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Improvement of low frequency oscillation damping in power systems via an adaptive critic based neurofuzzy controller 8th

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8th International Conference on Knowledge-Based Intelligent Information and engineering Systems, KES 2004

作者： Rashidi, Farzan Moshiri, Behzad Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Iran

ISBN: (纸本)9783540232056

This paper proposes an Adaptive Critic Based Neuro-Fuzzy controller (ACBNFC) to Thyristor controlled Series Capacitor (TCSC), which might have a significant impact on power system dynamics. The function of the ACBNFC is to control a firing angle of the TCSC. The proposed method is used for damping the low frequency oscillations caused by disturbances such as a sudden change of small or large loads or an outage in the generators or transmission lines. To evaluate the usefulness of the proposed method, the computer simulation for single machine infinite system is performed and compared with fuzzy PD controller. Obtained results show the proposed control strategy is very robust, flexible and could be used to get the desired performance levels. The response time is also very fast. Simulation results that have been compared with fuzzy PD controller show that our method has the better control performance than fuzzy PD controller. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Damping

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Intelligent modeling and control of washing machine using LLNF modeling and modified BELBIC

Intelligent modeling and control of washing machine using LL...

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5th International Conference on control and Automation, ICCA'05

作者： Mohammadi-Milasi, Rasoul Lucas, Caro Araabi, Babak Nadjar Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Iran Department of Electrical and Computer Engineering School of Cognitive Sciences University of Tehran Iran

ISBN: (纸本)0780391381

Intelligent control of home appliances, have in recent years, attracted much theoretical attention, as well as becoming a major factor for industrial and economic success and rapid market penetration. Washing machines represent an important market. Intelligent control techniques are capable of providing useful means for both easier use and energy and water conservation. In this paper, we use two techniques that we have successfully used in other intelligent modeling and control applications. Firstly, we use a neurofuzzy locally linear model system for data driven modeling of the machine. Secondly, we use a neural computing technique, based on a mathematical model of amygdala and the limbic system, for emotional control of the washing machine. The obtained results indicate the applicability of the proposed techniques in this important business sector. © 2005 IEEE.

关键词： Washing machines

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Identifying Critical Components of Combined Cycle Power Plants for Implementation of Reliability-centered Maintenance

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CSEE Journal of Power and Energy Systems 2016年第2期2卷 87-97页

作者： Hamed Sabouhi Mahmud Fotuhi-Firuzabad Payman Dehghanian Center of Excellence in Power System Management and Control Department of Electrical EngineeringSharif University of TechnologyTehranIran Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX77843USA

Maintenance scheduling and asset management practices play an important role in power systems,specifically in power generating *** paper presents a novel riskbased framework for a criticality assessment of plant components as a means to conduct more focused maintenance *** components in power plants that influence overall system performance are identified by quantifying their failure impact on system reliability,electric safety,cost,and the *** of plant components according to the proposed risk-based method ensures that the most effective and techno-economic investment decisions are ***,in turn,helps to initiate modern maintenance approaches,such as reliability-centered maintenance(RCM).The proposed method is applied to a real combined cycle power plant(CCPP)in Iran,composed of two gas turbine power plants(GTPP)and one steam turbine power plant(STPP).The results demonstrate the practicality and applicability of the presented approach in real world practices.

关键词： Combined cycle power plant(CCPP) critical component gas turbine power plant(GTPP) reliability-centered maintenance(RCM) risk safety steam turbine power plant(STPP)

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Parallel Building: A Complex System Approach for Smart Building Energy Management

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1452-1461页

作者： Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid

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Skull assembly and completion using template-based surface matching

Skull assembly and completion using template-based surface m...

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International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission

作者： Wei, Li Yu, Wei Li, Maoqing Li, Xin Xiamen University Department of Automation China Louisiana State University Department of Electrical and Computer Engineering Center for Computation and Technology United States

ISBN: (纸本)9780769543697

We present a skull assembly and completion framework based on shape matching. In order to assemble fragmented skulls, we need to compute rigid transformations from these fragments to their assembled geometry. We develop a reliable assembly pipeline where each fragment is matched and transformed to be aligned with the template. In order to further complete the assembled skull with several damaged regions, we use the template to repair damaged regions on the assembled skull. The entire pipeline has been conducted on several real skull models and demonstrated great robustness and effectiveness. © 2011 IEEE.

关键词： Pipelines

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Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 64-74页

作者： Zhengguo Sheng Saskia Pfersich Alice Eldridge Jianshan Zhou Daxin Tian Victor C.M.Leung the Department of Engineering and Design University of Sussex Infineon Technologies AG the Department of Music University of Sussex Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety ControlSchool of Transportation Science and EngineeringBeihang University the Department of Electrical and Computer Engineering The University of British Columbia

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity *** scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ *** and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination *** tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.

关键词： Acoustic sensor networks edge computing energy efficiency

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A Modified Dempster Shafer Approach to Classification in Surgical Skill Assessment 31

A Modified Dempster Shafer Approach to Classification in Sur...

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31st International Conference on electrical engineering, ICEE 2023

作者： Iranfar, Arash Soleymannejad, Mohammad Moshiri, Behzad Taghirad, Hamid D. School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada Industrial Control Center of Excellence Faculty of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350312560

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is. © 2023 IEEE.

关键词： Classification (of information)

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A spectrum sensing and transmission model for secondary users in cognitive radios

A spectrum sensing and transmission model for secondary user...

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2012 20th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2012

作者： Gandhi, Pratik Alshawabkeh, Ibrahim Chandra, Kavitha Thompson, Charles Center for Advanced Computation and Telecommunications Department of Electrical and Computer Engineering University of Massachusetts Lowell Lowell MA 01854 United States

ISBN: (纸本)9789532900347

An analysis of spectrum sensing and transmission of a secondary user sharing a channel with a licensed primary user is presented. The primary user access patterns are modeled as an on-off Markov process. A five state Markov model is derived to describe the performance of a secondary user that aims to utilize the channel during its idle state. Assuming perfect knowledge of the primary user access patterns, the model is analyzed with respect to the size of a back-off counter chosen when the channel is sensed busy and the size of the window allocated for secondary transmission when the channel is sensed idle. An analytical region is derived for the secondary users to operate in while the primary channel is free. © 2012 University of Split.

关键词： Cognitive radio

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Dynamic harmonic texture mapping using methods of fundamental solutions

Dynamic harmonic texture mapping using methods of fundamenta...

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6th International Conference on computer Science and Education, ICCSE 2011

作者： Xu, Huanhuan Li, Xin Department of Electrical and Computer Engineering Center for Computation and Technology Louisiana State University Baton Rouge LA 70803 United States

ISBN: (纸本)9781424497188

Feature-aligned surface mapping improves the effect of texture mapping by enforcing feature correspondence between 3D meshes and images. We present a novel constrained texture mapping computation framework based on the method of fundamental solution (MFS). We first flatten the surface onto planar domain, then compose a 2D harmonic map with user specified features aligned with corresponding texels in the texture image. We demonstrate that our framework can efficiently conduct feature-aligned texture mapping with dynamic deforming meshes or texture images. © 2011 IEEE.

关键词： Harmonic functions

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Modelling time dependent swelling potential of mudrock using laboratory tests and artificial neural network

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Transactions of the Institutions of Mining and Metallurgy, Section A: Mining Technology 2008年第1期117卷 32-41页

作者： Doostmohammadi, R. Moosavi, M. Araabi, B.N. Mining Engineering Department Zanjan University 5 km Tabriz road Zanjan Iran School of Mining Engineering University of Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Iran

The focus of this research work is modelling time dependent swelling strain and pressure simultaneously in weak rock. Various swelling potential under different boundary conditions is considered. Free and semiconfined swelling strain and swelling pressure under oedometric condition are the most proposed tests for determination of the swelling potential of weak rock. In this paper, a modified swelling strain and pressure test is introduced using springs to provide deformable axial stiffness. The swelling strain and pressure of mudrock are modelled by artificial neural network for various swellable clay, carbonate, initial water content and different boundary stiffness, by using a combination of the proposed modified test along with the existing tests. This method correlates the results of laboratory tests with an acceptable accuracy. The proper operation of the developed model for predicting the time dependent swelling strain and pressure of mudrock under deformable and rigid axial boundaries is confirmed. © 2008 Institute of Materials, Minerals and Mining.

关键词： Neural networks

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