Propagation-Based Fault Detection and Sensor Optimization for Complex Industrial Systems
TS-072175 — The Need
Modern industrial and energy systems are increasingly complex, making timely fault detection and discrimination critical for safety, reliability, and cost control. Existing fault diagnosis methods often struggle with transient states, require extensive historical data, or lack interpretabil…
- College: College of Engineering (COE)
- Inventors: Smidts, Carol; Diao, Xiaoxu; Li, Boyuan
- Licensing Officer: Giles, David
Efficient Cyclic Redundancy Check Encoding with Low-Complexity LFSR Design
TS-071388 — The Need
Cyclic redundancy checks (CRC) are utilized in digital communication and storage systems for error detection. Current CRC encoding and decoding methods in digital communication and storage systems are complex and require a high gate count, leading to inefficiencies in hardware design. There…
- College: College of Engineering (COE)
- Inventors: Zhang, Xinmiao; Cai, Jiaxuan; Tang, Yok Jye
- Licensing Officer: Giles, David
A Generalized Mistuning Model for Bladed Disk Systems
TS-070971 — The Need
Modern gas turbines and compressors rely on bladed disks, which are highly sensitive to mistuning caused by manufacturing tolerances, wear, or damage. Existing modeling tools are fragmented, complex, and often limited to specific mistuning types. Industry will greatly benefit from a unified…
- College: College of Engineering (COE)
- Inventors: D'Souza, Kiran; Krizak, Troy
- Licensing Officer: Giles, David
Joint Activity Testing (JAT): A Testing & Evaluation Methodology for Human-Machine Teams
TS-068443 —
In high-stakes industries, the integration of humans and advanced automation systems demands evaluation methods that reliably predict performance under varying challenges. Current testing methods often focus on individual components, failing to assess how human-machine teams operate as a unit, par…
- College: College of Engineering (COE)
- Inventors: Morey, Dane; Rayo, Michael
- Licensing Officer: Giles, David
Vision System Using MDP-Based Tracking for Automated Dimensional Analysis
TS-068440 —
Accurate measurement of physical attributes, such as dimensions, alignments, and surface features, is essential for ensuring quality and consistency across industries. Traditional measurement methods, often reliant on manual tools and human oversight, are time-intensive, prone to errors, and lac…
- College: College of Engineering (COE)
- Inventors: Allen, Theodore "Ted"; Rodriguezbuno, Ramiro; Zhang, Yifei
- Licensing Officer: Giles, David
Optimal and Pure Leaf Classification Trees for Machine Learning (ML) Decision-Making
TS-067550 — A method to improve the performance and accuracy of ML-based decision trees.
Decision trees are popular machine learning (ML) methods used in classification and regression problems, and they have numerous applications in the real world. Various industries use decision trees to help decide strategies, investments, and operations. In addition, they are used in healthcare to he…
- College: College of Engineering (COE)
- Inventors: Allen, Theodore "Ted"; Arrey, Evelyn; Booth, Matthew; Liu, Enhao; Mashayekhi, Medhi
- Licensing Officer: Giles, David
A Cybersecurity Vulnerability Prioritization System Including Identifying "Super-Critical" Vulnerabilities, predicting "Dark Host" Vulnerabilities, and Addressing Economic Costs
TS-066063 — Our cybersecurity vulnerability maintenance system stands as a pillar of modern security strategy, transforming reactive security measures into a preemptive defense mechanism. This integration of technology and economics ensures that your most critical assets are protected efficiently and effectively, making it an invaluable tool for any organization serious about security.
In today’s hyper-connected world, the escalation in cyber threats poses significant risks to organizational data and systems. Vulnerabilities within network infrastructures can lead to massive security breaches, as demonstrated by incidents like the 2017 Equifax hack. Effective vulnerability…
- College: College of Engineering (COE)
- Inventors: Allen, Theodore "Ted"; Liu, Enhao
- Licensing Officer: Giles, David
SimulationAI -- AI-Enabled Software Solution for Physics-Based Simulations
TS-066058 — By adopting our AI-driven solution, engineering teams can achieve more in less time, push the boundaries of innovation, and significantly cut down costs, all while maintaining or increasing the reliability and accuracy of their structural and material analysis. This is not just an evolution in FEM technology—it's a revolution.
In an era where precision and efficiency drive the success of engineering projects, the finite element method (FEM) remains indispensable but is burdened by high operational and computational costs. These costs often lead to overlooked uncertainty factors, suboptimal designs, and significant finan…
- College: College of Engineering (COE)
- Inventors: Soghrati, Soheil; vemparala, Balavignesh; Yang, Ming
- Licensing Officer: Giles, David
Novel Deep Learning Model for Reconnaissance of Infrastructure on Drones
TS-063007 — The Need
In disaster-stricken areas, timely and accurate reconnaissance is paramount for effective response and recovery efforts. Traditional methods of assessing damage to critical infrastructure, such as power distribution poles, often involve time-consuming manual inspections, leading to delays …
- College: College of Engineering (COE)
- Inventors: Shafieezadeh, Abdollah; Bagheri Jeddi, Ashkan
- Licensing Officer: Giles, David
Concept Discovery from Text via Knowledge Transfer
TS-050856 — A better way for systems to organize, file, or index documents or content based on actual or anticipated information needed in the form of a user query or natural language question.
Data Processing and (IT)-related activities, ranging from web hosting to automated data entry services are more important than ever due to the large amounts of data collected through technology. According to IBIS World, "Companies will increasingly capture more data, requiring the outside exp…
- College: College of Engineering (COE)
- Inventors: Das, Manirupa; Fosler-Lussier, Eric; Ramnath, Rajiv
- Licensing Officer: Giles, David
Show More Technologies