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In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material.

At Boeing’s commercial aircraft production in Everett Washington, the organization that supplies parts to the factory floor (known internally as Company 625) is revising their methods. A new process will deliver parts in kits that correspond to the installation plans used by the mechanics. Several alternative methods are under review. The authors used simulation methods to evaluate and compare these alternatives. This study focuses on the category of parts known as standard fasteners (‘standards’). Through direct observation, interviews with experts, as well as time and motion study, the process flow of the kitting operation was mapped A simulation model was created using the simulation software ARENA to examine two scenarios: the current kitting operation in the factory cribs and the proposed centralization of kitting operation in the Company 625.

We are developing a flexible and general methodology for real-time monitoring of acoustic emissions in machining applications. The goal of this work is to develop an approach to in-process monitoring which allows continuous assessment of tool wear and early warning of process exceptions. The nature of metal removal processes creates short-lived vibrations that carry information about the condition of the cutting tool and quality of cut. We wish to extract and represent these transient events without loss of important spectral structure. Other challenges include the need for system training data selection in the absence of expert labeled data, the modeling of short-term time evolution, and efficient real-time operation on an inexpensive computing platform.