wirocomm Research Group Department of Electronics&Communication EngineeringIndraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Department of Computer Science&Applied Mathematics Indraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Optical Zeitgeist Laboratory Institute National de la Recherche ScientifiqueMontreal H5A 1K6Canada

The digital twin(DT)is envisaged as a catalyst for pioneering ecosystems of service provision within an immersive environment born from the convergence of virtual and physical ***,DT could enhance the performance of edge-intelligent connected vehicular networks by allocating network resources efficiently based on the key performance indicators(KPIs)of vehicular data ***,this work addresses the key challenge of computation and spectrum resource allocation for vehicular *** allocate the optimal resource allocation,we subdivided the problem into:traffic classification,collective learning,and resource allocation *** order to do so,this paper concentrates on two crucial vehicular applications:brake application and lane-change *** utilize a random forest model to collectively learn vehicular data traffic in the upcoming time ***,a time-ahead resource allocation algorithm is proposed to improve the quality of service(QoS)by intelligently offloading vehicular data traffic to a DT-based integrated fiber-wireless(Fi-Wi)connected vehicular *** evaluate the performance of the resource allocation strategy in terms of resources required by the network alongside the packet loss *** was observed that there was a 44.74%increase in cost as the total computation resources increased from F=50 to 100 GHz,whereas the PLR of the network decreased by 71.43%.

关键词： connected vehicles digital twin edgeintelligence fiber-wireless machine learning resource allocation

来源：评论

学校读者我要写书评

暂无评论

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

引用

Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

来源：评论

学校读者我要写书评

暂无评论

Optimized management of green supply chains by the use of Ant Colonies multi-objective algorithm: The integration of the economic, environmental and social impacts of multimodal transport 8

Optimized management of green supply chains by the use of An...

引用

8th International Conference on Optimization and Applications, ICOA 2022

作者： El Khaldi, Adam Hachimi, Hanaa Sultan Moulay Sliman University Systems Engineering Laboratory Beni Mellal Morocco Sultan Moulay Slimane University Applied Mathematics & Computer Science Systems Engineering Laboratory Beni Mellal Morocco

ISBN: (数字)9781665476812

ISBN: (纸本)9781665476812

In a global context marked by climate change and by peak globalization materializing through highly interconnected and interdependent chains of value. Our subject is naturally aligned in a global dynamic characterized by existential issues and public-level challenges. This motivates us to contribute in this global reflection by proposing concrete and viable responses and measures to safeguard our planet and to perpetuate our heritage for future generations. It is our duty and collective responsibility. Indeed, the balance between environmental, economic and social impacts has become the optimum objective in all contemporary strategies, policies, actions and decisions. This balance is critical in logistics and transport field because it is one of the largest emitters of greenhouse gases directly causing the acceleration of global warming. And also because it's one of the most crucial and sensitive sectors for mankind due to the fact that our survival and comfort depend on it! Our aim is to build a decision aid tool in order to optimally choose a route (also called path) in a multimodal transport network of goods and/or passengers. The system should be as efficient as possible: with a path that causes the least damage and aggression to the environment while being economically and socially beneficial to man. For a multimodal transport network, a mathematical model is established in order to calculate the ecological and socio-economic criteria to be considered‥. Then a multi-objective optimization algorithm is built to find the shortest path by optimizing the defined criteria: An ant colony algorithm is chosen because it is the most optimal and efficient in a complex scenario that takes into account a large number of variable parameters and criteria. Naturally, an implementation on a multimodal transport network is carried out in order to assess the algorithm's performances. Finally, problematic questions are asked in order to incite reflection and explore future perspe

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Predicting Student Success in mathematics Using Machine Learning Techniques: A Comparative Study of Algorithms

Predicting Student Success in Mathematics Using Machine Lear...

引用

Innovative and Intelligent Information Technologies (IC3IT), International Conference on

作者： Zouaoui Samia Zouaoui Nour El Houda Mestour Hareth Computer Science Department University of Batna 2 Batna Algeria LINFI Laboratory of Biskra University Mathematics Department Laboratory of Applied Mathematics University of Biskra Algeria Mathematics Department University of Batna 2 Batna Algeria

ISBN: (数字)9798331541064

ISBN: (纸本)9798331541071

Achieving low results in mathematics is a real and urgent concern, as mathematics is the foundation of science and technology, and any weakness in it reflects negatively on the progress of society in various ways. This study evaluates how different mathematics study methods affect students educational success and achievement. We employed three different machine learning models to analyze data related to mathematics study techniques and student outcomes. These methods included: support vector machine (SVM), A K-nearest neighbors (KNN), and Linear Regression (LR). The results showed that KNN provided the best accuracy rate with 97.6% and an F1-score of 96.7%. These results highlight the significant contribution of forming study groups and trying to cooperate with each other, this factor is considered important and influential in the success of students in mathematics.

关键词： Support vector machines Logistic regression Machine learning algorithms Education Machine learning Nearest neighbor methods Predictive models Prediction algorithms Mathematical models Object recognition

来源：评论

学校读者我要写书评

暂无评论

Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

引用

npj Computational Materials 2023年第1期9卷 733-743页

作者： Evan R.Antoniuk Gowoon Cheon George Wang Daniel Bernstein William Cai Evan J.Reed Materials Science Division Physical and Life Sciences DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA Department of Chemistry Stanford UniversityStanfordCAUSA Department of Applied Physics Stanford UniversityStanfordCAUSA Google Research Mountain ViewCAUSA Department of Physics Stanford UniversityStanfordCAUSA Department of Mathematics Stanford UniversityStanfordCAUSA Department of Computer Science Stanford UniversityStanfordCAUSA Department of Materials Science and Engineering Stanford UniversityStanfordCAUSA

Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials *** this work,we develop a deep learning synthesizability model(SynthNN)that leverages the entire space of synthesized inorganic chemical *** reformulating material discovery as a synthesizability classification task,SynthNN identifies synthesizable materials with 7×higher precision than with DFT-calculated formation *** a head-to-head material discovery comparison against 20 expert material scientists,SynthNN outperforms all experts,achieves 1.5×higher precision and completes the task five orders of magnitude faster than the best human ***,without any prior chemical knowledge,our experiments indicate that SynthNN learns the chemical principles of charge-balancing,chemical family relationships and ionicity,and utilizes these principles to generate synthesizability *** development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

关键词： inorganic crystalline utilize

来源：评论

学校读者我要写书评

暂无评论

Adjoint-Free 4D-Var Methods Via Line Search Optimization For Non-Linear Data Assimilation

arXiv

引用

arXiv 2023年

作者： Nino-Ruiz, Elias D. Diaz-Rodriguez, Jairo Applied Math and Computer Science Laboratory Department of Computer Science Universidad del Norte Colombia Department of Mathematics and Statistics York University Canada

This paper proposes two practical implementations of Four-Dimensional Variational (4D-Var) Ensemble Kalman Filter (4D-EnKF) methods for non-linear data assimilation. Our formulations' main idea is to avoid the intrinsic need for adjoint models in the context of 4D-Var optimization and, even more, to handle non-linear observation operators during the assimilation of observations. The proposed methods work as follows: snapshots of an ensemble of model realizations are taken at observation times, these snapshots are employed to build control spaces onto which analysis increments can be estimated. Via the linearization of observation operators at observation times, a line-search based optimization method is proposed to estimate optimal analysis increments. The convergence of this method is theoretically proven as long as the dimension of control-spaces equals model one. In the first formulation, control spaces are given by full-rank square root approximations of background error covariance matrices via the Bickel and Levina precision matrix estimator. In this context, we propose an iterative Woodbury matrix formula to perform the optimization steps efficiently. The last formulation can be considered as an extension of the Maximum Likelihood Ensemble Filter to the 4D-Var context. This employs pseudo-square root approximations of prior error covariance matrices to build control spaces. Experimental tests are performed by using the Lorenz 96 model. The results reveal that, in terms of Root-Mean-Square-Error values, both methods can obtain reasonable estimates of posterior error modes in the 4D-Var optimization problem. Moreover, the accuracies of the proposed filter implementations can be improved as the ensemble sizes are increased. © 2023, CC BY.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Gradient-free methods for nonconvex nonsmooth stochastic compositional optimization 24

Gradient-free methods for nonconvex nonsmooth stochastic com...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhuanghua Liu Luo Luo Bryan Kian Hsiang Low Department of Computer Science National University of Singapore CNRS@CREATE LTD Singapore School of Data Science Fudan University Shanghai Key Laboratory for Contemporary Applied Mathematics Department of Computer Science National University of Singapore

ISBN: (纸本)9798331314385

Stochastic compositional optimization (SCO) problems are popular in many real-world applications, including risk management, reinforcement learning, and meta-learning. However, most of the previous methods for SCO require the smoothness assumption on both the outer and inner functions, which limits their applications to a wider range of problems. In this paper, we study the SCO problem in that both the outer and inner functions are Lipschitz continuous but possibly nonconvex and nonsmooth. In particular, we propose gradient-free stochastic methods for finding the (δ, ε)-Goldstein stationary points of such problems with non-asymptotic convergence rates. Our results also lead to an improved convergence rate for the convex nonsmooth SCO problem. Furthermore, we conduct numerical experiments to demonstrate the effectiveness of the proposed methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LSTM and Linearized Segmented Model for Short-Term Power Output Forecasting of Wind Turbines

LSTM and Linearized Segmented Model for Short-Term Power Out...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Regis Donald Hontinfinde Amavi G. Hounkpe Houenou Mahugnon G. Azehoun Pazou Faith Y. P. J. Houessou K. B. Audace Didavi Macaire B. Agbomahena Laboratory of Science Engineering and Mathematics University of Abomey (UNSTIM) Abomey Republic of Benin Laboratory of Electronics Telecommunication and Applied Computer Science University of Abomey Calavi (UAC) Lokossa Republic of Benin

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In this paper, an indirect model for predicting wind turbine output power is proposed, based on a LSTM and a mathematical model for wind power estimation. The LSTM model is used to forecast future changes in wind speed and direction, and the linearized segmented model to estimate wind turbine output power based on forecast wind speeds. To train the LSTM model, NASA meteorological data were used for a period from January 1,2015 to December 31, 2022 for a coastal site in Benin. The results obtained showed that the forecasts made by the LSTM model are fairly close to the actual values. Values of 0.36 m/s and 8.1° for the Mean Square Error (RMSE) and 0.92 and 0.71 for the Coefficient of Determination (R2) were obtained for wind speed and direction forecasts respectively. The predicted speeds were then used to estimate the power output of the NE-200S wind turbine.

关键词： Wind speed Estimation Predictive models Wind power generation Mathematical models Generators Wind turbines Wind forecasting Forecasting Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Teaching An Old Dog New Tricks: Porting Legacy Code to Heterogeneous Compute Architectures With Automated Code Translation

arXiv

引用

arXiv 2025年

作者： Nytko, Nicolas Reisner, Andrew Moulton, J. David Olson, Luke N. West, Matthew Department of Computer Science University of Illinois at Urbana-Champaign United States Applied Computer Science Los Alamos National Laboratory New Zealand Applied Mathematics and Plasma Physics Los Alamos National Laboratory New Zealand Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign United States

Legacy codes are in ubiquitous use in scientific simulations;they are well-tested and there is significant time investment in their use. However, one challenge is the adoption of new, sometimes incompatible computing paradigms, such as GPU hardware. In this paper, we explore using automated code translation to enable execution of legacy multigrid solver code on GPUs without significant time investment and while avoiding intrusive changes to the codebase. We developed a thin, reusable translation layer that parses Fortran 2003 at compile time, interfacing with the existing library Loopy (Klöckner 2014) to transpile to C++/GPU code, which is then managed by a custom MPI runtime system that we created. With this low-effort approach, we are able to achieve a payoff of an approximately 2–3× speedup over a full CPU socket, and 6× in multi-node settings. © 2025, CC BY-SA.

关键词： Investments

来源：评论

学校读者我要写书评

暂无评论

Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

引用

IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：