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30th European Simulation and Modelling Conference, ESM 2016

作者： Pózna, Anna I. Gerzson, Miklós Leitold, Adrien Hangos, Katalin M. Department of Electrical Engineering and Information Systems Hungary Deparment of Mathematics University of Pannonia P.O.B. 158 VeszprémH-8201 Hungary Process Control Research Group Computer and Automation Research Institute P.O.B. 63 BudapestH-1518 Hungary

ISBN: (纸本)9789077381953

An improved diagnosis method of technological systems is proposed in this paper that is based on their qualitative colored Petri-net model (Leitold et al. 2014). The proposed diagnosis method can be used when more than one fault occur in the system and if the fault evolves during the operation of the system. In order to make the diagnosis of complex process systems computationally feasible, a structural decomposition method is introduced to reduce the size of their occurrence graphs and to make the diagnosis easier. The proposed methods are illustrated on examples.

关键词： Fault detection

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Grey-Box Modeling for Identification and control: An Emerging Discipline or an Established Technology?

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IFAC Proceedings Volumes 1994年第8期27卷 1193-1198页

作者： S. Bay Jorgensen K.M. Hangos Department of Chemical Engineering Technical University of Denmark Systems and Control Research Laboratory: Computer and Automation Institute HAS

Grey box modeling is a widely used term, which presently reflects that both a priori and experimental knowledge is being incorporated into the model building process. A brief investigation into various uses of grey box modeling approaches reveals that they mainly differ with respect to the required model accuracy. The goal of the model application also has to be considered in the model building, since it constrains the necessary accuracy required by the model. In this paper these two viewpoints are combined thus leading to a proposed new definition of grey box modeling.

关键词： Qualitative differential and algebraic equations Uncertainty representation Verbatim models Robust identification and control Modeling cycle

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Stable ℋ∞ controller design based on a novel parameterization of the controller set

Stable ℋ∞ controller design based on a novel parameterizat...

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European control Conference (ECC)

作者： T. Péni Z. Szabó J. Bokor Institute for Computer Science and Control MTA-BME Control Engineering Research Group

This paper proposes a method for improving the LMI-based robust control synthesis. It is well-known that the design of a ℋ ∞ controller can be formulated as a two-step procedure. In the first step the performance level is minimized and the closed loop Lyapunov function is determined. The second step is the construction of the controller. The currently available implementations of this procedure generate only one controller candidate and leave no room for additional tuning between the design steps. If the controller obtained is not acceptable, (e.g. it is unstable) then the designer has to modify the original control problem (changing, e.g., the performance weighting functions) and then has to perform the entire synthesis again. This paper presents a novel parameterization of all controllers corresponding to the performance level and Lyapunov function determined in the first design step. This parameterizaton makes it possible to construct tuning algorithms for finding the “best” controller in the parameterized set. Now, this paper focuses only on the stability of the controller and provides an iterative tuning algorithm, which finds a stable controller having poles on the prescribed domain of the left half complex plane. The applicability of the proposed algorithm is demonstrated on a simple robust control problem.

关键词： Linear matrix inequalities Tuning Symmetric matrices Optimization Algorithm design and analysis Eigenvalues and eigenfunctions Output feedback

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An optimal permissible source region strategy for multispectral bioluminescence tomography

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Optics Express 2008年第20期16卷 15640-15654页

作者： Feng, Jinchao Jia, Kebin Yan, Guorui Zhu, Shouping Qin, Chenghu Lv, Yujie Tian, Jie College of Electronic Information and Control Engineering Beijing University of Technology Medical Image Processing Group Institute of Automation Chinese Academy of Sciences P. O. Box 2728 Beijing 100190 China Life Science Research Center Xidian University Xi'an. 710071 China

Multispectral bioluminescence tomography (BLT) attracts increasing more attention in the area of small animal studies because multispectral data acquisition could help in the 3D location of bioluminescent sources. Generally, BLT problem is ill-posed and a priori information is indispensable to reconstruction bioluminescent source uniquely and quantitatively. In this paper, we propose a spectrally solved bioluminescence tomography algorithm with an optimal permissible source region strategy. Being the most different from earlier studies, an optimal permissible source region strategy which is automatically selected without human intervention is developed to reduce the ill-posedness of BLT and therefore improves the reconstruction quality. Furthermore, both numerical stability and computational efficiency benefit from the strategy. In the numerical experiments, a heterogeneous phantom is used to evaluate the proposed algorithm and the synthetic data is produced by Monte Carlo method for avoiding the inverse crime. The results demonstrate the feasibility and potential of our methodology for reconstructing the distribution of bioluminescent sources. © 2008 Optical Society of America.

关键词： Monte Carlo methods

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DL-RLSTM: An Anomaly Detection Framework for High Dimensional Time Series Data

DL-RLSTM: An Anomaly Detection Framework for High Dimensiona...

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2021 China automation Congress, CAC 2021

作者： Wang, Shuai Liu, Zhihong Xiao, Qianke Lin, Jie Long, Dan An, Dou School of Computer Science and Technology Xi'an Jiaotong University China Automation Research Institute Co. Ltd. of China South Industries Group Corporation China School of Automation Science and Engineering Xi'an Jiaotong University China

ISBN: (纸本)9781665426473

With the development of communication technology and the popularization of Internet of Things (IoT) applications in daily life, anomaly detection for time series data has been paid more attentions. To mitigate the imbalance and incorrectness of data labels in anomaly detection, unsupervised anomaly detection has been widely used to detect abnormal data. Although a number of efforts on unsupervised anomaly detection has been developed, most of these schemes focus on detecting abnormal data with low-dimensional and achieve undulatory efficiency on detecting abnormal data with high-dimension. In addition, most of the existing schemes determine the data as abnormalities with the thresholds defined by expertise experience, resulting in undulatory efficiency on abnormal data detection as well. To address these issues, in our paper, an advanced Double Layer-RLSTM framework, namely DL-RLSTM, is proposed to detect the abnormal time series data with high-dimension effectively. Particularly, two neural network layers are considered in our DL-RLSTM framework, in which the RLSTM network is introduced in the first neural network layer to extract the temporal feature of time series data. The second neural network layer in our DL-RLSTM framework are served as the autoencoder to reconstruct temporal feature of time series data extracted by the first neural network layer. Different from the traditional projection-based dimensionality reduction, our DL-RLSTM framework can maximize the retention of temporal feature of time series data. Additionally, K-Means is introduced in our DL-RLSTM framework to determine the abnormal time series data according to the anomaly scores obtained from the autoencoder. By doing this, the participation of expertise experience on abnormality thresholds determination can be minimized. Via evaluations, the results show that our DL-RLSTM framework can achieve better detection efficiency on high dimensional abnormal time series data in comparison with existing

关键词： Feature extraction

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Heuristic regression method for descriptive data analysis 16

Heuristic regression method for descriptive data analysis

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16th International Conference on Environment and Electrical engineering, EEEIC 2016

作者： Gomes, Flavio A. Gomes, Viviane M. Assis, Alfredo De O. Reis, Marcio R. Da C. Da Cruz, Gelson Calixto, Wesley P. Electrical Mechanical and Computer Engineering School University of Goias Brazil Experimental and Technological Research and Study Group Federal Institute of Goias Brazil Program in Electronic Systems and Automation University of Brasilia Brazil

ISBN: (纸本)9781509023196

The purpose of this paper is to use the hybridized optimization method in order to find mathematical structures for analysis of experimental data. The heuristic optimization method will be hybridized with deterministic optimization method in order to that structures found require not knowledge about data generated experimentally. Three case studies are proposed and discussed to validate the results. The proposed method has viable solution for the analysis of experimental data and extrapolation, with mathematical expression reduced. © 2016 IEEE.

关键词： Heuristic methods

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An Asymmetric Direct-Connected Network for Rolling Bearing Fault Diagnosis

An Asymmetric Direct-Connected Network for Rolling Bearing F...

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2021 China automation Congress, CAC 2021

作者： Long, Dan Qiu, Feng Xu, Manfei Lin, Jie Wang, Shuai An, Dou School of Computer Science and Technology Xi'an Jiaotong University China Automation Research Institute Co. Ltd. of China South Industries Group Corporation China School of Automation Science and Engineering Xi'an Jiaotong University China

ISBN: (纸本)9781665426473

Rolling bearings have be extensively applied in applications of intelligent manufacturing, such as rotating machinery, and the fault diagnosis of rolling bearings is critical to mitigate the production and economics losses. Deep learning, taking the advantage in signal processing, have been widely used in fault diagnosis. However, existing deep-learning-based fault diagnosis cannot be directly and easily applied to process the signal of rolling bearing. In addition, some existing scheme suffer from severe performance degradation in variable noisy environments or variable working loads. To address these issues, in this paper, an asymmetric direct-connected network, namely ADNet, is proposed to effectively diagnose faults of rolling bearing with deep learning technologies. To be specific, in our ADNet, the one-dimensional bearing signal firstly is converted into the recurrence plot (i.e., two-dimensional image), and multiple direct connection blocks (DCBs) that are nested with asymmetric convolution transformation (ACT) modules are proposed to extracted the detailed convolved feature map of faults. Finally, through the softmax classifier, the faults can be effectively identified by our ADNet based on the detailed convolved feature map of faults. Via evaluations, the data demonstrate that our ADNet can achieve great scalability and accuracy on faults diagnosis of rolling bearing in both variable noisy environments and variable working loads. © 2021 IEEE

关键词： Failure analysis

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Modeling and identification of a nuclear reactor with temperature effects and Xenon poisoning

Modeling and identification of a nuclear reactor with temper...

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Annual Conference of Industrial Electronics Society

作者： Attila Gábor Csaba Fazekas Gábor Szederkényi Katalin M. Hangos Process Control Research Group Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

This paper presents the modeling and identification procedure for a VVER-type pressurized water reactor. The modeling goal is to produce a mathematical description in nonlinear state-space form that is suitable for control-oriented model analysis and preliminary controller design experiments. The proposed model takes temperature effects and xenon poisoning into consideration and thus it is an extension of formerly published simpler model structures. Real transient measurement data from the plant has been used for the identification that is based on standard prediction error minimization. It is shown that the model is fairly well identifiable and the newly inserted model components significantly improve the quality of fit between the measured and computed model outputs. Furthermore, the estimated parameter values fall into physically meaningful ranges.

关键词： Temperature Xenon Inductors Neutrons Parameter estimation Power system modeling Steady-state Mathematical model Power generation Delay

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Identifiability of a Hodgkin-Huxley type ion channel under voltage step measurement conditions

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IFAC Proceedings Volumes 2010年第5期43卷 332-337页

作者： D. Csercsik Gábor Szederkényi Katalin M. Hangos Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences P.O. Box 63 H-1518 Budapest Hungary

In this paper, we analyze the identifiability properties of a Hodgkin-Huxley (HH) type voltage dependent ion channel model under voltage clamp circumstances. The elimination of the differential variables is performed, and the identifiability of various parameters is analyzed using the differential algebra approach and the algorithm based on the Taylor series expansion of the output. It is shown that the model is structurally non-identifiable using certain commonly applied parametrizations, and hidden dependences between physical parameters are unravelled. Using the results of the identifiability analysis, physically meaningful examples are shown when the model parameters are different but the system outputs are identical.

关键词： Modeling and Identification control Applications in Biological Processes

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Plug-and-Play Based Optimization Algorithm for New Crime Density Estimation

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Journal of computer Science & Technology 2019年第2期34卷 476-493页

作者： Xiang-Chu Feng Chen-Ping Zhao Si-Long Peng Xi-Yuan Hu Zhao-Wei Ouyang School of Mathematics and Statistics Xidian University Xi’an 710126 China School of Mathematical Science Henan Institute of Science and Technology Xinxiang 453003 China Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China Research Center of Beijing Visystem Co. Ltd. Beijing 100190 China

Different from a general density estimation,the crime density estimation usually has one important factor:the geographical *** this paper,a new crime density estimation model is formulated,in which the regions where crime is impossible to happen,such as mountains and lakes,are *** further optimize the estimation method,a learning-based algorithm,named Plug-and-Play,is implanted into the augmented Lagrangian scheme,which involves an off-the-shelf filtering *** selections of the filtering operator make the algorithm correspond to several classical estimation ***,the proposed Plug-and-Play optimization based estimation algorithm can be regarded as the extended version and general form of several classical *** the experiment part,synthetic examples with different invalid regions and samples of various distributions are first *** under complex geographic constraints,we apply the proposed method with a real crime dataset to recover the density *** state-of-the-art results show the feasibility of the proposed model.

关键词： crime density estimation augmented Lagrangian strategy Plug-and-Play filtering operator

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