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An apparatus for performing statistical time-series analysis of complex electrical signal waveforms, permitting prompt and accurate determination of statistical characteristics of the signal is presented.
Investigations related to satellite remote sensing of vegetation have been concerned with questions of signature identification and extension, cover inventory accuracy, and change detection and monitoring. Attention is given to models of ecological succession, present directions in successional modeling and analysis, nondynamic spatial models, issues in the analysis of spatial data, and aspects of spatial modeling. Issues in time-series analysis are considered along with dynamic spatial models, and problems of model specification and identification.
Observational evidence favoring the local regulation of solar-wind heat flux at 1 AU is reviewed, and four months of IMP 6 plasma and magnetic-field data are merged and analyzed in order to investigate what might be regulating the heat flux. A statistical analysis of the data shows that the solar-wind Alfven speed is probably regulating the heat flux locally at 1 AU and that the Alfven speed, the velocity difference between the peak of low-energy electrons and the bulk plasma velocity, and the solar-wind velocity component projected along the local spiral angle are statistically well correlated for Alfven speeds not exceeding about 70 km/s. A time-series analysis of the data indicates that only the Alfven speed and the velocity difference between the peak of low-energy electrons and the bulk plasma velocity are well correlated both qualitatively and quantitatively on a microscopic time scale. It is strongly suggested that, at times, the solar-wind heat flux is locally regulated by the magnitude of the Alfven speed at 1 AU. Uncertainties in the results are discussed.
The basic techniques of forecasting the air passenger traffic are outlined. These techniques can be broadly classified into four categories: judgmental, time-series analysis, market analysis and analytical. The differences between these methods exist, in part, due to the degree of formalization of the forecasting procedure. Emphasis is placed on describing the analytical method.
The multivariable instrumental variable/approximate maximum likelihood (IV/AML) method or recursive time-series analysis is used to identify the multivariable (four inputs-three outputs) dynamics of the Pratt and Whitney F100 engine. A detailed nonlinear engine simulation is used to determine linear engine model structures and parameters at an operating point using open loop data. Also, the IV/AML method is used in a direct identification mode to identify models from actual closed loop engine test data. Models identified from simulated and test data are compared to determine a final model structure and parameterization that can predict engine response for a wide class of inputs. The ability of the IV/AML algorithm to identify useful dynamic models from engine test data is assessed.
The multivariable instrumental variable/approximate maximum likelihood (IV/AML) method of recursive time-series analysis is used to identify the multivariable (four inputs-three outputs) dynamics of the Pratt and Whitney F100 engine. A detailed nonlinear engine simulation is used to determine linear engine model structures and parameters at an operating point using open loop data. Also, the IV/AML method is used in a direct identification made to identify models from actual closed loop engine test data. Models identified from simulated and test data are compared to determine a final model structure and parameterization that can predict engine response for a wide class of inputs. The ability of the IV/AML algorithm to identify useful dynamic models from engine test data is assessed. Previously announced in STAR as N82-20339
Time-series analysis reveals two dominant, stable long-term periodicities approximately equal to 33 + or - 3 and 260 + or - 25 million years in the known series of geological and biological upheavals during the Phanerozoic Eon. Because the cycles of these episodes agree in period and phase with the cycles of impact cratering on earth, these results suggest that periodic comet impacts strongly influence global tectonism and biological evolution. These two periodicities could arise from interactions of the solar system with interstellar clouds as the solar system moves cyclically through the Galaxy.
Recent suggestions of periodic cometary showers coincident with mass extinctions have prompted a review of the terrestrial cratering record. From the point of view of completeness of search and crater retention, the record used to argue for statistical periodicities is incomplete and may not be representative. Estimates of crater ages are of variable accuracy and precision and, in some cases, different dating methods yield discordant results. It is possible to define through time-series analysis a number of periodicities of different magnitude (about 29, 21, 18.5, and 13.5 m.y.) and phase, depending on which craters are considered the most representative sample of the record. Arguing against periodic cometary showers is the observation that estimates of the average cratering rate in recent time and observations on Apollo bodies are equivalent. In addition, siderophile element data from impact melt rocks suggest a variety of projectile types were responsible for relatively large terrestrial craters. Given these observations, it is concluded that statements regarding periodic cometray showers are not categorical and require evidence beyond arguments based on time-series analyses of an incomplete cratering record.
Ages of craters in the record of impacts on earth may be uniformly periodic, totally random, or a mixture of the two. These alternatives are studied through numerical simulation wherein time-series analysis is performed on real and simulated sequences to which random noise has been added to represent age-dating uncertainty. It is concluded that the real record is most likely to have been generated by a mixture of random and periodic impacts, with the random events constituting the majority.
Eight major episodes of biological extinction of marine families over the past 250 million years stand significantly above local background (P less than 0.05). These events are more pronounced when analyzed at the level of genus, and generic data exhibit additional apparent extinction events in the Aptian (Cretaceous) and Pliocene (Tertiary) Stages. Time-series analysis of these records strongly suggests a 26-million-year periodicity. This conclusion is robust even when adjusted for simultaneous testing of many trial periods. When the time series is limited to the four best-dated events (Cenomanian, Maestrichtian, upper Eocene, and middle Miocene), the hypothesis of randomness is also rejected for the 26-million-year period (P less than 0.0002).
Eight major episodes of biological extinction of marine families over the past 250 million years stand significantly above local background (P < 0.05). These events are more pronounced when analyzed at the level of genus, and generic data exhibit additional apparent extinction events in the Aptian (Cretaceous) and Pliocene (Tertiary) Stages. Time-series analysis of these records strongly suggests a 26-million-year periodicity. This conclusion is robust even when adjusted for simultaneous testing of many trial periods. When the time series is limited to the four best-dated events (Cenomanian, Maestrichtian, upper Eocene, and middle Miocene), the hypothesis of randomness is also rejected for the 26-million-year period (P < 0.0002).
Time-series spectral analysis has been performed on the dates of geomagnetic reversals of the last 20 Myr BP and earlier. Possible evidence is found from the presence of high spectral peaks for two very long periodicities, 0.4 Myr and 1.3 Myr, that may be associated with slow variations of the earth's orbital eccentricity as predicted by Berger. However, statistical significance tests and a number of other arguments do not confirm the two detections.
Experiments and analyses concerning electromagnetic induction in the moon and other extraterrestrial bodies are summarized. The theory of classical electromagnetic induction in a sphere is first considered, and this treatment is extended to the case of the moon, where poloidal eddy-current response has been found experimentally to dominate other induction modes. Analysis of lunar poloidal induction yields lunar internal electrical conductivity and temperature profiles. Two poloidal-induction analytical techniques are discussed: a transient-response method applied to time-series magnetometer data, and a harmonic-analysis method applied to data numerically Fourier-transformed to the frequency domain, with emphasis on the former technique. Attention is given to complicating effects of the solar wind interaction with both induced poloidal fields and remanent steady fields. The static magnetization field induction mode is described, from which are calculated bulk magnetic permeability profiles. Magnetic field measurements obtained from the moon and from fly-bys of Venus and Mars are studied to determine the feasibility of extending theoretical and experimental induction techniques to other bodies in the solar system.
Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.
Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.
A time-series technique is presented for identifying the dynamic characteristics of the human operator in manual control tasks from relatively short records of experimental data. Control of system excitation signals used in the identification is not required. The approach is a multi-channel identification technique for modeling multi-input/multi-output situations. The method presented includes statistical tests for validity, is designed for digital computation, and yields estimates for the frequency responses of the human operator. A comprehensive relative power analysis may also be performed for validated models. This method is applied to several sets of experimental data; the results are discussed and shown to compare favorably with previous research findings. New results are also presented for a multi-input task that has not been previously modeled to demonstrate the strengths of the method.
A time-series technique is presented for identifying the dynamic characteristics of the human operator in manual control tasks from relatively short records of experimental data. Control of system excitation signals used in the identification is not required. The approach is a multi-channel identification technique for modeling multi-input/multi-output situations. The method presented includes statistical tests for validity, is designed for digital computation, and yields estimates for the frequency response of the human operator. A comprehensive relative power analysis may also be performed for validated models. This method is applied to several sets of experimental data; the results are discussed and shown to compare favorably with previous research findings. New results are also presented for a multi-input task that was previously modeled to demonstrate the strengths of the method.
A program, designed specifically to study the respective effects of some common data problems on results obtained through stepwise iterative Fourier transformation of synthetic data with known waveform composition, was outlined. Included in this group were the problems of gaps in the data, different time-series lengths, periodic but nonsinusoidal waveforms, and noisy (low signal-to-noise) data. Results on sinusoidal data were also compared with results obtained on narrow band noise with similar characteristics. The findings showed that the analytic procedure under study can reliably reduce data in the nature of (1) sinusoids in noise, (2) asymmetric but periodic waves in noise, and (3) sinusoids in noise with substantial gaps in the data. The program was also able to analyze narrow-band noise well, but with increased interpretational problems. The procedure was shown to be a powerful technique for analysis of periodicities, in comparison with classical spectrum analysis techniques. However, informed use of the stepwise procedure nevertheless requires some background of knowledge concerning characteristics of the biological processes under study.