Engineering Commons^™

An Assessment Of The Decision Making Units’ Efficiency In Service Systems (The Case Of Cellular Telecom), Maoloud Dabab, Timothy R. Anderson

Student Research Symposium

Most tools and models on performance and quality of service management are generic and do not solve the complex technical systems, which the most critical component on the network and where these tools should be applied. The objective of this research is to assess the cellular performance and Base Transceiver Station (BTS) efficiency by proposing a robust model that is derived from multiple Key Performance Indicators (KPIs) based on technical and financial aspects. The novelty of this research provides a comprehensive multidimensional model for tuning the BTS parameters, which can lead to developing a standard global mobile network KPI. The …

Explanation Methods For Neural Networks, Jack H. Chen, Christof Teuscher

Student Research Symposium

Neural Networks (NNs) have become a basis of almost all state-of-the-art machine learning algorithms and classifiers. While NNs have been shown to generalize well to real-world examples, researchers have struggled to show why they work on an intuitive level. We designed several methods to explain the decisions of two state-of-the-art NN classifiers, ResNet and an All-CNN, in the context of the Japanese Society of Radiological Technology (JSRT) lung nodule dataset and the CIFAR-10 image dataset. Leading explanation methods LIME and Grad-CAM generate variations of heat maps which represent the regions of the input determined salient by the NN. We analyze …

Reliable Explanations Via Adversarial Examples On Robust Networks, Walt Woods, Jack H. Chen, Christof Teuscher

Student Research Symposium

Neural Networks (NNs) are increasingly used as the basis of advanced machine learning techniques in sensitive fields such as autonomous vehicles and medical imaging. However, NNs have been found vulnerable to a class of imperceptible attacks, called adversarial examples, which arbitrarily alter the output of the network. To close the schism between needing reliability in real-world applications and the fragility of NNs, we propose a new method for stabilizing networks, and show that as an added bonus, our technique results in reliable, high-fidelity explanations for the NN's decision. Compared to the state-of-the-art, this technique increased the area under the curve …

Development Of A Design Guideline For Pile Foundations Subjected To Liquefaction-Induced Lateral Spreading, Milad Souri, Arash Khosravifar

Student Research Symposium

Past earthquakes confirmed that seismically induced kinematic loads from soil lateral spreading and inertial loads from structure can cause severe damages to pile foundations. The research questions are:

• How to combine inertial and kinematic loads in design of pile foundations in liquefied soil?
• How the combination of inertia and kinematics changes with depth?
• How this combination is affected by long-duration earthquakes?
• How this combination affects inelastic demands in piles?

Diagnostic Imaging Of Structural Concrete Using Ground Penetrating Radar And Ultrasonic Array, Sina Mehdinia, Thomas Schumacher, Eric Wan, Xubo Song

Student Research Symposium

Structural concrete is the most widely used construction material in the world. Many structures critical to a society such as bridges, hospitals, and airports are built with concrete. While this material is well understood from a mechanical design point of view, still no accurate quantitative tools exist to assess it for damage and deterioration. This is of particular concern for an urban area like Portland with a mega-thrust earthquake waiting to occur. Non-destructive evaluation tools that can quickly and accurately give a full picture of the integrity of structural concrete elements will be key to help plan effective and safe …

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods, Christof Teuscher

Student Research Symposium

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

Fair Resource Allocation In An Mec-Enabled Ultra-Dense Iot Network With Noma, Qun Wang

Student Research Symposium

Ultra-dense Internet of Things (IoT) network has greatly facilitated the development of smart environments and the realization of diverse sophisticated applications. Power constrained and resource-limited IoT devices often need to perform computation-intensive and delay-sensitive tasks in such a network. Mobile edge computing and non-orthogonal multiple access are two promising techniques to address the corresponding challenges. In order to improve the fairness and resource efficiency among IoT users, resource allocation problems are formulated in ultra-dense MEC-enabled IoT networks with NOMA considered. An iterative algorithm based on successive convex approximation technique is proposed to solve those challenging non-convex problems.

Computational Prediction Of Host-Pathogen Protein Interactions Between Melioidosis Pathogen Burkholderia Pseudomallei And Human, Chathumadavi Ediriweera

Student Research Symposium

Background:

Burkholderia pseudomallei (Bp) is a critical biothreat agent, causes Melioidosis: a disease associated with high case-fatality rates in animals and humans; even with treatment, its mortaility is up to 50%. It also infects plants. Yet few effector proteins have been functionally characterized to date and little work has been carried out to understand the host-pathogen protein-protein interactions (PPI) which are important to develop efficient prevention measures. To address these issues, we deployed diverse in-silico methodologies to identify virulence factors and their interactions with host proteins to characterize bacterial infection mechanisms.

Propulsion Unit Optimization For Small Uas, Cory Goates

Student Research Symposium

Selection of a propulsion system for unmanned aerial vehicles (UAV) is a complex and iterative process. Attempts have been made to provide effective modelling of propulsion systems. However, due to the discrete nature of the design space, effective optimization tools have yet to be developed. We present an optimizer which presents designers with feasible propulsion systems based on given parameters. This optimizer also allows designers to view trend in the design space and more quickly determine an optimum solution.

Exploiting Wall Street: An Algorithmic Approach To Investing, Sterling Lemon

Student Research Symposium

Technology has advanced dramatically over the years, yet the old investment strategy of "buy and hold" has remained prevalent. While this strategy performed well historically, it serves as a stark contrast to modern investing which, by utilizing machine learning and advanced data analytics, is generating unprecedented returns This research project will demonstrate the strength behind algorithmic investing and how the investment strategy of "buy and hold" will become a thing of the past.

Augmenting Anaerobic Digestion Of Microalgal Biomass, Anna Doloman

Student Research Symposium

Anaerobic digestion of microalgal biomass cannot be achieved without specialized hydrolytic microorganisms. Potentially algalytic bacteria belonging to Citrobacter and Alcaligenes species were isolated from a wastewater lagoon system. A combination of two potentially algalytic bacteria was successfully incorporated into the granular anaerobic consortia. A series of anaerobic cultures were prepared with different microbial combinations to test the methane production from algal biomass collected from a local wastewater treating trickling filter. The anaerobic microbial community mixed with two algalytic bacteria produced 10% more methane when compared to the methane produced by a native granular consortium. The presence of the algalytic bacteria …

Robust Structured Group Local Sparse Tracker Using Convolutional Neural Network Features, Mohammadreza Javanmardi

Student Research Symposium

Sparse representation has recently been successfully applied in visual tracking. It utilizes a set of templates to represent target candidates and find the best one with the minimum reconstruction error as the tracking result. In this presentation, we propose a robust deep features-based structured group local sparse tracker, which exploits the convolutional neural network (CNN) features of local patches inside target candidates and represents them by a set of templates in the particle filter framework. To extract the local CNN features, we first set the size of each target candidate to 64 by 64 pixels to contain sufficient object-level information …