Computer Sciences Commons^™

Intelligent Protection Scheme Using Combined Stockwell-Transform And Deep Learning-Based Fault Diagnosis For The Active Distribution System, Latha Maheswari Kandasamy, Kanakaraj Jaganathan

Turkish Journal of Electrical Engineering and Computer Sciences

This study aims to perform fast fault diagnosis and intelligent protection in an active distribution network (ADN) with high renewable energy penetration. Several time-domain simulations are carried out in EMTP-RV to extract time-synchronized current and voltage data. The Stockwell transform (ST) was used in MATLAB/SIMULINK to preprocess these input datasets to train the adaptive fault diagnosis deep convolutional neural network (AFDDCNN) for fault location identification, fault type identification, and fault phase-detection for different penetration levels. Based on the AFDDCNN output, the intelligent protection scheme (IDOCPS) generates the signal for isolating a faulty section of the ADN. An intelligent fault diagnosis …

Consensus-Based Virtual Leader Tracking Algorithm For Flight Formation Control Of Swarm Uavs, Berat Yıldız, Akif Durdu, Ahmet Kayabaşi

Turkish Journal of Electrical Engineering and Computer Sciences

Technological developments in industrial areas also impact unmanned aerial vehicles (UAVs). Recent improvements in both software and hardware have significantly increased the use of many UAVs in social and military fields. In particular, the widespread use of these vehicles in social areas such as entertainment, shipping, transportation, and delivery and military areas such as surveillance, tracking, and offensive measures has accelerated the research on swarm systems. This study examined the previous investigations on swarm UAVs and aimed to create a more efficient algorithm. The effectiveness of the proposed algorithm was compared with other leader-based applications. A swarm consisting of 5 …

Cascade Controller Design Via Controller Synthesis For Load Frequency Control Of Electrical Power Systems, Yavuz Güler, Mustafa Nalbantoğlu, Ibrahim Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

The regulation of tie-line electricity flow and frequency of electrical power systems (EPS) is crucial for ensuring their robustness to parameter changes and efficient management of disturbances. To this end, a novel cascade control design approach utilizing a serial Proportional-Integral-Derivative controller with a filter (PIDF) is proposed in this paper. The parameters of the controllers are derived analytically, and it is employed in both loops of the cascade control system to regulate the Load Frequency Control (LFC) of EPS. The implementation of PIDF controllers in both loops is utilized in the cascade control scheme for various power systems featuring different …

Atomic Comagnetometer Gyroscopes For Inertial Navigation Systems: A Review, Murat Salim Karabinaoglu, Bekir Çakir, Mustafa Engin Başoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, developments in quantum sensing, laser, and atomic sensor technologies have also enabled advancement in the field of quantum navigation. Atomic-based gyroscopes have emerged as one of the most critical atomic sensors in this respect. In this review, a brief technology statement of spin exchange relaxation free (SERF) and nuclear magnetic resonance (NMR) type atomic comagnetometer gyroscope (CG) is presented. Related studies in the literature have been gathered, and the fundamental compositions of CGs with technical basics are presented. A comparison of SERF and NMR CGs is provided. A basic simulation of SERF CG was carried out because …

Advanced Hyperthermia Treatment: Optimizing Microwave Energy Focus For Breast Cancer Therapy, Burak Acar, Tuba Yilmaz Abdolsaheb, Ali Yapar

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a fast antenna phase optimization scheme to enable microwave power focusing for breast cancer hyperthermia. The power focusing is achieved through the maximization of the deposited electric field on the target malignant tumor tissue. To do so, a malignant breast tumor, the surrounding breast medium, and the skin of the breast are modeled as a cylindrical structure composed of eccentric cylinders, and electric field distribution is computed analytically in terms of cylindrical harmonics. This approach minimized the computational cost and simplified the breast medium model. To ensure applicability across various breast types, the dielectric properties (DPs) of …

Uncovering And Mitigating Spurious Features In Domain Generalization, Saeed Karimi, Hamdi̇ Di̇bekli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Domain generalization (DG) techniques strive to attain the ability to generalize to an unfamiliar target domain solely based on training data originating from the source domains. Despite the increasing attention given to learning from multiple training domains through the application of various forms of invariance across those domains, the enhancements observed in comparison to ERM are nearly insignificant under specified evaluation rules. In this paper, we demonstrate that the disentanglement of spurious and invariant features is a challenging task in conventional training since ERM simply minimizes the loss and does not exploit invariance among domains. To address this issue, we …

Lower Data Attacks On Advanced Encryption Standard, Orhun Kara

Turkish Journal of Electrical Engineering and Computer Sciences

The Advanced Encryption Standard (AES) is one of the most commonly used and analyzed encryption algorithms. In this work, we present new combinations of some prominent attacks on AES, achieving new records in data requirements among attacks, utilizing only 2 4 and 2 16 chosen plaintexts (CP) for 6-round and 7-round AES 192/256, respectively. One of our attacks is a combination of a meet-in-the-middle (MiTM) attack with a square attack mounted on 6-round AES-192/256 while another attack combines an MiTM attack and an integral attack, utilizing key space partitioning technique, on 7-round AES-192/256. Moreover, we illustrate that impossible differential (ID) …

Comparing Task Graph Scheduling Algorithms: An Adversarial Approach, Jared Coleman, Bhaskar Krishnamachari

Computer Science Faculty Works

Scheduling a task graph representing an application over a heterogeneous network of computers is a fundamental problem in distributed computing. It is known to be not only NP-hard but also not polynomial-time approximable within a constant factor. As a result, many heuristic algorithms have been proposed over the past few decades. Yet it remains largely unclear how these algorithms compare to each other in terms of the quality of schedules they produce. We identify gaps in the traditional benchmarking approach to comparing task scheduling algorithms and propose a simulated annealing-based adversarial analysis approach called PISA to help address them. We …

Q-Rung Neutrosophic Sets And Topological Spaces, Michael Gr. Voskoglou, Florentin Smarandache, Mona Mohamed

Neutrosophic Systems with Applications

The concept of the q-rung orthopair neutrosophic set is introduced in this paper, and fundamental properties of it are studied. Also, the ordinary notion of topological space is extended to the q-rung orthopair neutrosophic environment, as well as the fundamental concepts of convergence, continuity, compactness, and Hausdorff topological space. All these generalizations are illustrated by suitable examples.

Q-Rung Neutrosophic Sets And Topological Spaces, Michael Gr. Voskoglou, Florentin Smarandache, Mona Mohamed

Neutrosophic Systems with Applications

The concept of the q-rung orthopair neutrosophic set is introduced in this paper, and fundamental properties of it are studied. Also, the ordinary notion of topological space is extended to the q-rung orthopair neutrosophic environment, as well as the fundamental concepts of convergence, continuity, compactness, and Hausdorff topological space. All these generalizations are illustrated by suitable examples.

A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes

Graduate Industrial Research Symposium

The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute it into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects, but does not necessarily indicate the initial point of interference within the network. The objective of this project is to take advantage of large scale and genome-wide perturbational datasets by using them to train a tuned machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of …

Graph Theory And Graph Neural Network Assisted High-Throughput Crystal Structure Prediction And Screening For Energy Conversion And Storage, Joshua Ojih, Mohammed Al-Fahdi, Yagang Yao, Jianjun Hu, Ming Hu

Faculty Publications

Prediction of crystal structures with desirable material properties is a grand challenge in materials research, due to the enormous search space of possible combinations of elements and their countless arrangements in 3D space. Despite the recent progress of a few crystal structure prediction algorithms, most of those methods only target a few specific material families or are restricted to simple systems with limited element diversity. Moreover, these algorithms are usually coupled with first principles calculations and thus are computationally expensive and very time consuming. Therefore, establishing a workflow that can generate a large number of hypothetical structures with diverse elements …

Relative Vectoring Using Dual Object Detection For Autonomous Aerial Refueling, Derek B. Worth, Jeffrey L. Choate, James Lynch, Scott L. Nykl, Clark N. Taylor

Faculty Publications

Once realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous attempts at establishing vision-based solutions have come close but rely heavily on near perfect extrinsic camera calibrations that often change midflight. In this paper, we propose dual object detection, a technique that overcomes such requirement by transforming aerial refueling imagery directly into receiver aircraft reference frame probe-to-drogue vectors regardless of camera position and orientation. These vectors are precisely what autonomous agents need to successfully maneuver the tanker and receiver aircraft in synchronous flight during refueling operations. Our method follows a common 4-stage process …

Small Unmanned Aircraft System Detection And Tracking With Audio, Computer Vision, And Deep Learning Techniques, Anthony C. Brunson

Theses and Dissertations

sUAS present significant risks to local and federal agencies when under the control of negligent, reckless, or criminal operators. In the face of an escalating presence of sUAS in shared airspace with traditional aircraft, and their deployment in protected airspace as potential weapons, safeguarding personnel, facilities, and assets becomes paramount. This research seeks to address this emerging threat by investigating the efficacy of integrating low-cost distributed sensors and Machine learning (ML) models to enhance battlespace awareness and complement existing sensing platforms for real-time sUAS detection, classification, and localization. The thesis introduces the conceptualization and development of a Drone Detection Command …

Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs

Theses and Dissertations

Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …

Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil

Theses and Dissertations

This thesis investigates the impact of adjusting artificial intelligence explainability levels’ outputs on user perception. The overarching study extends within the Explainable Artificial Intelligence (XAI) domain. It examines a spectrum of variables, including performance, cognizance, familiarity, transparency, system bias, and the overall impact of AI, to understand their collective and individual effects that enable effective professional use in an organization. The study aims to illuminate the relationship between the degree of explainability provided by large language models such as ChatGPT, Bard, and Bing AI and the performance of these models when tasked with XAI adjustments.

Standardization Of Risk Classifications For Unmanned Space Vehicle Missions, Collin A. Gwaltney

Theses and Dissertations

This paper seeks to model risk classification levels (A-D) for 122 Space Vehicle programs. Models include multinomial logistic regression as well as random forest, a machine learning technique based on decision trees. We use independent variables (IVs) which are theoretically correlated to risk class for the regression and one random forest model. We then include all IVs and allow the random forest technique to use those which provide the most information on risk class before paring down the number of IVs to only 7. We show that the accuracy of predictions increases from 62% to 87% by using random forest …

Performance Of Humans And Agents In A Systems Modeling Language V2 Task: A User-Centered Evaluation Approach, Allen W. Dukes

Theses and Dissertations

The Department of Defense is adopting Digital Engineering practices for its workforce. Simultaneously, the larger Systems Engineering community strives to modernize and define those Digital Engineering practices. These efforts to move from traditionally document-based approaches to pure digital implementations will require enhanced capabilities to manage and digitally track the lifecycle of a program or product. However, this growth must address tool design through an iterative process focusing on usability for many user types. Many tools and technologies exist but often lack an assessment of usability when engineers design tools for other engineers. Including usability when developing solutions for Digital Engineering …

Continual Online Learning-Based Optimal Tracking Control Of Nonlinear Strict-Feedback Systems: Application To Unmanned Aerial Vehicles, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel optimal trajectory tracking scheme is introduced for nonlinear continuous-time systems in strict feedback form with uncertain dynamics by using neural networks (NNs). The method employs an actor-critic-based NN back-stepping technique for minimizing a discounted value function along with an identifier to approximate unknown system dynamics that are expressed in augmented form. Novel online weight update laws for the actor and critic NNs are derived by using both the NN identifier and Hamilton-Jacobi-Bellman residual error. A new continual lifelong learning technique utilizing the Fisher Information Matrix via Hamilton-Jacobi-Bellman residual error is introduced to obtain the significance of weights in …

Weakly Supervised Attention-Based Recognition Under Spectral, Turbulence, And Resource Variations, Kshitij Naresh Nikhal

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

While supervised optimization paradigms are ubiquitous across diverse recognition systems, the risk of over-fitting and increasing bias have limited their applicability.

This dissertation focuses on unsupervised learning—learning without precisely curated data—and argues that unsupervised learning methods can enable both discriminability and generalizability. Through the use of attention-based machine learning and advanced clustering, unsupervised methods are able to focus on fine-grained information in images without any explicit supervision. The dissertation introduces a domain-bridging framework for tasks like cross-spectrum matching and long-range recognition, utilizing intra-domain clustering and inter-domain matching to generate pseudo-labels. Additionally, a hash-based network is proposed to accelerate the search …

Analyzing Biomedical Datasets With Symbolic Tree Adaptive Resonance Theory, Sasha Petrenko, Daniel B. Hier, Mary A. Bone, Tayo Obafemi-Ajayi, Erik J. Timpson, William E. Marsh, Michael Speight, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

Biomedical Datasets Distill Many Mechanisms Of Human Diseases, Linking Diseases To Genes And Phenotypes (Signs And Symptoms Of Disease), Genetic Mutations To Altered Protein Structures, And Altered Proteins To Changes In Molecular Functions And Biological Processes. It Is Desirable To Gain New Insights From These Data, Especially With Regard To The Uncovering Of Hierarchical Structures Relating Disease Variants. However, Analysis To This End Has Proven Difficult Due To The Complexity Of The Connections Between Multi-Categorical Symbolic Data. This Article Proposes Symbolic Tree Adaptive Resonance Theory (START), With Additional Supervised, Dual-Vigilance (DV-START), And Distributed Dual-Vigilance (DDV-START) Formulations, For The Clustering Of …

U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan

Theses and Dissertations

The current system for providing US Army ROTC cadets their branches leaves significant uncertainty until the final pronouncement of branch assigned. This uncertainty can be alleviated by providing a prediction model for cadets to input personal data and desired branch to identify likelihood of receiving the request. This thesis produces a machine learning model capable of producing branch prediction for cadets.

A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike

Theses and Dissertations

This research examines a 2v2 air combat maneuvering problem (ACMP) in a Beyond Visual Range (BVR) environment. A discrete-time, infinite-horizon Markov Decision Process (MDP) model represents the BVR-ACMP, seeking to determine high-quality policies for a pair of autonomous aircraft to execute tactical maneuvers and firing decisions. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) characterizes the complex six-degree of freedom (6-DOF) aircraft operations, encompassing kinematics, sensors, and weapons. Given the high dimensionality and continuous nature of the state and decision variables, a deep reinforcement learning (RL) solution approach is adopted wherein the value function is approximated via a Neural …

A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae

Theses and Dissertations

A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …

Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O

Theses and Dissertations

This thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military …

A Model-Based And System-Theoretic Approach To Design Advanced Autonomy For Air Dominance Missions: A Loyal Wingman Case Study, Elizabeth S. Pennington

Theses and Dissertations

In contested air environments, safe coordination between decision-makers is paramount. Although the Department of Defense (DoD) prioritizes the development of Artificially Intelligent (AI) wingmen for air combat, a lack of methodology exists to design safe, holistic coordination between human and autonomous wingmen in the same environment. This thesis delivers a framework using Systems Theoretic Process Analysis Extended for Coordination (STPA-Coord) to analyze and design holistic coordination for the Loyal Wingman concept in an Air Dominance mission. STPA-Coord is a safety and hazard analysis process that uses Systems Theory to analyze and design coordination between decisionmakers in a system-of-systems architecture. Using …

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng

Research Collection School Of Computing and Information Systems

Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hotspot regions of pick-up points, which can make it easier for drivers to pick-up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory …

Pa2blo: Low-Power, Personalized Audio Badge, Hemanth Sabbella, Dulaj Sanjaya Weerakoon, Manoj Gulati, Archan Misra

Research Collection School Of Computing and Information Systems

We present the hardware design and software pipeline for an ultra-low power device, in the form factor of a wearable badge, that supports energy efficient sensing, processing and wireless transfer of human voice commands and interactions. The proposed system, called PA2BLO, is envisioned to support both: (a) real-time, scalable, authorized voice based interaction and control of devices and appliances, and (b) longitudinal, low-power logging of natural voice interactions. PA2BLO in-troduces two key novel capabilities. First, it includes a low power, low-complexity voice authentication module that is able to reliably authenticate an authorized user only using low sampling rate (500 Hz) …

Random Forests For Detecting Weak Signals And Extracting Physical Information: A Case Study Of Magnetic Navigation, Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai, Aaron P. Nielsen

Faculty Publications

It has been recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth’s anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment. The accuracy of the detected anomaly field corresponds to a positioning accuracy in the range of 10–40 m. To increase the accuracy and reduce the uncertainty of weak signal detection as well as to directly obtain the position information, we exploit the machine-learning model of random forests that combines the output of multiple decision trees to give optimal values of the physical …