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Events for the 3rd week of December
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Astani Department of Civil and Environmental Engineering Ph.D. Dissertation
Mon, Dec 13, 2021 @ 04:00 PM - 06:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Preetham Aghalaya Manjunatha , Astani Department of Civil and Environmental Engineering
Talk Title: Vision-Based and Data-driven Analytical and Experimental Studies into Condition Assessment and Change Detection of Evolving Civil, Mechanical and Aerospace Infrastructures
Abstract: Civil, mechanical, and aerospace infrastructures are subjected to applied loads and environmental forces like earthquakes, wind, and water waves in their operating lifespan. These factors will slowly deteriorate the structures during their service period, and often subtle observations of substantial damages are challenging. Due to the cost-effectiveness of high-resolution color, depth cameras, location sensors, and Micro Aerial Vehicles (MAVs), image processing, computer vision, and robotics techniques are gaining interest in Non-Destructive Testing (NDT) and condition assessment of infrastructures. In this study, several promising vision-based and data-driven, automated, and semi-automated condition assessment techniques are proposed and evaluated to detect and quantify a class of problems under the umbrella of infrastructure condition assessment.
A synthetic crack generation methodology is introduced to generate zero-labeled samples for training the classical classifiers. This classifier was tested on a real-world dataset using the gradient-based hierarchical hybrid Multi-scale Fractional Anisotropy Tensor (MFAT) filter to segment the cracks. The results demonstrate the promising capabilities of the proposed synthetic crack generation method. Furthermore, the textural noise suppression and refinement are carried out by using an anisotropic diffusion filter. Guidelines are provided to select the parameters for the anisotropic diffusion filter. Further, this study presents the semantic segmentation of the cracks on concrete surface images using a deep Convolutional Neural Network (CNN) that has fewer parameters to learn. Several illustrative examples are presented to demonstrate the capabilities of the CNN-based crack segmentation procedure. The CNN was tested on the four real-world datasets, and the results show the proposed CNN's superiority against four state-of-the-art methods.
As a part of this study, an efficient and autonomous crack change detection, tracking, and evolution methodology is introduced. Among the image registration methods, feature-based registration is robust to the noise, intensity change, and partial affine motion model. This study uses an efficient $k$-d tree-based nearest neighbor search which is faster than the quadratic computational complexity of the current pairwise search. Furthermore, unlike other methods, the fixed camera assumption is relaxed in this study. Another significant contribution is a probabilistic measure of the reliability of the analysis results that can aid the prognosis damage detection models.
After the nearest neighbor search, the SURF-based keypoints are extracted from the images in the previous database and the current one. This is followed by the Random Sample Consensus (RANSAC)-based outliers rejection, bundle adjustment to refine the homographies, gain exposure compensation and multi-band blending for the seamless registration images. Lastly, the registered image is compared to the current images for the change detection in crack physical properties. To demonstrate the capabilities of the proposed method, two datasets were utilized; a real-world dataset, and a synthetic dataset. The experimental results show that the performance of the proposed methodology is suitable for detecting the crack changes in two datasets.
This work also studies the condition assessment of public sewer pipelines. The visual-bags-of-words model was evaluated for classifying the defective and non-defective sewer pipeline images using two feature descriptors. Three classical classifiers are trained and tested on a moderate-sized dataset of 14,404 images. The experimental results demonstrate that the classification accuracy of the visual-bags-of-words model is satisfactory and comparable to deep learning methods given the moderate dataset size.
Lastly, defect detection of the three-dimensional surface of mechanical parts is studied. A preliminary study on a vision-based semi-autonomous spatio-temporal method to detect, locate and quantify the defects such as loose bolts, displacements, pipe chafing, or deformation is proposed. In addition, a probabilistic reliability quantification method based on the ensemble averaging of the Cloud-to-Cloud (C2C) distances is introduced for mechanical systems. Several quantitative and qualitative examples are presented to illustrate the capabilities of the proposed method. The results show that the proposed method is promising and robust to register the complex shapes, and detect and locate the changes in the mechanical systems.
Webcast: https://usc.zoom.us/j/98823087239? Meeting ID: 988 2308 7239 Passcode: 729479WebCast Link: https://usc.zoom.us/j/98823087239? Meeting ID: 988 2308 7239 Passcode: 729479
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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Drop-In Weekly Office Hours [Virtual] Posted By: Center for Advanced Research Computing
Tue, Dec 14, 2021 @ 02:30 PM - 05:00 PM
Technology & Applied Computing Program (TAC)
Workshops & Infosessions
Every Tuesday, office hours are an opportunity for CARC users to ask questions about research computing. No appointment/registration is necessary, but you must use your USC credentials to access the Zoom meeting by clicking "Register" below. For in-person support, we are also in Leavey Library room 3M (basement) during this same time period. Register Here!
Audiences: Everyone Is Invited
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Virtual First-Year Admission Information Session
Tue, Dec 14, 2021 @ 04:00 PM - 05:00 PM
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.
Register here!Audiences: Everyone Is Invited
Contact: Viterbi Admission
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DEN@Viterbi - 'Limited Status: How to Get Started' Virtual Info Session
Tue, Dec 14, 2021 @ 06:00 PM - 07:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi for our upcoming Limited Status: How to Get Started Virtual Information Session via WebEx to learn about the Limited Status enrollment option. The Limited Status enrollment option allows individuals with an undergraduate degree in engineering or related field, with a 3.0 GPA or above to take courses before applying for formal admission into a Viterbi graduate degree program.
USC Viterbi representatives will provide a step-by-step guide for how to get started as a Limited Status student and enroll in courses online via DEN@Viterbi as early as the Fall 2021 semester.
Register Now!WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=e2dd0e1b1527d4f16bbd4241ef282076b
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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MS CSCI/DSCI Drop-in Academic Advising
Wed, Dec 15, 2021 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Workshops & Infosessions
Virtual Academic Advising Drop-in Hours for continuing MS students in CS or Data Science Programs will be available for the month of December. If you have a quick question that doesn't require a 20 minute appointment, please come to our drop in hours. Students may be placed into the waiting room upon arrival.
Zoom access link for all sessions:
ZOOM LINK SENT TO STUDENTS DIRECTLY. CHECK EMAIL FOR LINK.
Wednesday, December 1st --- 10am -- 11am
Wednesday, December 1st --- 2:30pm -- 3:30pm
Wednesday, December 8th --- 10am -- 11am
Wednesday, December 8th --- 2:30pm -- 3:30pm
Wednesday, December 15th --- 10am -- 11am
Wednesday, December 15th --- 2:30pm -- 3:30pmLocation: Online
Audiences: Graduate
Contact: USC Computer Science
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Astani Department of Civil and Environmental Engineering Ph.D. Dissertation
Wed, Dec 15, 2021 @ 12:00 PM - 02:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Xiaoshu Zeng, CE Ph.D. Candidate, Viterbi School- Astani Department of Civil and Environmental Engineering
Talk Title: Efficient Inverse Analysis with Dynamic and Stochastic Reductions for Large-Scale Models of Multi-Component Systems
Abstract: This work has two main goals: the primary goal of dealing with inverse analysis for large-scale models of multi-component systems and the second goal of dealing with the multi-fidelity uncertainty
quantification (UQ) for models with dissimilar parameterization. The primary goal involves efficient structural dynamic analysis, probabilistic modeling, and inverse analysis for the following reasons:
1. The context is the integrity assessment of the internals of a complex multi-scale structure, a fullyloaded spent nuclear fuel canister (FLSNFC), with the assessment based on dynamic signals on the
exterior surface of the FLSNFC before and after transportation.
2. Since the observed data is inevitably subjected to errors, the inverse analysis usually requires an accurate probabilistic model to capture the uncertainty propagated to the quantities of interest (QoI).
3. An efficient forward model, a dynamic model, is essential to construct a probabilistic model for complex systems.
A first attempt to build an efficient dynamic model is through constructing a global reduced-order basis (ROB). Usually, the complex multi-scale structures are characterized by numerous local vibration modes (or elastic modes) and the usual long-wavelength global vibration modes. Accordingly, a methodology that does not require the computation of the numerous local modes builds a global reduced-order model (ROM) by constructing a global ROB. In this method, the kinematics of the structure is modified to filter the local vibrations. Moreover, the reduced kinematics is combined with the idea of static condensation to achieve higher accuracy.
Due to the high accuracy requirement, a second attempt for dynamic modeling is carried out. For the FLSNFC, a honeycomb basket is placed inside the cylindrical canister, and a fuel assembly (FA)
that holds nearly 100 fuel rods is inserted in each of the 68 basket cells. A multi-level nested CraigBampton (CB) sub-structuring method with shift-invert Lanczos (SIL) eigenvalue solver and filtering of the local vibration modes of the substructures is proposed. This method is adapted to the multi-scale nature and localized connections between the substructures. The CB sub-structuring technique takes advantage of the limited degrees of freedom (DOF) of internal boundary and is applied to modal analysis for two structural levels, the system and the FA levels. As a result, the integrated method achieves a computational gain of four orders of magnitude for the FLSNFC at the expense of negligible errors.
For probabilistic modeling, polynomial chaos expansion (PCE) is an efficient method, but it suffers from the curse of dimensionality. However, a basis adaptation method proposed by Tipireddy and Ghanem (2014) [6] can reduce the dimension of the problem by rotating the input Gaussian random variables such that the quantity of interest (QoI) in the new space has concentrated representation. In this study, we proposed two novel approaches that can accelerate the convergence of the basis adaptation method. In the first approach, the mean and Gaussian coefficients in the adapted space are corrected by information obtained from a pilot PCE. The second approach updates the rotation matrix by taking advantage of the probabilistic information embedded in the higher dimensional adaptation gleaned from an initial adaptation. As a result, both approaches achieve accelerated convergence of the basis adaptation method with negligible additional costs.
The basis adaptation is adequate to reduce the dimension for the scalar QoI problems. To deal with UQ problems with high dimensional QoI and high dimensional parameter space, the integration of Karhunen- Loève expansion (KLE) and basis adaptation is proposed. The KLE first approximates the QoI to reduce the dimension of the QoI to a limited number of KL terms. Then, for each KL term, an adapted PCE is built with the accelerated basis adaptation method to reduce the dimension of the input variables. The PCE models of the KL terms then can be substituted back to the KLE of the QoI to obtain a surrogate probabilistic model of the QoI. Finally, the accuracy of the surrogate model is verified.
The Bayesian method will be used for the inverse analysis of parameter inference given observed data of a possibly damaged model. The process usually involves evaluating the forward model
numerous times, which is intractable for the complex system considered in the present study, even if the dynamic ROM is used. Thanks to the accurate surrogate probabilistic model of the QoI, the physical model required to be evaluated in Bayesian analysis can be replaced by the surrogate model to achieve several orders of computational efficiency. Nevertheless, generating posterior samples of high dimensional parameters can still be challenging. Thus, a block-update Markov Chain Monte Carlo (MCMC) method is applied to address this issue. By appropriately designing the inverse problem, the location and damage types of the internals can be identified based on dynamic signals on the exterior
surface.
The second goal of the work is to propose a novel multi-fidelity UQ method for dissimilar parameterization models. In multi-fidelity UQ, credible prediction and analyses of high-fidelity (HF) models are obtained by leveraging evaluations of a large number of efficient low-fidelity (LF) models.
The efficacy of the technique relies heavily on the correlation of the HF and LF models. We propose using the basis adaption method in the multi-fidelity technique to independently identify the important directions (or adapted variables) for each model, and the important directions assemble a common lower-dimensional space. Since important directions have concentrated information of the QoI and are aligned for different models, the samples generated on the common lower-dimensional manifold have
enhanced correlations. Thus, the proposed method can increase the performance of the multi-fidelity technique, especially for models with dissimilar parameterization.
The two main goals in the thesis are relevant since both involve stochastic reduction for complex multi- component models.
Webcast: Join Zoom Meeting https://usc.zoom.us/j/95953335264?pwd=SnNEY3JST0hVVFZCdzg2MWpwOG5zUT09 Meeting ID: 959 5333 5264 Passcode: 275995Location: Zoom Meeting
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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MS CSCI/DSCI Drop-in Academic Advising
Wed, Dec 15, 2021 @ 02:30 PM - 03:30 PM
Thomas Lord Department of Computer Science
Workshops & Infosessions
Virtual Academic Advising Drop-in Hours for continuing MS students in CS or Data Science Programs will be available for the month of December. If you have a quick question that doesn't require a 20 minute appointment, please come to our drop in hours. Students may be placed into the waiting room upon arrival.
Zoom access link for all sessions:
ZOOM LINK SENT TO STUDENTS DIRECTLY. CHECK EMAIL FOR LINK.
Wednesday, December 1st --- 10am -- 11am
Wednesday, December 1st --- 2:30pm -- 3:30pm
Wednesday, December 8th --- 10am -- 11am
Wednesday, December 8th --- 2:30pm -- 3:30pm
Wednesday, December 15th --- 10am -- 11am
Wednesday, December 15th --- 2:30pm -- 3:30pm
Location: Online
Audiences: Graduate
Contact: USC Computer Science
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Drop-In Q&A for Prospective Graduate Students
Thu, Dec 16, 2021 @ 09:00 AM - 09:30 AM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
This webinar is designed for those that have specific questions they want answered. Questions will be submitted using the Q&A function and will be answered verbally by a USC Viterbi representative.
WebCast Link: https://usc.zoom.us/webinar/register/WN_a0cbNz_jQieaByFuoP32WQ
Audiences: Everyone Is Invited
Contact: William Schwerin
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Drop-In Q&A for Prospective Graduate Students
Thu, Dec 16, 2021 @ 04:00 PM - 04:30 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
This webinar is designed for those that have specific questions they want answered. Questions will be submitted using the Q&A function and will be answered verbally by a USC Viterbi representative.
WebCast Link: https://usc.zoom.us/webinar/register/WN_MYe4mGEpQGWUDZnivSKgNw
Audiences: Everyone Is Invited
Contact: William Schwerin
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DEN@Viterbi: How to Apply Virtual Info Session
Thu, Dec 16, 2021 @ 05:00 PM - 06:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi representatives for a step-by-step guide and tips for how to apply for formal admission into a Master's degree or Graduate Certificate program. The session is intended for individuals who wish to pursue a graduate degree program completely online via USC Viterbi's flexible online DEN@Viterbi delivery method.
Attendees will have the opportunity to connect directly with USC Viterbi representatives and ask questions about the admission process throughout the session.
Register Now!WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=ec1322172ce9fee3f985d36b87e395e3f
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs