2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)最新文献_第10页

Exploiting Symmetry in Dependency Graphs for Model Reduction in Supervisor Synthesis 利用依赖图的对称性进行监督综合模型约简

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216953

L. Moormann, J. V. D. Mortel-Fronczak, W. Fokkink, J. Rooda

引用次数: 1

A Generic Online Parameter (Re-)calibration Framework Using PPL 一种基于PPL的通用在线参数(重)校准框架

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216771

Seyed Mahdi Shamsi, N. Napp

{"title":"A Generic Online Parameter (Re-)calibration Framework Using PPL","authors":"Seyed Mahdi Shamsi, N. Napp","doi":"10.1109/CASE48305.2020.9216771","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9216771","url":null,"abstract":"Parameter calibration is a burdensome yet essential part of robotics development which traditionally was done through manual calibration routines. Over the recent years, many authors have proposed to automatically calibrate parameters directly from the input data, during the operation of the robot. While the majority of methods are discussed within the context of a specific application and benefit from the particularity of the problem, some generic approaches are proposed that are applicable to a wide spectrum of problems. However in practice, they require re-implementation and customization every time applied to a different domain, due to coupling of formulations with the model, e.g. linearization steps, matrix decomposition, closed form solving, etc. In this paper, we exploit the expressiveness of general purpose probabilistic programming languages (PPLs) to build a generic online calibration framework that can estimate the parameters of arbitrary robotic systems during operation. The proposed approach, based on Bayes filter and Monte Carlo methods, only requires model specification and works as a black-box otherwise. Hence, it spans the generality to the implementation aspect of the calibration problem which facilitates a range of new applications, e.g. fast prototyping of arbitrary robots. We show a short PPL program is capable of calibrating kinematic, extrinsic, and noise parameters of a classic SLAM dataset with minimum knowledge about the system and the parameters.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129551626","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: 1

Crowd Counting with Spatial Normalization Network 基于空间归一化网络的人群计数

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216769

Pengcheng Xia, Dapeng Zhang

{"title":"Crowd Counting with Spatial Normalization Network","authors":"Pengcheng Xia, Dapeng Zhang","doi":"10.1109/CASE48305.2020.9216769","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9216769","url":null,"abstract":"Crowd counting, which requires to estimate crowd density from an image, is still a challenging task in computer vision. Most of the current methods are focused on large scale variation of people and ignore the huge distribution difference of crowd. To tackle these two problems together, we propose a novel framework named Spatial Normalization Network (SNNet). We normalize multi-scale features from parallel subnetworks to a particular scale and then fuse them to acquire rich spatial information for final accurate density map predictions. Furthermore, we propose a novel normalization layer called Spatial Group Normalization (SGN), which firstly split feature maps along the spatial dimension and then perform group-wise normalization. It’s useful to solve statistic shift problems caused by the great difference of distribution in crowd counting. Moreover, SGN can be naturally plugged into existing solutions and brings significant improvement in crowd counting. Our proposed SNNet achieves state-of-the-art performance on four challenging crowd counting datasets (ShanghaiTech, UCFQNRF, GCC and TRANCOS datasets), which demonstrates the effectiveness and robust feature learning capability of our methods.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129657417","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

Compact Belief State Representation for Task Planning 任务规划的紧凑信念状态表示

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216994

E. Safronov, Michele Colledanchise, L. Natale

引用次数: 0

Tightened Formulation and Resolution of Energy-Efficient Job-Shop Scheduling 节能作业车间调度的强化制定与解决

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9217035

B. Yan, Mikhail A. Bragin, P. Luh

{"title":"Tightened Formulation and Resolution of Energy-Efficient Job-Shop Scheduling","authors":"B. Yan, Mikhail A. Bragin, P. Luh","doi":"10.1109/CASE48305.2020.9217035","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9217035","url":null,"abstract":"Job shops are an important production environment for low-volume high-variety manufacturing. When there are urgent orders, the speeds of certain machines can be adjusted with a high energy and wear and tear cost. Scheduling in such an environment is to achieve on-time deliveries and low energy costs. The problem is, however, complicated because part processing time depends on machine speeds, and machines need to be modeled individually to capture energy costs. This paper is to obtain near-optimal solutions efficiently. The problem is formulated as a Mixed-Integer Linear Programming (MILP) form to make effective use of available MILP methods. This is done by modeling machines in groups for simplicity while approximating energy costs, and by linking part processing status and machine speed variables. Nevertheless, the resulting problem is still complicated. The formulation is therefore transformed by extending our previous tightening approach for machines with constant speeds. The idea is that if constraints can be transformed to directly delineate the convex hull, then the problem can be solved by linear programming methods. To solve the problem efficiently, our advanced decomposition and coordination method is used. Numerical results show that nearoptimal solutions are obtained, demonstrating significant benefits of our approach on on-time deliveries and energy costs.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129032960","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: 2

Online Computation Performance Analysis for Distributed Machine Learning Pipelines in Fog Manufacturing 雾制造中分布式机器学习管道的在线计算性能分析

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216979

Lening Wang, Yutong Zhang, Xiaoyu Chen, R. Jin

{"title":"Online Computation Performance Analysis for Distributed Machine Learning Pipelines in Fog Manufacturing","authors":"Lening Wang, Yutong Zhang, Xiaoyu Chen, R. Jin","doi":"10.1109/CASE48305.2020.9216979","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9216979","url":null,"abstract":"Smart manufacturing enables real-time data streaming from interconnected manufacturing processes to improve manufacturing quality, throughput, flexibility, and cost reduction via computation services. In these computation services, machine learning pipelines integrate various types of computation method options to match the contextualized, on-demand computation needs for the maximum prediction accuracy or the best model structure interpretation. On the other hand, there is a pressing need to integrate Fog computing in manufacturing, which will reduce communication time latency and dependency on connections, improve responsiveness and reliability of the computation services, and maintain data privacy. However, there is a knowledge gap in using machine learning pipelines in Fog manufacturing. Existing offloading strategies are not effective, due to the lack of accurate prediction model for the performance of computation services before the execution of those heterogeneous computation tasks. In this paper, machine learning pipelines are implemented in Fog manufacturing. The computation performance of each sub-step of pipelines is predicted and analyzed via linear regression models and random forest regression models. A Fog manufacturing testbed is adopted to validate the performance of the employed models. The results show that the models can adequately predict the performance of computation services, which can be further integrated into Fog manufacturing to better support offloading strategies for machine learning pipelines.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"794 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117038362","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: 5

An entropy-based sensor selection algorithm for structural damage detection 基于熵的结构损伤检测传感器选择算法

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216828

Jimmy Tjen, Francesco Smarra, A. D’innocenzo

引用次数: 10

Comparing Position- and Image-Based Visual Servoing for Robotic Assembly of Large Structures 大型结构机器人装配中基于位置和图像的视觉伺服比较

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9217028

Yuan-Chih Peng, Devavrat Jivani, R. Radke, J. Wen

引用次数: 6

Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose 基于图像的OpenPose俯卧撑计数器正确与错误动作参数分类

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216833

Ho-Jun Park, Jang-Woon Baek, Jong-Hwan Kim

引用次数: 8

On-road Trajectory Planning with Spatio-temporal RRT* and Always-feasible Quadratic Program 基于时空RRT*和始终可行二次规划的道路轨迹规划

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9217044

Bai Li, Qi Kong, Youmin Zhang, Zhijiang Shao, Yumeng Wang, Xiaoyan Peng, Daxun Yan

引用次数: 11