Conferences related to Smart Structures

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2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting

The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science


2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe)

Energy conversion and conditioning technologies, power electronics, adjustable speed drives and their applications, power electronics for smarter grid, energy efficiency,technologies for sustainable energy systems, converters and power supplies


2020 IEEE International Conference on Plasma Science (ICOPS)

IEEE International Conference on Plasma Science (ICOPS) is an annual conference coordinated by the Plasma Science and Application Committee (PSAC) of the IEEE Nuclear & Plasma Sciences Society.


2020 IEEE Power & Energy Society General Meeting (PESGM)

The Annual IEEE PES General Meeting will bring together over 2900 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutorials and more


2020 Joint Conference of the IEEE International Frequency Control Symposium and International Symposium on Applications of Ferroelectrics (IFCS-ISAF)

Ferroelectric materials and applications


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Periodicals related to Smart Structures

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


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Most published Xplore authors for Smart Structures

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Xplore Articles related to Smart Structures

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Distributed fibre grating sensor for smart structures applications

IEE Colloquium on Optical Techniques for Smart Structures and Structural Mmonitoring (Digest No. 1997/033), 1997

Optical fibre sensors have been widely used to measure strain and temperature for smart structures applications. Distributed sensors using fibre Bragg gratings have previously been demonstrated. These sensors have many advantages, including high spatial resolution and no dead-zones, allowing a complete spatial image of a measurand to be obtained. Interrogation systems using a tunable narrowband source have been previously demonstrated, ...


Investigation on damage self-diagnosis of fiber smart structures based on LS-SVM

2010 2nd IEEE International Conference on Information Management and Engineering, 2010

The self-diagnosis function is one of main research contents of smart structures. And it is the foundation of other functions realization of smart structures. Aiming at the localization of present structural damage detection methods and the virtue of Least Square Support Vector Machine arithmetic, Least Square Support Vector Machine (LS-SVM) used to detect damages in fiber smart structures was proposed ...


IEE Colloquium on Optical Techniques for SMART Structures and Structural Monitoring (Ref. No.1997/033)

IEE Colloquium on Optical Techniques for Smart Structures and Structural Mmonitoring (Digest No. 1997/033), 1997

None


Fuzzy Least Square Support Vector Machine Applied to Detect Damage for Fiber Smart Structures

2008 International Symposium on Intelligent Information Technology Application Workshops, 2008

The research on realizing the self-detecting damage function is one of the main research contents of smart structures, and an important issue related to the self-detecting damage function is the method of damage detection. It has been of an important theoretical meaning and a great practical value for applications of smart structures to research on this issue. Due to the ...


Parallel Bionic Algorithm Used to Optimize Sensor Placement for Self-diagnostic Smart Structures

2008 International Conference on Computer Science and Information Technology, 2008

Smart structures are a multi-sensor architecture, and the problem of multi- sensor optimal placement involves the cost-effective factor. It has been of great practical value and is worth to researching further to optimize sensors' locations and number for smart structures. For piezoelectric smart composite laminated plates, least square wavelet support vector machine (LS-WSVM) with wavelet kernel function is proposed to ...


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Educational Resources on Smart Structures

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IEEE-USA E-Books

  • Distributed fibre grating sensor for smart structures applications

    Optical fibre sensors have been widely used to measure strain and temperature for smart structures applications. Distributed sensors using fibre Bragg gratings have previously been demonstrated. These sensors have many advantages, including high spatial resolution and no dead-zones, allowing a complete spatial image of a measurand to be obtained. Interrogation systems using a tunable narrowband source have been previously demonstrated, but are limited to monotonically varying wavelength profiles. Volanthen et al. (1996) demonstrated a broadband interrogation system for distributed grating sensors, capable of measuring arbitrary strain fields. Low-coherence interferometry selected the location under interrogation and a tunable filter measured the local wavelength. Further to previously published work, two improved systems are presented. The first system uses electronic processing for rapid and accurate wavelength measurement, whereas the second system uses a commercially available optical coherence domain reflectometer (OCDR) as part of the interrogation system. The latest sensing results from both systems are presented. The ultimate performance limits are examined experimentally and compared to the theoretically derived values. Both thermal and strain measurements have been made using single fibre gratings up to 40 cm in length. Multiple grating sensors have also been combined into a single sensing network, producing a sensor system that is both distributed and multiplexed.

  • Investigation on damage self-diagnosis of fiber smart structures based on LS-SVM

    The self-diagnosis function is one of main research contents of smart structures. And it is the foundation of other functions realization of smart structures. Aiming at the localization of present structural damage detection methods and the virtue of Least Square Support Vector Machine arithmetic, Least Square Support Vector Machine (LS-SVM) used to detect damages in fiber smart structures was proposed in this paper and was compared with the improved BP neural network. The experimental research results show that this proposed method is feasible and effective for detecting damages in smart structures. Least Square Support Vector Machine provides the more advanced method for realizing the self-diagnosis function in fiber smart structures.

  • IEE Colloquium on Optical Techniques for SMART Structures and Structural Monitoring (Ref. No.1997/033)

    None

  • Fuzzy Least Square Support Vector Machine Applied to Detect Damage for Fiber Smart Structures

    The research on realizing the self-detecting damage function is one of the main research contents of smart structures, and an important issue related to the self-detecting damage function is the method of damage detection. It has been of an important theoretical meaning and a great practical value for applications of smart structures to research on this issue. Due to the structure damage detectionpsilas essence as pattern recognition or nonlinear regression, damagepsilas nondeterministic attribute, and least square support vector machinepsilas (LS-SVM) excessive sensitivity to isolated data points, fuzzy least square support vector machine (FLS-SVM) is proposed to detect damage locations for fiber smart structures by introducing fuzzy memberships to LS-SVM. The testing results show that, FLS-SVM possesses the higher damage locating accuracy, and the bitter dissemination ability than LS-SVM under the same conditions. And FLS-SVM obtains the better noise immunity, and thus strengthens its own robustness.

  • Parallel Bionic Algorithm Used to Optimize Sensor Placement for Self-diagnostic Smart Structures

    Smart structures are a multi-sensor architecture, and the problem of multi- sensor optimal placement involves the cost-effective factor. It has been of great practical value and is worth to researching further to optimize sensors' locations and number for smart structures. For piezoelectric smart composite laminated plates, least square wavelet support vector machine (LS-WSVM) with wavelet kernel function is proposed to establish the performance function of damage detection, and then an improved genetic algorithm (GA) is applied to optimize the performance function. To enhance the algorithm speed, LS-WSVM adopted as parallel mode are combined with GA, that is, a parallel genetic algorithm integrated neural network is constructed to realize the optimal sensor placement corresponding to its primal sensor placement based on damage detection. Simulation results show that the optimal placements of different number of sensors are consistent with engineering judgments, and the optimal sensors' number corresponding to its primal sensorspsila number can be determined through the method of parallel genetic algorithm integrated neural network, and the method possesses the advantages of faster speed, etc..

  • Temperature compensation for fiber optic strain sensor in smart structures

    The applications of Fabry-Perot (F-P) fiber-optic white light interference strain sensor are mainly restricted by the cross-sensitivity effects of the cavity length changes, which caused by the changes of the temperature and stress. In this paper, the sensing principles of the white light interference F-P fiber-optic strain sensor, used to measure the smart structure, is briefly introducted, then the methods to eliminate or weaken the temperature sensitivity of is described. The temperature compensation formula and project implementation are presented during the fiber-optic strain measurement. The temperature compensation method mentioned in this paper, can effectively removes the strain measurement error due to the temperature changes.

  • ILC based active vibration control of smart structures

    Vibration control of flexible structures is observed to be a good way to maintain structural integrity as well as to optimize performance for space structures currently. Taking the piezoelectric flexible structure as research object, this paper focuses on the methodology and implementation of a vibration suppression system. An efficient method to simulate and analyze the piezoelectric plate is presented with ANSYS applied, while an iterative learning control (ILC) algorithm is proposed as the control strategy. A finite element model of the flexible plate is constructed by an APDL (ANSYS parameter design language) program. By transient analysis of piezoelectric flexible plate, the models of the control channel and the primary path are obtained. Finally, an ILC based controller, having good cancellation of the vibration of the flexible plate, is developed. The ANSYS experiments suggest that analyzing the piezoelectric flexible plate by ANSYS is a cost-effective method. The SIMULINK results show that the open-loop PID-type learning algorithm is effective for the active vibration control.

  • Kernel optimization of LS-SVM based on damage detection for smart structures

    The method of damage detection is an important issue related to the self- detecting damage function for smart structures. Based on smart structures' nonlinear, parallel features, and the existed intrinsic flaws of conventional neural networks, research on Support Vector Machine (SVM) used to detect damages for smart structures has become one of main researches recently. Aimed at the key and difficult research problem on SVM - the selection and construction of kernel functions, a mixed kernel function used to Least Square Support Vector Machine (LS-SVM) is constructed through analyzing the existed kernel functions of LS-SVM. Based on damage detection for smart structures, the parameters of LS-SVM with the mixed kernel are optimized by Genetic Algorithms (GA), and the detecting results are compared with that of LS-SVM based on RBF kernel. The result shows that, the accuracy of damage detection based on LS-SVM with mixed kernel is higher than that based on LS-SVM with RBF kernel under the same condition. Compared with LS-SVM with RBF kernel, LS-SVM with mixed kernel possesses the better dissemination ability and stronger learning ability by absorbing the advantages of RBF kernel and polynomial kernel function.

  • Design, controlling and detection of smart structures

    The application of smart structures is one of the most challenging areas to be involved in today. Enabling technologies for smart structures include materials, advanced actuation, sensing techniques, adaptive computer algorithms with the ability to learn and control outputs, and finally methods whereby energy usage can be optimized. In this paper, some technologies are introduced, such as optical design of wave-front sensors for adaptive optical systems, surface control and vibration suppression of a large mm-wave telescope for smart structures.

  • Optimal Sensor Placement Based on Parallel Quantum Genetic Algorithm Integrated LS-SVMs for Self-diagnostic Smart Structures

    It has been of great practical value to optimize sensors' locations and number for the self-diagnostic smart structures. Based on damage detection, Least Square Support Vector Machine (LS-SVM) is proposed to establish the performance function of damage detection for the piezoelectric smart structures, and then quantum genetic algorithm (QGA) is applied to optimize the performance function. To enhance the algorithm speed, LS-SVMs adopted as parallel mode are combined with QGA, that is, a parallel QGA integrated LS- SVMs is constructed to realize optimal sensor placement corresponding to its primal sensor placement. For the more sensors' primal placement, the number of sensors can be reduced effectively through the method of parallel QGA integrated LS-SVMs, and thus leads to cost savings. Compared with traditional GA, QGA possesses the better searching ability and the faster convergence speed.



Standards related to Smart Structures

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