Engineering Commons^™

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 …

Lightweight Distributed Computing Framework For Orchestrating High Performance Computing And Big Data, Muhammed Numan İnce, Meli̇h Günay, Joseph Ledet

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the need for the ability to work remotely and subsequently the need for the availability of remote computer-based systems has increased substantially. This trend has seen a dramatic increase with the onset of the 2020 pandemic. Often local data is produced, stored, and processed in the cloud to remedy this flood of computation and storage needs. Historically, HPC (high performance computing) and the concept of big data have been utilized for the storage and processing of large data. However, both HPC and Hadoop can be utilized as solutions for analytical work, though the differences between these may …

Symbolic Ns-3 For Efficient Exhaustive Testing, Jianfei Shao

School of Computing: Dissertations, Theses, and Student Research

Exhaustive testing is an important type of simulation, where a user exhaustively simulates a protocol for all possible cases with respect to some uncertain factors, such as all possible packet delays or packet headers. It is useful for completely evaluating the protocol performance, finding the worst-case performance, and detecting possible design or implementation bugs of a protocol. It is, however, time consuming to use the brute force method with current NS-3, a widely used network simulator, for exhaustive testing. In this paper, we present our work on Sym-NS-3 for more efficient exhaustive testing, which leverages a powerful program analysis technique …

Material Synthesis And Machine Learning For Additive Manufacturing, Jaime Eduardo Regis

Open Access Theses & Dissertations

The goal of this research was to address three key challenges in additive manufacturing (AM), the need for feedstock material, minimal end-use fabrication from lack of functionality in commercially available materials, and the need for qualification and property prediction in printed structures. The near ultraviolet-light assisted green reduction of graphene oxide through L-ascorbic acid was studied with to address the issue of low part strength in additively manufactured parts by providing a functional filler that can strengthen the polymer matrix. The synthesis of self-healing epoxy vitrimers was done to adapt high strength materials with recyclable properties for compatibility with AM …

Competition And Third-Party Platform-Integration In Ride-Sourcing Markets, Yaqian Zhou, Hai Yang, Jintao Ke, Hai Wang, Xinwei Li

Research Collection School Of Computing and Information Systems

Recently, some third-party integrators attempt to integrate the ride services offered by multiple independent ride-sourcing platforms. Accordingly, passengers can request ride through the integrators and receive ride service from any one of the ride-sourcing platforms. This novel business model, termed as third-party platform-integration in this work, has potentials to alleviate market fragmentation cost resulting from demand splitting among multiple platforms. Although most existing studies focus on operation strategies for one single monopolist platform, much less is known about the competition and platform-integration and their implications on operation strategy and system efficiency. In this work, we propose mathematical models to describe …

Message-Locked Searchable Encryption: A New Versatile Tool For Secure Cloud Storage, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Rongmao Chen, Xixiang Lv

Research Collection School Of Computing and Information Systems

Message-Locked Encryption (MLE) is a useful tool to enable deduplication over encrypted data in cloud storage. It can significantly improve the cloud service quality by eliminating redundancy to save storage resources, and hence user cost, and also providing defense against different types of attacks, such as duplicate faking attack and brute-force attack. A typical MLE scheme only focuses on deduplication. On the other hand, supporting search operations on stored content is another essential requirement for cloud storage. In this article, we present a message-locked searchable encryption (MLSE) scheme in a dual-server setting, which achieves simultaneously the desirable features of supporting …

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 …

Missing Samples Reconstruction Using An Efficient And Robust Instantaneous Frequency Estimation Algorithm, Sadiq Ali, Nabeel Ali Khan

Turkish Journal of Electrical Engineering and Computer Sciences

In order to recover missing samples in a nonstationary signal, this paper employs a time-signal analysis and filtering method. The instantaneous frequency of a multicomponent signal is first estimated by employing a robust and computationally efficient method. Then the time-frequency filtering is performed using a dechirping operation to recover missing samples. These steps are repeated until convergence. The proposed method achieves better performance than the state of art methods both in terms of the accuracy of the recovered signal and computational efficiency.

Stochastic Day-Ahead Optimal Scheduling Of Multimicrogrids: An Alternating Direction Method Of Multipliers (Admm) Approach, Amin Safari, Hossein Nasiraghdam

Turkish Journal of Electrical Engineering and Computer Sciences

Multimicrogrid system is a novel notion in modern power systems as a result of developing renewable-based generation units and accordingly microgrids in distribution networks. Their energy management might be challenging due to presence of independent units. Thus, in this paper, a decentralized method for energy management of multimicrogrid systems has been proposed. Decentralized methods can enhance the privacy of users and reduce the burden of calculations. Alternating direction method of multipliers (ADMM) is selected as a decentralized approach which has the capability of breaking problems with complicating constraints in order to facilitate the solving process. Using decentralized approach not only …

Priority Enabled Content Based Forwarding In Fog Computing Via Sdn, Yasi̇n İnağ, Metehan Güzel, Feyza Yildirim Okay, Mehmet Demi̇rci̇, Suat Özdemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

As the number of Internet of Things (IoT) applications increases, an efficient transmitting of the data generated by these applications to a centralized cloud server can be a challenging issue. This paper aims to facilitate transmission by utilizing fog computing (FC) and software defined networking (SDN) technologies. To this end, it proposes two novel content based forwarding (CBF) models for IoT networks. The first model takes advantage of FC to reduce transmission and computational delay. Based on the first model, the second model makes use of the prioritization concept to address the timely delivery of critical data while ensuring the …

Estimation Of Mode Shape In Power Systems Under Ambient Conditions Using Advanced Signal Processing Approach, Rahul S, Sunitha R

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a dynamic approach for the monitoring and estimation of electromechanical oscillatory modes in the power system in real time with less computational burden. Extensive implementation of phasor measurement units (PMU) and the utilization of advanced signal processing techniques help in identifying the dynamic behaviors of oscillatory modes. Conventional nonstationary analysis techniques are computationally weak to handle a larger quantity of data in real-time. This research utilizes the variational mode decomposition (VMD) for signal decomposition, which is highly tolerant to noise and computationally more robust. The predefined parameters of the VMD process are assigned using FFT analysis of …

Residential Energy Management System Based On Integration Of Fuzzy Logic And Simulated Annealing, Ömer Ci̇han Kivanç, Beki̇r Tevfi̇k Akgün, Semi̇h Bi̇lgen, Sali̇h Bariş Öztürk, Suat Baysan, Ramazan Nejat Tuncay

Turkish Journal of Electrical Engineering and Computer Sciences

With the increase in prosperity level and industrialization, energy need continues to overgrow in many countries. To meet the rapidly increasing energy needs, countries attach great importance to using limited natural resources rationally, diversifying their energy production using novel technologies, improving the efficiency of existing technologies, and implementing policies and strategies toward alternative energy sources. In particular, individual energy prosumers (someone that both produces and consumes energy) head toward smart home energy management systems (SHEMS) that include renewable energy sources in their homes. By integrating PV solar panels into houses, there is a need to optimize home energy production/consumption scenarios …

The Iwar Range + 21 Years: Cyber Defense Education In 2022, Joseph H. Schafer, Chris Morrell, Ray Blaine

Military Cyber Affairs

Twenty-one years ago, The IWAR Range paper published by CCSC described nascent information assurance (now cybersecurity[1]) education programs and the inspiration and details for constructing cyber ranges and facilitating cyber exercises. This paper updates the previously published work by highlighting the dramatic evolution of the cyber curricula, exercise networks and ranges, influences, and environments over the past twenty years.

[1] In 2014, DoD adopted “cybersecurity” instead of “information assurance.” [34:1]

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 …

Framework Of Hardware Trojan Detection Leveraging Structural Checking Tool, Rafael Dacanay Del Carmen

Graduate Theses and Dissertations

Since there is a significant demand for obtaining third-party soft Intellectual Property (IP) by first-party integrated circuit (IC) vendors, it is becoming easier for adversaries to insert malicious logic known as hardware Trojans into designs. Due to this, vendors need to find ways to screen the third-party IPs for possible security threats and then mitigate them. The development of the Structural Checking (SC) tool provides a solution to this issue. This tool analyzes the structure of an unknown soft IP design and creates a network of all the signals within the design and how they are connected to each other. …

Optimized Damage Assessment And Recovery Through Data Categorization In Critical Infrastructure System., Shruthi Ramakrishnan

Graduate Theses and Dissertations

Critical infrastructures (CI) play a vital role in majority of the fields and sectors worldwide. It contributes a lot towards the economy of nations and towards the wellbeing of the society. They are highly coupled, interconnected and their interdependencies make them more complex systems. Thus, when a damage occurs in a CI system, its complex interdependencies make it get subjected to cascading effects which propagates faster from one infrastructure to another resulting in wide service degradations which in turn causes economic and societal effects. The propagation of cascading effects of disruptive events could be handled efficiently if the assessment and …

Implementing The Cms+ Sports Rankings Algorithm In A Javafx Environment, Luke Welch

Industrial Engineering Undergraduate Honors Theses

Every year, sports teams and athletes get cut from championship opportunities because of their rank. While this reality is easier to swallow if a team or athlete is distant from the cut, it is much harder when they are right on the edge. Many times, it leaves fans and athletes wondering, “Why wasn’t I ranked higher? What factors when into the ranking? Are the rankings based on opinion alone?” These are fair questions that deserve an answer. Many times, sports rankings are derived from opinion polls. Other times, they are derived from a combination of opinion polls and measured performance. …

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 …

Machine Learning-Based Device Type Classification For Iot Device Re- And Continuous Authentication, Kaustubh Gupta

School of Computing: Dissertations, Theses, and Student Research

Today, the use of Internet of Things (IoT) devices is higher than ever and it is growing rapidly. Many IoT devices are usually manufactured by home appliance manufacturers where security and privacy are not the foremost concern. When an IoT device is connected to a network, currently there does not exist a strict authentication method that verifies the identity of the device, allowing any rogue IoT device to authenticate to an access point. This thesis addresses the issue by introducing methods for continuous and re-authentication of static and dynamic IoT devices, respectively. We introduce mechanisms and protocols for authenticating a …

Characterizing And Predicting Human Visual Perception Of Unmanned Aerial Vehicle Gestures, Paul Fletcher

School of Computing: Dissertations, Theses, and Student Research

Unmanned Aerial Vehicles (UAVs) are being used in public domains and hazardous environments where effective communication strategies are critical. UAV gesture techniques have been shown to communicate meaning to human observers and may be ideal in contexts that require lightweight systems such as unmanned aerial flight, however, this work may be limited to an idealized range of viewer perspectives. As gesture is a visual communication technique it is necessary to consider how the perception of a robot gesture may suffer from obfuscation or self-occlusion from some viewpoints. This thesis presents the results of three online user-studies that examine participants’ ability …

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 …