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A distributed real-time data prediction framework for large-scale time-series data using stream processing

International Journal of Intelligent Computing and Cybernetics 2017年第2期10卷 145-165页

作者： Kehe Wu Yayun Zhu Quan Li Ziwei Wu School of Control and Computer Engineering North China Electric Power UniversityBeijingChina

Purpose-The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources,e.g.,sensor networks,securities exchange,electric power secondary system,***,the proposed framework should handle several difficult requirements including the management of gigantic data sources,the need for a fast self-adaptive algorithm,the relatively accurate prediction of multiple time series,and the real-time ***/methodology/approach-First,the autoregressive integrated moving average-based prediction algorithm is ***,the processing framework is designed,which includes a time-series data storage model based on the HBase,and a real-time distributed prediction platform based on ***,the work principle of this platform is ***,a proof-of-concept testbed is illustrated to verify the proposed ***-Several tests based on Power Grid monitoring data are provided for the proposed *** experimental results indicate that prediction data are basically consistent with actual data,processing efficiency is relatively high,and resources consumption is ***/value-This paper provides a distributed real-time data prediction framework for large-scale time-series data,which can exactly achieve the requirement of the effective management,prediction efficiency,accuracy,and high concurrency for massive data sources.

关键词： Prediction Real-time Autoregressive integrated moving average Storm Stream processing Time series

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Recursive network estimation from binary-valued observation data

arXiv

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arXiv 2019年

作者： Xing, Yu He, Xingkang Fang, Haitao Johansson, Karl Henrik Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden

This paper studies the problem of recursively estimating the weighted adjacency matrix of a network out of a temporal sequence of binary-valued observations. The observation sequence is generated from nonlinear networked dynamics in which agents exchange and display binary outputs. Sufficient conditions are given to ensure stability of the observation sequence and identifiability of the system parameters. It is shown that stability and identifiability can be guaranteed under the assumption of independent standard Gaussian disturbances. Via a maximum likelihood approach, the estimation problem is transformed into an optimization problem, and it is verified that its solution is the true parameter vector under the independent standard Gaussian assumption. A recursive algorithm for the estimation problem is then proposed based on stochastic approximation techniques. Its strong consistency is established and convergence rate analyzed. Finally, numerical simulations are conducted to illustrate the results and to show that the proposed algorithm is insensitive to small unmodeled factors. Copyright © 2019, The Authors. All rights reserved.

关键词： Approximation theory

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Principal Component Analysis on Graph-Hessian

Principal Component Analysis on Graph-Hessian

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Yichen Pan Yicong Zhou Weifeng Liu Liqiang Nie College of Control Science and Engineering China University of Petroleum (East China) Qingdao China Faculty of Science and Technology University of Macau Macau China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Principal Component Analysis (PCA) is a widely used linear dimensionality reduction method, which assumes that the data are drawn from a low-dimensional affine subspace of a high-dimensional space. However, it only uses the feature information of the samples. By exploiting structural information of data and embedding it into the PCA framework, the local positional relationship between samples in the original space can be preserved, so that the performance of downstream tasks based on PCA can be improved. In this paper, we introduce Hessian regularization into PCA and propose a new model called Graph-Hessian Principal Component Analysis (GHPCA). Hessian can correctly use the intrinsic local geometry of the data manifold. It is better able to maintain the neighborhood relationship between data in high-dimensional space. Compared with other Laplacian-based models, our model can obtain more abundant structural information after dimensionality reduction, and it can better restore low-dimensional structures. By comparing with several methods of PCA, GLPCA, RPCA and RPCAG, through the K-means clustering experiments on USPS handwritten digital dataset, YALE face dataset and COIL20 object image dataset, it is proved that our models are superior to other principal component analysis models in clustering tasks.

关键词： Principal component analysis Sparse matrices Robustness Matrix decomposition Analytical models Manifolds Task analysis

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Network weight estimation for binary-valued observation models

arXiv

引用

arXiv 2019年

作者： Xing, Yu He, Xingkang Fang, Haitao Johansson, Karl Henrik Key Lab of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden

This paper studies the estimation of network weights for a class of systems with binary-valued observations. In these systems only quantized observations are available for the network estimation. Furthermore, system states are coupled with observations, and the quantization parts are unknown inherent components, which hinder the design of inputs and quantizers. To fulfill the estimation, we propose a recursive algorithm based on stochastic approximation techniques. More precisely, to deal with the temporal dependency of observations and achieve the recursive estimation of network weights, a deterministic objective function is constructed based on the likelihood function by extending the dimension of observations and applying ergodic properties of Markov chains. It is shown that this function is strictly concave and has unique maximum identical to the true parameter vector. Finally, the strong consistency of the algorithm is established. Our recursive algorithm can be applied to online tasks like real-time decision-making and surveillance for networked systems. This work also provides a new scheme for the identification of systems with quantized observations. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Adaptive Fault-Tolerant control of A Two-car High-speed Train Model with Inter-car Flexible Link and Traction Actuator Failures*

Adaptive Fault-Tolerant Control of A Two-car High-speed Trai...

引用

Asian control Conference

作者： Zehui Mao Gang Tao Bin Jiang Xing-Gang Yan Yali Xue Ministry of Industry and Information Technology and Jiangsu Key Laboratory of Internet of Things and Control Technologies (NUAA) Key Laboratory of Navigation Control and Health Management Technologies of Advanced Aircraft (NUAA) Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA School of Engineering and Digital Arts University of Kent Canterbury United Kingdom

ISBN: (纸本)9781728102634

This paper studies the adaptive fault-tolerant tracking control problem for the high-speed trains with intercar flexible link and traction actuator failures. This study is focused on a benchmark model which, as a main dynamic unit of the CRH (China Railway High-speed) train, is a two-car dynamic system with a flexible link between two cars, for which the input acts on the second car and the output is the speed of the first car. This model is under parameter uncertainties and subject to uncertain actuator failures. For such an underactuated system, to ensure the first car tracking a desired speed trajectory, a coordinate transformation method is employed to decompose the system model into a control dynamics subsystem and a zero dynamics subsystem. Stability analysis is conducted to show that such a zero dynamic system is Lyapunov stable and is partially input-to-state stable. An adaptive fault-tolerant controller is developed which is able to ensure the closed-loop system signal boundedness and desired speed tracking, in the presence of the actuator failures and unknown system parameters. Simulation results from a realistic train dynamic model are presented to verify the effectiveness of the adaptive controller.

关键词： Automobiles Actuators Fault tolerance Fault tolerant systems Adaptation models Aerodynamics Adaptive systems

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RAM: A region-aware deep model for vehicle re-identification

arXiv

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arXiv 2018年

作者： Liu, Xiaobin Zhang, Shiliang Huang, Qingming Gao, Wen School of Electronics Engineering and Computer Science Peking University China School of Computer and Control Engineering University of Chinese Academy of Sciences China

Previous works on vehicle Re-ID mainly focus on extracting global features and learning distance metrics. Because some vehicles commonly share same model and maker, it is hard to distinguish them based on their global appearances. Compared with the global appearance, local regions such as decorations and inspection stickers attached to the windshield, may be more distinctive for vehicle Re-ID. To embed the detailed visual cues in those local regions, we propose a Region-Aware deep Model (RAM). Specifically, in addition to extracting global features, RAM also extracts features from a series of local regions. As each local region conveys more distinctive visual cues, RAM encourages the deep model to learn discriminative features. We also introduce a novel learning algorithm to jointly use vehicle IDs, types/models, and colors to train the RAM. This strategy fuses more cues for training and results in more discriminative global and regional features. We evaluate our methods on two large-scale vehicle Re-ID datasets, i.e., VeRi and VehicleID. Experimental results show our methods achieve promising performance in comparison with recent works. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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RAM: A REGION-AWARE DEEP MODEL FOR VEHICLE RE-IDENTIFICATION

RAM: A REGION-AWARE DEEP MODEL FOR VEHICLE RE-IDENTIFICATION

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IEEE International Conference on Multimedia and Expo

作者： Xiaobin Liu Shiliang Zhang Qingming Huang Wen Gao School of Electronics Engineering and Computer Science Peking University China School of Computer and Control Engineering University of Chinese Academy of Sciences China

关键词： Vehicle Re-ID Deep Convolutional Neural Network (DCNN) Region-Aware Deep Model

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Abnormal Behavior Detection Based on the Potential Field Forces in Surveillance Scenarios

引用

Journal of Physics: Conference Series 2020年第1期1487卷

作者： Yong Qiang Shumin Fei Liuwen Li Key Laboratory of Measurement and Control of CSE (Ministry of Education) School of Automation Southeast University Nanjing 210096 China School of Computer Engineering Jinling Institute of Technology Nanjing 211169 China

In a social environment, there is a natural mutual force between people. This paper proposes a method for detecting group abnormal behavior based on the potential field method. The movement of a pedestrian is described as the movement towards the target under the combined action of several forces. Forces on pedestrians include the attractive force for themselves and the repulsive force between pedestrians. By calculating the resultant force between them, the behavior of pedestrians is judged according to a set threshold. It is verified through experiments on public data sets that our method has high accuracy and robustness.

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The Application of Blockchain in Higher Education Field

The Application of Blockchain in Higher Education Field

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3rd International Conference on Politics,Economics and Law(ICPEL 2018)

作者： Wenqing ZHAO Zhuoying MA School of Control and Computer Engineering North China Electric Power University

The blockchain technology is seen as revolutionary technology, which obtains attention from various fields in many countries. In China, students information in higher education needs to be managed by the third part-China Credentials Verification. Although this website brings a lot of convenience to students, it still exists some disadvantages. Through analyzing the application of blockchain technology in finance field, the key feature – decentralized can also be effectively applied in the education field to improve the work efficiency of verifying degree,storing considerable data or transmitting students' information in the way of point-to-point and so on. Through exploring the modern of ‘blockchain+education', considerable students' information in database can be stored in the blockchain and can be traced by companies to conveniently query their records. This paper will discuss the application landscape of blockchain technology in higher education in new view.

关键词： Blockchain Technology Education Application ‘Blockchain+education’ Modern

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A Power Forecasting Approach for PV Plant based on Irradiance Index and LSTM

A Power Forecasting Approach for PV Plant based on Irradianc...

引用

第37届中国控制会议

作者： Hui He Ran Hu Yaning Zhang Ying Zhang Runhai Jiao School of Control and Computer Engineering North China Electric Power University

A novel power forecasting approach for PV plant based on irradiance index and LSTM is presented in this paper. Firstly, we come up with a clustering algorithm according to the irradiance index after analyzing the periodic characteristics of PV plant daily power curves. Then, the Long Short-Term Memory（LSTM） is employed to build forecasting models for each type of weather. An empirical study on a real dataset shows that the proposed method can effectively use multivariate time series information to predict the power for PV plants and obtain better performance than Extreme Learning Machine（ELM） and Artificial Neural Networks（ANN）.

关键词： Irradiance Index Long Short-Term Memory（LSTM） PV Power Forecasting PV Plant

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