776 resultados para log measuring


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Non-uniformity of steps within a flight is a major risk factor for falls. Guidelines and requirements for uniformity of step risers and tread depths assume the measurement system provides precise dimensional values. The state-of-the-art measurement system is a relatively new method, known as the nosing-to-nosing method. It involves measuring the distance between the noses of adjacent steps and the angle formed with the horizontal. From these measurements, the effective riser height and tread depth are calculated. This study was undertaken for the purpose of evaluating the measurement system to determine how much of total measurement variability comes from the step variations versus that due to repeatability and reproducibility (R&R) associated with the measurers. Using an experimental design quality control professionals call a measurement system experiment, two measurers measured all steps in six randomly selected flights, and repeated the process on a subsequent day. After marking each step in a flight in three lateral places (left, center, and right), the measurers took their measurement. This process yielded 774 values of riser height and 672 values of tread depth. Results of applying the Gage R&R ANOVA procedure in Minitab software indicated that the R&R contribution to riser height variability was 1.42%; and to tread depth was 0.50%. All remaining variability was attributed to actual step-to-step differences. These results may be compared with guidelines used in the automobile industry for measurement systems that consider R&R less than 1% as an acceptable measurement system; and R&R between 1% and 9% as acceptable depending on the application, the cost of the measuring device, cost of repair, or other factors.

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Real-time battery impedance spectrum is acquired using a one-time record. Fast Summation Transformation (FST) is a parallel method of acquiring a real-time battery impedance spectrum using a one-time record that enables battery diagnostics. An excitation current to a battery is a sum of equal amplitude sine waves of frequencies that are octave harmonics spread over a range of interest. A sample frequency is also octave and harmonically related to all frequencies in the sum. The time profile of this signal has a duration that is a few periods of the lowest frequency. The voltage response of the battery, average deleted, is the impedance of the battery in the time domain. Since the excitation frequencies are known and octave and harmonically related, a simple algorithm, FST, processes the time record by rectifying relative to the sine and cosine of each frequency. Another algorithm yields real and imaginary components for each frequency.

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Methods of rapidly measuring an impedance spectrum of an energy storage device in-situ over a limited number of logarithmically distributed frequencies are described. An energy storage device is excited with a known input signal, and aresponse is measured to ascertain the impedance spectrum. An excitation signal is a limited time duration sum-of-sines consisting of a select number offrequencies. In one embodiment, magnitude and phase of each frequency ofinterest within the sum-of-sines is identified when the selected frequencies and sample rate are logarithmic integer steps greater than two. This technique requires a measurement with a duration of one period of the lowest frequency. In another embodiment, where selected frequencies are distributed in octave steps, the impedance spectrum can be determined using a captured time record that is reduced to a half-period of the lowest frequency.

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Real time battery impedance spectrum is acquired using one time record, Compensated Synchronous Detection (CSD). This parallel method enables battery diagnostics. The excitation current to a test battery is a sum of equal amplitude sin waves of a few frequencies spread over range of interest. The time profile of this signal has duration that is a few periods of the lowest frequency. The voltage response of the battery, average deleted, is the impedance of the battery in the time domain. Since the excitation frequencies are known, synchronous detection processes the time record and each component, both magnitude and phase, is obtained. For compensation, the components, except the one of interest, are reassembled in the time domain. The resulting signal is subtracted from the original signal and the component of interest is synchronously detected. This process is repeated for each component.

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OBJECTIVE: To evaluate the reliability and validity of a novel ultrasound (US) imaging method to measure metacarpophalangeal (MCP) and proximal interphalangeal (PIP) finger joint cartilage. METHODS: We examined 48 patients with rheumatoid arthritis (RA), 18 patients with osteoarthritis (OA), 24 patients with unclassified arthritis of the finger joints, and 34 healthy volunteers. The proximal cartilage layer of MCP and PIP joints for fingers 2-5 was bilaterally visualized from a posterior view, with joints in approximately 90 degrees flexion. Cartilage thickness was measured with integrated tools on static images. External validity was assessed by measuring radiologic joint space width (JSW) and a numeric joint space narrowing (JSN) score in patients with RA. RESULTS: Precise measurement was possible for 97.5% of MCP and 94.2% of PIP joints. Intraclass correlation coefficients for bilateral total joint US scores were 0.844 (95% confidence interval [95% CI] 0.648-0.935) for interobserver comparisons and 0.928 (95% CI 0.826-0.971) for intraobserver comparisons (using different US devices). The US score correlated with JSN for both hands (adjusted R(2) = 0.513, P < 0.001) and JSW of the same finger joints (adjusted R(2) = 0.635, P < 0.001). Reduced cartilage shown by US allowed discrimination of early symptomatic OA versus early RA and healthy joints. In patients with RA, US scores correlated with duration of treatment-resistant, progressive RA. CONCLUSION: The US method of direct visualization and quantification of cartilage in MCP and PIP joints is objective, reliable, valid, and can be useful for diagnostic purposes in patients with arthritis.

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As more and more open-source software components become available on the internet we need automatic ways to label and compare them. For example, a developer who searches for reusable software must be able to quickly gain an understanding of retrieved components. This understanding cannot be gained at the level of source code due to the semantic gap between source code and the domain model. In this paper we present a lexical approach that uses the log-likelihood ratios of word frequencies to automatically provide labels for software components. We present a prototype implementation of our labeling/comparison algorithm and provide examples of its application. In particular, we apply the approach to detect trends in the evolution of a software system.