Digital Commons Network^™

A Real-Time Iot-Based Data Acquisition And Monitoring System For Photovoltaic Applications, Adam Barbosa, Hamza Mubarak, Fazel Mohammadi, Mohammad J. Sanjari, Mehrdad Saif

Electrical & Computer Engineering and Computer Science Faculty Publications

The transition to low-carbon energy systems, driven by climate change and fossil fuel scarcity, highlights technologies, such as Photovoltaic (PV) technology, for sustainable energy generation. This paper focuses on enhancing the efficiency of PV monitoring systems by leveraging Internet of Things (IoT) technology for accurate and real-time monitoring of essential parameters, such as voltage, current, and output power. Significant gaps in cost-effective and reliable IoT integration for PV monitoring are addressed, with an emphasis on predictive modeling. In this regard, a low-cost real-time IoT-based data acquisition and monitoring system for PV systems, as a proof of concept for future endeavors …

Hardware-Oriented Protection And Acceleration For Machine Learning Application, Antian Wang

All Dissertations

The security of Machine Learning (ML) grows along with the development of high-performance models and expanding application scenarios. Numerous users are benefiting from the convenience brought by transformative ML applications. In the meantime, various attackers are trying to find vulnerabilities within ML deployment service models, thereby undermining the performance of ML and jeopardizing stakeholders’ interests. The dissertation focuses on the two aspects of secure ML applications: acceleration and protection. Homomorphic Encryption (HE) emerges as a widely recognized security primitive suitable for the cloud computing service model, where the computation can be performed over ciphertext without decryption. However, evaluations in the …

Convex Approach To Data-Driven Optimal Control With Safety Constraints Using Linear Transfer Operator, Joseph Raphel Moyalan

All Dissertations

This thesis is concerned with the data-driven solution to the optimal control problem with safety constraints for a class of control-affine nonlinear systems. Designing optimal control satisfying safety constraints is a problem of interest in various applications, including robotics, power systems, transportation networks, and manufacturing. This problem is known to be non-convex. One of this thesis's main contributions is providing a convex formulation to this non-convex problem. The second main contribution is providing a data-driven framework for solving the control problem with safety constraints. The linear operator theoretic framework involving Perron-Frobenius and Koopman operators provides the convex formulation and associated …

Data-Driven Enhanced Energy Management System Applications For Energy Control Centers, Dulip Madurasinghe

All Dissertations

This dissertation explores the pressing needs of modern bulk power system operation and control and delves into enhancements to EMS applications with minimal infrastructure development. The dissertation investigates three main EMS application enhancements. The transmission network topology processing (TNTP) is a foundational application of the EMS. A physics-based hierarchical transmission network topology processing (H-TNTP), including substation configuration identification, to improve efficiency and reliability is proposed. Secondly, a multi-level distributed linear state estimation (D-LSE) approach solely based on PMUs is proposed utilizing H-TNTP as the network modeling tool. The D-LSE can conduct linear state estimation (LSE) at either substation, area or …

Ensemble Machine Learning At The Edge Using The Codec Classifier Structure And Weak Learners Guided By Mutual Information, Aj Beckwith

All Graduate Theses and Dissertations, Fall 2023 to Present

The Codec Classifier is a low-computation, low-memory tree ensemble method that dramatically improves feasibility of image classification on resource-constrained edge devices. It achieves advantages over other tree ensemble methods due the separation of encoder and decoder tasks in the classifier. The encoder partitions feature space, and the decoder labels the regions in the partition. This functional separation of tasks enables the encoder design (partitioning) to be guided by maximizing the mutual information (MI) between class labels and the features (i.e. the encoded representation of the data) without regard to the error performance of the classifier. Experiments show maximizing MI leads …

Gps-Denied Navigation Using Location Estimation And Texel Image Correction, Nikolas I. Jensen

All Graduate Theses and Dissertations, Fall 2023 to Present

In recent years, the use of small drones, also categorized as small Unmanned Aerial Vehicles (sUAV), has surged. They are used for tasks like surveying land, collecting data from a distance, and performing maneuvers for military operations. These drones are popular because they are affordable, small, easy to use, and can navigate well in complex areas. These factors make them a cheap and quick option for tasks like surveying and surveillance when compared to traditional methods.

This thesis introduces a system that uses algorithms to figure out where the drone is. Typically, this relies on sensors and GPS, but GPS …

A Position Allocation Approach To The Battery Electric Bus Charging Problem, Alexander Brown

All Graduate Theses and Dissertations, Fall 2023 to Present

With an increasing adoption of Battery Electric Bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this work, a BEB charging scheduling framework that considers fixed route schedules, multiple charger types, and battery dynamics is modeled as a Mixed Integer Linear Program (MILP). The MILP is modeled after the Berth Allocation Problem (BAP) in a modified form known as the Position Allocation Problem (PAP). The optimization coordinates BEB charging to ensure that each vehicle remains above a specified charge percentage. The model also minimizes the total number of chargers …

Magnetic Sensor Compensation Utilizing Factor Graph Estimation, Frederic W. Lathrop, Clark N. Taylor, Aaron Nielsen

Faculty Publications

Recently, there has been significant interest in the ability to navigate without GPS using the magnetic anomaly field of the Earth (magnav). One of the key technical bottlenecks to achieving magnav is obtaining an accurate magnetic sensor calibration, taking into account own-ship and sensor effects. The Tolles-Lawson magnetic calibration method continues to be the industry standard and was developed when airborne magnetic survey aircraft were first employed over 70 years ago. In this paper, we present a magnetic calibration algorithm based on a factor graph optimization using inertial measurements as well as inputs from both a vector and scalar magnetometer. …

Training Uav Teams With Multi-Agent Reinforcement Learning Towards Fully 3d Autonomous Wildfire Response, Bryce Hopkins

All Theses

As climate-exacerbated wildfires increasingly threaten landscapes and communities, there is an urgent and pressing need for sophisticated fire management technologies. Coordinated teams of Unmanned Aerial Vehicles (UAVs) present a promising solution for detection, assessment, and even incipient-stage suppression – especially when integrated into a multi-layered approach with other recent wildfire management technologies such as geostationary/polar-orbiting satellites and CCTV detection networks. However, there remains significant challenges in developing the necessary sensing, navigation, coordination, and communication subsystems that enable intelligent UAV teams. Further, federal regulations governing UAV deployment and autonomy pose constraints on real-world aerial testing, creating a disconnect between theoretical research …

Using Efficient Vision Transformers To Improve Perception Systems In Autonomous Off-Road Vehicles, Adam S. Pickeral

All Theses

The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety reasons, these systems must not only be accurate, but also be quick to make decisions. In Autonomous Vehicle research, the environment perception system is one of the key com- ponents of development. The environment perception system allows the vehicle to understand its surroundings using cameras, light detection and ranging (LiDAR), and other sensor systems or modalities. Deep learning computer vision algorithms have shown to be the …

Secondary Electron Yield Of Metals, Alloys, And Metal Oxides From First Principles Based Monte Carlo Simulations, Raul E. Gutierrez

Electrical and Computer Engineering ETDs

Density Functional Theory (DFT) based Monte Carlo (MC) simulations of the Sec-
ondary Electron Yield (SEY) of metals, alloys, and metal oxides are performed to
find material properties that could help reduce or influence the multipactor effect.
In order to accurately model the SEY of materials, knowledge of the frequency- and
momentum-dependent Energy Loss Function (qDepELF) is required. The qDepELF
is difficult to determine from experiment; however, it can be calculated from first
principles. The DFT-MC approach for simulating the secondary electron genera-
tion, propagation, and emission processes is described herein. Material properties,
which are calculated using DFT and used …

Professor Yong Yang, The Editorial Board Member Of Journal Of Electrochemistry, Is Elected As A Fellow Of The Electrochemical Society, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

First Announcement Of 76th Annual Meeting Of The International Society Of Electrochemistry, International Society Of Electrochemistry (Ise)

Journal of Electrochemistry

No abstract provided.

Series Reports From Professor Wei’S Group Of Chongqing University: Advancements In Electrochemical Energy Conversions (1/4): Report 1: High-Performance Oxygen Reduction Catalysts For Fuel Cells, Fa-Dong Chen, Zhuo-Yang Xie, Meng-Ting Li, Si-Guo Chen, Wei Ding, Li Li, Jing Li, Zi-Dong Wei

Journal of Electrochemistry

Two major challenges, high cost and short lifespan, have been hindering the commercialization process of low-temperature fuel cells. Professor Wei’s group has been focusing on decreasing cathode Pt loadings without losses of activity and durability, and their research advances in this area over the past three decades are briefly reviewed herein. Regarding the Pt-based catalysts and the low Pt usage, they have firstly tried to clarify the degradation mechanism of Pt/C catalysts, and then demonstrated that the activity and stability could be improved by three strategies: regulating the nanostructures of the active sites, enhancing the effects of support materials, and …

Data-Driven Viewpoint For Developing Next-Generation Mg-Ion Solid-State Electrolytes, Fang-Ling Yang, Ryuhei Sato, Eric Jian-Feng Cheng, Kazuaki Kisu, Qian Wang, Xue Jia, Shin-Ichi Orimo, Hao Li

Journal of Electrochemistry

Magnesium (Mg) is a promising alternative to lithium (Li) in solid-state batteries due to its abundance and high theoretical volumetric capacity. However, the sluggish Mg-ion conduction in the lattice of solid-state electrolytes (SSEs) is one of the key challenges that hamper the development of Mg-ion solid-state batteries. Though various Mg-ion SSEs have been reported in recent years, key insights are hard to be derived from a single literature report. Besides, the structure-performance relationships of Mg-ion SSEs need to be further unraveled to provide a more precise design guideline for SSEs. In this Viewpoints article, we analyze the structural characteristics of …

Kai Wu, The Editorial Board Member Of Journal Of Electrochemistry And Chief Scientist Of Catl, Receives The National Science And Technology Progress Award, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Sorbitol-Electrolyte-Additive Based Reversible Zinc Electrochemistry, Qiong Sun, Hai-Hui Du, Tian-Jiang Sun, Dian-Tao Li, Min Cheng, Jing Liang, Hai-Xia Li, Zhan-Liang Tao

Journal of Electrochemistry

The unstable zinc (Zn)/electrolyte interfaces formed by undesired dendrites and parasitic side reactions greatly hinder the development of aqueous zinc ion batteries. Herein, the hydroxy-rich sorbitol was used as an additive to reshape the solvation structure and modulate the interface chemistry. The strong interactions among sorbitol and both water molecules and Zn electrode can reduce the free water activity, optimize the solvation shell of water and Zn²⁺ ions, and regulate the formation of local water (H₂O)-poor environment on the surface of Zn electrode, which effectively inhibit the decomposition of water molecules, and thus, achieve the thermodynamically stable …

A New Approach: Ordinal Predictive Maintenance With Ensemble Binary Decomposition (Opmeb), Ozlem Ece Yurek, Derya Birant

Turkish Journal of Electrical Engineering and Computer Sciences

Predictive maintenance (PdM), a fundamental element of modern industrial systems, employs machine learning to monitor equipment conditions, estimate failure probabilities, and optimize maintenance schedules. Its core objective is to enhance equipment reliability, extend lifespan, and minimize costs through data-driven insights by enabling efficient maintenance scheduling, reducing downtime, and optimizing resource allocation. In this paper, we propose a novel ordinal predictive maintenance with ensemble binary decomposition (OPMEB) method for the PdM domain, considering the hierarchical nature of class labels reflecting the machine's health status, including categories like healthy, low risk, moderate risk, and high risk. The proposed OPMEB method was validated …

Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, Okan Çi̇ftçi̇, Fati̇h Soygazi̇, Selma Teki̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Recent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering …

Ensemble Learning For Accurate Prediction Of Heart Sounds Using Gammatonegram Images, Sinam Ashinikumar Singh, Sinam Ajitkumar Singh, Aheibam Dinamani Singh

Turkish Journal of Electrical Engineering and Computer Sciences

The analysis of heart sound signals constitutes a pivotal domain in healthcare, with the prediction of imbalanced heart sounds offering critical diagnostic insights. However, the inherent diversity in cardiac sound patterns presents a substantial challenge in predicting imbalanced signals. Many scientific disciplines have focused a great deal of emphasis on the problem of class inequality. We introduce an ensemble learning approach employing a convolutional neural network model-based deep learning algorithm to effectively tackle the challenges associated with predicting imbalanced heart sound signals. We use a Gammatone filter bank to extract relevant features from the heard sound signal. Our approach leverages …

Detection And Classification Of Unauthorized Use Of Irrigation Motors In Agricultural Irrigation, Önder Ci̇velek, Sedat Görmüş, Hali̇l İbrahi̇m Okumuş, Orhan Gazi̇ Kederoglu

Turkish Journal of Electrical Engineering and Computer Sciences

The decarbonisation of electricity generation requires the real-time monitoring and control of grid components in order to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge …

A New Dynamic Classifier Selection Method For Text Classification, İsmai̇l Terzi̇, Alper Kürşat Uysal

Turkish Journal of Electrical Engineering and Computer Sciences

The primary objective of employing multiple classifier systems (MCS) in pattern recognition is to enhance classification accuracy. Dynamic classifier selection (DCS) and dynamic ensemble selection (DES) are two purposeful forms of multiple classifier systems. While DES involves the selection of a classifier set followed by decision combination, DCS opts for the choice of a single competent classifier, eliminating the necessity for classifier combination. As a consequence, DCS methods exhibit superior efficiency in terms of processing time and memory usage compared to DES methods. Moreover, a substantial performance gap exists between the performance of Oracle and both DES and DCS methods. …

A Real-Time Embedded System Designed For Nilm Studies With A Novel Competitive Decision Process Algorithm, Sai̇d Mahmut Çinar, Rasi̇m Doğan, Emre Akarslan

Turkish Journal of Electrical Engineering and Computer Sciences

This paper explores the determination of any load or load combination in a power system at any moment. This process requires measurements at the main electric utility service entry of a house, known as nonintrusive measurement. To accurately identify loads, total harmonic distortion, RMS, third harmonic currents, and power consumption are considered their fingerprints. Based on these fingerprints, an algorithm called the competitive decision process is developed and integrated into an embedded system. This algorithm has a two-level decision mechanism. In the first stage, the winner loads with the highest similarity scores from each feature are determined, and the loads …

Multi-Label Voice Disorder Classification Using Raw Waveforms, Gökay Di̇şken

Turkish Journal of Electrical Engineering and Computer Sciences

Automated voice disorder systems that distinguish pathological voices from healthy ones have been developed with the aid of machine learning methods. Both clinicians and patients can benefit from these systems as they provide many advantages, compared to the invasive techniques. These systems can produce binary (healthy/pathological) or multi-class (healthy/selected pathologies) decisions. However, multiple disorders might exist in an individual’s voice. Multi-label classification should be considered in such cases. By this time, only a single report is available on this topic, where hand-crafted features were used, and a data augmentation technique was utilized to overcome class imbalances. In this study, a …

Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, …

Efficient Deep Neural Network Compression For Environmental Sound Classification On Microcontroller Units, Shan Chen, Na Meng, Haoyuan Li, Weiwei Fang

Turkish Journal of Electrical Engineering and Computer Sciences

Environmental sound classification (ESC) is one of the important research topics within the non-speech audio classification field. While deep neural networks (DNNs) have achieved significant advances in ESC recently, their high computational and memory demands render them highly unsuitable for direct deployment on resource-constrained Internet of Things (IoT) devices based on microcontroller units (MCUs). To address this challenge, we propose a novel DNN compression framework specifically designed for such devices. On the one hand, we leverage pruning techniques to significantly compress the large number of model parameters in DNNs. To reduce the accuracy loss that follows pruning, we propose a …

Distributed Energy Trading Models Utilizing Reinforcement Learning And Game-Theoretic Based Approaches In Smart Grids, Nicholas Kemp

Electrical and Computer Engineering ETDs

Community-driven energy initiatives have become crucial for effective energy management, particularly in trading and management. The rise of Distributed Energy Resources in smart grids demands a redesign of traditional Demand Response Management (DRM) models to account for prosumers' dynamic behavior in energy markets. Decentralization, including peer-to-peer (P2P) energy trading, is vital for resilience and sustainability. This thesis introduces two coalitional DRM models: one based on hedonic community formation games, and the other combining matching theory with coalition games. These models empower prosumers to autonomously select energy trading communities using partially available data. To optimize energy consumption, two additional models are …

Deep Integration Of Technological Innovation And Industrial Innovation In Modern Industrial System: Inspiration From Global New Generation Lithography Systems, Jiang Yu, Feng Chen, Yue Guo

Bulletin of Chinese Academy of Sciences (Chinese Version)

Utilizing technological innovation to lead the construction of a modern industrial system is a strategic choice for seizing the opportunities of the new round of technological revolution and industrial transformation. It is also a necessary step for winning the strategic initiative towards high-level self-reliance and self-improvement. Technological innovation is the intrinsic driving force behind industrial innovation, and industrial innovation is the value embodiment of technological innovation. The deep integration of technological innovation and industrial innovation is the key to constructing and improving a modern industrial system. Taking the global extreme ultra-violet (EUV) lithography system as an example, based on the …

Insights On Strategy And Approach For China To Construct A Modern Integrated Circuits Industrial System, Ximing Yin, Beibei Zhang, Tailun Chen, Jiang Yu, Jin Chen

Bulletin of Chinese Academy of Sciences (Chinese Version)

The integrated circuit (IC) industry is highly complex and systematic, and its key core technology breakthroughs are highly dependent on the support of systematic capabilities. The West especially the United States has accelerated the promotion of the “small-yard, high-fence” strategy, the “New Washington Consensus”, the “de-risking”, and other systematic policies to curb China’s rise. China’s IC industry chain is facing extreme risks such as rupture or blockage. Meanwhile, facing the new mission and requirements of Chinese modernization and new-quality productivity, China needs to accelerate the modernization of the IC industry with new development paradigms, new strategies, and new approaches. Based …

Exploration Of Semiconductor Gain Medium, Resonator, Pump, And Frequency Stabilization For Laser Guide Star Applications, Mingyang Zhang

Optical Science and Engineering ETDs

Laser Guide Star (LGS) systems are essential for adaptive optics in ground-based astronomical observation. This dissertation demonstrates the feasibility of semiconductor-based LGS systems using the membrane external-cavity surface-emitting laser (MECSEL) platform, employing multiple quantum wells. Various laser cavity configurations were analyzed through simulations and experiments. The in-well pumping method was explored to reduce the quantum defect and address thermal limitations. Multi-pass pumping schemes were designed with Zemax modeling and demonstrated experimentally. To simplify multi-pass pumping, the hybrid-MECSEL (H-MECSEL) design was introduced. COMSOL modeling studied thermal management and thermal lensing effect.

The H-MECSEL achieved approximately 30 W of output power at …