IU-Advise: A Web Based Advising Tool For Academic Advisors and Students

Thesis (M.S.) -- Indiana University South Bend, 2009.Academic advising is an important activity of an academic institution. It guides the students to explore potential careers, academic disciplines and opportunities in the college environment. An accurate and full featured advising system can be an effective tool to both students and faculty advisors. The dynamic nature of academic programs, especially in regards to changes in the general education and other degree requirements, poses a continuous challenge to faculty advisors to remain up-to-date. The goal of this thesis is to implement a web-based advising system which facilitates academic advisors in their efforts to providing quality, accurate and consistent advising services to their students. The proposed system was implemented using a set of open source software packages to create a low cost, flexible, and customizable system

Robust Vibration Output-only Structural Health Monitoring Framework Based on Multi-modal Feature Fusion and Self-learning

Output-only structural health monitoring is a highly active research direction because it is a promising methodology for building digital twin applications providing near-real-time monitoring results of the structure. However, one of the technical bottlenecks is how to work effectively with multiple high-dimensional vibration signals. To address this question, this study develops a two-stage data-driven framework based on various advanced techniques, such as time-series feature extractions, self-learning, graph neural network, and machine learning algorithms. At first, multiple features in statistical, time, and spectral domains, are extracted from raw vibration data; then, they subsequently enter a graph convolution network to account for the spatial correlation of sensor locations. After that, the high-performance adaptive boosting machine learning algorithm is leveraged to assess structures' health states. This method allows for learning a lower-dimensional yet informative representation of vibration data; thus, the subsequent monitoring tasks could be performed with reduced time complexity and economical computational resources. The performance of the proposed method is qualitatively and quantitatively demonstrated through two examples involving both numerical and experimental structural data. Furthermore, comparison and robustness studies are carried out, showing that the proposed approach outperforms various machine learning/deep learning-based methods in terms of accuracy and noise/missing-robustness

Enhancing the Fairness and Performance of Edge Cameras with Explainable AI

The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug. Our research presents a diagnostic method using Explainable AI (XAI) for model debugging, with expert-driven problem identification and solution creation. Validated on the Bytetrack model in a real-world office Edge network, we found the training dataset as the main bias source and suggested model augmentation as a solution. Our approach helps identify model biases, essential for achieving fair and trustworthy models.Comment: IEEE ICCE 202

Stabilization for equal-order polygonal finite element method for high fluid velocity and pressure gradient

This paper presents an adapted stabilisation method for the equal-order mixed scheme of finite elements on convex polygonal meshes to analyse the high velocity and pressure gradient of incompressible fluid flows that are governed by Stokes equations system. This technique is constructed by a local pressure projection which is extremely simple, yet effective, to eliminate the poor or even non-convergence as well as the instability of equal-order mixed polygonal technique. In this research, some numerical examples of incompressible Stokes fluid flow that is coded and programmed by MATLAB will be presented to examine the effectiveness of the proposed stabilised method

Secrecy outage probability of a NOMA scheme and impact imperfect channel state information in underlay cooperative cognitive networks

Security performance and the impact of imperfect channel state information (CSI) in underlay cooperative cognitive networks (UCCN) is investigated in this paper. In the proposed scheme, relay R uses non-orthogonal multiple access (NOMA) technology to transfer messages e1, e2 from the source node S to User 1 (U-1) and User 2 (U-2), respectively. An eavesdropper (E) is also proposed to wiretap the messages of U-1 and U-2. The transmission's security performance in the proposed system was analyzed and performed over Rayleigh fading channels. Through numerical analysis, the results showed that the proposed system's secrecy performance became more efficient when the eavesdropper node E was farther away from the source node S and the intermediate cooperative relay R. The secrecy performance of U-1 was also compared to the secrecy performance of U-2. Finally, the simulation results matched the Monte Carlo simulations well.Web of Science203art. no. 89

Support Vector Machine for Regression of Ultimate Strength of Trusses: A Comparative Study

Thanks to the rapid development of computer science, direct analyses have been increasingly used in the design of structures in lieu of member-based design methods using the effective length factor. In a direct analysis, the ultimate strength of a whole structure can be sufficiently estimated, so that the need for member capacity checks is eliminated. However, in complicated structural design problems where many structural analyses are required, the use of direct analyses requires an excessive computation cost. In such cases, Machine Learning (ML) algorithms are used to build metamodels that can predict the structural responses without performing costly structural analysis. In this paper, the support vector machine (SVM) algorithm is employed for the first time to develop a metamodel for predicting the ultimate strength of trusses using direct analysis. Several kernel functions for the SVM model, including linear, sigmoid, polynomial, radial basis function (RBF), are considered. A planar 39-bar nonlinear inelastic steel truss is taken to study the performance of the kernel functions. The results confirm the applicability of the SVM-based metamodel for predicting the ultimate strength of trusses. In particular, the RBF appears to be the best kernel among others. This investigation also provides a deeper understanding of the effect of the parameters on the efficiency of the kernel functions

Experimental and Probabilistic Investigations of the Effect of Fly Ash Dosage on Concrete Compressive Strength and Stress-strain Relationship

The effect of fly ash (FA) dosage on concrete’s compressive strength and stress-strain relationship is investigated in two steps in this article. First, an experimental program was conducted on concrete mixtures designed with 0% (control batch of 30 MPa mean cylinder compressive strength), 10, 20, 30, and 40% of ordinary Portland cement (OPC) mass replaced by FA, which is taken from a new source in an Asia country. The test results showed that compared to other investigated dosages, concrete using 20% FA/OPC mass-replacement gained the most improvement in the 28-day compressive strength and tensile split strength, as well as the compressive strength development. Second, a probabilistic investigation was conducted using Dropout Neural Network, Bayesian Neural Network, and Gaussian Process models. These artificial intelligence-based models were compared to other models reviewed from the literature, showing relatively good results in terms of the statistical metric R2, which are 0.92, 0.9, and 0.88, respectively. The three models were tested and validated with a dataset of 1032 experimental results on FAC collected from the literature. When testing with the experimental results obtained in the first step, a good correlation between the predicted values and the experimental results was observed within the confidence interval of (5%, 95%), showing the reliability of the proposed models. Thus, the stress-strain relationship of fly ash concrete can also be investigated in a probabilistic manner. It is proved in this study that among the proposed models, Dropout Neural Network has the best balance between performance and time complexity

XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection

Visual quality inspection systems, crucial in sectors like manufacturing and logistics, employ computer vision and machine learning for precise, rapid defect detection. However, their unexplained nature can hinder trust, error identification, and system improvement. This paper presents a framework to bolster visual quality inspection by using CAM-based explanations to refine semantic segmentation models. Our approach consists of 1) Model Training, 2) XAI-based Model Explanation, 3) XAI Evaluation, and 4) Annotation Augmentation for Model Enhancement, informed by explanations and expert insights. Evaluations show XAI-enhanced models surpass original DeepLabv3-ResNet101 models, especially in intricate object segmentation.Comment: IEEE ICCE 202