2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)最新文献_第5页

Implan: Scalable Incremental Motion Planning for Multi-Robot Systems 多机器人系统的可扩展增量运动规划

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2016-04-01 DOI: 10.1109/ICCPS.2016.7479105

I. Saha, Rattanachai Ramaithitima, Vijay R. Kumar, George J. Pappas, S. Seshia

{"title":"Implan: Scalable Incremental Motion Planning for Multi-Robot Systems","authors":"I. Saha, Rattanachai Ramaithitima, Vijay R. Kumar, George J. Pappas, S. Seshia","doi":"10.1109/ICCPS.2016.7479105","DOIUrl":"https://doi.org/10.1109/ICCPS.2016.7479105","url":null,"abstract":"We consider the collision-free motion planning problem for a group of robots using a library of motion primitives. To cope with the complexity of the problem, we introduce an incremental algorithm based on an SMT solver, where we divide the robots into small groups based on a priority assignment algorithm. The priority assignment algorithm assigns priorities to the robots in such a way that the robots do not block the cost-optimal trajectories of the other robots. While the priority assignment algorithm attempts to assign distinct priorities to the robots, the algorithm ends up with assigning the same priority to some robots due to the dependencies among themselves. The algorithm includes the robots with the same priority in the same group. Our incremental algorithm then considers the robot groups one by one based on their priority and synthesizes the trajectories for the group of robots together. While synthesizing the trajectories for the robots in one group, the algorithm considers the higher priority robots as dynamic obstacles, and introduces a minimal delay in executing the cost-optimal trajectories to avoid collision with the higher priority robots. We apply our method to synthesize trajectories for a group of quadrotors in our lab space. Experimental results show that we can synthesize trajectories for tens of robots with complex dynamics in a reasonable time.","PeriodicalId":6619,"journal":{"name":"2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)","volume":"62 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78749504","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: 46

Demo Abstract: Building IoT Applications with Accessors in CapeCode 演示摘要:在CapeCode中使用访问器构建物联网应用

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2016-04-01 DOI: 10.1109/ICCPS.2016.7479096

Marten Lohstroh, Christopher X. Brooks, Edward A. Lee

引用次数: 0

Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems 网络物理能源系统的数据驱动建模、控制和工具

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2016-01-20 DOI: 10.1109/ICCPS.2016.7479093

Madhur Behl, Achin Jain, R. Mangharam

{"title":"Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems","authors":"Madhur Behl, Achin Jain, R. Mangharam","doi":"10.1109/ICCPS.2016.7479093","DOIUrl":"https://doi.org/10.1109/ICCPS.2016.7479093","url":null,"abstract":"Demand response (DR) is becoming important as the volatility on the grid continues to increase. Current DR approaches are either completely manual or involve deriving first principles based models which are extremely cost and time prohibitive to build. We consider the problem of data-driven DR for large buildings which involves predicting the demand response baseline, evaluating fixed DR strategies and synthesizing DR control actions. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large buildings. Our data-driven control synthesis algorithm outperforms rule- based DR by 17% for a large DoE commercial reference building and leads to a curtailment of 380 kW and over $45,000 in savings. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the building's facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. DR-Advisor achieves 92.8% to 98.9% prediction accuracy for 8 buildings on Penn's campus. We compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE's benchmarking data-set for energy prediction.","PeriodicalId":6619,"journal":{"name":"2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)","volume":"12 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2016-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84982334","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: 27

An Unsupervised Spatiotemporal Graphical Modeling Approach to Anomaly Detection in Distributed CPS 分布式CPS异常检测的无监督时空图形建模方法

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2015-12-24 DOI: 10.1109/ICCPS.2016.7479069

Chao Liu, Sambuddha Ghosal, Zhanhong Jiang, S. Sarkar

引用次数: 52

Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering 信息估计器在人机协同信息收集中的深层价值

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2015-12-23 DOI: 10.1109/ICCPS.2016.7479095

Kin Gwn Lore, Nicholas Sweet, Kundan Kumar, N. Ahmed, S. Sarkar

{"title":"Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering","authors":"Kin Gwn Lore, Nicholas Sweet, Kundan Kumar, N. Ahmed, S. Sarkar","doi":"10.1109/ICCPS.2016.7479095","DOIUrl":"https://doi.org/10.1109/ICCPS.2016.7479095","url":null,"abstract":"Effective human-machine collaboration can significantly improve many learning and planning strategies for information gathering via fusion of 'hard' and 'soft' data originating from machine and human sensors, respectively. However, gathering the most informative data from human sensors without task overloading remains a critical technical challenge. In this context, Value of Information (VOI) is a crucial decision- theoretic metric for scheduling interaction with human sensors. We present a new Deep Learning based VOI estimation framework that can be used to schedule collaborative human-machine sensing with efficient online inference and minimal policy hand-tuning. Supervised learning is used to train deep convolutional neural networks (CNNs) to extract hierarchical features from 'images' of belief spaces obtained via data fusion. These features can be associated with soft data query choices to reliably compute VOI for human interaction. The CNN framework is described in detail, and a performance comparison to a feature- based POMDP scheduling policy is provided. The practical feasibility of our method is also demonstrated on a mobile robotic search problem with language-based semantic human sensor inputs.","PeriodicalId":6619,"journal":{"name":"2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)","volume":"13 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2015-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85035119","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: 21

Microgrid Losses: When the Whole Is Greater Than the Sum of Its Parts 微电网损失:当整体大于部分之和

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2015-11-04 DOI: 10.1145/2821650.2821676

Maxim Buevich, Xiao Zhang, Oliver Shih, Dan Schnitzer, Tristan Escalada, Arthur Jacquiau-Chamski, Jon Thacker, Anthony G. Rowe

{"title":"Microgrid Losses: When the Whole Is Greater Than the Sum of Its Parts","authors":"Maxim Buevich, Xiao Zhang, Oliver Shih, Dan Schnitzer, Tristan Escalada, Arthur Jacquiau-Chamski, Jon Thacker, Anthony G. Rowe","doi":"10.1145/2821650.2821676","DOIUrl":"https://doi.org/10.1145/2821650.2821676","url":null,"abstract":"Non-Technical Loss (NTL) represents a major challenge when providing reliable electrical service in developing countries, where it often accounts for 11-15% of total generation capacity [1]. NTL is caused by a variety of factors such as theft, unmetered homes, and inability to pay, which at volume can lead to system instability, grid failure, and major financial losses for providers. In this paper, we investigate error sources and techniques for separating NTL from total losses in microgrids. We adopt and compare two classes of approaches for detecting NTL: (1) model- driven and (2) data- driven. The model-driven class considers the primary sources of state uncertainty including line losses, meter consumption, meter calibration error, packet loss, and sample synchronization error. In the data-driven class, we use two approaches that learn grid state based on training data. The first approach uses a regression technique on an NTL-free period of grid operation to capture the relationship between state error and total consumption. The second approach uses an SVM trained on synthetic NTL data. Both classes of approaches can provide a confidence interval based on the amount of detected NTL. We experimentally evaluate and compare the approaches on wireless meter data collected from a 525-home microgrid deployed in Les Anglais, Haiti. We see that both are quite effective, but that the data-driven class is significantly easier to implement. In both cases, we are able to experimentally evaluate to what degree we can reliably separate NTL from total losses.","PeriodicalId":6619,"journal":{"name":"2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)","volume":"30 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76817740","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: 13

GreenPlanning: Optimal Energy Source Selection and Capacity Planning for Green Datacenters 绿色规划:绿色数据中心的最佳能源选择和容量规划

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2014-06-16 DOI: 10.1145/2591971.2592025

Fanxin Kong, Xue Liu

{"title":"GreenPlanning: Optimal Energy Source Selection and Capacity Planning for Green Datacenters","authors":"Fanxin Kong, Xue Liu","doi":"10.1145/2591971.2592025","DOIUrl":"https://doi.org/10.1145/2591971.2592025","url":null,"abstract":"Cloud service providers such as Microsoft and Google are beginning to power up their datacenters using multiple energy sources. To reduce cost and emission, they incorporate green energy sources into the power supply, while to improve service availability, they back up datacenters using traditional (usually brown) energy sources. However, challenge arises due to distinct characteristics of energy sources used for different goals. How to select optimal energy sources and plan their capacity for constructing datacenters to meet cost, emission and service availability requirement remains to be fully explored. This work provides a holistic solution to address this problem. We present GreenPlanning, a framework to strike a judicious balance among multiple energy sources, grid power and energy storage devices for a datacenter in terms of the above three goals. GreenPlanning investigates different features and operations of a wide spectrum of green and brown energy sources available to datacenters. The framework minimizes the lifetime total cost including both capital and operational cost for a datacenter. We conduct extensive simulations to evaluate GreenPlanning with real-life computational workload and meteorological data traces. Results demonstrate that GreenPlanning can reduce the lifetime total cost and emission by more than 50% compared to traditional configurations, while still satisfying service availability requirement.","PeriodicalId":6619,"journal":{"name":"2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)","volume":"12 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76384969","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: 24

WiP Abstract: Supply-demand Planning Method in cooperation with factory production schedule aimed at the realization of Symbiosis-Autonomous Decentralized System 摘要:以实现共生-自治分散系统为目标的工厂生产计划协同供需计划方法

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2014-04-15 DOI: 10.1109/ICCPS.2014.6843731

I. Yu, Iijima Koichiro, Fukumoto Takashi, Yoshioka Masahiro

引用次数: 1

Real-time adaptive signaling for isolated intersections 孤立交叉口的实时自适应信号

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2013-04-08 DOI: 10.1145/2502524.2502575

Sai Prathyusha Peddi

引用次数: 0

Architecture of a cyberphysical avatar 网络物理化身的架构

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2013-04-08 DOI: 10.1145/2502524.2502550

Song Han, A. Mok, Jianyong Meng, Yi-Hung Wei, Pei-Chi Huang, Xiuming Zhu, L. Sentis, Kwan-Suk Kim, R. Miikkulainen, J. Menashe

引用次数: 23