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This presentation reviews metrics pertinent to noise from eVTOL aircraft, including Noise dose metrics; DNL, CTL; Statistical metrics; Spectrum-based metrics; metrics based on event frequency; and metrics based on partial loudness and detection.
Noise certification metrics such as Effective Perceived Noise Level and Sound Exposure Level are used to ensure that helicopters meet regulations, but these metrics may not be good indicators of annoyance since noise complaints against helicopters persist. Sound quality (SQ) metrics, specifically fluctuation strength, tonality, impulsiveness, roughness, and sharpness, are explored to determine their relationship with annoyance. A psychoacoustic test was conducted at the NASA Langley Research Center Exterior Effects Room to assess annoyance to helicopter-like sounds over a range of SQ metric values. The amplitude, phase, and frequency of the AS350 helicopter main and tail rotor blade passage signal harmonics were manipulated to produce 105 unique helicopter-like sounds with prescribed values of SQ metrics. All sounds were set to roughly the same loudness level. These sounds were played to 40 subjects who rated each sound for annoyance. Analyses given in this paper point to which SQ metrics are important to the helicopter noise annoyance response.
METRIC comprises a suite of two instruments in a Lunar lander that perform X-ray diffraction (XRD) for mineral structure, X-ray fluorescence (XRF) for elemental composition, and X-ray Micro Computed Tomography (XCT) for 3D internal micromorphology. The instruments are accompanied by optical-near IR cameras to provide local geologic context. The Honeybee Robotics PlanetVac pneumatic sampling and transfer system [1],positioned on a lander footpad, will deliver sieved regolith to the X-ray instruments for analysis. The instrument suite is intended for delivery to the lunar surface on a Commercial Lunar Payload Services (CLPS) lander. The METRICXRD/F instrument draws on heritage from the Mars Science Laboratory CheMin instrument [2] and improves upon the design in multiple ways [3]. Like CheMin, Rietveld refinement and full-pattern fitting of METRIC XRD data can identify minerals at a detection limit of ~1 wt.%, quantify their abundances when present at >3 wt.%, and determine mineral composition (e.g., Fo#in olivine) from lattice parameters for minerals present at >5 wt.%[4,5]. An optimized XRF geometry provides for improved detection and quantification of major, minor and trace elements. The METRIC XCT instrument is a miniaturized X-ray Computed Tomography scanner[6]. Lunar regolith is delivered to a 3 mm diameter, 10 mm long graphite tube inside the instrument. The tube is rotated through 360°in 0.9deg.incrementsand a divergent point source X-ray beam is directed through the material. A Charge Coupled Device (CCD)records attenuation images whose brightness and contrast area function of average atomic number and density. Quantitative data, including particle and void sizes, 3D particle shape parameters, modal volumes and pore geometry can be derived from the resulting 3D reconstructions(voxel resolution: 30 μm). Crystal morphologies derived from METRIC XCT data complement the bulk mineralogy determined by the METRIC XRD/F and provide a measure of grain size distribution for the different phases. Taken together, the METRIC instrument suite determines crystal structure, elemental composition and morphology, three principal characteristics of geological materials that are highly useful in determining the origin and subsequent processing of lunar regolith.
A common set of statistical metrics has been used to summarize the performance of models or measurements- the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying uncertainty. The authors demonstrate that these metrics can be directly derived from the parameters of the simple linear error model. Since a correct error model captures the full error information, it is argued that the specification of a parametric error model should be an alternative to the metrics-based approach. The error-modeling methodology is applicable to both linear and nonlinear errors, while the metrics are only meaningful for linear errors. In addition, the error model expresses the error structure more naturally, and directly quantifies uncertainty. This argument is further explained by highlighting the intrinsic connections between the performance metrics, the error model, and the joint distribution between the data and the reference.
This document contains about 7,600 references on the metric system and conversion to the metric system. These references include all known documents on the metric system as of December 1975, the month of enactment of the Metric Conversion Act of 1975. This bibliography includes books, reports, articles, presentations, periodicals, legislation, motion pictures, TV series, film strips, slides, posters, wall charts, education and training courses, addresses for information, and sources for metric materials and services. A comprehensive index is provided.
The purpose of this paper is to examine several aspects of helicopter noise quantification from the standpoint of psychoacoustics. Noise metrics in common use to describe far-field aircraft noise and the noise characteristics which they consider are discussed. Some findings of recent psychoacoustic research related specifically to helicopter noise quantification are presented. Criteria for the accuracy of noise metrics to quantify helicopter noise are discussed. Finally, the prospects for improved metrics and research needed to develop and validate improved metrics or existing metrics are discussed.
Software metrics are used to evaluate the software development process and the quality of the resulting product. Five metrics were used during the testing phase of the Shuttle Mission Control Center Upgrade at the NASA Johnson Space Center. All but one metric provided useful information. Based on the experience, it is recommended that metrics be used during the test phase of software development and additional candidate metrics are proposed for further study.
Software metrics provide an effective method for characterizing software. Metrics have traditionally been composed through the definition of an equation. This approach is limited by the fact that all the interrelationships among all the parameters be fully understood. This paper explores an alternative, neural network approach to modeling metrics. Experiments performed on two widely accepted metrics, McCabe and Halstead, indicate that the approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics.
The principal NASA metrication activities for FY 1989 were a revision of NASA metric policy and evaluation of the impact of using the metric system of measurement for the design and construction of the Space Station Freedom. Additional studies provided a basis for focusing follow-on activity. In FY 1990, emphasis will shift to implementation of metric policy and development of a long-range metrication plan. The report which follows addresses Policy Development, Planning and Program Evaluation, and Supporting Activities for the past and coming year.
NASA's principal metrication accomplishments for FY 1990 were establishment of metrication policy for major programs, development of an implementing instruction for overall metric policy and initiation of metrication planning for the major program offices. In FY 1991, development of an overall NASA plan and individual program office plans will be completed, requirement assessments will be performed for all support areas, and detailed assessment and transition planning will be undertaken at the institutional level. Metric feasibility decisions on a number of major programs are expected over the next 18 months.
The major NASA metrication activity of 1988 concerned the Space Station. Although the metric system was the baseline measurement system for preliminary design studies, solicitations for final design and development of the Space Station Freedom requested use of the inch-pound system because of concerns with cost impact and potential safety hazards. Under that policy, however use of the metric system would be permitted through waivers where its use was appropriate. Late in 1987, several Department of Defense decisions were made to increase commitment to the metric system, thereby broadening the potential base of metric involvement in the U.S. industry. A re-evaluation of Space Station Freedom units of measure policy was, therefore, initiated in January 1988.
Methods are needed for evaluating the quality of augmented visual displays (AVID). Computational quality metrics will help summarize, interpolate, and extrapolate the results of human performance tests with displays. The FLM Vision group at NASA Ames has been developing computational models of visual processing and using them to develop computational metrics for similar problems. For example, display modeling systems use metrics for comparing proposed displays, halftoning optimizing methods use metrics to evaluate the difference between the halftone and the original, and image compression methods minimize the predicted visibility of compression artifacts. The visual discrimination models take as input two arbitrary images A and B and compute an estimate of the probability that a human observer will report that A is different from B. If A is an image that one desires to display and B is the actual displayed image, such an estimate can be regarded as an image quality metric reflecting how well B approximates A. There are additional complexities associated with the problem of evaluating the quality of radar and IR enhanced displays for AVID tasks. One important problem is the question of whether intruding obstacles are detectable in such displays. Although the discrimination model can handle detection situations by making B the original image A plus the intrusion, this detection model makes the inappropriate assumption that the observer knows where the intrusion will be. Effects of signal uncertainty need to be added to our models. A pilot needs to make decisions rapidly. The models need to predict not just the probability of a correct decision, but the probability of a correct decision by the time the decision needs to be made. That is, the models need to predict latency as well as accuracy. Luce and Green have generated models for auditory detection latencies. Similar models are needed for visual detection. Most image quality models are designed for static imagery. Watson has been developing a general spatial-temporal vision model to optimize video compression techniques. These models need to be adapted and calibrated for AVID applications.
This paper looks closely at each of the software metrics generated by the McCabe object-Oriented Tool(TM) and its ability to convey timely information to developers. The metrics are examined for meaningfulness in terms of the scale assignable to the metric by the rules of measurement theory and the software dimension being measured. Recommendations are made as to the proper use of each metric and its ability to influence development at an early stage. The metrics of the McCabe Object-Oriented Tool(TM) set were selected because of the tool's use in a couple of NASA IV&V projects.
The success of any effort depends upon the effective initial definition of its purpose, in terms of the needs to be satisfied and the goals to be fulfilled. If the desired product is "A System" that is well-characterized, these high-level need and goal statements can be transformed into system requirements by traditional systems engineering techniques. The satisfaction of well-designed requirements can be tracked by fairly straightforward cost, schedule, and technical performance metrics. Unfortunately, some types of efforts, including those that NASA terms "Programs," tend to resist application of traditional systems engineering practices. In the NASA hierarchy of efforts, a "Program" is often an ongoing effort with broad, high-level goals and objectives. A NASA "project" is a finite effort, in terms of budget and schedule, that usually produces or involves one System. Programs usually contain more than one project and thus more than one System. Special care must be taken in the formulation of NASA Programs and their projects, to ensure that lower-level project requirements are traceable to top-level Program goals, feasible with the given cost and schedule constraints, and measurable against top-level goals. NASA Programs and projects are tasked to identify the advancement of technology as an explicit goal, which introduces more complicating factors. The justification for funding of technology development may be based on the technology's applicability to more than one System, Systems outside that Program or even external to NASA. Application of systems engineering to broad-based technology development, leading to effective measurement of the benefits, can be valid, but it requires that potential beneficiary Systems be organized into a hierarchical structure, creating a "system of Systems." In addition, these Systems evolve with the successful application of the technology, which creates the necessity for evolution of the benefit metrics to reflect the changing baseline. Still, economic metrics for technology development in these Programs and projects remain fairly straightforward, being based on reductions in acquisition and operating costs of the Systems. One of the most challenging requirements that NASA levies on its Programs is to plan for the commercialization of the developed technology. Some NASA Programs are created for the express purpose of developing technology for a particular industrial sector, such as aviation or space transportation, in financial partnership with that sector. With industrial investment, another set of goals, constraints and expectations are levied on the technology program. Economic benefit metrics then expand beyond cost and cost savings to include the marketability, profit, and investment return requirements of the private sector. Commercial investment criteria include low risk, potential for high return, and strategic alignment with existing product lines. These corporate criteria derive from top-level strategic plans and investment goals, which rank high among the most proprietary types of information in any business. As a result, top-level economic goals and objectives that industry partners bring to cooperative programs cannot usually be brought into technical processes, such as systems engineering, that are worked collaboratively between Industry and Government. In spite of these handicaps, the top-level economic goals and objectives of a joint technology program can be crafted in such a way that they accurately reflect the fiscal benefits from both Industry and Government perspectives. Valid economic metrics can then be designed that can track progress toward these goals and objectives, while maintaining the confidentiality necessary for the competitive process.
The Metric is one of several measures employed by the NASA to assess the Agency s progress as mandated by the United States Congress and the Office of Management and Budget. Because any measure must have a reference point, whether explicitly defined or implied, the Metric is a comparison between a selected ALS Project life support system and an equivalently detailed life support system using technology from the Environmental Control and Life Support System (ECLSS) for the International Space Station (ISS). This document provides the official calculation of the Advanced Life Support (ALS) Research and Technology Development Metric (the Metric) for Fiscal Year 2004. The values are primarily based on Systems Integration, Modeling, and Analysis (SIMA) Element approved software tools or reviewed and approved reference documents. For Fiscal Year 2004, the Advanced Life Support Research and Technology Development Metric value is 2.03 for an Orbiting Research Facility and 1.62 for an Independent Exploration Mission.
Software Quality Assurance (SQA) is a planned and systematic set of activities that ensures conformance of software life cycle processes and products conform to requirements, standards and procedures. In software development, software quality means meeting requirements and a degree of excellence and refinement of a project or product. Software Quality is a set of attributes of a software product by which its quality is described and evaluated. The set of attributes includes functionality, reliability, usability, efficiency, maintainability, and portability. Software Metrics help us understand the technical process that is used to develop a product. The process is measured to improve it and the product is measured to increase quality throughout the life cycle of software. Software Metrics are measurements of the quality of software. Software is measured to indicate the quality of the product, to assess the productivity of the people who produce the product, to assess the benefits derived from new software engineering methods and tools, to form a baseline for estimation, and to help justify requests for new tools or additional training. Any part of the software development can be measured. If Software Metrics are implemented in software development, it can save time, money, and allow the organization to identify the caused of defects which have the greatest effect on software development. The summer of 2004, I worked with Cynthia Calhoun and Frank Robinson in the Software Assurance/Risk Management department. My task was to research and collect, compile, and analyze SQA Metrics that have been used in other projects that are not currently being used by the SA team and report them to the Software Assurance team to see if any metrics can be implemented in their software assurance life cycle process.
This paper presents the application of Bounded Linear Stability Analysis (BLSA) method for metrics-driven adaptive control. The bounded linear stability analysis method is used for analyzing stability of adaptive control models, without linearizing the adaptive laws. Metrics-driven adaptive control introduces a notion that adaptation should be driven by some stability metrics to achieve robustness. By the application of bounded linear stability analysis method the adaptive gain is adjusted during the adaptation in order to meet certain phase margin requirements. Analysis of metrics-driven adaptive control is evaluated for a second order system that represents a pitch attitude control of a generic transport aircraft. The analysis shows that the system with the metrics-conforming variable adaptive gain becomes more robust to unmodeled dynamics or time delay. The effect of analysis time-window for BLSA is also evaluated in order to meet the stability margin criteria.
This document defines an initial set of metrics for use by the NASA Airspace Systems Program (ASP). ASP consists of the NextGen-Airspace Project and the NextGen-Airportal Project. The work in each project is organized along multiple, discipline-level Research Focus Areas (RFAs). Each RFA is developing future concept elements in support of the Next Generation Air Transportation System (NextGen), as defined by the Joint Planning and Development Office (JPDO). In addition, a single, system-level RFA is responsible for integrating concept elements across RFAs in both projects and for assessing system-wide benefits. The primary purpose of this document is to define a common set of metrics for measuring National Airspace System (NAS) performance before and after the introduction of ASP-developed concepts for NextGen as the system handles increasing traffic. The metrics are directly traceable to NextGen goals and objectives as defined by the JPDO and hence will be used to measure the progress of ASP research toward reaching those goals. The scope of this document is focused on defining a common set of metrics for measuring NAS capacity, efficiency, robustness, and safety at the system-level and at the RFA-level. Use of common metrics will focus ASP research toward achieving system-level performance goals and objectives and enable the discipline-level RFAs to evaluate the impact of their concepts at the system level.