The document is a review of recent advances in using neural network techniques for damage identification of bridges. It begins with an introduction discussing traditional structural identification methods versus soft computing methods such as neural networks, genetic algorithms, and fuzzy logic. It then provides numerous examples of recent studies from 2008-2013 that have used soft computing methods like neural networks, genetic algorithms and fuzzy neural networks for structural assessment of bridges. The document discusses nonlinear feedforward neural network models and traditional learning methods. It also discusses a probabilistic interpretation of neural networks that allows for Bayesian inference and a hierarchical multi-level Bayesian approach for neural network modeling.
Steganographic Application of improved Genetic Shifting algorithm against RS ...Vladislav Kaplan
Steganography is a “science”, the method of hiding sent information. Unlike cryptography that deals with coding of information, the main idea of steganography is hiding the fact that the message exists. It embeds the secret message in cover media (image, audio, video, text, etc.). During the last years with the development of digital image processing, methods of digital steganography have gained a lot of popularity. The most popular steganography method is LSB (Last Significant Bit) replacement in the cover image. With extensive evolution of steganography, Steganalysis methods have a lot of importance. Steganalysis algorithms role is to detect a hidden secret message inside any media. The most notable Steganalysis algorithm is the RS method [1], which detects stegamesage by the statistical analysis applied on image pixels.
Shen Wang and others [2] created a new algorithm based on Genetic Shifting method (GSM). GSM performs manipulation and modification of the original image pixels. GSM algorithm keeps image statistic after inserting a hidden message and is hard to be detected by the RS analysis. The goal of the project is to demonstrate effectiveness and stability of GSM algorithm against RS analysis by using mathematical and statistical methods.
Image restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
Practical steps for non-machine learners on how to prepare your medical image dataset for deep learning modelling.
Here we use a fundus image dataset as an example that might have controls (healthy eyes) and glaucomatous fundus images with three different severities. In glaucoma, the optic disc is of a special interest so we want to annotate that from the images using a bounding box to help the deep learning training.
In this paper, a new steganography algorithm has been suggested to enforce the security of data hiding and to increase the amount of payloads. This algorithm is based on four safety layers; the first safety layer has been initiated through compression and an encryption of a confidential message using a set partition in hierarchical trees (SPIHT) and advanced encryption standard (AES) mechanisms respectively. An irregular image segmentation algorithm (IIS) on a cover-image (Ic) has been constructed successfully in
the second safety layer, and it is based on the adaptive reallocation segments' edges (ARSE) by applying an
adaptive finite-element method (AFEM) to find the numerical solution of the proposed partial differential equation (PDE). An intelligent computing technique using a hybrid adaptive neural network with a modified ant colony optimizer (ANN_MACO) has been proposed in the third safety layer to construct a
learning system. This system accepts entry using support vector machine (SVM) to generate input patterns as features of byte attributes and produces new features to modify a cover-image. The significant innovation of the proposed novel steganography algorithm is applied efficiently on the forth
safety layer which is more robust for hiding a large amount of confidential message reach to six bits per pixel (bpp) into color images. The new approach of hiding algorithm works against statistical and visual attacks with high imperceptible of hiding data into stego-images (Is). The experimental results are
discussed and compared with the previous steganography algorithms; it demonstrates that the proposed algorithm has a significant improvement on the effect of the security level of steganography by making an arduous task of retrieving embedded confidential message from color images.
This talk will provide an introduction to the DReAMS reserach line at NECSTLab. At NECSTLab we are working at developing a Coursera specialization. The set of four courses will introduce the students to the FPGA technologies, to the concept of reconfigurability in FPGAs, presenting the available mechanisms and technologies at the device level and the tools and design methodologies required to design FPGA-based computing systems. The course will present the different aspects of the design of FPGA-based systems, starting from basic knowledge to advanced design methodologies to implement complex design via SDAccel on Amazon AWS F1 instances. This talk will start describing the work done so far and the future plans in realizing the specialization.
We will then focus on two research projects that will be also used during the online classes.
We will first present CAOS, a framework which helps the application designer in identifying acceleration opportunities and guides through the implementation of the final FPGA-based system. The CAOS platform targets the full stack of the application optimization process, starting from the identification of the kernel functions to accelerate, to the optimization of such kernels and to the generation of the runtime management and the configuration files needed to program the FPGA. After CAOS will present the HUGenomics projects. The unique genetic profile of a species is leading to the development of customized treatments, from personalized medicine to agrigenomics, but the exponential growth of available genomic data requires a computational effort that may limit the progress of these fields. The HUGenomics framework aims at facilitating genome assembly process by means of both hardware accelerated algorithms and scientific data visualization tools. Indeed, the system raises the level of abstraction allowing users to easily integrate custom algorithms into the hardware pipeline without any knowledge of the underneath architecture.
Steganographic Application of improved Genetic Shifting algorithm against RS ...Vladislav Kaplan
Steganography is a “science”, the method of hiding sent information. Unlike cryptography that deals with coding of information, the main idea of steganography is hiding the fact that the message exists. It embeds the secret message in cover media (image, audio, video, text, etc.). During the last years with the development of digital image processing, methods of digital steganography have gained a lot of popularity. The most popular steganography method is LSB (Last Significant Bit) replacement in the cover image. With extensive evolution of steganography, Steganalysis methods have a lot of importance. Steganalysis algorithms role is to detect a hidden secret message inside any media. The most notable Steganalysis algorithm is the RS method [1], which detects stegamesage by the statistical analysis applied on image pixels.
Shen Wang and others [2] created a new algorithm based on Genetic Shifting method (GSM). GSM performs manipulation and modification of the original image pixels. GSM algorithm keeps image statistic after inserting a hidden message and is hard to be detected by the RS analysis. The goal of the project is to demonstrate effectiveness and stability of GSM algorithm against RS analysis by using mathematical and statistical methods.
Image restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
Practical steps for non-machine learners on how to prepare your medical image dataset for deep learning modelling.
Here we use a fundus image dataset as an example that might have controls (healthy eyes) and glaucomatous fundus images with three different severities. In glaucoma, the optic disc is of a special interest so we want to annotate that from the images using a bounding box to help the deep learning training.
In this paper, a new steganography algorithm has been suggested to enforce the security of data hiding and to increase the amount of payloads. This algorithm is based on four safety layers; the first safety layer has been initiated through compression and an encryption of a confidential message using a set partition in hierarchical trees (SPIHT) and advanced encryption standard (AES) mechanisms respectively. An irregular image segmentation algorithm (IIS) on a cover-image (Ic) has been constructed successfully in
the second safety layer, and it is based on the adaptive reallocation segments' edges (ARSE) by applying an
adaptive finite-element method (AFEM) to find the numerical solution of the proposed partial differential equation (PDE). An intelligent computing technique using a hybrid adaptive neural network with a modified ant colony optimizer (ANN_MACO) has been proposed in the third safety layer to construct a
learning system. This system accepts entry using support vector machine (SVM) to generate input patterns as features of byte attributes and produces new features to modify a cover-image. The significant innovation of the proposed novel steganography algorithm is applied efficiently on the forth
safety layer which is more robust for hiding a large amount of confidential message reach to six bits per pixel (bpp) into color images. The new approach of hiding algorithm works against statistical and visual attacks with high imperceptible of hiding data into stego-images (Is). The experimental results are
discussed and compared with the previous steganography algorithms; it demonstrates that the proposed algorithm has a significant improvement on the effect of the security level of steganography by making an arduous task of retrieving embedded confidential message from color images.
This talk will provide an introduction to the DReAMS reserach line at NECSTLab. At NECSTLab we are working at developing a Coursera specialization. The set of four courses will introduce the students to the FPGA technologies, to the concept of reconfigurability in FPGAs, presenting the available mechanisms and technologies at the device level and the tools and design methodologies required to design FPGA-based computing systems. The course will present the different aspects of the design of FPGA-based systems, starting from basic knowledge to advanced design methodologies to implement complex design via SDAccel on Amazon AWS F1 instances. This talk will start describing the work done so far and the future plans in realizing the specialization.
We will then focus on two research projects that will be also used during the online classes.
We will first present CAOS, a framework which helps the application designer in identifying acceleration opportunities and guides through the implementation of the final FPGA-based system. The CAOS platform targets the full stack of the application optimization process, starting from the identification of the kernel functions to accelerate, to the optimization of such kernels and to the generation of the runtime management and the configuration files needed to program the FPGA. After CAOS will present the HUGenomics projects. The unique genetic profile of a species is leading to the development of customized treatments, from personalized medicine to agrigenomics, but the exponential growth of available genomic data requires a computational effort that may limit the progress of these fields. The HUGenomics framework aims at facilitating genome assembly process by means of both hardware accelerated algorithms and scientific data visualization tools. Indeed, the system raises the level of abstraction allowing users to easily integrate custom algorithms into the hardware pipeline without any knowledge of the underneath architecture.
Falling costs with rising quality via hardware innovations and deep learning.
Technical introduction for scanning technologies from Structure-from-Motion (SfM), Range sensing (e.g. Kinect and Matterport) to Laser scanning (e.g. LiDAR), and the associated traditional and deep learning-based processing techniques.
Note! Due to small font size, and bad rendering by SlideShare, better to download the slides locally to your device
Alternative download link for the PDF:
https://www.dropbox.com/s/eclyy45k3gz66ve/proptech_emergingScanningTech.pdf?dl=0
Kate Storrs - Closing the loop between biological and artificial vision - Cre...Luba Elliott
This talk by Kate Storrs from MRC Cognition & Brain Sciences Unit at Cambridge on "Closing the loop between biological and artificial vision" was part of the Creative AI meetup on the 24th May held at IDEA London.
These are my slides for the 2012 meeting of all german DFG founded research training groups (Graduiertenkolleg) in computer science. I present the group METRIK.
Deconstructing SfM-Net architecture and beyond
"SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates."
Alternative download:
https://www.dropbox.com/s/aezl7ro8sy2xq7j/sfm_net_v2.pdf?dl=0
Shallow introduction for Deep Learning Retinal Image AnalysisPetteriTeikariPhD
Overview of retinal imaging techniques such as fundus photography, optical coherence tomography (OCT) along with future upgrades such as multispectral imaging, OCT angiography, adaptive optics imaging and polarization-sensitive OCT. This is followed by an overview of deep learning image analysis methods suitable to be used with retinal imaging techniques.
Alternative download link: https://www.dropbox.com/s/n01w02cjaf68vbo/retina_deepLearning_pipeline.pdf?dl=0
Sidang Skripsi, Gia Muhamad Agusta, 10909100144, Program Studio Teknik Informatika. 10 Oktober 2012. Universitas Islam Negeri Syarif Hidayatullah Jakarta
10,00 Modelling and analysis of geophysical data using geostatistics and mach...Beniamino Murgante
10,00 Modelling and analysis of geophysical data using geostatistics and machine learning
Vasily Demyanov – Heriot–Watt Institute, Edinburgh (U.K.)
Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)
Calcestruzzo Armato e Calcestruzzo Armato Precompresso secondo l'Eurocodice 2. Tullio Antonini. INTERSCIENZE Edizioni Scientifiche. Presentazione di Giulio Maier e Pietro Gambarova.
This lecture illustrates the structural use of aluminium extrusions for the replacement of damaged concrete bridge decks as realized in a case in Sweden; it describes the technical and economic advantages of the chosen light-weight aluminium design. Basic knowledge of structural engineering and extrusion design; and some familiarity with TALAT lectures no. 1302, 1501, 2200, 2300 and 2400 is assumed.
VALUTAZIONI CRITICHE DEI MODELLI NUMERICI E DEI RISULTATIStroNGER2012
Lezione di Chiara Crosti
ANALISI STRUTTURALE DI PONTI E DI OPERE COMPLESSE DI INGEGNERIA CIVILI
Roma 10 ottobre 2014,
Facoltà di Ingegneria Civile ed Industriale
Sapienza - Università di Roma
Via Eudossiana, 18 Roma
Falling costs with rising quality via hardware innovations and deep learning.
Technical introduction for scanning technologies from Structure-from-Motion (SfM), Range sensing (e.g. Kinect and Matterport) to Laser scanning (e.g. LiDAR), and the associated traditional and deep learning-based processing techniques.
Note! Due to small font size, and bad rendering by SlideShare, better to download the slides locally to your device
Alternative download link for the PDF:
https://www.dropbox.com/s/eclyy45k3gz66ve/proptech_emergingScanningTech.pdf?dl=0
Kate Storrs - Closing the loop between biological and artificial vision - Cre...Luba Elliott
This talk by Kate Storrs from MRC Cognition & Brain Sciences Unit at Cambridge on "Closing the loop between biological and artificial vision" was part of the Creative AI meetup on the 24th May held at IDEA London.
These are my slides for the 2012 meeting of all german DFG founded research training groups (Graduiertenkolleg) in computer science. I present the group METRIK.
Deconstructing SfM-Net architecture and beyond
"SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates."
Alternative download:
https://www.dropbox.com/s/aezl7ro8sy2xq7j/sfm_net_v2.pdf?dl=0
Shallow introduction for Deep Learning Retinal Image AnalysisPetteriTeikariPhD
Overview of retinal imaging techniques such as fundus photography, optical coherence tomography (OCT) along with future upgrades such as multispectral imaging, OCT angiography, adaptive optics imaging and polarization-sensitive OCT. This is followed by an overview of deep learning image analysis methods suitable to be used with retinal imaging techniques.
Alternative download link: https://www.dropbox.com/s/n01w02cjaf68vbo/retina_deepLearning_pipeline.pdf?dl=0
Sidang Skripsi, Gia Muhamad Agusta, 10909100144, Program Studio Teknik Informatika. 10 Oktober 2012. Universitas Islam Negeri Syarif Hidayatullah Jakarta
10,00 Modelling and analysis of geophysical data using geostatistics and mach...Beniamino Murgante
10,00 Modelling and analysis of geophysical data using geostatistics and machine learning
Vasily Demyanov – Heriot–Watt Institute, Edinburgh (U.K.)
Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)
Calcestruzzo Armato e Calcestruzzo Armato Precompresso secondo l'Eurocodice 2. Tullio Antonini. INTERSCIENZE Edizioni Scientifiche. Presentazione di Giulio Maier e Pietro Gambarova.
This lecture illustrates the structural use of aluminium extrusions for the replacement of damaged concrete bridge decks as realized in a case in Sweden; it describes the technical and economic advantages of the chosen light-weight aluminium design. Basic knowledge of structural engineering and extrusion design; and some familiarity with TALAT lectures no. 1302, 1501, 2200, 2300 and 2400 is assumed.
VALUTAZIONI CRITICHE DEI MODELLI NUMERICI E DEI RISULTATIStroNGER2012
Lezione di Chiara Crosti
ANALISI STRUTTURALE DI PONTI E DI OPERE COMPLESSE DI INGEGNERIA CIVILI
Roma 10 ottobre 2014,
Facoltà di Ingegneria Civile ed Industriale
Sapienza - Università di Roma
Via Eudossiana, 18 Roma
Il DM2008 e gli Eurocodici da tempo prevedono analisi strutturali da sviluppare con metodi più generali comprensivi ad esempio di fenomeni come l’interazione suolo-struttura, ed in grado di recepire i vari tipi di non linearità geometriche e meccaniche; inoltre è notevolmente aumentato il numero di stati limite da considerare, queste due circostanze impongono il ricorso a strumenti software che consentano di mantenere il controllo dell’analisi al crescere della complessità sia qualitativa che quantitativa della stessa.
Nella prassi applicativa, tali metodi iniziano ad avere una più ampia applicazione grazie alla disponibilità di software con algoritmi di calcolo sempre più potenti il cui utilizzo è
stato reso più semplice rispetto al passato e che consente di utilizzarli anche al di fuori delle applicazioni di Ricerca e Sviluppo. Il loro utilizzo richiede una visione ingegneristica
approfondita e matura da saper cogliere quando tale maggior generalità sia rilevante ai fini del dimensionamento, della misura della sicurezza o del livello di prestazioni della
struttura. Simili analisi presuppongono una solida conoscenza delle teorie di base e richiedono sia una certa esperienza sui problemi di gestione delle analisi, sia una
buona capacità critica nella valutazione dei risultati ottenuti.
Sulla base di queste premesse, il seminario approfondirà due aspetti:
- evidenziare come sia indispensabile un controllo ed una valutazione critica dei modelli numerici e dei risultati .
- mostrare le potenzialità di un moderno codice attraverso applicazioni del software LUSAS, dotato di versioni specializzate ai diversi campi dell’ingegneria civile (Bridge,
Civil & Structural).
61Resilienza dei centri urbani e rilievo delle costruzioni: un binomio indivi...StroNGER2012
GLI ATTORI DEL DIVENIRE URBANO
Facoltà di Ingegneria
Sapienza Università di Roma
Sala del Chiostro 26 NOVEMBRE 2015
a cura di
Alessandro Cutini - Franco Bontempi
INFRASTRUTTURE IN AMBITO URBANO: COMPLESSITA’ DI PROGETTO E DURABILITA’StroNGER2012
GLI ATTORI DEL DIVENIRE URBANO
Facoltà di Ingegneria
Sapienza Università di Roma
Sala del Chiostro 26 NOVEMBRE 2015
a cura di
Alessandro Cutini - Franco Bontempi
I Restauri e la Città: l’esempio del Colosseo e della Casa di AugustoStroNGER2012
GLI ATTORI DEL DIVENIRE URBANO
Facoltà di Ingegneria
Sapienza Università di Roma
Sala del Chiostro 26 NOVEMBRE 2015
a cura di
Alessandro Cutini - Franco Bontempi
Corso di dottorato & Corso di formazione StroNGER2012
Basi di OTTIMIZZAZIONE STRUTTURALE, 6 luglio 2016 (totale di 8 ore)
&
LA PROGETTAZIONE STRUTTURALE ATTRAVERSO L’ANALISI DI CASI CRITICI, 7 e 8 luglio (totale di 16 ore)
SISTEMILA RETE STRADALE URBANA:UN’EMERGENZA DEL QUOTIDIANO O UN’OPPORTUNITA’ ...StroNGER2012
GLI ATTORI DEL DIVENIRE URBANO
Facoltà di Ingegneria
Sapienza Università di Roma
Sala del Chiostro 26 NOVEMBRE 2015
a cura di
Alessandro Cutini - Franco Bontempi
Ispezione dei Ponti. Indagini mirate a particolari critici ai fini della sicu...Franco Bontempi
Scopo del Corso è di presentare una serie di criticità
associate a determinate e diffuse tipologie di ponti
attualmente in esercizio e di opere ausiliarie a corredo
dei ponti stessi, quali parapetti e guardiavia.
Di massima, ogni tipo di patologia sarà presentato in
funzione del contesto nel quale si manifesta e verrà
studiato con brevi cenni a impostazioni di calcolo che
ne consentono un’analisi razionale. Ai fini dell’esito
delle ispezioni, verranno infine indicate le soglie di
criticità ammissibili per la permanenza in esercizio
di un ponte e le indicazioni sui rimedi applicabili per
ripristinare prestazioni certe in termini di capacità
portante, di funzionalità e di durabilità.
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
Application of Artificial Neural Networking for Determining the Plane of Vibr...IOSRJMCE
In this paper a new approach for Artificial Neural Networking using Feed Forward Back Propagation Method and Levenberg-Marquardt backpropagation training function has been developed using Java Programming, where by directly feeding the RMS and Phase values of vibration, the unbalance plane can be detected with minimum error. In a Machine Fault Simulator RMS value and phase values of vibrations are collected from the four accelerometers placed in X and Y direction of Left and Right Bearings .Further these data are fed into the neural network for training purpose. In the testing phase of the neural network, the plane of vibration has been determined using different training algorithms available in MATLAB. Their prediction values have been compared with the actual value, errors for different training algorithms are calculated and a conclusion has been drawn for the best training function available for this current research work.
NS2 ieee 2017 project titles | Final Year IEEE 2018 Network Simulator ProjectsJAYAPRAKASH JPINFOTECH
NS2 ieee 2017 project titles | Final Year IEEE 2018 Network Simulator Projects
To get this project in ONLINE or through TRAINING Sessions, Contact:
JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank.
Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch.
Mobile: (0) 9952649690,
Email: jpinfotechprojects@gmail.com,
web: http://www.jpinfotech.org
2017 ieee ns2 project titles | Latest Final Year ieee network Simulator Proje...JPINFOTECH JAYAPRAKASH
2017 ieee ns2 project titles | Latest Final Year ieee network Simulator Project Titles 2018, NS2 Projects in chennai, Ns2 Projects in Pondicherry, Bulk IEEE Projects
To get this project in ONLINE or through TRAINING Sessions, Contact:
JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank.
Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch.
Mobile: (0) 9952649690,
Email: jpinfotechprojects@gmail.com,
web: http://www.jpinfotech.org
"Development of a EEG-Based Biometric Authentication & Security System"
presebnted the poster in my university tech fiesta- 2016
I haven't developed it.. but still working on it.
If anyone interested please knock me at
Facebook:
https://www.facebook.com/mubin.hasan.33
New Research Articles 2019 October Issue Signal & Image Processing An Interna...sipij
Signal & Image Processing: An International Journal (SIPIJ)
ISSN: 0976 – 710X [Online]; 2229 - 3922 [Print]
http://www.airccse.org/journal/sipij/index.html
Current Issue; October 2019, Volume 10, Number 5
Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction
Besma Sadou1, Atidel Lahoulou2, Toufik Bouden1, Anderson R. Avila3, Tiago H. Falk3 and Zahid Akhtar4, 1Non Destructive Testing Laboratory, University of Jijel, Algeria, 2LAOTI laboratory, University of Jijel, Algeria, 3University of Québec, Canada and 4University of Memphis, USA
Test-cost-sensitive Convolutional Neural Networks with Expert Branches
Mahdi Naghibi1, Reza Anvari1, Ali Forghani1 and Behrouz Minaei2, 1Malek-Ashtar University of Technology, Iran and 2Iran University of Science and Technology, Iran
Robust Image Watermarking Method using Wavelet Transform
Omar Adwan, The University of Jordan, Jordan
Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs
Itaru Kaneko1, Yutaka Yoshida2 and Emi Yuda3, 1&2Nagoya City University, Japan and 3Tohoku University, Japan
http://www.airccse.org/journal/sipij/vol10.html
Expert guidance to the projects in computer science, engineering for MTech, ME & PhD research scholars for their academic requirement.
We also help research scholars to make custom and semi-custom computer science, IEEE projects for submission in final year. For more info Visit at:-http://www.techsparks.co.in/
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
Novel conservative reversible error control circuits based on molecular QCAVIT-AP University
Quantum-dot cellular automata are a prominent part of the nanoscale regime. They
use a quantum cellular based architecture which enables rapid information process with high
device density. This paper targets the two kinds of novel error control circuits such as Hamming
code, parity generator and checker. To design the HG-PP (HG = Hamming gate, PP = parity
preserving), NG-PP (NG = new gate) are proposed for optimising the circuits. Based on the
proposed gates and a few existing gates, the Hamming code and parity generator and checker
circuits are constructed. The proposed gates have been framed and verified in QCA. The
simulation outcomes signify that their framed circuits are faultless. In addition to verification,
physical reversible is done. The reversible major metrics such as gate count, quantum cost, unit
delay, and garbage outputs, uses best optimisation results compared to counterparts. They can be utilised as a low power error control circuit applied in future communication systems.
Artificial Neural Network Based Graphical User Interface for Estimation of Fa...ijsrd.com
This paper addresses the problem of estimation of fabrication time in Rig construction projects through application of Artificial Neural Network (ANNs) as this is the most crucial activity for successful project management planning. ANN is a non-linear, data driven, self adaptive approach as opposed to the traditional model based methods, also fast becoming popular in forecasting where relationship between input and output is not known but vast collection of data is available. Around 960 data regarding fabrication activity has been collected from ABG Shipyard Ltd., Dahej. 3 input parameters have been considered for estimation of output as fabrication time. 11 Feed Forward Back Propagation neural networks with different network architectures were made. Network N10 was able to predict the output with MSE 1.35337e-2. Coding was done for the Graphical User Interface (GUI) so that the GUI runs, simulates network N10, and displays the fabrication time for different combination of inputs.
Similar to Neural network-based techniques for the damage identification of bridges: a review of recent advances, Arangio S. (20)
Roma e le sue acque:il punto di vista della Protezione CivileStroNGER2012
GLI ATTORI DEL DIVENIRE URBANO
Facoltà di Ingegneria
Sapienza Università di Roma
Sala del Chiostro 26 NOVEMBRE 2015
a cura di
Alessandro Cutini - Franco Bontempi
Una visione ampia dei sistemi: robustezza e resilienza.StroNGER2012
GLI ATTORI DEL DIVENIRE URBANO
Facoltà di Ingegneria
Sapienza Università di Roma
Sala del Chiostro 26 NOVEMBRE 2015
a cura di
Alessandro Cutini - Franco Bontempi
L’investigazione antincendio sugli aspetti strutturali: una proposta di codificaStroNGER2012
I numerosi incendi che si innescano e danneggiano
le strutture hanno rivoluzionato, da una parte,
molte procedure sulla prevenzione definendo metodologie
gestionali più efficaci e stanno, dall’altra,
portando ad affinare procedure investigative
codificate atte a ridurre il rischio di errori/omissioni
durante le indagini.
Lo scopo di questo articolo è quello di esporre
una metodologia codificata di Structural Fire Investigation
(Investigazione sugli aspetti strutturali in
caso di incendio) atta ad individuare le cause scatenanti,
pregresse e latenti, che hanno determinato
l’evento accidentale.
L’iter investigativo, associato a determinate operazioni
strutturali e forensi che partono dalla raccolta
delle informazioni iniziali al repertamento e
controllo documentale per poi completarsi con le
verifiche computazionali, sicuramente aiuta a determinare,
in maniera rigorosa, le cause e l’origine
di un incendio. La modellazione degli incendi con
il software del NIST, Fire Dynamics Simulator
(FDS) e l’analisi strutturale con vari codici di calcolo,
permettono di verificare determinate ipotesi
maturate durante il repertamento e di avvalorare
scientificamente l’analisi semiotica rilevata sulla
scena, fornendo dati forensi utili in fase dibattimentale.
Quindi un’attività investigativa pianificata, permette
a qualsiasi utente, (VV.F., personale delle Forze
dell’Ordine, Consulente, Perito, CTU o Libero
Professionista), di svolgere indagini in maniera appropriata
secondo una linea guida che permette
di non tralasciare controlli a volte rilevanti per la
stesura della documentazione complessiva in forma
di report finale.
29 May 2015 - Rome
Research Meeting with
University of Brasilia–Brazil
University of Nebraska-Lincoln (Omaha Campus)
University of Rome La Sapienza
StroNGER
29 May 2015 - Rome
Research Meeting with
University of Brasilia–Brazil
University of Nebraska-Lincoln (Omaha Campus)
University of Rome La Sapienza
StroNGER
29 May 2015 - Rome
Research Meeting with
University of Brasilia–Brazil
University of Nebraska-Lincoln (Omaha Campus)
University of Rome La Sapienza
StroNGER
29 May 2015 - Rome
Research Meeting with
University of Brasilia–Brazil
University of Nebraska-Lincoln (Omaha Campus)
University of Rome La Sapienza
StroNGER
Uso delle fibre di basalto nel risanamento degli edifici storiciStroNGER2012
Intervento di Stefania Arangio a:
Miglioramento e adeguamento sismico di strutture esistenti attraverso l'utilizzo di materiali compisiti in FRP
Ordine degli Ingegneri della Provincia di Roma
14 aprile 2015
IDENTIFICAZIONE STRUTTURALE DEL COMPORTAMENTO SPERIMENTALE DI CENTINE INNOVAT...StroNGER2012
Contributo a IF CRASC'15 di Alessandra Castelli e Francesco Petrini.
14-16 maggio 2015.
Universita' degli Studi di Roma La Sapienza
Facolta' di Ingegneria Civile e Industriale
ifcrasc15@stronger2012.com
Corso Ottimizzazione Strutturale Sapienza 2015StroNGER2012
Il corso vuole introdurre in maniera semplice i concetti, i metodi, gli strumenti necessari all’ottimizzazione di una struttura in termini di capacità prestazionali e sicurezza. L’attenzione è focalizzata sulle idee e sulle applicazioni, nella convinzione che gran parte dei dettagli algoritmici, seppure fondamentali nelle applicazioni più sofisticate, possano essere rimandati a successivi approfondimenti: questo anche alla luce degli strumenti computazionali moderni che permettono di concentrarsi sulla progettazione concettuale dei sistemi strutturali nelle forme più attuali. Gli studenti potranno quindi essere capaci di impostare e comprendere i processi ideativi alla base delle moderne forme strutturali che si presentano per le coperture, i ponti e gli edifici alti.
MIGLIORAMENTO ED ADEGUAMENTO SISMICO DI STRUTTURE ESISTENTI ATTRAVERSO L’UTIL...StroNGER2012
MIGLIORAMENTO ED ADEGUAMENTO SISMICO DI STRUTTURE ESISTENTI ATTRAVERSO L’UTILIZZO DI MATERIALI COMPOSITI IN FRP.
14 e 21 Aprile 2015.
https://www.ording.roma.it/seminario.aspx?id=14727
Design Knowledge Gain by Structural Health MonitoringStroNGER2012
The design of complex structures should be based on advanced approaches able to take into account the behavior of the constructions during their entire life-cycle. Moreover, an effective design method should consider that the modern constructions are usually complex systems, characterized by strong interactions among the single components and with the design environment.
A modern approach, capable of adequately considering these issues, is the so-called performance-based design (PBD). In order to profitably apply this design philosophy, an effective framework for the evaluation of the overall quality of the structure is needed; for this purpose, the concept of dependability can be effectively applied.
In this context, structural health monitoring (SHM)
assumes the essential role to improve the knowledge on the structural system and to allow reliable evaluations of the structural safety in operational conditions. SHM should be planned at the design phase and should be performed during the entire life-cycle of the structure.
In order to deal with the large quantity of data coming from the continuous monitoring various processing techniques exist. In this work different approaches are discussed and in the last part two of them are applied on the same dataset.
It is interesting to notice that, in addition to this first level of knowledge, structural health monitoring allows obtaining a further more general contribution to the design knowledge of the whole sector of structural engineering.
Consequently, SHM leads to two levels of design knowledge gain: locally, on the specific structure, and globally, on the general class of similar structures.
2° WORKSHOP GRUPPO ITALIANO IABMAS - IABMAS ITALIAN GROUPStroNGER2012
Negli ultimi anni un crescente sviluppo di studi e ricerche ha consentito significativi progressi nell’ambito della modellazione, analisi, progettazione, monitoraggio, manutenzione e riparazione di ponti, viadotti e infrastrutture. Nell’ambito della comunità scientifica e del mondo professionale questi sviluppi sono percepiti come centrali per l’ingegneria civile, per la quale si sta attuando una transizione verso una filosofia di progettazione che considera l’intero ciclo di vita, secondo canoni sostenibili tali da consentire la realizzazione di opere intrinsecamente durevoli, robuste e resilienti.
L’Associazione IABMAS – International Association for Bridge Maintenance And Safety – opera in questo ambito dalla sua fondazione nel 1999 e rappresenta la principale organizzazione internazionale nei settori della progettazione, manutenzione e gestione dei ponti, con oltre 1000 membri individuali e 80 membri collettivi da 55 paesi (http://www.iabmas.org).
Per un migliore coordinamento delle sue attività, l’Associazione IABMAS prevede la possibilità di istituire gruppi nazionali che consentono di meglio interpretare e promuovere le competenze e le potenzialità che ciascun paese desidera esprimere nell’ambito dell’Associazione. Oltre all’Italia, i paesi che ospitano i gruppi nazionali IABMAS sono Portogallo, Giappone, Cina e Brasile.
Il Gruppo Italiano IABMAS è stato costituito nel 2012 con l’intento di istituire un riferimento privilegato per studiosi, ricercatori e progettisti in grado di promuovere una fruttuosa sinergia tra teoria e pratica nel settore dei ponti e viadotti, favorendo il dialogo tra comunità accademica, comunità professionale, operatori del mondo delle costruzioni, produttori di materiali avanzati, enti di gestione e amministrazioni di reti infrastrutturali pubbliche e private (http://www.iabmas-italy.it).
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
By Design, not by Accident - Agile Venture Bolzano 2024
Neural network-based techniques for the damage identification of bridges: a review of recent advances, Arangio S.
1. Neural networks based techniques for
damage identification of bridges:
a review of recent advances
Sapienza University of Rome – StroNGER s.r.l.
S. Arangio
stefania.arangio@uniroma1.it, stefania.arangio@stronger2012.com
Cagliari, September 5th 2013
2. 2/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Introduction
Part I
Conclusions
Part II
Neural networks and Bayesian enhancements
Outline
Case study:
Bayesian neural networks
for the assessment of the bridge of the ANCRiSST benchmark problem
Soft computing approaches for the structural assessment of bridges
3. 3/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Introduction
Part I
Conclusions
Part II
Neural networks and Bayesian enhancements
Outline
Case study:
Bayesian neural networks
for the assessment of the bridge of the ANCRiSST benchmark problem
Soft computing approaches for the structural assessment of bridges
4. 4/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Methods for structural identification and damage detection
Input – output
techniques
• The structure has to be artificially excited and
in case of large structures it is not always
possible
• The operation of the structure has to be
interrupted
Only output
techniques
• The excitation is given by the ambient
vibration
• Measurements in real operational conditions
• Suitable in case of continuous monitoring
Traditional
methods
Soft computing
methods
• Time domain
approaches
• Frequency
domain
approaches
• Neural
networks
• Genetic
algorithms
• Fuzzy Logic
Introduction
5. 5/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Examples of structural assessment
by using soft computing methods (2008-2013)
Adeli H., Jiang X., Intelligent infrastructures – Neural Networks, wavelets, and Chaos Theory for Intelligent Transportation Systems and
Smart Structures, CRC Press, Taylor & Francis, Boca Raton, Florida, 2009
Al-Rahmani A.H., Rasheed H.A., Najjar A.Y., A combined soft computing-mechanics approach to inversely predict damage in bridges,
Procedia Computer Science, 8, 461 – 466, 2012
Arangio S., Beck J.L. Bayesian neural networks for bridges integrity assessment, Structural Control & Health Monitoring, 2012; 19(1), 3-21.
Arangio S., Bontempi F. Soft Computing based Multilevel Strategy for Bridge Integrity Monitoring, Computer-Aided Civil and Infrastructure
Engineering 2010; 25, 348-362.
Bhattacharyya P., Banerji P., Improved Damage Classification and Detection on a Model Bridge using Fuzzy Neural Networks, 4th
International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-4), 22-24 July 2009, Zurich, Switzerland,
2009.
Cheng J., An artificial neural network based genetic algorithm for estimating the reliability of long span suspension bridges, Finite Elements
in Analysis and Design, 46, 658–667, 2010.
Cheng J., Li Q.S., Reliability analysis of structures using artificial neural network based genetic algorithms, Comput. Methods Appl. Mech.
Engrg., 197, 3742–3750, 2008.
Firouzi A., Rahai A., An integrated ANN-GA for reliability based inspection of concrete bridge decks considering extent of corrosion-induced
cracks and life cycle costs, Scientia Iranica, 19 (4), 974–981, 2012.
Flood I., Towards the next generation of artificial neural networks for civil engineering, Advanced Engineering Informatics 22, 4–14, 2008
Freitag S., Graf W., Kaliske M. Recurrent neural networks for fuzzy data, Integrated Computer-Aided Engineering - Data Mining in
Engineering, 2011; 18(3), 265-280.
Graf W.S., Freitag S., Sickert U., Kaliske M., Structural Analysis with Fuzzy Data and Neural Network Based Material Description,
Computer-Aided Civil and Infrastructure Engineering 27, 640–654, 2012.
Li S., Li H., Liu Y., Lan C., Zhou W., Ou J., SMC structural health monitoring benchmark problem using monitored data from an actual cable-
stayed bridge, Structural Control and Health Monitoring, published online form March 26th 2013, DOI:10.1002/stc.1559
Mehrjoo M., Khaji N., Moharrami H., Bahreininejad A., Damage detection of truss bridge joints using Artificial Neural Networks, Expert
Systems with Applications 35, 1122–1131, 2008.
Park J.H., Kim J.T, Honga D.S., Hoa D.D., Yib J.H., Sequential damage detection approaches for beams using time-modal features and
artificial neural networks, Journal of Sound and Vibration, 323, 451–474, 2009.
Sgambi L., Gkoumas K., Bontempi F. Genetic Algorithms for the Dependability Assurance in the Design of a Long-Span Suspension Bridge,
Computer-Aided Civil and Infrastructure Engineering 2012; 27(9), 655-675.
Tsompanakis Y., Lagaros N.D., Stavroulakis G. Soft computing techniques in parameter identification and probabilistic seismic analysis of
structures, Advances in Engineering Software 2008, 39(7), 612-624.
Wang Y.M., Elhag T.M.S., An adaptive neuro-fuzzy inference system for bridge risk assessment, Expert Systems with Applications 34,
3099–3106, 2008.
Zhou H.F., Ni Y.Q., Ko J.M., Constructing input to neural networks for modeling temperature-caused modal variability: Mean temperatures,
effective temperatures
Introduction
6. 6/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Introduction
Part I
Conclusions
Part II
Neural networks and Bayesian enhancements
Outline
Case study:
Bayesian neural networks
for the assessment of the bridge of the ANCRiSST benchmark problem
Soft computing approaches for the structural assessment of bridges
7. 7/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII
Nonlinear feed-forward basis functions
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8. 8/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Traditional learning
t
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9. 9/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Probabilistic interpretation
( ){ }
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10. 10/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Network learning as inference
10
=),,( MwDp β
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POSTERIOR: BAYES’ THEOREM
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12. 12/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Bayesian techniques for neural networks
• Level 1 Model fitting: inferring the model parameters given the
model and the data
• Level 2 Optimization of the hyperparameters α and β
• Level 3 Model class selection: optimal model complexity
• Level 4 Automatic relevance determination (ARD):
evaluation of the relative importance of different inputs
Network learning as inference (model fitting) is only one level in
which Bayesian inference can be applied in the neural network
field
Hierarchical multi-level approach
PartI
13. POSTERIOR FOR α, β
TRAINING: OPTIMIZATION
w = wMAP?
?( ) ( )DMEVDMEV ii 1−>
INFERENCE OF NEW DATA
CHOOSE MODEL Mi-1
?
POSTERIOR FOR Mi
α, β = αMP, βMP
DATA PRE- PROCESSING
OUTPUT
NETWORK MODEL Mi
N HIDDEN = i
N INPUT = k
POSTERIOR FOR w
yes
DATA POST PROCESSING
PROBABILISTIC MODEL
no
INPUT
CHOOSE INITIAL α, β
INITIALIZE WEIGHTS w
RE-ESTIMATION OF α, β
yes
no
Wγ ≈
yes
no
i= i+1
is α1,…,αk
‘very large’?
k= k-1
yes
no
( ) ( )
( )MDp
MwpMwDp
Dwp
,,
,,,
),,,(
βα
αβ
βα =M
1st level
Model fitting
14. POSTERIOR FOR α, β
TRAINING: OPTIMIZATION
w = wMAP?
?( ) ( )DMEVDMEV ii 1−>
INFERENCE OF NEW DATA
CHOOSE MODEL Mi-1
?
POSTERIOR FOR Mi
α, β = αMP, βMP
DATA PRE- PROCESSING
OUTPUT
NETWORK MODEL Mi
N HIDDEN = i
N INPUT = k
POSTERIOR FOR w
yes
DATA POST PROCESSING
PROBABILISTIC MODEL
no
INPUT
CHOOSE INITIAL α, β
INITIALIZE WEIGHTS w
RE-ESTIMATION OF α, β
yes
no
Wγ ≈
yes
no
i= i+1
is α1,…,αk
‘very large’?
k= k-1
yes
no
( ) ( )
( )MDp
MwpMwDp
Dwp
,,
,,,
),,,(
βα
αβ
βα =M
1st level
Model fitting
2nd level
Evaluating the hyperparameters α, β
( ) ( )
( )MDp
MpMDp
Dp
βαβα
βα
,,,
),,( =M
15. 15/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Issues in neural network design: selection of the optimal model
RULES OF THUMBS
-…between the input layer size and the output
layer size (Blum, 1992)
- (Software Neuroshell, 2000)
- (Berry and Lynoff, 1997)
- n = dimension needed to capture 70-80% of the
variance
(Boger and Guterman, 1997)
OPTIMAL NUMBER OF UNITS
(“OCKHAM’S RAZOR”)
)(
3
2
oI NNn +=
INn ⋅<2
examplesNn ⋅<
30
1
They aren’t rigorous methods
INPUT
LAYER
OUTPUT
LAYER
HIDDEN
LAYERS
PartI
16. POSTERIOR FOR α, β
TRAINING: OPTIMIZATION
w = wMAP?
?( ) ( )DMEVDMEV ii 1−>
INFERENCE OF NEW DATA
CHOOSE MODEL Mi-1
?
POSTERIOR FOR Mi
α, β = αMP, βMP
DATA PRE- PROCESSING
OUTPUT
NETWORK MODEL Mi
N HIDDEN = i
N INPUT = k
POSTERIOR FOR w
yes
DATA POST PROCESSING
PROBABILISTIC MODEL
no
INPUT
CHOOSE INITIAL α, β
INITIALIZE WEIGHTS w
RE-ESTIMATION OF α, β
yes
n
o
Wγ ≈
yes
no
i= i+1
is α1,…,αk
‘very large’?
k= k-1
yes
no
( ) ( )
( )MDp
MwpMwDp
Dwp
,,
,,,
),,,(
βα
αβ
βα =M
1st level
Model fitting
2nd level
Evaluating the hyperparameters α, β
3rd level
Model class selection
( ) ( )MpMDpDMp ∝)(
prior = constantevidence
( ) ( )
( )MDp
MpMDp
Dp
βαβα
βα
,,,
),,( =M
17. POSTERIOR FOR α, β
TRAINING: OPTIMIZATION
w = wMAP?
?( ) ( )DMEVDMEV ii 1−>
INFERENCE OF NEW DATA
CHOOSE MODEL Mi-1
?
POSTERIOR FOR Mi
α, β = αMP, βMP
DATA PRE- PROCESSING
OUTPUT
NETWORK MODEL Mi
N HIDDEN = i
N INPUT = k
POSTERIOR FOR w
yes
DATA POST PROCESSING
PROBABILISTIC MODEL
no
INPUT
CHOOSE INITIAL α, β
INITIALIZE WEIGHTS w
RE-ESTIMATION OF α, β
yes
n
o
Wγ ≈
yes
no
i= i+1
is α1,…,αk
‘very large’?
k= k-1
yes
no
( ) ( )
( )MDp
MwpMwDp
Dwp
,,
,,,
),,,(
βα
αβ
βα =M
1st level
Model fitting
2nd level
Evaluating the hyperparameters α, β
3rd level
Model class selection
( ) ( )MpMDpDMp ∝)(
prior = constantevidence
is α1,…,αk
‘very large’?
4th level
Automatic Relevance Determination
( ) ( )
( )MDp
MpMDp
Dp
βαβα
βα
,,,
),,( =M
18. 18/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Introduction
Part I
Conclusions
Part II
Neural networks and Bayesian enhancements
Outline
Case study:
Bayesian neural networks
for the assessment of the bridge of the ANCRiSST benchmark problem
Soft computing approaches for the structural assessment of bridges
19. 19/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
The ANCRiSST benchmark problem
• Consortium of 20 research institutions
• Established in 2002 with the purpose of:
• assessing current progresses on smart materials and structures technology
• Developing synergies that facilitate joint research projects that cannot easily carried
out by individual centers
In October 2011 they opened for
researchers in the SHM community a
benchmark problem based on a real
bridge: the TianjinYonghe bridge
http://smc.hit.edu.cn/
PartII
20. 20/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Description of the Tianjin Yonghe bridge
Tianjin Hangu
25.15 99.85 260 99.85 25.15
• Cable-stayed bridge
• Opened to traffic since December 1987
• After 19 years of operation damages were detected and the bridge was
retrofitted
• A sophisticated SHM system has been designed and implemented by the
Research Center of Structural Health Monitoring and Control of the Harbin
Institute of Technology
PartII
21. 21/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Structural Health Monitoring System
Tianjin Hangu
2515 5600 5885 5900 5600 5600 5900 5885 5600 2515
1 (3) 2 (4) 3 (5) 7 (9) 9 (10) 11 (12) 13 (14)
Uniaxial/biaxial accelerometers
Hygrothermograph
Anemometer
1, 3, 5, 7, 9 11, 13 2, 4, 6, 8, 10, 12, 14
During 2008:
• Continuous monitoring system
• 14 uniaxial accelerometers on the bridge deck (downward and upward)
• On the top of the tower: 1 biaxial accelerometer; 1 anemometer; 1 temperature
sensor
downward and upward
PartII
22. 22/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
damaged area
Damage situation 1
Cracks at the closure segment
at both side spans
August 2008:
2 damages are detected
PartII
24. 24/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Available data set
Health condition Damaged condition
• Time histories of the accelerations
recorded at the 14 deck sensors
on January 1st and January 17th 2008
(registrations of 1 h carried out for 24 h )
• Environmental information
(wind, temperature)
• Biaxial accelerations at the top of the
tower
• Time histories of the accelerations
recorded at the same 14 deck sensors
on July 30th 2008
(registrations of 1 h carried out for 24 h)
• Accelerations collected by field testing
August 7th to 10th 2008 (not used)
PartII
25. 25/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Procedure for neural network training
time history of the
acceleration recorded at
sensor #
Structural system
Ambient excitation
1+−dtf 2−tf tf1−tf 1+tfTraining of the neural
network model in
undamaged condition
2+tf
Test of the trained neural
network model on a new time
history
26. 26/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Neural network based damage detection strategy
14 groups of networks have been created
(one for each measurement point e one for each hour of measurements)
14 (points) x24 (hours) = 336 neural network models
Tianjin Hangu
1 (3) 2 (4) 3 (5) 7 (9) 9 (10) 11 (12) 13 (14)
accelerometers
27. 27/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Detection of anomalies
If ∆e ≈ 0
the structure is considered as undamaged
If ∆e is large an anomaly is detected
28. 28/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII
Damaged area
Error in the approximation of the accelerations in the undamaged sections
Training Undamaged
Damage detection
Tianjin Hangu
29. 29/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Bayesian model class selection
The most plausible class can be obtained applying Bayes’ Theorem:
( ) ( )( , ) |j jj
p M D p D M p M∝M M
prior = cost
evidence
provided by D
The various model can be compared by evaluating their evidence
−
+
+−−
γγ
α
N
E MP
W
2
ln
2
12
ln
2
1
ln
2
1
A
++++− jjMP
MP
D HH
N
E ln2!lnln
2
ββ( )=iMDpln
Data fit term
Penalizing term
“Ockham factor”
30. 30/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Bayesian model class selection
The chosen model has 3 hidden units
Model 1 2 3 4 5
N parameters 7 13 19 25 31
gamma 2,00 3,03 4,02 5,00 6,00
MP
j
MP
D
MP
j
β
N
Eβ ln
2
+− 20770 22682 25078 22153 23500
( ) MP
j
MP
j
HH ln2!ln + 2,08 3,99 5,95 8,01 10,16
data fit term 20772 22686 25084 22161 23510
MP
j
MP
W
MP
j
α
W
Eα ln
2
ln
2
1
++− A -13,08 -79,32 -158 -213 -266
−
+
γNγ
2
ln
2
12
ln
2
1
-3,31 -3,51 -3,66 -3,8 -3,86
penalizing term -16 -83 -162 -217 -270
log evidence 20756 22603 24922 21944 23240
32. 32/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Error in the approximation of the undamaged conditions
downriver
upriver
∆e at the various locations
Data for training: January 1st 2008 (H1 to H24)
Data for testing: January 17th 2008 (H1 to H24)
Undamaged conditions
33. 33/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Error in the approximation of the damaged conditions
∆e at the various locations
Data for training: January 1st 2008 (H1 to H24)
Data for testing: July 30th 2008 (H1 to H24
Damaged conditions!
34. 34/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Difference of the errors
The difference of error in the approximation suggests the presence of structural
anomalies around sensor #10
35. 35/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Validation of the results: Structural assessment by applying
the Enhanced Frequency Domain Decomposition
• Data collection and signal preprocessing
• Construction of the the Power Spectral
Density matrix (PSD)
• Whelch averaged modified periodgram method
• 50 % overlapping and periodic Hamming windowing
• Singular Value Decomposition (SVD) of the PSD
• Identification of modal frequencies and mode shapes
• Evaluation of the damping
PartII
36. 36/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
0
0,2
0,4
0,6
0 0,5 1 1,5 2 2,5 3
H6
H11
H15
H17
H19
H21
SingularValues(health)
f [Hz]
0
0,1
0,2
0,3
0 0,5 1 1,5 2
AverageSingularValues(health)
f [Hz]
EFDD: Singular Values DecompositionPartII
0
0,5
1
1,5
2
0 0,5 1 1,5 2
AverageSingularValues(damaged)
f [Hz]
0
0,5
1
1,5
2
0 0,5 1 1,5 2 2,5 3
H6
H9
H12
H15
H18
H20
H22
H23
H24
Undamaged conditions Damaged conditions
Average Singular values Average Singular values
37. 37/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Comparison of the mode shapesPartII
The decrease of the frequencies suggests the presence of damage
f=0.4075 Hz
FEM (“AS BUILT” CONDITION)
FEM Mode 1 - f=0.452 Hz FEM Mode 2 - f=0.632 Hz FEM Mode 3 - f=0.937 Hz
Mode 1 - Mode 2 - f=0.594 Hz Mode 3 - f=0.896 Hz
Mode 1 - f=0.262 Hz Mode 2 - f=0.388 Hz Mode 3 - f=0.664 Hz
UNDAMAGED CONDITION
DAMAGED CONDITION
38. 38/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Introduction
Part I
Conclusions
Part II
The ANCRIiST benchmark problem
Description of the bridge and available monitoring data
Outline
Neural network based damage detection strategy
Results
39. 39/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Conclusions
Soft computing approaches, like the neural networks model, have
proven to be effective for dealing with large quantities of data and,
recently, have been widely used for the structural assessment of Civil
structures and infrastructures.
Neural networks can be significantly improved by applying Bayesian
inference at different levels in a hierarchical way:
Bayesian Neural Networks (BNN)
The BNNs have been applied for processing the monitoring data
coming from the bridge of the ANCRiSST SHM benchmark problem
and have shown to be able to detect the presence of an anomaly.
The current work is focused on the development of methods for the
localization of the detected damage
Conclusions
40. POSTERIOR FOR α, β
TRAINING: OPTIMIZATION
w = wMAP?
?( ) ( )DMEVDMEV ii 1−>
INFERENCE OF NEW DATA
CHOOSE MODEL Mi-1
?
POSTERIOR FOR Mi
α, β = αMP, βMP
DATA PRE- PROCESSING
OUTPUT
NETWORK MODEL Mi
N HIDDEN = i
N INPUT = k
POSTERIOR FOR w
yes
DATA POST PROCESSING
PROBABILISTIC MODEL
no
INPUT
CHOOSE INITIAL α, β
INITIALIZE WEIGHTS w
RE-ESTIMATION OF α, β
yes
n
o
Wγ ≈
yes
no
i= i+1
is α1,…,αk
‘very large’?
k= k-1
yes
no
email: stefania.arangio@uniroma1.it
stefania.arangio@stronger2012.com
Prof. Bontempi and his research team www.francobontempi.org of
Sapienza University of Rome are gratefully acknowledged.
This research was partially supported by StroNGER s.r.l. from the
fund “FILAS - POR FESR LAZIO 2007/2013 - Support for the
research spin off”.