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2019第十一届计算机建模与仿真国际会议

作者： Wenxin Sun Yanlong Zhai Tianhong Bao Muhammad Mudassar Jun Shen Kai Yang School of Computer Science Beijing Institute of Technology School of Computing & Information Technology University of Wollongong Science and Technology on Special System Simulation Laboratory Beijing Simulation Center

Modeling and simulation are essential methods to better understand a complex system in the real world. Many simulation systems have strong demand for involving physical devices or equipment in the system to improve the fidelity. But most of the current cloud based simulation systems are designed for equipment with adequate resources and suffering from high latency for their centralized communication. Edge computing is getting much attention as a new paradigm for distributed systems involving heterogeneous devices and requiring real-time communication. However, edge computing is still in its initial stage and there is no consensus on the edge framework and implementation. To the best of our knowledge, no detailed studies have explored the edge-side simulation and modeling. In this paper, we study and design an edge-side simulation framework, which exploits light-weight microservices architecture to build scalable and real-time LVC(Live, Virtual and Constructive) simulations. Specifically, the application model is designed to illustrate how LVC simulation integrates with edge computing and cloud computing. The autonomous negotiation service and time synchronization services are discussed in details. Though it is a preliminary edge computing framework for LVC simulation, no similar research has been conducted for modeling and simulation using edge computing.

关键词： Edge-side simulation microservices LVC simulation IoT

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Correction to: Bifurcations, chaotic behavior, sensitivity analysis and new optical solitons solutions of Sasa-Satsuma equation

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Nonlinear Dynamics 2024年第17期112卷 15521-15521页

作者： Li, Peiluan Shi, Sairu Xu, Changjin Rahman, Mati ur School of Mathematics and Statistics Henan University of Science and Technology Luoyang People’s Republic of China Longmen Laboratory Luoyang People’s Republic of China Guizhou Key Laboratory of Economics System Simulation Guizhou University of Finance and Economics Guiyang People’s Republic of China School of Mathematical Sciences Jiangsu University Zhenjiang People’s Republic of China Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

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Soil liquefaction assessment by using hierarchical Gaussian Process model with integrated feature and instance based domain adaption for multiple data sources

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AI in Civil Engineering 2022年第1期1卷 50-81页

作者： Hongwei Guo Timon Rabczuk Yanfei Zhu Hanyin Cui Chang Su Xiaoying Zhuang Department of Geotechnical Engineering Tongji University1239 Siping RoadShanghai 200092People’s Republic of China Computational Science and Simulation Technology Institute of PhotonicsLeibniz Universität HannoverAppelstr.1130167 HannoverGermany Shanghai Tunnel Engineering Co. Ltd.No 1099 Wanping South RoadXuhuiShanghai 200032People’s Republic of China Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Tongji University1239 Siping RoadShanghai 200092People’s Republic of China ALISR Laboratory College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh 11543Saudi Arabia State Key Laboratory of Acoustics Institute of AcousticsChinese Academy of SciencesHaidian DistrictNo.21 North 4th Ring RoadBeijing 100190People’s Republic of China.

For soil liquefaction prediction from multiple data sources,this study designs a hierarchical machine learning model based on deep feature extraction and Gaussian Process with integrated domain adaption *** proposed model first combines deep fisher discriminant analysis(DDA)and Gaussian Process(GP)in a unified framework,so as to extract deep discriminant features and enhance the model performance for *** deliver fair evalu-ation,the classifier is validated in the approach of repeated stratified K-fold cross ***,five different data resources are presented to further verify the model’s robustness and *** reuse the gained knowledge from the existing data sources and enhance the generality of the predictive model,a domain adaption approach is formu-lated by combing a deep Autoencoder with TrAdaboost,to achieve good performance over different data records from both the in-situ and laboratory *** comparing the proposed model with classical machine learn-ing models,such as supported vector machine,as well as with the state-of-art ensemble learning models,it is found that,regarding seismic-induced liquefaction prediction,the predicted results of this model show high accuracy on all datasets both in the repeated cross validation and Wilcoxon signed rank ***,a sensitivity analysis is made on the DDA-GP model to reveal the features that may significantly affect the liquefaction.

关键词： Liquefaction Machine learning Deep fisher discriminant analysis Gaussian Process Ensemble methods Domain adaption

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General quantum algorithms for Hamiltonian simulation with applications to a non-Abelian lattice gauge theory

arXiv

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arXiv 2022年

作者： Davoudi, Zohreh Shaw, Alexander F. Stryker, Jesse R. Department of Physics University of Maryland College ParkMD20742 United States Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Joint Center for Quantum Information and Computer Science National Institute of Standards and Technology University of Maryland College ParkMD20742 United States The NSF Institute for Robust Quantum Simulation University of Maryland College ParkMD20742 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

With a focus on universal quantum computing for quantum simulation, and through the example of lattice gauge theories, we introduce rather general quantum algorithms that can efficiently simulate certain classes of interactions consisting of correlated changes in multiple (bosonic and fermionic) quantum numbers with non-trivial functional coefficients. In particular, we analyze diagonalization of Hamiltonian terms using a singular-value decomposition technique, and discuss how the achieved diagonal unitaries in the digitized time-evolution operator can be implemented. The lattice gauge theory studied is the SU(2) gauge theory in 1+1 dimensions coupled to one flavor of staggered fermions, for which a complete quantum-resource analysis within different computational models is presented. The algorithms are shown to be applicable to higher-dimensional theories as well as to other Abelian and non-Abelian gauge theories. The example chosen further demonstrates the importance of adopting efficient theoretical formulations: it is shown that an explicitly gauge-invariant formulation using loop, string, and hadron degrees of freedom simplifies the algorithms and lowers the cost compared with the standard formulations based on angular-momentum as well as the Schwinger-boson degrees of freedom. The loop-string-hadron formulation further retains the non-Abelian gauge symmetry despite the inexactness of the digitized simulation, without the need for costly controlled operations. Such theoretical and algorithmic considerations are likely to be essential in quantumly simulating other complex theories of relevance to nature. © 2022, CC BY.

关键词： Hamiltonians

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DeepCP: Deep learning driven cascade prediction based autonomous content placement in closed social network

arXiv

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arXiv 2020年

作者： Wu, Qiong Wu, Muhong Chen, Xu Zhou, Zhi He, Kaiwen Chen, Liang School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion Department of Financial Technology Tencent Inc. Shenzhen China

—Online social networks (OSNs) are emerging as the most popular mainstream platform for content cascade diffusion. In order to provide satisfactory quality of experience (QoE) for users in OSNs, much research dedicates to proactive content placement by using the propagation pattern, user’s personal profiles and social relationships in open social network scenarios (e.g., Twitter and Weibo). In this paper, we take a new direction of popularity-aware content placement in a closed social network (e.g., WeChat Moment) where user’s privacy is highly enhanced. We propose a novel data-driven holistic deep learning framework, namely DeepCP, for joint diffusion-aware cascade prediction and autonomous content placement without utilizing users’ personal and social information. We first devise a time-window LSTM model for content popularity prediction and cascade geo-distribution estimation. Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users’ QoE. We conduct extensive experiments using cascade diffusion traces in WeChat Moment (WM). Evaluation results corroborate that the proposed DeepCP framework can predict the content popularity with a high accuracy, generate efficient placement decision in a real-time manner, and achieve significant content access latency reduction over existing schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Social aspects

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Identifying Climatological Interactions in Global Influenza Across Temperate and Tropical Regions 9th

Identifying Climatological Interactions in Global Influenza ...

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9th International Conference on Mobile Wireless Middleware, Operating Systems and Applications, MOBILWARE 2020

作者： Yuan, Zhilu Tang, Shengjun Huang, Qiuyang Zhang, Chijun Ertem, Zeynep Du, Zhanwei Bai, Yuan Research Institute for Smart Cities and Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services School of Architecture and Urban Planning Shenzhen University Shenzhen China College of Computer Science and Technology Jilin University Changchun130012 China School of Management Science and Information Engineering Jilin University of Finance and Economics Changchun130117 China Marshall School of Business The University of Southerm California California United States Key Laboratory of Urban Land Resources Monitoring and Simulation MNR Shenzhen China

ISBN: (纸本)9783030622046

Recently, global epidemic models that uses climatological factors have been shown to explain influenza activities for both temperate and tropical regions. In this paper, we extend these global models by including interactions of climatological factors. We find that countries in Europe and Australia have higher forecast skill, indicating the stronger relationship of influenza with climatological factors, than regions in other continents. The influenza activities of 47 (83%) countries can be explained with a closer match using multi-factor interactions along with original factors than only using the original factors. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Tropics

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Hybrid optimization algorithm of particle swarm optimization with lagrangian relaxation for solving the multidimensional knapsack problem 2nd

Hybrid optimization algorithm of particle swarm optimization...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Luo, Jinyan Lin, Geng Zhang, Fuquan Xu, Lin Department of Mathematics Minjiang University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Digital Performance and Simulation Technology Laboratory School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783030045845

A hybrid algorithm that integrates PSO with Lagrangian relaxation is proposed for solving the multidimensional knapsack problem (MKP). An efficiency measure for MKP based on the LR dual information is defined to combine the object function and the constraints of the MKP together. The efficiency measure is used to determine the core problem for MKP with the aim of reducing the problem scale. Then a hybrid algorithm combines the Quantum Particle Swarm Optimization with a local search method is presented to solve the core problem. Numerical experiments are made on certain knapsack problems and computational results show that the proposed algorithm is very promising. © Springer Nature Switzerland AG 2019.

关键词： Efficiency

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Complementary Coordinated Dispatch of Multi-energy Systems Considering Power Flexibility Margin

Complementary Coordinated Dispatch of Multi-energy Systems C...

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2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

作者： Xiao, Bai Xiao, Zhiwen Yan, Gangui Jiang, Zhuo Wang, Maochun Yang, Hongzhi Dong, Ling Key Laboratory of Modern Power System Simulation and Control Renewable Energy Technology Northeast Electric Power University Jilin China School of Computer Science and Technology Beihua University Jilin China State Grid Qinghai Electric Power Company Xining China

ISBN: (纸本)9781728135205

In the light of wind and photovoltaic curtailment caused by the lack of flexibility in multi-energy power systems, a method for measuring the flexibility margin of power source is proposed. Aiming at the lowest cost of fuel and atmospheric emission control, a coordinated optimal dispatching model of wind, photovoltaic, hydropower and thermal was constructed. Flexibility balance constraints are introduced into the model, and the model is divided into three dispatching layers: non- water renewable energy, hydropower and thermal power. The net load is updated step by step. Heuristic algorithm is used to determine the thermal power unit combination and particle swarm optimization algorithm is improved to optimize the unit output. Taking a provincial power grid in China as an example, the calculation results show that the model and method can not only as far as possible utilize renewable energy, but also reduce system redundancy and environmental pollution. © 2019 IEEE.

关键词： Heuristic algorithms

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BLAC: A Named Entity Recognition Model Incorporating Part-of-Speech Attention in Irregular Short Text

BLAC: A Named Entity Recognition Model Incorporating Part-of...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Ming Zhu Huakang Li Xiaoyu Sun Zhuo Yang School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Urban Land Resources Monitoring and Simulation Shenzhen China Suzhou Privacy Information Technology Company Suzhou China School of Computers Guangdong University of Technology Guangzhou China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

Irregular text refers to text with incomplete sequence information of the sentence or text that does not meet normal grammatical specifications, such as Weibo text. Existing named entity recognition algorithms recognize such text, the effect is poor due to the lack of context information. Because the attention mechanism has advantages in obtaining contextual information, we merge part-of-speech attention with the BI-LSTM-CRF model and propose a BLAC model. We tested on several public datasets and compared the results with the basic model BI-LSTM-CRF. The results show that the method we proposed has a certain improvement in the entity recognition of irregular short text.

关键词： Hidden Markov models Text recognition Task analysis Computational modeling Neural networks Deep learning Data models

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PARDA: A dataset for scholarly PDF document metadata extraction evaluation 14th

PARDA: A dataset for scholarly PDF document metadata extract...

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Fan, Tiantian Liu, Junming Qiu, Yeliang Jiang, Congfeng Zhang, Jilin Zhang, Wei Wan, Jian School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9783030129804

Metadata extraction from scholarly PDF documents is the fundamental work of publishing, archiving, digital library construction, bibliometrics, and scientific competitiveness analysis and evaluations. However, different scholarly PDF documents have different layout and document elements, which make it impossible to compare different extract approaches since testers use different source of test documents even if the documents are from the same journal or conference. Therefore, standard datasets based performance evaluation of various extraction approaches can setup a fair and reproducible comparison. In this paper we present a dataset, namely, PARDA(Pdf Analysis and Recognition DAtaset), for performance evaluation and analysis of scholarly documents, especially on metadata extraction, such as title, authors, affiliation, author-affiliation-email matching, year, date, etc. The dataset covers computer science, physics, life science, management, mathematics, and humanities from various publishers including ACM, IEEE, Springer, Elsevier, arXiv, etc. And each document has distinct layouts and appearance in terms of formatting of metadata. We also construct the ground truth metadata in Dublin Core XML format and BibTex format file associated this dataset. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Metadata

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