Artificial Intelligence and Robotics Commons^™

Du Undergraduate Showcase: Research, Scholarship, And Creative Works: Abstracts, Emma Aggeler, Elena Arroway, Daisy T. Booker, Justin Bravo, Kyle Bucholtz, Megan Burnham, Nicole Choi, Spencer Cockerell, Rosie Contino, Jackson Garske, Kaitlyn Glover, Caroline Hamilton, Haley Hartmann, Madalyne Heiken, Colin Holter, Leah Huzjak, Alyssa Jeng, Cole Jernigan, Chad Kashiwa, Adelaide Kerenick, Emily King, Abigail Langeberg, Maddie Leake, Meredith Lemons, Alec Mackay, Greer Mckinley, Ori Miller, Guy Milliman, Katherine Miromonti, Audrey Mitchell, Lauren Moak, Megan Morrell, Gelella Nebiyu, Zdenek Otruba, Toni V. Panzera, Kassidy Patarino, Sneha Patil, Alexandra Penney, Kevin Persky, Caitlin Pham, Gabriela Recinos, Mary Ringgenberg, Chase Routt, Olivia Schneider, Roman Shrestha, Arlo Simmerman, Alec Smith, Tessa Smith, Nhi-Lac Thai, Kyle Thurmann, Casey Tindall, Amelia Trembath, Maria Trubetskaya, Zachary Vangelisti, Peter Vo, Abby Walker, David Winter, Grayden Wolfe, Leah York

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Showcase.

Artificial Intelligence, Controls, And Sensor Fusion For Optimization And Modeling Of Space Missions And Particle Accelerators, Reza Pirayeshshirazinezhad

Mechanical Engineering ETDs

This PhD dissertation is devoted to developing artificial intelligence (AI) applications for space missions and particle accelerators considering constraints on the computational resources. The space mission studied in this research, the Virtual Telescope for X-ray Observations (VTXO), is the mission exploiting 2 6U-CubeSats operating in a precision formation. The goal of the VTXO project is to develop a space-based, X-ray imaging telescope with high angular resolution precision. VTXO space mission is designed and the mission is optimized to increase the performance of the mission. Trajectory optimization with AI, hybrid control, control algorithms, and high performance computing are all used to …

Optimization Of Orbital Trajectories Using Neuroevolution Of Augmenting Topologies, Nathan Wetherell

University Scholar Projects

This project aims to determine the feasibility of using NeuroEvolution of Augmenting Topologies (NEAT), an advanced neural network evolution scheme, to optimize orbital transfer trajectories. More specifically, this project compares a genetically evolved neural network to a standard Hohmann transfer between Earth and Mars. To test these two methods, an N-body simulation environment was created to accurately determine the result of gravitational interactions on a theoretical spacecraft when combined with planned engine burns. Once created, this simulation environment was used to train the neural networks created using the NEAT Python module. A genetic algorithm was used to modify the topology …

Analysis Of Gpu Memory Vulnerabilities, Jarrett Hoover

Computer Science and Computer Engineering Undergraduate Honors Theses

Graphics processing units (GPUs) have become a widely used technology for various purposes. While their intended use is accelerating graphics rendering, their parallel computing capabilities have expanded their use into other areas. They are used in computer gaming, deep learning for artificial intelligence and mining cryptocurrencies. Their rise in popularity led to research involving several security aspects, including this paper’s focus, memory vulnerabilities. Research documented many vulnerabilities, including GPUs not implementing address space layout randomization, not zeroing out memory after deallocation, and not initializing newly allocated memory. These vulnerabilities can lead to a victim’s sensitive data being leaked to an …

Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Software developers often use social media (such as Twitter) to shareprogramming knowledge such as new tools, sample code snippets,and tips on programming. One of the topics they talk about is thesoftware library. The tweets may contain useful information abouta library. A good understanding of this information, e.g., on thedeveloper’s views regarding a library can be beneficial to weigh thepros and cons of using the library as well as the general sentimentstowards the library. However, it is not trivial to recognize whethera word actually refers to a library or other meanings. For example,a tweet mentioning the word “pandas" may refer to …

Radiomic Features To Predict Overall Survival Time For Patients With Glioblastoma Brain Tumors Based On Machine Learning And Deep Learning Methods, Lina Chato

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for …

Hierarchical Value Decomposition For Effective On-Demand Ride Pooling, Hao Jiang, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

On-demand ride-pooling (e.g., UberPool, GrabShare) services focus on serving multiple different customer requests using each vehicle, i.e., an empty or partially filled vehicle can be assigned requests from different passengers with different origins and destinations. On the other hand, in Taxi on Demand (ToD) services (e.g., UberX), one vehicle is assigned to only one request at a time. On-demand ride pooling is not only beneficial to customers (lower cost), drivers (higher revenue per trip) and aggregation companies (higher revenue), but is also of crucial importance to the environment as it reduces the number of vehicles required on the roads. Since …

Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn

Graduate Theses and Dissertations

Machine learning approaches for prediction play an integral role in modern-day decision supports system. An integral part of the process is extracting interest variables or features to describe the input data. Then, the variables are utilized for training machine-learning algorithms to map from the variables to the target output. After the training, the model is validated with either validation or testing data before making predictions with a new dataset. Despite the straightforward workflow, the process relies heavily on good feature representation of data. Engineering suitable representation eases the subsequent actions and copes with many practical issues that potentially prevent the …

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

Data-Driven Framework For Understanding & Modeling Ride-Sourcing Transportation Systems, Bishoy Kelleny

Civil & Environmental Engineering Theses & Dissertations

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for …

Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw

Electrical & Computer Engineering Theses & Dissertations

Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …

Development Of Modeling And Simulation Platform For Path-Planning And Control Of Autonomous Underwater Vehicles In Three-Dimensional Spaces, Sai Krishna Abhiram Kondapalli

Mechanical & Aerospace Engineering Theses & Dissertations

Autonomous underwater vehicles (AUVs) operating in deep sea and littoral environments have diverse applications including marine biology exploration, ocean environment monitoring, search for plane crash sites, inspection of ship-hulls and pipelines, underwater oil rig maintenance, border patrol, etc. Achieving autonomy in underwater vehicles relies on a tight integration between modules of sensing, navigation, decision-making, path-planning, trajectory tracking, and low-level control. This system integration task benefits from testing the related algorithms and techniques in a simulated environment before implementation in a physical test bed. This thesis reports on the development of a modeling and simulation platform that supports the design and …

Identification Of Return-Oriented Programming Attacks Using Risc-V Instruction Trace Data, Daniel F. Koranek, Scott R. Graham, Brett J. Borghetti, Wayne C. Henry

Faculty Publications

An increasing number of embedded systems include dedicated neural hardware. To benefit from this specialized hardware, deep learning techniques to discover malware on embedded systems are needed. This effort evaluated candidate machine learning detection techniques for distinguishing exploited from non-exploited RISC-V program behavior using execution traces. We first developed a dataset of execution traces containing Return Oriented Programming (ROP) exploitation on the RISC-V Instruction Set Architecture (ISA) and then developed several deep learning bidirectional Long Short-Term Memory (LSTM) models capable of distinguishing exploited traces from non-exploited traces, each using subsets of features from the execution traces. An objective of this …

Data-Driven Design And Analysis Of Next Generation Mobile Networks For Anomaly Detection And Signal Classification With Fast, Robust And Light Machine Learning, Muhammed Furkan Küçük

USF Tampa Graduate Theses and Dissertations

This research focuses on machine (and deep) learning applications (including clustering,anomaly detection and signal classification) for self-organizing and next generation mobile networks in wireless communications. Specifically, this dissertation document will address the three different topics.

First, in the study titled “Performance analysis of neural network topologies and hyperparameters for deep clustering”, we explore the relationship between the clustering performance and network complexity. Deep learning found its initial footing in supervised applications such as image and voice recognition successes of which were followed by deep generative models across similar domains. In recent years, researchers have proposed creative learning representations to utilize …

Study On Near-Body Pressure Characteristics Of Bionic Robotic Fish Undulating In Near Wall Region, Ou Xie, Aiguo Song, Qixin Zhu

Journal of System Simulation

Abstract: To avoid unbalanced workload assignment, we studied the vehicle routing problem with refined oil secondary distribution considering workload balance. A bi-objectivemixed integer programming model was built to minimize the total distribution cost and the maximum difference in vehicle route length. A heuristic variable neighborhood tabu search algorithm was designed. An improved Solomon_I1 insertion algorithm was developed to generate afeasible initial solution such that the total distribution cost was as small as possible. Then, the variable neighborhood tabu search algorithm was used to improve the initial solution and thereby obtain the approximate optimal solution. The simulation results show that in …

Multi-Modality Affective Computing Model Based On Personality And Memory Mechanism, Sijin Zhou, Dicheng Chen, Geng Tu, Dazhi Jiang

Journal of System Simulation

Abstract: With the development of affective computing, the correlation of memory, individuation and emotion is more and more important. Focus on the machine emotion shortcomings in the perception, understanding and expression, an emotion computing model integrating the emotion perception, understanding and expression is proposed. The model is a memory-oriented deep network perception model that accepts multiple modal inputs (visual, auditory, lexical) and applies a fuzzy emotion integration decision to realize the understanding of uncertain emotions. The simulation experiments prove that the model has a good performance in all kinds of multimodal affective computing.

Virtual Scene Stereoscopic Panorama Generation And Viewport Rendering Algorithm, Haoxiang Li, Chunyi Chen, Xiaojuan Hu, Yunbiao Liu, Qiwei Xing

Journal of System Simulation

Abstract: Aiming at the nonuniform sampling in map projection and the redundancy in aspheric projection, spherical Fibonacci lattices is used to sample the visible spherical area to generate spherical Fibonacci lattice panorama with low-redundancy and high-quality. On the basis of panoramic stereo imaging model, the binocular ray direction generation algorithm for spherical stereo panorama is proposed. With Fibonacci grid, an adaptive filtering method of light-visibility map for panorama is designed to generate spherical panorama with approximate soft shadow. The nearest neighbor interpolation is used to render the viewport of panorama. Extensive experiments show that the frame frequency of the viewport …

Simulation On Cold Chain Distribution Path Of Fresh Agricultural Products Under Low-Carbon Constraints, Tao Ning, Tao Gou, Xiangdong Liu

Journal of System Simulation

Abstract: The freshness distribution requirements of fresh agricultural products may increase the carbon emissions of the cold chain distribution process. A cold chain distribution scheduling strategy and simulation method for the fresh agricultural products under low-carbon constraints is proposed. Based on the quantitative analysis of the carbon tax mechanism, a mathematical model of minimizing the carbon emissions and minimizing the overall cost of distribution is established. Comprehensively analyzing the conventional factors such as the product delivery volume, delivery time and loading and unloading time in logistics distribution, an improved quantum ant colony algorithm based on adaptive rotation angle …

Simulation Optimization On Joint Production And Preventive Maintenance Scheduling For Distributed Job-Shop, Fei Ye, Ziqing Li, Yuanjun Laili

Journal of System Simulation

Abstract: Distributed job-shop production scheduling is the key to high efficient production. Preventive maintenance, an essential means to ensure the safety and reliability of equipment, should be the necessary content of the production decision-making. Aiming at the production delay caused by equipment maintenance, a simulation-corrected optimization method is proposed. The mathematical model and simulation model for the joint production and preventive maintenance scheduling are established. The sequence exchange-based genetic algorithm is combined with the simulation-corrected optimization method to form a fast simulation optimization scheme. Experimental results on typical cases show that the proposed simulation optimization method can improve the solution …

Research On Discrete Workshop Task Assignment Based On Improved Water Filling Algorithm, Kaituan Feng, Jie Yuan

Journal of System Simulation

Abstract: Aiming at the unsatisfactory the results of the real-time dynamic task allocation in discrete workshops are not ideal, an improved water filling algorithm is proposed. Compared with the equal cost allocation of the water injection algorithm, the processing rate and cost factors are added to the improved water injection algorithm to coordinate the processing rate, the cost and the workpieces. The allocation of the different cost workpieces is realized and the result is adjusted, which can meet the requirements of discrete distribution. The improved water injection algorithm can dynamically allocate the newly added workpieces in real time. The proposed …

Key Technology Research On Stall Spin Simulation Training System Of An Aircraft, Guangxu Xi, Yongyi Liu, Chong Wu, Junjie Zhang, Yinghao Chen

Journal of System Simulation

Abstract: In order to realize the simulation continuity of the multi-state evolution of traffic and to solve the problem that multi-state traffic can only be simulated by single state traffic through multiple times, the middleware model of continuous traffic event simulation is built by the secondary development interface of VISSIM simulation software, and the original two related traffic simulation events are jointly driven. The secondary development of VISSIM-com is carried out by C# and database. The survey data of the intersection of Ganghua Road and Baihua Road in the Yuzhong District of Chongqing is selected as the example, and the …

Simulation On Mutual Interference Of Laser Radar In Road Environments, Xuesong Mao, Runlong Lei, Shaowei Huang, Xuetao Mao

Journal of System Simulation

Abstract: Transmission line inspection is an important work to ensure the safe operation of the power grid. It involves the live-work, the power-outage-maintenance and the fault-diagnosis. Training using only 3D virtual technology cannot achieve realistic results. It is necessary to combine 3D virtual scenes with digital grids. To this end, using the collected monitoring data and multi-time-scale calculation models, a twin state digital power grid has been constructed for the live-work, a parallel state digital power grid has been constructed for the power-outage-maintenance, and corresponding analog devices have been added to the parallel digital power grid to complete the training …

Research On Credibility Assessment Of Cloud Simulation System, Wei Li, Huan Zhang, Ping Ma, Ming Yang

Journal of System Simulation

Abstract: The cloud simulation platform supports the "cloud simulation" mode, which can automatically find simulation resources and dynamically build the cloud simulation system. Because the cloud simulation platform has the characteristics of simulation services establishment on demand and multi-granularity resources sharing, the credibility evaluation of the cloud simulation system faces new challenges in the credibility of the simulation resources, the credibility of the simulation subsystems and the reliability of the simulation environment, etc. From the whole life cycle perspective of the cloud simulation system, the credibility evaluation process model and the credibility evaluation index system of the cloud simulation system …

Research On Physical Layer Security Of Full-Duplex Uav Relaying, Shu Ye, Xiaodong Ji, Wenhua Li

Journal of System Simulation

Abstract: Aiming at the physical layer security of a full-duplex UAV relaying system, a novel scheme based on the joint optimization of the transmit power and UAV flight trajectory is proposed. Under the condition of limited transmit power and flight trajectory, a joint optimization that maximizing the average secrecy rate of the system is constructed. The non-concave problem that cannot be solved directly is resolved into two sub-problems of transmit power and flight trajectory optimization, which can be transformed into the concave problem by the successive convex approximation method. An iterative algorithm is proposed to obtain the numerical solution of …

Optimal Operation For Park Integrated Energy System Considering Interruptible Loads, Lixin Ma, Ying Cheng

Journal of System Simulation

Abstract: The operating mode of thermal power generation units has certain limitations in peak shaving capacity. Interruptible load (IL), as a power resource to be tapped, can be applied to the park integrated energy management and microgrid systems to guide users to reduce peak electricity consumption. The IL function is introduced into the park integrated energy system with combined heat and power units to improve the system's peak shaving ability, and the corresponding model is established with the optimization goal of economy. Taking an ecological park of northern region as the example, the adaptive chaotic particle swarm algorithm is used …

Radar Remote Sensing Data Augmentation Method Based On Generative Adversarial Network, Xu Kang, Xiaofeng Zhang

Journal of System Simulation

Abstract: In the research field of radar remote sensing, both the completeness and diversity of radar data samples cannot meet the requirement of effective training of deep learning models, and the models are prone to over-fitting, which significantly limits the wide application of deep learning techniques in this field. Targeting on the needs of intelligent application in radar remote sensing, a microwave imaging radar suited data augmentation method is proposed to solve the issue of insufficient radar data samples by leveraging the general framework of generative adversarial network. Aiming at the features of radar samples being not obvious, the label …

A Devs-Based Formal Description Method For Complex Product Behavior Models, Qingquan Lin, Jiaran Yang, Heming Zhang

Journal of System Simulation

Abstract: For the online optimization of pedestrian flow control in subway station, an algorithm frame for pedestrian flow control in subway station based on machine learning is designed. The pedestrian flow control process of a subway station during morning rush hour is selected,and the agent-based model is built to simulate the control process. The training data is collected through the multiple runs of the model, which is used as the input of deep reinforcement learning network, and the mature net is obtained through adequate training to provide the optimizing scheduling policy. Linking the actual data with the mature net …

An Unmanned Swarm Search Method Based On Human-Robot Cooperation, Xin Zhou, Weiping Wang, Yifan Zhu, Tao Wang, Tian Jing

Journal of System Simulation

Abstract: Human-robot collaboration is a research hotspot and the human and unmanned swarm collaborative search is a typical scenario. It can carry out the more complex tasks by combining the human complex reasoning capabilities with repeated and precise execution capabilities of unmanned swarm. Based on the high-value target search of the uncertain scenarios, the concept definition for the collaborative search of human and unmanned swarm is given. A multi-agent dynamic programming model under uncertain with unknown prior knowledge is proposed, established to describe how the multi-agent system carries out the search under human support. A dynamic programming algorithm …

Research On Space Launch Visualization Simulation Analysis Technology And Application, Feng Wu, Xiuluo Liu, Jia Wang, Yang Liu, Sujiang Li, Yan Zhong

Journal of System Simulation

Abstract: Through the typical cases in the field of military, society, manufacturing etc., this paper introduces the concept and features of complex engineering system of systems as well as the significance of modeling and simulation for the study of complex engineering system of systems. Taking complex product manufacturing as an example, the evolution process from systems engineering to model-based systems engineering (MBSE) and then to modeling and simulation-based system of systems engineering is analyzed. The characteristics and challenges of modeling and simulation of complex engineering system of systems is discussed. Some research topics in filed of MSBS2E is introduced, which …

Hybrid Variable Neighborhood Search Algorithm For The Multi-Trip And Heterogeneous-Fleet Electric Vehicle Routing Problem, Weiquan Wang, Ding Ding, Shuyan Cao

Journal of System Simulation

Abstract: Based on the real business practice, the multi-trip and heterogeneous-fleet electric vehicle routing problem (MTHF-EVRP) with time windows in green logistics is studied. A path-based mixed-integer linear model is built for the precise solution to the small-scale instances. A hybrid variable neighborhood search algorithm (Hybrid VNS) combined the variable neighborhood search algorithm with the labeling algorithm is proposed for the large-scale instances. The algorithm generates a modified insertion heuristic with random factor to construct the initial solution, allows the time window and range violation, adopts the neighborhood operators for the local search, and applies a labeling algorithm to solve …