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This paper investigates the application of K-means clustering algorithms to traditional aircraft conceptual-level weight estimation techniques. K-Means clustering was utilized as a means for statistical discovery within the dataset to explore potential paths for improved weight estimation using conceptual-level information. As a proof-of-concept demonstration, scope was limited to fuselage basic weight estimation. A variety of weight sources were parsed and curated to produce a large, diverse dataset consisting of 82 separate aircraft. A corresponding new universal fuselage basic weight regression was generated as a baseline for comparison. K-Means clustering was then employed to sort aircraft into groupings based on configuration and topology with an associated weight equation created for each grouping. Configuration-based groupings utilized information such as a high-level abstraction of the structural layout as well as whether the aircraft is a fixed-wing or rotary-wing vehicle. Topology-based groupings utilized information such as landing gear location and possession of a cargo ramp or wing. The configuration-based groupings showed modest improvement compared to the baseline regression which were in turn outperformed by the topology-based regressions across a range of data groupings. Under all conditions, a subset of the data associated with fixed-wing aircraft was shown to be an outlier in regard to error as a result of a large range of weight and speed scales, in addition to possible secondary pressurization impacts. Special treatment of the winged dataset led to further reduction in error based on unique design features. The weight estimation processes explored within this paper present a methodology that leverages machine learning algorithms that can improve and inform existing best practices. [ABSTRACT FROM AUTHOR]

We report on the design of a data acquisition (DAQ) module for a small animal PET camera developed at our institutes. During the design an important guideline was to develop a system which is built up from strictly identical DAQ modules, and which has no built-in hardware limitation on the maximum number of modules. The developed DAQ module comprises of an LSO scintillator crystal block, a position sensitive PMT, analog signal conditioning circuits, a digitizer, an field programmable gate array (FPGA) for digital signal processing, and a communication module through which the collected data are sent to a cluster of computers for postprocessing and storage. Instead of implementing hardware coincidence detection between the modules, we attach a precise time stamp to each event in our design, and the coincidence is determined by the data collecting computers during postprocessing. The digital CFD algorithm implemented in the FPGA gives a time resolution of 2 ns FWHM for real detector signals. Index Terms--Data acquisition (DAQ), digital CFD, Ethernet, PET, small animal.

We report on the design of a small animal PET scanner being developed at our institutes. The existing setup is the first version of the miniPET machine consisting of four detector modules. Each detector module consists of an 8 x 8 LSO scintillator crystal block, a position sensitive photomultiplier, a digitizer including a digital signal processing board and an Ethernet interface board. There is no hardware coincidence detection implemented in the system and coincidence is determined based on a time stamp attached to every event by a digital CFD algorithm. The algorithm is implemented in the digital signal processing board and generates a time stamp with a coincidence resolution of less than 2 ns. The data acquisition system is based on Ethernet network and is highly scalable in size and performance. Index Terms--Ethernet-based data acquisition, FPGA, free running pulse sampling, LSO, positron emission tomography.

The uncontrolled development of nanoscale roughness during plasma exposure of polymer surfaces is a major issue in the field of semiconductor processing. In this paper, we investigated the question of a possible relationship between the formation of nanoscale roughening and the simultaneous introduction of a nanometer-thick, densified surface layer that is formed on polymers due to plasma damage. Polystyrene films were exposed to an Ar discharge in an inductively coupled plasma reactor with controllable substrate bias and the properties of the modified surface layer were changed by varying the maximum Ar+ ion energy. The modified layer thickness, chemical, and mechanical properties were obtained using real-time in situ ellipsometry, x-ray photoelectron spectroscopy, and modeled using molecular dynamics simulation. The surface roughness after plasma exposure was measured using atomic force microscopy, yielding the equilibrium dominant wavelength λ and amplitude A of surface roughness. The comparison of measured surface roughness wavelength and amplitude data with values of λ and A predicted from elastic buckling theory utilizing the measured properties of the densified surface layer showed excellent agreement both above and below the glass transition temperature of polystyrene. This agreement strongly supports a buckling mechanism of surface roughness formation. [ABSTRACT FROM AUTHOR]