Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation最新文献_第3页

Towards Discrete-Event, Aggregating, and Relational Control Interfaces for Traffic Simulation 面向交通仿真的离散事件、聚合和关系控制接口

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3591116

Zhuoxiao Meng, Anibal Siguenza-Torres, Mingyue Gao, Margherita Grossi, Alexander Wieder, Xiaorui Du, S. Bortoli, C. Sommer, Alois Knoll

{"title":"Towards Discrete-Event, Aggregating, and Relational Control Interfaces for Traffic Simulation","authors":"Zhuoxiao Meng, Anibal Siguenza-Torres, Mingyue Gao, Margherita Grossi, Alexander Wieder, Xiaorui Du, S. Bortoli, C. Sommer, Alois Knoll","doi":"10.1145/3573900.3591116","DOIUrl":"https://doi.org/10.1145/3573900.3591116","url":null,"abstract":"The use of IoT and AI/ML to extract insights for Data-Driven Decision-Making (DDDM) in Intelligent Traffic Systems (ITS) is becoming increasingly popular. While simulation is a cost-effective and safe way to evaluate such approaches, existing simulators are often impractical due to inefficient control interfaces. In this work, we propose a Discrete-Event, Aggregating, and Relational Control Interfaces (DAR-CI) framework for achieving efficient traffic management simulations through a coupled approach. It enables a non-blocking interaction mode based on a discrete-event synchronization architecture. The overhead caused by data exchange is substantially reduced by supporting the direct retrieval of temporal metrics, data batch processing and customized in-situ aggregation. Combined with flexible, extendable, easy-to-understand, and implementation-friendly semantic specifications, we propose DAR-CI to serve as a universal tool for the traffic simulation community, taking the use and control of traffic simulation to a new level. A proof-of-concept study on the simulation of an adaptive traffic light control system demonstrates a 9.53X speedup compared to TraCI, a widely used protocol for controlling traffic simulators.","PeriodicalId":246048,"journal":{"name":"Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"4 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120926776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards accessible Parallel Discrete Event Simulation of Spiking Neural Networks 尖峰神经网络可达并行离散事件仿真研究

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3593637

Adriano Pimpini

引用次数: 0

Towards a Performance-Aware Partitioning Algorithm for Cloud-Based Microscopic Vehicle Traffic Simulations 基于云的微观车辆交通模拟的性能感知划分算法

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3593629

Anibal Siguenza-Torres, Wentong Cai, Alois Knoll

引用次数: 0

Zero Lookahead? Zero Problem. The Window Racer Algorithm 零超前?零的问题。Window Racer算法

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3591115

Philipp Andelfinger, Till Köster, A. Uhrmacher

{"title":"Zero Lookahead? Zero Problem. The Window Racer Algorithm","authors":"Philipp Andelfinger, Till Köster, A. Uhrmacher","doi":"10.1145/3573900.3591115","DOIUrl":"https://doi.org/10.1145/3573900.3591115","url":null,"abstract":"Synchronization algorithms for parallel simulation struggle to attain speedup if the simulation entities are tightly coupled and their interactions are difficult to predict. Window Racer is a novel parallel synchronization algorithm for shared-memory architectures specifically targeted toward attaining speedup in these challenging cases. The key idea is to speculatively process sequences of dependent events even across partition boundaries through fine-grained locking and low-overhead rollbacks, while negotiating a global synchronization window that rules out transitive rollbacks. In performance measurements using a variant of the PHold benchmark model, Window Racer outperforms an established implementation of the Time Warp algorithm in model configurations where events are often scheduled with near-zero delay. In an ablation study, we pinpoint the performance impact of our algorithm’s individual features by reducing Window Racer to two existing algorithms. We further study the algorithm’s ability to attain speedup in simulations of bio-chemical reaction networks, a particularly challenging class of simulations with tightly coupled state transitions.","PeriodicalId":246048,"journal":{"name":"Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116402817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Learning to Calibrate Hybrid Hyperparameters: a Study on Traffic Simulation 学习校正混合超参数:交通仿真研究

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3591113

Wanpeng Xu, Hua Wei

引用次数: 0

Automatic Model Generation and Data Assimilation Framework for Cyber-Physical Production Systems 信息物理生产系统的自动模型生成和数据同化框架

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3591112

Wen Jun Tan, Moon Gi Seok, Wentong Cai

{"title":"Automatic Model Generation and Data Assimilation Framework for Cyber-Physical Production Systems","authors":"Wen Jun Tan, Moon Gi Seok, Wentong Cai","doi":"10.1145/3573900.3591112","DOIUrl":"https://doi.org/10.1145/3573900.3591112","url":null,"abstract":"The recent development of new technologies within the Industry 4.0 revolution drives the increased digitization of manufacturing plants. To effectively utilize the digital twins, it is essential to guarantee a correct alignment between the physical system and the associated simulation model along the whole system life cycle. Data assimilation is frequently used to incorporate observation data into a running model to produce improved estimates of state variables of interest. However, it assumes a closed system and cannot handle structural changes in the system, e.g., machine breakdown. Instead of combining the observation data into an existing model, we aim to automatically generate the model concurrently with the data assimilation procedure. This can reduce the time and cost of building the model. In addition, it can generate a more accurate model when sudden operational changes are not reflected at the higher planning levels. Component-based model generation approach is used with the application of data and process mining techniques to generate a complete process model from the data. A new data assimilation method is proposed to iteratively generate new models based on the arrival of further data. Each model is simulated to obtain the system performance, which will be compared to the real system performance to select the best-estimated model. Identical twin experiments of a wafer-fab simulation are conducted under different scenarios to evaluate the feasibility of the proposed approach.","PeriodicalId":246048,"journal":{"name":"Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125344044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach 超视距空战的自主智能体:一种深度强化学习方法

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-04-19 DOI: 10.1145/3573900.3593631

Joao P. A. Dantas, M. Maximo, Takashi Yoneyama

引用次数: 1

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation 2023年ACM SIGSIM高级离散仿真原理会议论文集

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 1900-01-01 DOI: 10.1145/3573900

引用次数: 0