Multi-Stage Corn-to-Syrup Process Monitoring and Yield Prediction Using Machine Learning and Statistical Methods.

Corn syrup is a cost-effective sweetener ingredient for the food industry. In producing syrup from corn, process control to enhance and/or maintain a constant dextrose equivalent value (DE) is a constant challenge, especially in semi-automated/batch production settings, which are common in small to medium-size factories. Existing work has focused on continuous process control to keep parameter values within a setpoint.

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors.

Manufacturing systems are becoming increasingly flexible, necessitating the adoption of new technologies that allow adaptations to a turbulent and complex modern market. Consequently, modern concepts of production systems require horizontal and vertical integration, extending across value networks and within a factory or production shop. The integration of these environments enables the acquisition of a substantial amount of data containing information pertaining to production, processes, and equipment located on the shop floor.

Cyber-Enabled Optimization of HVAC System Control in Open Space of Office Building.

Thermal comfort is crucial to well-being and work productivity. Human thermal comfort is mainly controlled by HVAC (heating, ventilation, air conditioning) systems in buildings. However, the control metrics and measurements of thermal comfort in HVAC systems are often oversimplified using limited parameters and fail to accurately control thermal comfort in indoor climates.

Cyber-Physical System for Smart Traffic Light Control.

In recent years, researchers have proposed smart traffic light control systems to improve traffic flow at intersections, but there is less focus on reducing vehicle and pedestrian delays simultaneously. This research proposes a cyber-physical system for smart traffic light control utilizing traffic detection cameras, machine learning algorithms, and a ladder logic program. The proposed method employs a dynamic traffic interval technique that categorizes traffic into low, medium, high, and very high volumes.

FEA and Machine Learning Techniques for Hidden Structure Analysis.

This study focuses on investigating and predicting two hidden structures: plant root system architecture and non-visible bubbles in plexiglass. Current approaches are damaging, expensive, or time-consuming. Infrared imaging was used to study the root structure and depth of small plants and to detect the diameter and depth of bubbles in plexiglass.

The fabrication and thermal properties of bismuth-aluminum oxide nanothermometers.

Bismuth (Bi) nanowires, well controlled in length and diameter, were prepared by using an anodic aluminum oxide (AAO) template-assisted molding injection process with a high cooling rate. A high performance atomic layer deposition (ALD)-capped bismuth-aluminum oxide (Bi-AlO) nanothermometer is demonstrated that was fabricated via a facile, low-cost and low-temperature method, including AAO templated-assisted molding injection and low-temperature ALD-capped processes. The thermal behaviors of Bi nanowires and Bi-AlO nanocables were studied by in situ heating transmission electron microscopy.

Thermography and machine learning techniques for tomato freshness prediction.

The United States and China are the world's leading tomato producers. Tomatoes account for over $2 billion annually in farm sales in the U.S.

Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences.

Unlabelled: Bioinformatics research often requires conservative analyses of a group of sequences associated with a specific biological function (e.g. transcription factor binding sites, micro RNA target sites or protein post-translational modification sites).