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10th ifac symposium on control of Power and Energy systems (CPES)

作者： zhu, Mingjuan Shen, Jiong Lee, Kwang Y. Wu, Xiao Southeast Univ Key Lab Energy Thermal Convers & Control Ministry Educ Nanjing Jiangsu Peoples R China Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

Fossil fuel power plants dominate the energy structure in China However, the water scarcity, in Southeast/Southwest China, results in the development of the dry cooling power plants. Direct air cooling condenser (DACC) is appealing for its water saving ability. In DACC, the exhaust steam is cooled and condensed to water by the air flow, which is provided by the fan array at the cost of electricity. there exists a varying optimal exhaust steam pressure under different air temperatures and loads. the static model of the turbine-DACC system is built to quantify the influences of the air temperatures and loads. An on-line optimization is used here to set the reference value of the exhaust steam pressure for the turbine-DACC system for economic operation. the objective is to maximize the net power production while satisfying the constraints on the safety pressure and the available air flowrate. the optimization is conducted in a model 660MW power plant selected on a random summer/winter day. the hourly optimal exhaust steam pressure and the operation instructions are calculated as the reference values for the control layer. the optimization results show that the optimal exhaust steam pressure may be too close to the safety limits in winter, suggesting the safety also to be considered in the optimization In summer, the DACC system cannot satisfy the condensation load sometimes. thus, a complementary cooling method, for example, the wet cooling, is needed to reduce the burden on the DACC system. (C) 2018, ifac (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Direct air cooling condenser Turbine variations Condensation pressure optimization Cooling fan array

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Parity Space-Based Fault Detection by Minimum Error Minimax Probability Machine

Parity Space-Based Fault Detection by Minimum Error Minimax ...

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10th International-Federation-of-Automatic-control (ifac) symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Zhong, Maiying Song, Yang Xue, Ting Yang, Rui Li, Wenbo Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100191 Peoples R China Univ Duisburg Essen Inst Automat Control & Complex Syst D-47057 Duisburg Germany Beijing Inst Control Engn Beijing 100190 Peoples R China

this paper deals with the problem of parity space-based fault detection (FD) for linear discrete time systems in the framework of minimum error minimax probability machine (MEMPM). Traditional parity space-based FD is usually difficult to achieve acceptable tradeoff between missed alarm rate (MAR) and false alarm rate (FAR) without the exact stochastic properties of unknown input. To solve this problem, this paper proposes a novel method of parity space-based FD by MEMPM. Firstly, the integrated design of parity space vector, threshold, MAR and FAR is formulated as a problem of binary classification, i.e., the fault-free case and faulty case. By using the method of MEMPM, a bank of parity space vectors corresponding to different faulty scenarios and a threshold are then obtained, while an optimal trade-off between MAR and FAR is achieved in the worst-case setting. To show the effectiveness of proposed method, a satellite attitude control system subject to roll momentum wheel fault and pitch gyroscope fault is considered. (C) 2018, ifac (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Parity space fault detection minimum error minimax probability machine missed alarm rate false alarm rate

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A Data-Driven Causality Analysis Tool for Fault Diagnosis in Industrial Processes

A Data-Driven Causality Analysis Tool for Fault Diagnosis in...

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10th International-Federation-of-Automatic-control (ifac) symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Alizadeh, Esmaeil El Koujok, Mohamed Ragab, Ahmed Amazouz, Mouloud Nat Resources Canada CanmetENERGY Varennes PQ J3X 1S6 Canada Ecole Polytech Montreal Math & Ind Engn Dept Montreal PQ H3T 1J4 Canada Univ Menoufia Ind Elect & Control Engn Dept Fac Elect Engn Menoufia 32952 Egypt

Data-driven causality analysis is an important step towards fault diagnosis in complex industrial processes. Although many causality analysis tools were developed in different domains, only a few of them are applied in the industry. Accordingly, there is a need to develop a causality analysis tool that serves for fault diagnosis in large-scale chemical plants. this paper develops a decision-support tool to perform causality analysis by extracting useful information from the process historical data. the aim is to help the process operator to understand the underlying systems conditions with minimal efforts and to take appropriate actions in a short response time. the tool is implemented as a graphical user-friendly interface (GUI) that exploits the multivariate time series data and provides the user with stationarity tests and Granger causality analysis. It also offers various visualization charts such as pairwise causality relationships and most importantly the final causal graph. In order to demonstrate the easiness and usability of the developed tool, two different case studies are considered. the first case study is a time varying simulated model and the second one is the Tennessee Eastman Process as a well-known benchmark. the results show that the cause-and-effect information obtained by the developed tool can assist the user to deeply analyze causal variables and diagnose the corresponding fault with minimal involvement. (C) 2018, ifac (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Causality Analysis Causal Graphs Industrial Processes Data-Driven Fault Diagnosis Time Series Analysis Tennessee Eastman Process

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Health-aware LPV-MPC based on a Reliability-based Remaining Useful Life Assessment

Health-aware LPV-MPC based on a Reliability-based Remaining ...

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10th International-Federation-of-Automatic-control (ifac) symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Pour, Fatemeh Karimi Puig, Vicenc Cembrano, Gabriela Univ Politecn Cataluna Automat Control Dept Inst Robot & Informat IndCSIC Supervis Safety & Automat Control Res Ctr CS2AC C Llorens & Artigas 4-6 E-08028 Barcelona Spain

One of the relevant information provided by the prognostics and health management algorithms is the estimation of the Remaining Useful Life (RUL). the prediction of the expected RUL is very useful to decrease maintenance cost, operational downtime and safety hazards. this paper proposes a new strategy of health-aware Model Predictive control (MPC) for a Linear Parameter Varying (LPV) system that includes as an additional goal extending the system RUL via their estimation using reliability tools. In this approach, the RUL maximization is included in the objective function of the LPV-MPC controller. the RUL is included in the MPC model as an extra parameter varying equation that considers the control action as scheduling variable. the proposed control approach allows the controller to accommodate to the parameter changes. through computing an estimation of the state variables during prediction, the MPC model can be modified to the estimated state evolution at each time instant. Moreover, for solving the optimization problem by using a series of Quadratic Programs (QP) in each time instant, a new iterative approach is exhibited, which improves the computational efficiency. A pasteurization plant control system is used as a case study to illustrate the performance of the proposed approach. (C) 2018, ifac (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Remaining Useful Life (RUL) Model Predictive control Linear Parameter Varying Reliability

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Optical coherent tomography and fluorescent microscopy for the study of meningeal lymphatic systems

Optical coherent tomography and fluorescent microscopy for t...

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Saratov Fall Meeting (SFM) on Laser Physics and Photonics XVIII;and Computational Biophysics and Analysis of biomedical Data IV / 5th International symposium on Optics and Biophotonics

作者： Semyachkina-Glushkovskaya, O. Abdurashitov, A. Namykin, A. Fedosov, I. Pavlov, A. Karavaev, A. Sindeeva, O. Shirokov, A. Ulanova, M. Shushunova, N. Khorovodov, A. Agranovich, I. Bodrova, A. Sagatova, M. Shareef, Ali Esmat Saranceva, E. Dvoryatkina, M. Tuchin, V. Saratov NG Chernyshevskii State Univ Astrakhanskaya Str 83 Saratov 410012 Russia Yuri Gagarin State Tech Univ Saratov Politech Skaya Str 77 Saratov 410054 Russia Russian Acad Sci Inst Bioorgan Chem Entusiastov Str 13 Saratov 410049 Russia Natl Res Tomsk State Univ Lenins Av 36 Tomsk 634050 Russia Russian Acad Sci Inst Precis Mech & Control Rabochaya Str 24 Saratov 410028 Russia

ISBN: (数字)9781510620049

ISBN: (纸本)9781510620049

the development of novel technologies for the imaging of meningeal lymphatic vessels is one of the amazing trends of biophotonics thanks to discovery of brain lymphatics over several years ago. However, there is the limited technologies exist for the study of lymphatics in vivo because lymphatic vessels are transparent with a low speed flow of lymph. Here we demonstrate the successful application of fluorescent microscopy for the imaging of lymphatic system in the mouse brain in vivo.

关键词： cerebral lymphatic vessels glymphatic system optical imaging optical coherent tomography fluorescent microscopy

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Self-organization in multilayer network with adaptation mechanisms based on competition

Self-organization in multilayer network with adaptation mech...

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Saratov Fall Meeting (SFM) on Laser Physics and Photonics XVIII;and Computational Biophysics and Analysis of biomedical Data IV / 5th International symposium on Optics and Biophotonics

作者： Pitsik, Elena N. Makarov, Vladimir V. Nedaivozov, Vladimir O. Kirsanov, Daniil V. Goremyko, Mikhail V. Yuri Gagarin State Tech Univ Saratov REC Artificial Intelligence Syst & Neurotechnol Politech Skaya Str 77 Saratov 410056 Russia Saratov State Tech Univ Dept Automatizat Control & Mechatron Politech Skaya Str 77 Saratov 410056 Russia

ISBN: (数字)9781510620049

ISBN: (纸本)9781510620049

the paper considers the phenomena of competition in multiplex network whose structure evolves corresponding to dynamics of it's elements, forming closed loop of self-learning with the aim to reach the optimal topology. Numerical analysis of proposed model shows that it is possible to obtain scale-invariant structures for corresponding parameters as well as the structures with homogeneous distribution of connections in the layers. Revealed phenomena emerges as the consequence of the self-organization processes related to structure-dynamical self-learning based on homeostasis and homophily, as well as the result of the competition between the network's layers for optimal topology. It was shown that in the mode of partial and cluster synchronization the network reaches scale-free topology of complex nature that is different from layer to layer. However, in the mode of global synchronization the homogeneous topologies on all layer of the network are observed. this phenomenon is tightly connected with the competitive processes that represent themselves as the natural mechanism of reaching the optimal topology of the links in variety of real-world systems.

关键词： Multiplex network complex network Kuramoto model synchronization competition

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Energy Consumption Forecasting Using ARIMA and Neural Network Models 5

Energy Consumption Forecasting Using ARIMA and Neural Networ...

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5th International symposium on Electrical and Electronics Engineering (ISEEE)

作者： Nichiforov, Cristina Stamatescu, Iulia Fagarasan, Ioana Stamatescu, Grigore Univ Politehn Bucuresti Fac Automat Control & Comp Dept Automat & Ind Informat Bucharest Romania

ISBN: (纸本)9781538620595

Energy forecast is essential for a good planning of the electricity consumption as well as for the implementation of decision support systems which can lead the decision making process of energy system. Energy consumption time series prediction problems represent a difficult type of predictive modelling problem due to the existence of complex linear and non-linear patterns. this paper presents two approaches for energy consumption forecast: an autoregressive integrated moving average (ARIMA) model and a non-linear autoregressive neural network (NAR) model. the two models are deeply described and finally compared in order to evaluate their performance.

关键词： forecasting energy consumption artificial neural networks arima time series

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Human-Robot Impact Model: For safety assessment of collaborative robot design 5

Human-Robot Impact Model: For safety assessment of collabora...

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5th IEEE International symposium on Robotics and Intelligent Sensors (IEEE IRIS)

作者： Vemula, Bhanoday Reddy Ramteen, Marcus Spampinato, Giacomo Fagerstrom, Bjorn Malardalen Univ Eskilstuna Sweden ABB Corp Res Vasteras Sweden ABB Robot Vasteras Sweden ABB Power Consultant Vasteras Sweden

ISBN: (纸本)9781538613429

In this research, a novel impact simulation model based on compliant contact force (CCF) modelling approach is presented. this model can simulate the physical impact between non-homogeneous and layered elastic bodies representing the robot and human body parts. the proposed CCF model is intended to be used by the robot designers to execute safety evaluation tasks during the design and development of collaborative robot systems. the main theoretical contribution from this CCF impact model is related to the formulations, which can account for the contact behavior due to the non-homogeneous nature of the impacting bodies. the relevance of the proposed impact simulation is evaluated based on a comparative analysis with other available relevant models from the literature as well as with Finite element based simulation model. Finally, the influence of various robot design parameters on the impact severity is analyzed for different impact scenarios by adopting the proposed CCF model.

关键词： Collaboborative robot Safety assessments Impact modelling

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Regressive cross-correlation of pressure signals in the region of stenosis: Insights from particle image velocimetry experimentation

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biomedical SIGNAL PROCESSING AND control 2017年第0期32卷 143-149页

作者： Docherty, P. D. Geoghegan, P. H. Huetter, L. Jermy, M. Sellier, M. Univ Canterbury Dept Mech Engn Private Bag 4800 Christchurch 8140 New Zealand Karlsruhe Inst Technol Dept Mech Engn POB 3640 D-76021 Karlsruhe Germany

Various anomalies in arterial geometry can cause serious hemodynamic dysfunction. In particular, stenosed arteries can cause reduced blood flow, excess stress on the heart, and elements can shear off causing blockage, which in the brain leads to stroke. this research assesses whether pressure signals obtained close to a stenosis are distinct from signals observed in other areas of the artery. Particle image velocimetry was used to determine the fluid velocity field within a compliant phantom that mimicked a stenosis in the carotid artery during physiological pulsatile pressure waves. the Navier-Stokes representation of the velocity fields were used to determine the pressure responses across the domain. A three-parameter regressive cross-correlation was used to calibrate the output pressure responses against the pressure input signal. the transform between the input-output pressure signals allowed detection of the region immediately downstream of the stenosis. In particular, if the cross correlative parameter that relates the instantaneous transfer across the input-output signals was greater than the delayed transfer parameter a stenosis is present. In contrast, the delayed transfer parameter was larger for the region upstream of the stenosis. this outcome is particularly valuable as it does not require calibration of the absolute pressure, which can be difficult to determine physiologically due to factors such as arterial geometry and intrathoracic pressure. However, the outcomes need to be validated in more geometries prior to clinical validation. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Particle image velocimetry Hemodynamics Autoregressive correlation modelling Fluid dynamics Experimental fluids

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Incremental modelling of transcriptional regulatory networks 5

Incremental modelling of transcriptional regulatory networks

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5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017

作者： Prabhu, H.R. Sachin Wei, Hua-Liang Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom

ISBN: (纸本)9781509059904

Models of transcriptional regulatory networks (TRN) have benefited a wide range of biological and biomedical research. Several system theoretical modelling approaches are available in literature including those that divide TRNs into subnet-works for computational advantages, estimate and recombine them. However, the problem of updating an existing model has not gained much attention. Such a situation is not rare in biological research where new protein-gene interactions are discovered on a regular basis. In such cases, existing models must be discarded and new system-wide models must be estimated. this may result in estimates that are inconsistent with previously validated models. this can be avoided by appending a previously validated model with a new subnetwork. In this paper, an optimisation based approach is developed to address this challenge. With the help of an experimental data set obtained from literature, it is shown that a convex combination of subnetworks can model system-wide data with signal reconstruction error comparable to that obtained with a modelling technique available in literature. © 2017 IEEE.

关键词： Signal reconstruction

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