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Semantic interoperability for asset communication within smart factories 22

Semantic interoperability for asset communication within sma...

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22nd IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2017

作者： Diedrich, Christian Belyaev, Alexander Schröder, Tizian Willmann, Alexander Koziolek, Heiko Pethig, Florian Niggemann, Oliver Vialkowitsch, Jens Usländer, Thomas Wende, Jörg Institute for Automation Engineering Otto-von-Guericke-Universität Magdeburg39106 Germany Industrial Internet of Things Group Fraunhofer FOKUS Berlin10589 Germany Corporate Research Center ABB Ladenburg68526 Germany Intelligent Automation Lab Big Data Lemgo32657 Germany Robert Bosch GmbH Stuttgart70442 Germany Information Management and DCS Fraunhofer IOSB Karlsruhe76131 Germany IBM Hybrid Cloud Software IBM Deutschland GmbH Dresden01067 Germany

ISBN: (纸本)9781509065059

Industrie 4.0 (I4.0) aims at a manufacturer-independent, vertical- And horizontal-oriented communication and cooperation within smart factories. This is only manageable using international standards. The so-called reference architecture model for Industrie 4.0 (RAMI4.0) and the requirement specification of an I4.0 component are already available. RAMI4.0 could be the basis for the interoperability of the interactions. The working group "Semantic and interaction model for I4.0 components" (GMA 7.20) made progress in this direction. The language used for the interaction of I4.0 components needs model definitions for the interaction consisting of the structure of the components, syntax for the description of the messages and the means to assign the meaning to the language elements. This paper discusses the results for a broader audience. © 2017 IEEE.

关键词： Semantics

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Structurally aware discretisation for Bayesian networks 22

Structurally aware discretisation for Bayesian networks

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22nd international Congress on Modelling and Simulation: Managing Cumulative Risks through Model-Based Processes, MODSIM 2017 - Held jointly with the 25th National conference of the Australian Society for Operations Research and the DST Group led Defence Operations Research Symposium, DORS 2017

作者： Mayfield, Helen Bertone, Edoardo Sahin, Oz Smith, Carl Cities Research Institute Griffith University QLD Australia School of Engineering and Cities Research Institute Griffith University QLD Australia Griffith Climate Change Response Program Griffith University QLD Australia UQ Business School University of Queensland QLD Australia

ISBN: (纸本)9780987214379

Bayesian networks represent a versatile probabilistic modelling technique widely used to tackle a range of problems in many different domains. However, they are discrete models, and a significant decision when designing a BN is how to split the continuous variables into discrete bins. Default options offered in most BN packages include assigning an equal number of cases to each bin or assigning equal sized bins. However, these methods discretise nodes independently of each other. When learning probabilities from data, this can result in conditional probability tables (CPTs) with missing or uninformed probabilities because data for particular bin combinations (scenarios) is either missing or scarce. This can result in poor model performance. We propose that the structure of the network is an important determinant in node discretisation, and that the best bin allocations for a simple naïve network will be different to those for more complicated networks that attempt to model relationships between the predictor variables. Furthermore, a good discretisation algorithm should not require the model designer to specify the exact number of bins as a target for discretisation. Rather, it should be flexible in determining bin allocations within limits specified by the model designer. BN performance can be improved if discretisation results in CPTs that contain fewer combinations with insufficient evidence to confidently estimate probabilities. We have developed a structure aware discretisation (SAD) algorithm that minimises the number of missing probabilities in CPTs by taking into account network structure. The algorithm requires some parameters to be set, such as the minimum number of cases in each bin, but determines the exact number of bins and their limits based on the data. It consists of two stages: a structurally unaware discretisation stage (SUD) that distributes the cases into bins until each bin has a minimum number of cases, followed by a structure aware discretisa

关键词： Bayesian networks

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Boosting Moving Average Reversion Strategy for Online Portfolio Selection: A Meta-learning Approach 22nd

Boosting Moving Average Reversion Strategy for Online Portfo...

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22nd international conference on database Systems for Advanced Applications (DASFAA)

作者： Lin, Xiao Zhang, Min Zhang, Yongfeng Gu, Zhaoquan Liu, Yiqun Ma, Shaoping Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Univ Massachusetts Amherst Coll Informat & Comp Sci Amherst MA 01003 USA Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783319556994;9783319556987

In this paper, we study the online portfolio selection problem from the perspective of meta learning for mean reversion. The online portfolio selection problem aims to maximize the final accumulated wealth by rebalancing the portfolio at each time period based on the portfolio prices announced before. Mean Reversion is a typical principle in portfolio theory and strategies that utilize this principle achieve the superior empirical performances so far. However there are some important limits of existing Mean Reversion strategies: First, the mean reversion strategies have to set a fixed window size, where the optimal window size can only be chosen in hindsight. Second, most existing mean reversion techniques ignore the temporal heterogeneity of historical price relatives from different periods. Moreover, most mean reversion methods suffer from noises and outliers in the data, which greatly affects the performances. In order to tackle the limits of previous approaches, we exploit mean reversion principle from a meta learning perspective and propose a boosting method for price relative prediction. More specifically, we generate several experts where each expert follows a specific mean reversion policy and predict the final price relatives with meta learning techniques. The sampling of multiple experts involves mean reversion strategies with various window sizes;while the meta learning technique brings temporal heterogeneity and stronger robustness for prediction. We adopt online passive-aggressive learning for portfolio optimization with the predicted price relatives. Extensive experiments have been conducted on real-world datasets and our approach outperforms the state-of-the-art approaches significantly.

关键词： Forecasting

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Edge-preserving rain removal for light field images based on RPCA

Edge-preserving rain removal for light field images based on...

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international conference on Digital Signal Processing (DSP)

作者： Cheen-Hau Tan Jie Chen Lap-Pui Chau School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Rain deteriorates outdoor vision and causes challenge for most vision based intelligent systems. In this paper we propose a method to efficiently remove the rain present in light field data. Firstly, the sub-view image sequence is globally aligned to the central view. Robust Principle Component Analysis (RPCA) are then applied to decompose the sequence into two parts, i.e., the low-rank data, and the sparse data. The decomposed sparse data contains both rain streaks and scene disparity edges. We propose to compute a dark view image to estimate the non-rain disparity edges, and the remaining part of the decomposed sparse data will be considered as rain. The disparity edges will then be added back to the low-rank data. The proposed method produces satisfactory rain removal visual results, and can efficiently preserve the light field perspective disparity at the same time.

关键词： Rain Image edge detection Cameras Robustness Matrix decomposition Sparse matrices Image sequences

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Partial co-training for virtual metrology

Partial co-training for virtual metrology

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international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Cuong Nguyen Xin Li Ronald D. Shawn Blanton Xiang Li Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Carnegie Mellon University Pittsburgh PA US Technology and Research (A*STAR) Singapore Institute of Manufacturing Technology Agency for Science Singapore

Virtual metrology is an important tool for industrial automation. To accurately build regression models for virtual metrology, we consider semi-supervised learning where labeled data are expensive to collect, but unlabeled data are abundant. In such a scenario, due to the scarcity of labeled data, traditional single-view learning methods face the risk of overfitting. To address the overfitting issue, we develop a Partial Co-training framework, which is an extension of the original co-training approach by means of an undirected probabilistic graphical model. Unlike other co-training techniques, this model creates a partial view by shrinking the original feature space, and makes use of this partial-view to provide guidance information for improving the complete-view model. Our approach is validated with data from two manufacturing applications. The results indicate that a consistent and robust estimation is achievable with very limited labeled data.

关键词： Linear regression Graphical models Classification algorithms data models Manufacturing Testing Maximum likelihood estimation

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Regression Kernel for Prognostics with Support Vector Machines

Regression Kernel for Prognostics with Support Vector Machin...

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IEEE international conference on Emerging Technologies and Factory Automation

作者： Josey Mathew Ming Luo Chee Khiang Pang Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Institute of Manufacturing Technology Singapore

Estimating the remaining useful life (RUL) of systems and/or equpipments has been an important goal for reliable, safe, and profitable operation of industrial plants. However, traditional mathematical and statistical modeling based approaches are difficult to design and they adapt poorly to the ever changing operating and environmental conditions in real-world industries. With recent developments in computational technologies, data storage, and industrial automation recording and storage of large amounts of historical plant data from embedded sensors and maintenance records have become easy. Availability of large data sets together with advancements in data driven machine learning algorithms has been the key driver for prognostic and diagnostic research in the industry as well as by academia. Nevertheless, developing generalized machine learning algorithms for the prognostic domain has been challenging due to the very nature of the problem. This paper describes some of these challenges and proposes a modified regression kernel that can be used by support vector regression (SVR) for prognostic problems. The method is tested on a simplified simulated time-series data set that is modeled to represent the challenges presented.

关键词： Regression Kernel Prognostics Vector Machines

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Gaussian mixture model for new fault categories diagnosis

Gaussian mixture model for new fault categories diagnosis

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international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Jun-Hong Zhou Chee Khiang Pang Weili Yan Manufacturing Execution and Control Group A*STAR Singapore Institute of Manufacturing Technology Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore

Fault diagnosis plays an important role to improve maintenance efficiency. The industry faces the challenges to collect history data that include all type of failures. To overcome the limitation of conventional diagnosis approaches, which misclassify new types of faults into existing categories from training, the unsupervised Gaussian mixture model (GMM) and the semi-supervised GMM diagnosis frameworks are presented in this paper for effective detection on new fault categories. For the unsupervised GMM framework, the component number is known and the hard assignment is applied to classify the new types of faults. For the semi-supervised GMM framework, the component number can be auto selected, and the soft assignment is able to first detect whether new types of faults occur and further categorize them in detail via the GMM update. The effectiveness of the two fault diagnosis frameworks is testified on an industrial fault simulator of rotary machine. Compared with existing hard clustering approaches, the semi-supervised GMM framework is able to achieve an average diagnosis accuracy of 99.3% without new fault categories and it can also classify new fault categories with diagnosis accuracy of 94.0%.

关键词： Training data Training Unsupervised learning Semisupervised learning Gaussian mixture model Testing

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A data-driven Prognostics Framework for Tool Remaining Useful Life Estimation in Tool Condition Monitoring

A Data-driven Prognostics Framework for Tool Remaining Usefu...

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IEEE international conference on Emerging Technologies and Factory Automation

作者： Chong Zhang Geok Soon Hong Huan Xu Kay Chen Tan Jun Hong Zhou Hian Leng Chan Haizhou Li Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computer Science City University of Hong Kong Kowloon Hong Kong Singapore Institute of Manufacturing Technology (SIMTech)

Tool Condition Monitoring (TCM) is an important topic in manufacturing industry, which improves product quality, production efficiency, reduces costs and downtime. This paper develops a new data-driven framework for estimating tool remaining useful life (RUL) in TCM. The framework includes the following modular components: data preprocessing with a proposed adaptive Baysian change point detection (ABCPD) for automatic data alignment, time window process, feature extraction, feature selection and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on a real-world gun drilling experimental dataset with multiple sensor measurements (i.e. thrust force, torque, 12 vibration signals). Different model selection, sensor selection, feature selection methods have been investigated in this paper. The simulation performance of the proposed framework is studied with the gun drilling dataset and it has been shown that the proposed framework has good performance.

关键词： Prognostics Framework Tool Remaining Condition Monitoring

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XGBoost: A Scalable Tree Boosting System 16

XGBoost: A Scalable Tree Boosting System

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Chen, Tianqi Guestrin, Carlos Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781450342322

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

关键词： Large-scale Machine learning

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Crazyflie 2.0 Quadrotor as a Platform for Research and Education in Robotics and Control engineering

Crazyflie 2.0 Quadrotor as a Platform for Research and Educa...

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international conference on Methods and Models in Automation and Robotics

作者： Wojciech Giernacki Mateusz Skwierczynski Wojciech Witwicki Pawel Wronski Piotr Kozierski Faculty of Electrical Engineering Poznan University of Technology Poznan Poland

In this paper a Crazyflie 2.0 nano quadrotor helicopter (quadcopter) as an open source experimental platform for research and education in robotics and control engineering has been presented. This low cost, easily expandable and upgradeable flying robot is here characterized in terms of hardware and software. Three aspects, which demonstrate the potential of broad use of this unmanned aerial vehicle (UAV) by researchers and students, are discussed in the paper. The first one is an acquisition of measurement data from test flights by the proposed, freely available "black-box" software. The second is the use of a new, advanced 4FLY Simulator in order to utilize the MA TLA B~?/Simulink environment to easily implement a mathematical model of Crazyflie 2.0 dynamics, as well as for a synthesis of various types of controllers with support of OpenGL cross-language in the visualization of simulations results. The 4FLY Simulator allows to test autonomous flights (and landings) with obstacles avoidance and to conduct learning and teaching the basics of Crazyflie 2.0 piloting. In the third aspect the authors outlined promising, preliminary results obtained in control of flying robot by pointing device (positioner) and with the support of a vision system, which basis only on a single Kinect sensor.

关键词： Crazyflie 2.0 nano quadrotor helicopter nano quadcopter flight simulator autonomous flights obstacles avoidance autonomous landing vision-based tracking

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