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Design Patterns for Multilevel Modeling and simulation

Design Patterns for Multilevel Modeling and Simulation

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international workshop/Symposium on distributed simulation and real-time applications (DS-RT)

作者： Luca Serena Moreno Marzolla Gabriele D'Angelo Stefano Ferretti Department of Computer Science and Engineering University of Bologna Italy Center for Inter-Department Industrial Research ICT University of Bologna Italy Department of Pure and Applied Sciences University of Urbino Carlo Bo Italy

Multilevel modeling and simulation (M&S) is becoming increasingly relevant due to the benefits that this methodology offers. Multilevel models allow users to describe a system at multiple levels of detail. From one side, this can make better use of computational resources, since the more detailed and time-consuming models can be executed only when/where required. From the other side, multilevel models can be assembled from existing components, cutting down development and verification/validation time. A downside of multilevel M&S is that the development process becomes more complex due to some recurrent issues caused by the very nature of multilevel models: how to make sub-models interoperate, how to orchestrate execution, how state variables are to be updated when changing scale, and so on. In this paper, we address some of these issues by presenting a set of design patterns that provide a systematic approach for designing and implementing multilevel models. The proposed design patterns cover multiple aspects, including how to represent different levels of detail, how to combine incompatible models, how to exchange data across models, and so on. Some of the patterns are derived from the general software engineering literature, while others are specific to the multilevel M&S application area.

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Symmetric distributed applications 8

Symmetric distributed applications

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8th ACM SIGPLAN international workshop on Reactive and Event-Based Languages and Systems, REBLS 2021, co-located with SPLASH 2021

作者： Sant'anna, Francisco Santos, Rodrigo Rodriguez, Noemi Rio de Janeiro State University Brazil Microsoft Brazil PUC-Rio Brazil

ISBN: (纸本)9781450391085

A program is deterministic if multiple re-executions with the same inputs always lead to the same state. Even concurrent instances of a deterministic program should observe identical behavior - -in real time - -if assigned the same set of inputs. In this work, we propose real-time reproducibility for distributed programs. Multiple instances of the same interactive application can broadcast asynchronous inputs and yet conform to identical behavior. Collaborative networked applications, such as watch parties, document editing, and video games can benefit from this approach. We name this class of applications as symmetric distributed applications. Using a standard event-driven API to wait and emit events, programmers write code as if the application executes in a single machine. Our middleware intercepts event generation and synchronizes all instances in a consistent timeline so that receipt is identically reproducible. Not only distributed applications benefit from consistency and determinism but also development and testing can be done in a single instance with the same guarantees. In our experiments, the middleware can handle applications with 25 FPS, distributed in up to 25 nodes over the Internet, with an event latency below 350ms. © 2021 ACM.

关键词： Middleware

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Spatio-Temporal Forecasting for Traffic simulation Framework

Spatio-Temporal Forecasting for Traffic Simulation Framework

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international workshop/Symposium on distributed simulation and real-time applications (DS-RT)

作者： Chengxin Wang Gary Tan Dept of Computer Science National University of Singapore

The recent expansion in sensor deployment throughout cities has produced extensive traffic data, empowering the traffic management center to closely monitor traffic conditions. These sensors generate massive spatio-temporal (ST) data, serving as the resource that can be harnessed to forecast traffic conditions within the city. Accurate forecasting of these conditions is crucial for traffic management centers, which can facilitate a wide range of urban applications, such as intelligent transportation, traffic simulation, and infrastructure planning. In this summary, we focus on leveraging ST traffic data through AI techniques for forecasting applications. We design novel methodologies to extract knowledge from the traffic data, providing support for a wide variety of real-world applications, including traffic simulation. To achieve it, we introduce the ST forecasting algorithms that operate at two distinct levels: the region-based level and the road-based level. These predictions can be then utilized as input to a traffic simulation framework, enabling the generation of proactive and advantageous planning strategies for urban transportation management. At the region-based level, a city is divided into multiple regions, i.e., even grid cells. Our goal is to generate accurate predictions concurrently for all regions within a city. We first investigate the inherent characteristics of traffic data, i.e., periodicity and ST dependency. We then devise a novel periodic residual learning network (PRNet) to better model the periodicity in traffic ST data. PRNet frames traffic condition forecasting as a periodic residual learning problem by modeling the variation between past and future conditions. Compared to directly predicting traffic conditions that are highly dynamic, learning more stationary variation is much easier, which facilitates model training. Also, the learned variation enables the network to produce residuals between future conditions and its corresponding weekl

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FRAME 2022: The 2nd workshop on Flexible Resource and Application Management on the Edge 22

FRAME 2022: The 2nd Workshop on Flexible Resource and Applic...

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31st international Symposium on High-Performance Parallel and distributed Computing (HPDC)

作者： Ferrucci, Luca Coppola, Massimo Kavalionak, Hanna Kontopoulos, Ioannis ISTI Consiglio Nazl Ric Pisa Italy Harokopio Univ Athens Athens Greece

ISBN: (纸本)9781450391993

The 2nd internationalworkshop on Flexible Resource and Application Management on the Edge (FRAME 2022) is dedicated to the so-called Cloud/Edge Continuum, where Cloud and Edge infrastructures can work together to fulfill requirements from a variety of NextGen applications. Clouds provide appropriate levels of performance to large groups of different users, whereas Edge resources act as a first layer of computing capacity that is closer to the user, to reduce the service latency. With respect to Clouds, Edge infrastructures typically are composed of heterogeneous and constrained resources and introduce new challenges from the viewpoint of security, orchestration and resource management. Tackling these new issues calls for innovative combinations of tools and abstractions, where AI and machine learning techniques complement algorithmic orchestration and optimization, bringing about new levels of distributed adaptivity and self-management. As real-time data-driven decisions can be promptly taken on the spot, without the need to wait for data to travel to the Cloud and back, also interactive and time-sensitive services like the immersive data processing of Extended reality (XR) applications can be partially extended toward the edge, thus exploiting a better computation to communication tradeoff and smoother connections to improve their QoE and remote collaboration. The FRAME'22 workshop proceedings are available at: https://***/***?id=3526059.

关键词： network mobility edge storage autonomic management federated learning cloud computing resource management artificial intelligence edge continuum virtualization 3d point cloud cloud federation edge computing machine learning edge orchestration

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Methodological Aspects of Multilevel Modeling and simulation

Methodological Aspects of Multilevel Modeling and Simulation

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international workshop/Symposium on distributed simulation and real-time applications (DS-RT)

作者： Luca Serena Department of Computer Science and Engineering University of Bologna Italy

Multilevel modeling and simulation (M&S) is emerging as a methodology for the study of complex systems that consist of multiple interacting components. While many simulation models are based on a monolithic architecture, with a single building block that reproduces all aspects of the simulation, in multilevel models different sub-components are in charge of reproducing semantically different aspects of the system. The advantages of multilevel M&S are multiple. First, a complex model built upon independent and reusable blocks might be faster to implement, as the development phase can be carried out in parallel, and to update, as changes will affect only a local portion of the model. Then, it is possible to make use of existing implementations, employing models that have already been tested and proved to be efficient for a specific goal, thus saving considerable time for implementation and the testing. Finally, a multilevel approach allows the developers to use the most suitable modeling paradigm for each part of the system. However, significant challenges exist: identifying the “best” partitioning of the model is not always straightforward, and the interaction among sub-models might be an issue. There is no definition of multilevel modeling that is agreed upon by the scientific community [1]. In fact, some authors refer to this concept with multiple terms (e.g., multiscale [2] or multi-resolution [3] modeling), while others use the expression “multilevel modeling” to indicate M&S schemes that do not necessarily require the presence of multiple cooperating models [4]. Specifically, the following types of models are referred as multilevel in the research community: •Models where different components are in charge of representing semantically different elements of a system [5]. •Models where the system is represented at different levels of detail [6]. This scenario occurs when it is computationally infeasible to carry out simulations where the whole system is described

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Collision Detection Algorithm Based on Particle System in Virtual simulation

Collision Detection Algorithm Based on Particle System in Vi...

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Emerging Technology (INCET), international Conference for

作者： Tao Wang Guangdong Mechanical & Electrical Polytechnic Guangzhou China

In recent years, with the rise of virtual reality, distributed interactive simulation and other technologies, collision detection, especially software collision detection, has become a research hotspot. Accurate collision detection plays an important role in improving the authenticity of the virtual environment and enhancing the immersion of the virtual environment. The complexity and real-time of the virtual environment put forward higher requirements for collision detection. Collision detection algorithm is a method used to determine whether two or more objects collide. Collision detection algorithms are usually used in computer games and simulations, such as the simulation of high-speed vehicles on highways. They can be applied in other fields, including virtual reality (VR) applications. Collision detection algorithm uses various mathematical models to determine when two objects collide with each other. The most common collision detection algorithm is based on detecting the intersection of two parallel lines representing objects at different time positions.

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11th Mining Humanistic Data workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international Conference on Artificial Intelligence applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

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11th Mining Humanistic Data workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international Conference on Artificial Intelligence applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

The proceedings contain 35 papers. The special focus in this conference is on Mining Humanistic Data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language Processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-Data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep Learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards realizing 5G Implementation;experimentation Scenarios for Machine Learning-Based Resource Management;efficient Data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise distributed Generation Monitoring and real-time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep Learning Based Facial Expressions An

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real-time simulation OF HULL STRUCTURE USING FINITE ELEMENT IN VIRTUAL realITY applications 40

REAL-TIME SIMULATION OF HULL STRUCTURE USING FINITE ELEMENT ...

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40th ASME international Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Li, Chuntong Wei, Naikun Luo, Xiaomeng Lv, Jianjun Yang, Xuelian Wang, Deyu Shanghai Jiao Tong Univ State Key Lab Ocean Engn Collaborat Innovat Ctr Adv Ship & Deep Sea Explor Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shipbldg Technol Res Inst Shanghai 200032 Peoples R China Shanghai Waigaoqiao Shipbuilding Co Ltd Shanghai 200137 Peoples R China

ISBN: (纸本)9780791885123

Visualization post-processing has become the primary means of awareness and understanding of CAE data intuitively and vividly. Visual analysis can help designers extract meaningful features and results from the complex data quickly and efficiently through intuitive visual images. Virtual reality (VR) -based simulation system can provide experience for the decision-makers and inexperienced workers. We propose a novel solution for real-time Finite Element Analysis (FEA) calculation between wave and hull structure. Firstly, an appropriate CAE system with all the FEA results of the hull is established in a single database. Secondly, based on the Unity 3d platform, effective data transmission technology and three-dimensional virtual models are constructed to achieve an immersive interactive experience. Finally, the third important player is hardware, making the Virtual reality navigation become possible in many different environments. This paper briefly describes the features of this system. The proposed system employs a distributed simulation framework to realize real-time virtual simulation and human-computer interaction (HCI) of hull structures based on the Finite Element (FE) calculations. In addition, the applicability and the accuracy of the proposed virtual simulation method have also been verified by conducting a case study.

关键词： Virtual reality (VR) Finite Element Analysis Visual analysis Hull structure

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distributed Autonomous Coordinated Dispatching Strategy for a Renewable Energy Cluster Considering Space-time Complementarity

Distributed Autonomous Coordinated Dispatching Strategy for ...

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2021 IEEE international Conference on Power Electronics, Computer applications, ICPECA 2021

作者： Han, Yue Luo, Yi Qian, Minhui Huazhong University of Sci. Tec. School of Electrical and Electronic Engineering Wuhan China China Electric Power Research Institute Renewable Energy Research Center Nanjing China

ISBN: (纸本)9781728190037

The fluctuating characteristic of renewable energy generation and its joint cluster-based and decentralized deployment have introduced significant challenges to the dispatching and operation of power systems. Traditionally, centralized optimization model has been used for their dispatch. However, the centralized optimization model is subject to several shortcomings in terms of computational complexity, communication reliability, expansion flexibility, and output volatility. To overcome these issues, a distributed coordinated dispatching strategy is proposed in this paper. Based on the analysis of the space-time characteristic of the renewable generation cluster, an active power dispatching model that considers space-time complementarity was formulated. To solve this model, a distributed interactive computing method in the cluster domain based on the consensus algorithm was designed. The convergence of the proposed strategy in various scenarios is verified by the result of numerical simulation. In addition, the effectiveness of the proposed model in improving the robustness and flexibility of communication in the cluster operation is validated. With the space-time complementarity of the cluster, the fluctuation of cluster output and the impact of real-time random factors on the operation are effectively reduced, leading to cluster-friendly grid integration, local autonomy, and adaptive coordination. © 2021 IEEE.

关键词： Renewable energy resources

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A Monitoring Method of Power Wireless Private Network Based on distributed Big Data Stream Processing 5th

A Monitoring Method of Power Wireless Private Network Based ...

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5th international Conference on Intelligent, interactive Systems and applications, IISA 2020

作者： Zheng, Weijun Liu, Junyu Liu, Zhe Fang, Jinghui Qi, Weiwu Xiao, Quan Jiaxing Power Supply Station State Grid Zhejiang Electric Power Co. Ltd. JiaxingZhejiang314033 China Beijing Fibrlink Communications Co. Ltd. Sihezhuang Road BeijingFengtai100070 China

ISBN: (纸本)9783030637835

Aiming at the problems of slow monitoring speed and low accuracy in the process of power wireless private network monitoring and automated operation and maintenance, a network monitoring method based on distributed big data stream processing is proposed. The monitoring data source of the system node to be tested is collected by sensors to grasp the operation status of the power grid and collect data in real time. At the same time, the Storm computing framework is used to process real-time detection of equipment faults in real time to meet the needs of rapid processing such as power grid status monitoring abnormal detection and fault analysis. simulation experiment results show that the distributed big data stream processing proposed in this paper has a great advantage in the processing of data of electric power wireless private network, which greatly improves the data efficiency of the power grid. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart power grids

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