Engineering Commons^™

Convolutional Neural Networks For Deflate Data Encoding Classification Of High Entropy File Fragments, Nehal Ameen

LSU New Orleans Theses and Dissertations

Data reconstruction is significantly improved in terms of speed and accuracy by reliable data encoding fragment classification. To date, work on this problem has been successful with file structures of low entropy that contain sparse data, such as large tables or logs. Classifying compressed, encrypted, and random data that exhibit high entropy is an inherently difficult problem that requires more advanced classification approaches. We explore the ability of convolutional neural networks and word embeddings to classify deflate data encoding of high entropy file fragments after establishing ground truth using controlled datasets. Our model is designed to either successfully classify file …

Stock Markets Performance During A Pandemic: How Contagious Is Covid-19?, Yara Abushahba

Theses and Dissertations

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock.

Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its …

Experience-Driven Control For Networking And Computing, Zhiyuan Xu

Dissertations - ALL

Modern networking and computing systems have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this thesis, we aim to study system control problems from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL), to develop experience-driven model-free approaches, which enable a network or a device to learn the best way to control itself from its own experience (e.g., runtime statistics data) rather than from accurate mathematical models, just as a human learns a new skill (e.g., driving, swimming, etc). To demonstrate the feasibility and superiority of this experience-driven control design …

Experience-Driven Control For Networking And Computing, Zhiyuan Xu

Dissertations - ALL

Modern networking and computing systems have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this thesis, we aim to study system control problems from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL), to develop experience-driven model-free approaches, which enable a network or a device to learn the best way to control itself from its own experience (e.g., runtime statistics data) rather than from accurate mathematical models, just as a human learns a new skill (e.g., driving, swimming, etc). To demonstrate the feasibility and superiority of this experience-driven control design …

Improving Additional Adversarial Robustness For Classification, Michael Guo

McKelvey School of Engineering Graduate Student Theses & Dissertations

Although neural networks have achieved remarkable success on classification, adversarial robustness is still a significant concern. There are now a series of approaches for designing adversarial examples and methods to defending against them. This paper consists of two projects. In our first work, we propose an approach by leveraging cognitive salience to enhance additional robustness on top of these methods. Specifically, for image classification, we split an image into the foreground (salient region) and background (the rest) and allow significantly larger adversarial perturbations in the background to produce stronger attacks. Furthermore, we show that adversarial training with dual-perturbation attacks yield …

Real-Time Monitoring Of Fdm 3d Printer For Fault Detection Using Machine Learning: A Bibliometric Study, Vaibhav Kisan Kadam, Satish Kumar, Arunkumar Bongale

Library Philosophy and Practice (e-journal)

Additive Manufacturing has wide application range including healthcare, Fashion, Manufacturing, Prototypes, Tooling etc. AM techniques are subjected to various defects that may be printing defects or anomalies in machine. There is gap between current AM techniques and smart manufacturing since current AM lacks in build sensors necessary for process monitoring and fault detection. Both of these issues can be solved by incorporating real-time monitoring into AM. So the study is carried out to identify recent work done in AM to improve current system. For this bibliometric study Scopus database is used, study is kept limited to year 2010-2021 and English …

Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi

Computer Science and Computer Engineering Undergraduate Honors Theses

Automatic Generation Control (AGC) is a key control system utilized in electric power systems. AGC uses frequency and tie-line power flow measurements to determine the Area Control Error (ACE). ACE is then used by the AGC to adjust power generation and maintain an acceptable power system frequency. Attackers might inject false frequency and/or tie-line power flow measurements to mislead AGC into falsely adjusting power generation, which can harm power system operations. Various data forgery detection models are studied in this thesis. First, to make the use of predictive detection models easier for users, we propose a method for automated generation …

Using Deep Learning To Analyze Materials In Medical Images, Carson Molder

Computer Science and Computer Engineering Undergraduate Honors Theses

Modern deep learning architectures have become increasingly popular in medicine, especially for analyzing medical images. In some medical applications, deep learning image analysis models have been more accurate at predicting medical conditions than experts. Deep learning has also been effective for material analysis on photographs. We aim to leverage deep learning to perform material analysis on medical images. Because material datasets for medicine are scarce, we first introduce a texture dataset generation algorithm that automatically samples desired textures from annotated or unannotated medical images. Second, we use a novel Siamese neural network called D-CNN to predict patch similarity and build …

Dynamic Task Allocation In Partially Defined Environments Using A* With Bounded Costs, James Hendrickson

Doctoral Dissertations and Master's Theses

The sector of maritime robotics has seen a boom in operations in areas such as surveying and mapping, clean-up, inspections, search and rescue, law enforcement, and national defense. As this sector has continued to grow, there has been an increased need for single unmanned systems to be able to undertake more complex and greater numbers of tasks. As the maritime domain can be particularly difficult for autonomous vehicles to operate in due to the partially defined nature of the environment, it is crucial that a method exists which is capable of dynamically accomplishing tasks within this operational domain. By considering …

A Hyperelastic Porous Media Framework For Ionic Polymer-Metal Composites And Characterization Of Transduction Phenomena Via Dimensional Analysis And Nonlinear Regression, Zakai J. Olsen

UNLV Theses, Dissertations, Professional Papers, and Capstones

Ionic polymer-metal composites (IPMC) are smart materials that exhibit large deformation in response to small applied voltages, and conversely generate detectable electrical signals in response to mechanical deformations. The study of IPMC materials is a rich field of research, and an interesting intersection of material science, electrochemistry, continuum mechanics, and thermodynamics. Due to their electromechanical and mechanoelectrical transduction capabilities, IPMCs find many applications in robotics, soft robotics, artificial muscles, and biomimetics. This study aims to investigate the dominating physical phenomena that underly the actuation and sensing behavior of IPMC materials. This analysis is made possible by developing a new, hyperelastic …

Autonomous Aerial Vehicle Vision And Sensor Guided Landing, Gabriel Bitencourt, Elijah J. Brown, Cedric Bleimling, Gilbert Lai, Arman Molki, Tolga Kaya

School of Computer Science & Engineering Faculty Publications

The use of autonomous landing of aerial vehicles is increasing in demand. Applications of this ability can range from simple drone delivery to unmanned military missions. To be able to land at a spot identified by local information, such as a visual marker, creates an efficient and versatile solution. This allows for a more user/consumer friendly device overall. To achieve this goal the use of computer vision and an array of ranging sensors will be explored. In our approach we utilized an April Tag as our location identifier and point of reference. MATLAB/Simulink interface was used to develop the platform …

Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity On 65 Nm Cmos, Luke Vincent

Graduate Theses and Dissertations

Machine learning is a rapidly accelerating tool and technology used for countless applications in the modern world. There are many digital algorithms to deploy a machine learning program, but the most advanced and well-known algorithm is the artificial neural network (ANN). While ANNs demonstrate impressive reinforcement learning behaviors, they require large power consumption to operate. Therefore, an analog spiking neural network (SNN) implementing spike timing-dependent plasticity is proposed, developed, and tested to demonstrate equivalent learning abilities with fractional power consumption compared to its digital adversary.

Digital Twin Technology Applications For Transportation Infrastructure - A Survey-Based Study, Hector Cruz

Open Access Theses & Dissertations

In the past couple of decades, various industries have taken advantage of emerging advanced technologies, such as digital twin (DT), to find more effective solutions in their respective areas. In the transportation infrastructure sector, the concept and implementation of DT technologies are slowly gaining traction but lagging behind other major industries. To better understand the limitations, opportunities and challenges for the adoption of DT in this sector, a survey questionnaire was distributed to collect information from industry professionals involved in transportation infrastructure projects. The purpose of this study is to understand how DT technology is being perceived by the industry. …

Improving Treatment Of Local Liver Ablation Therapy With Deep Learning And Biomechanical Modeling, Brian Anderson, Kristy Brock, Laurence Court, Carlos Eduardo Cardenas, Erik Cressman, Ankit Patel

Dissertations and Theses (Open Access)

In the United States, colorectal cancer is the third most diagnosed cancer, and 60-70% of patients will develop liver metastasis. While surgical liver resection of metastasis is the standard of care for treatment with curative intent, it is only avai lable to about 20% of patients. For patients who are not surgical candidates, local percutaneous ablation therapy (PTA) has been shown to have a similar 5-year overall survival rate. However, PTA can be a challenging procedure, largely due to spatial uncertainties in the localization of the ablation probe, and in measuring the delivered ablation margin.

For this work, we hypothesized …

A Deep Learning-Based Automatic Object Detection Method For Autonomous Driving Ships, Ojonoka Erika Atawodi

Master's Theses

An important feature of an Autonomous Surface Vehicles (ASV) is its capability of automatic object detection to avoid collisions, obstacles and navigate on their own.

Deep learning has made some significant headway in solving fundamental challenges associated with object detection and computer vision. With tremendous demand and advancement in the technologies associated with ASVs, a growing interest in applying deep learning techniques in handling challenges pertaining to autonomous ship driving has substantially increased over the years.

In this thesis, we study, design, and implement an object recognition framework that detects and recognizes objects found in the sea. We first curated …

Low-Power And Reconfigurable Asynchronous Asic Design Implementing Recurrent Neural Networks, Spencer Nelson

Graduate Theses and Dissertations

Artificial intelligence (AI) has experienced a tremendous surge in recent years, resulting in high demand for a wide array of implementations of algorithms in the field. With the rise of Internet-of-Things devices, the need for artificial intelligence algorithms implemented in hardware with tight design restrictions has become even more prevalent. In terms of low power and area, ASIC implementations have the best case. However, these implementations suffer from high non-recurring engineering costs, long time-to-market, and a complete lack of flexibility, which significantly hurts their appeal in an environment where time-to-market is so critical. The time-to-market gap can be shortened through …

Approximate Difference Rewards For Scalable Multigent Reinforcement Learning, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem ofmultiagent credit assignment in a large scale multiagent system. Difference rewards (DRs) are an effective tool to tackle this problem, but their exact computation is known to be challenging even for small number of agents. We propose a scalable method to compute difference rewards based on aggregate information in a multiagent system with large number of agents by exploiting the symmetry present in several practical applications. Empirical evaluation on two multiagent domains - air-traffic control and cooperative navigation, shows better solution quality than previous approaches.

A Matheuristic Algorithm For The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu

Research Collection School Of Computing and Information Systems

This paper studies the integration of the vehicle routing problem with cross-docking (VRPCD). The aim is to find a set of routes to deliver products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic based on column generation is proposed. The first phase focuses on generating a set of feasible candidate routes in both pickup and delivery processes by implementing an adaptive large neighborhood search algorithm. A set of destroy and repair operators are …

Mesoscopic Traffic Simulation Model And Calibration Considering Stretching-Segment Design, Zhaocheng He, Xuanhua Lin, Peilin Nie, Ronghui Zhang

Journal of System Simulation

Abstract: In order to make the simulation model fit the characteristics of urban traffic, both high accuracy and high performance, a lightweight mesoscopic traffic simulation system and the process of calibration are established. The speed-density model and vertical queue model are equivalent to the vehicle movement processes, the simulation accuracy and calibration efficiency are improved by the stretching-segment design at urban intersections of vertical queuing model, and the real individual vehicle information is used as the calibration data source. The application of the model in Xuancheng urban road network shows that, compared with the vertical queuing model, it can …

Modeling And Simulation Of Electric Vehicle Industry Development Based On System Dynamics, Yueqiang Fu, Tiantian Xia

Journal of System Simulation

Abstract: New energy electric vehicle are the main trend of automobile industry upgrading. It plays an important role in ensuring the energy security and improving the ecological environment. It is of theoretical and practical significance to carry out research on the development of new energy electric vehicles. The affecting factors are systematically analyzed, the causal relationship model and stock flow model are established, and the dynamic equation of the model are determined and the parameter assignments are made. The model is verified and the system simulation and analysis are performed. The development trend and main influencing factors of …

Small Fault Detection Based On Cumulative Sum Of Neighbor Statistic, Xiaoping Guo, Jiajun Gao, Jianbin Guo, Li Yuan

Journal of System Simulation

Abstract: Aiming at the small faults and the common data non-linear problems of industrial process, a fault detection method based oncumulative sum of neighbor statistic (CUSUM-NS) is proposed. Mutual information principal component analysis (MIPCA) is used to reduce the dimension of training data, and the principal components based on mutual information are extracted to construct a new sample space. For the new sample space after dimensionality reduction, the nonlinear features of the process data can be fully extracted through the distance square sum statistics of k nearest neighbors. Cumulative summation(CUSUM) method is used to accumulate the sum of squares of …

Visual Simulation Platform For Visible Light Reconnaissance Load Of Unmanned Aerial Vehicle, Yuzhou Chen, Li Yuan, Qinglin Wang, Zhang Qing, Jinyuan Zhang

Journal of System Simulation

Abstract: In view of the simulation and evaluation requirement of the visual system parameters on the performance of video imaging during the operation and reconnaissance of unmanned aerial vehicle, a visual simulation platform for the reconnaissance load is designed and constructed. The collected video is processed according to the visual system parameters and the flight parameters, and the support for the evaluation and the index design of the unmanned aerial vehicle reconnaissance load system is provided, and the guidance is provided for the flight parameters and the flight track setting when the unmanned aerial vehicle reconnaissance and operation …

Research On Some Questions Of Simulation Body Of Knowledge, Xiaogang Qiu, Duan Hong, Xie Xu, Bin Chen

Journal of System Simulation

Abstract: Simulation body of knowledge (BOK) is the knowledge required to conduct Modeling and Simulation (M&S) activities, which is a logic system consisting of concepts, propositions, and inferences that are tightly related to each other. The simulation BOK organizes the M&S knowledge in a hierarchical way, reflects the composition and structure of the knowledge in the M&S domain. The establishment of the simulation BOK is crucial to advance the M&S research and education. The requirements for establishing the simulation BOK are summarized, three basic features of the simulation knowledge, practical, systematical, and epochal are discussed, the challenges of establishing the …

Study On Composition Of Simulation Discipline Knowledge Areas, Duan Hong, Xiaogang Qiu, Xie Xu, Rusheng Ju

Journal of System Simulation

Abstract: Many disciplines, such as Software Engineering, Automation, have sorted out and formed their own knowledge areas and constructed their Body of Knowledge to steer teaching and study efforts. Describing the composition of Modeling and Simulation body of knowledge from the perspective of knowledge area plays an important role in the simulation engineering education and the development of simulation technology. According to the needs of simulation teaching, the body of knowledge of simulation discipline is divided into three levels, knowledge area, knowledge unit and knowledge topic. On the basis of reviewing the current status of the research and the role …

Research On Combat Simulation Body Of Knowledge, Xie Xu, Xiaogang Qiu, Duan Hong, Kedi Huang

Journal of System Simulation

Abstract: Combat simulation is an important research method in modern military domain, since it provides a virtual battlespace for entities of various types that are involved in a battle to interact with each other. Over last several decades, combat simulation has been widely applied in different applications, and as a result the body of knowledge of combat simulation has been extended a lot. The relevant articles and textbooks in combat simulation are extensively investigated, and a three-layer structure to organize the body of knowledge of combat simulation is proposed. Eleven knowledge areas that should be included in the combat simulation …

Personalized Game Recommendation Method Based On Implicit Feedback, Sha Jing, Gongli Zeng, Yang Yang, Wei Yao

Journal of System Simulation

Abstract: Traditional recommendation systems often use explicit feedback for personalized recommendations. But the explicit feedback data is not easy to obtain, and the quality is poor, and the recommendation results unable to meet the requitrment of the user. Implicit feedback data is easier to obtain and can provide users with the better content. A personalized game recommendation method based on implicit feedback data is proposed. The method builds an implicit feedback recommendation model for game user data based on implicit feedback data such as the game duration and game numbers. A personalized recommendation of the game is implemented through an …

Mesh Solid Construction Algorithm Of Spiral Bevel Gear Based On Virtual Collision Body, Cheng'en Li, Xiangjun Zou, Zeqin Zeng, Jianhua He, Li Hui, Zhaofeng Huang

Journal of System Simulation

Abstract: In order to improve the production automation, intelligence level and production efficiency of the tractor rear axle, the spiral bevel gear mesh entity based on the virtual collision body is constructed to carry out the human-machine interaction virtual simulation experiment of the tractor rear axle. The spiral bevel gear made by Gleason as is taken an example, the processing technology of arc spur gear is analyzed, the kinematics is used to establish a mathematical model of the spiral bevel gear forming process. The differential and interpolation methods are used to fit the gear curve and surface, the contour point …

Gas-Liquid Two-Phase Flow Pattern Recognition Method Based On Convolutional Neural Network, Weiguo Tong, Xuechun Pang, Genghong Zhu

Journal of System Simulation

Abstract: Aiming at the low recognition rate and subjectivity in two-phase flow pattern recognition, a method based on Landweber iterative image reconstruction algorithm and convolutional neural network is proposed. Landweber iterative image reconstruction algorithm is used to obtain the flow pattern images and build the flow pattern image database. By means of the flow pattern identification on, different convolution layers in VGG16 network and different size and resolution of the data set samples, the parameters of network frozen convolutional layer and input image are determined.The experimental results show that the combined method of resistance tomography and convolutional neural network …

Operation Resilience Optimization Of Power System Considering Generalized Energy Storage, Weiqing Sun, Zhang Jie, Ye Lei, Han Dong

Journal of System Simulation

Abstract: Based on the definition and principle of power system resilience, combining the traditional energy storage equipment with the demand response, an optimization method of power system operation resilience considering generalized energy storage is proposed. Five attack schemes based on topology are evaluated according to four indexes, and the most disadvantageous attack strategy is selected to simulate the damage of power system. Taking the minimum operating cost of the system as the objective, a combined scheduling model involving wind power station and energy storage unit is carried out. A generalized energy storage scheduling method is proposed to improve the …

Study On Relay Selection Algorithm Based On Swipt Wireless Cooperative Network, Qun Fang, Xukai Chen, He Xin, Yujun Zhu, Yiyang Liu, Yangyang Fang, Heju Li

Journal of System Simulation

Abstract: Although the wireless cooperative networks (WCN) significantly improve the communication quality and network throughput of wireless communications. However, it still faces the challenge of the network lifetime and the energy replenishment due to the energy limitation of relay nodes. In order to tackle this challenge, A multi-relay wireless cooperative network combined with the simultaneous wireless information and power transfer (SWIPT) is proposed and the theoretical performance under the Nakagami-m assumption is analyzed. The selection of the optimal relay node for wireless cooperative networks in the framework of the proposed system is studied. The outage probability of the system …