The parametric sequential classification of spectral patterns with application to signal detection on extraction.
Aerospace research and development projects selection and planning for success in manufacturing competition
Owsley, N. L.↗
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Automated Detection and Analysis of Resident Space Objects with the 1.3-Meter Eugene Stansbery-Meter Class Autonomous Telescope
Optical telescopes dedicated to the detection of orbital debris employ large-area detectors that generate a large number of images each night. Such surveys require automated data analysis pipelines that process the images and detect moving objects. We present an overview of the data analysis pipeline employed by the 1.3-meter Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) on Ascension Island, operated by NASA’s Orbital Debris Program Office. The pipeline enfolds the astrometric and photometric calibration of the images, star-trail removal, object detection, correlation over multiple sequential image frames, and orbital parameter estimation. The performance of the pipeline was investigated by means of Monte-Carlo simulations in which simulated object tracks were inserted into ES-MCAT images and then processed by the pipeline. This technique allows one to confidently estimate the completeness for the detection of resident space objects as a function of apparent magnitude and angular velocity. This paper discusses these techniques and provides examples using actual data.
Paul Hickson↗
Automated Detection and Analysis of Resident Space Objects with the 1.3-Meter Eugene Stansbery-Meter Class Autonomous Telescope
Optical telescopes dedicated to the detection of orbital debris employ large-area detectors that generate a large number of images each night. Such surveys require automated data analysis pipelines that process the images and detect moving objects. We present an overview of the data analysis pipeline employed by the 1.3-meter Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) on Ascension Island, operated by NASA’s Orbital Debris Program Office. The pipeline enfolds the astrometric and photometric calibration of the images, star-trail removal, object detection, correlation over multiple sequential image frames, and orbital parameter estimation. The performance of the pipeline was investigated by means of Monte-Carlo simulations in which simulated object tracks were inserted into ES-MCAT images and then processed by the pipeline. This technique allows one to confidently estimate the completeness for the detection of resident space objects as a function of apparent magnitude and angular velocity. This paper discusses these techniques and provides examples using actual data.
Paul Hickson↗
A Search Model for Imperfectly Detected Targets
Under the assumptions that 1) the search region can be divided up into N non-overlapping sub-regions that are searched sequentially, 2) the probability of detection is unity if a sub-region is selected, and 3) no information is available to guide the search, there are two extreme case models. The search can be done perfectly, leading to a uniform distribution over the number of searches required, or the search can be done with no memory, leading to a geometric distribution for the number of searches required with a success probability of 1/N. If the probability of detection P is less than unity, but the search is done otherwise perfectly, the searcher will have to search the N regions repeatedly until detection occurs. The number of searches is thus the sum two random variables. One is N times the number of full searches (a geometric distribution with success probability P) and the other is the uniform distribution over the integers 1 to N. The first three moments of this distribution were computed, giving the mean, standard deviation, and the kurtosis of the distribution as a function of the two parameters. The model was fit to the data presented last year (Ahumada, Billington, & Kaiwi, 2 required to find a single pixel target on a simulated horizon. The model gave a good fit to the three moments for all three observers.
Ahumada, Albert↗
Inverse Reinforcement Learning based Bayesian Goal Inference Method for Early Nuclear Proliferation Detection
Traditional methods for detection of nuclear proliferation indicators are usually applied after nuclear proliferation has already occurred. There is a need to advance these methods to perform early detection of nuclear proliferation indicators. In this project, we formulated an early detection problem as a sequential, decision-making, goal inference problem based on research publications of authors, to determine whether it is possible to infer whether an author will publish on a research activity before it has occurred. To develop and test our approach, we selected a civil nuclear activity for our case study. We constructed a state-action-state transition graph from publications of authors associated with the activity and the co-authors of their publications, using titles, abstracts, and author publication sequences. We then used inverse reinforcement learning to model the goal-directed behavior of authors in trajectories that terminate at selected goal states. Using a Bayesian formulation, we computed the probability that authors would reach each selected state from partially observed trajectories of their state transitions in their research topic space. The state with the highest probability was selected as the most probable goal state. Based on our results, we found that 60% of the times we can infer the correct goal state early; sometimes the inference is either delayed, or multiple states could be inferred as goal states. Overall, our results show that it is possible to perform early detection of research activities of authors in a nuclear technology area. Further research is necessary to establish a more accurate understanding of how topic modeling, topic space grid discretization, and the extent of overlap among trajectories of different goal states, affect the goal inference results. The methods developed in this work may be used to enhance data-driven methods for early detection of nuclear proliferation indicators.
97 MATHEMATICS AND COMPUTING↗
Early Inference of Nuclear Technology-Directed Research Activities of Authors from Scientific Publications
Nuclear research articles can provide information about early nuclear proliferation indicators such as influential research entities and technology capability levels of a country, but detection of nuclear activities typically occurs after they have started. We investigate the extent to which nuclear research articles can be used to infer whether a research entity will acquire or develop a nuclear technology before it happens. Early detection of nuclear proliferation or technology development indicators from data is challenging due to partial observability, sparse and unlabeled information, and confounding signals from multiple concurrent activities. This paper presents the early detection problem as a sequential decision-making, goal inference problem, where the objective is to characterize and predict an individual’s, organization’s, or a country’s intent (unobserved goal-directed behavior) towards developing a nuclear capability from partially observed sequences of their research publications, using inverse reinforcement learning and Bayesian goal inference methods. A computational framework is presented, and its application demonstrated using 29,196 Scopus records for a case study related to a civil nuclear capability. The case study results serve as a proof-of-concept demonstration for inference of technology-directed research activity of authors who publish in the nuclear domain. The inference method, combined with advanced computing, may be used to assess and monitor activities pertaining to early developmental stages of a nuclear technology or capability, which in turn can help to identify and prioritize activities with nuclear proliferation potential for further investigation.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Toward an algebraic theory of the analysis and testing of digital networks.
Logic networks of Boolean analogs analyzed by algebraic signal flow theory, introducing variational derivative for test detection in combinational and sequential networks
Brown, A.↗
A heuristic algorithm for the testing of asynchronous circuits
Heuristic algorithm for computation of failures detection tests in asynchronous sequential logic circuits
Putzolu, G. R.↗
Method and Apparatus for Reading Two Dimensional Identification Symbols Using Radar Techniques
A method and apparatus are provided for sensing two-dimensional identification marks provided on a substrate or embedded within a substrate below a surface of the substrate. Micropower impulse radar is used to transmit a high risetime, short duration pulse to a focussed radar target area of the substrate having the two dimensional identification marks. The method further includes the steps of listening for radar echoes returned from the identification marks during a short listening period window occurring a predetermined time after transmission of the radar pulse. If radar echoes are detected, an image processing step is carried out. If no radar echoes are detected, the method further includes sequentially transmitting further high risetime, short duration pulses, and listening for radar echoes from each of said further pulses after different elapsed times for each of the further pulses until radar echoes are detected. When radar echoes are detected, data based on the detected echoes is processed to produce an image of the identification marks.
Harry F Schramm Jr.↗
Inverse sequential procedures for the monitoring of time series
Climate changes traditionally have been detected from long series of observations and long after they happened. The 'inverse sequential' monitoring procedure is designed to detect changes as soon as they occur. Frequency distribution parameters are estimated both from the most recent existing set of observations and from the same set augmented by 1,2,...j new observations. Individual-value probability products ('likelihoods') are then calculated which yield probabilities for erroneously accepting the existing parameter(s) as valid for the augmented data set and vice versa. A parameter change is signaled when these probabilities (or a more convenient and robust compound 'no change' probability) show a progressive decrease. New parameters are then estimated from the new observations alone to restart the procedure. The detailed algebra is developed and tested for Gaussian means and variances, Poisson and chi-square means, and linear or exponential trends; a comprehensive and interactive Fortran program is provided in the appendix.
Radok, Uwe↗
Automatic detection of anomalies in Space Shuttle Main Engine turbopumps
A prototype expert system (developed on both PC and Symbolics 3670 lisp machine) for detecting anomalies in turbopump vibration data has been tested with data from ground tests 902-473, 902-501, 902-519, and 904-097 of the Space Shuttle Main Engine (SSME). The expert system has been utilized to analyze vibration data from each of the following SSME components: high-pressure oxidizer turbopump, high-pressure fuel turbopump, low-pressure fuel turbopump, and preburner boost pump. The expert system locates and classifies peaks in the power spectral density of each 0.4-sec window of steady-state data. Peaks representing the fundamental and harmonic frequencies of both shaft rotation and bearing cage rotation are identified by the expert system. Anomalies are then detected on the basis of sequential criteria and two threshold criteria set individually for the amplitude of each of these peaks: a prior threshold used during the first few windows of data in a test, and a posterior threshold used thereafter. In most cases the anomalies detected by the expert system agree with those reported by NASA. The two cases where there is significant disagreement will be further studied and the system design refined accordingly.
Lo, Ching F.↗
Model degradation effects on sensor failure detection
This paper discusses the effects of imperfect modeling on the detection and isolation of sensor failures. For systems with non-zero set points, deterministic inputs or non-zero noise biases, the model mismatch appears as a bias on the stochastic innovation process. This bias, if left unaccounted for, would be sufficient to declare a false alarm failure in one or more sensors. A practical design procedure based upon the Generalized Likelihood Ratio (GLR) form uses a finite data window sequential t-test to detect and isolate model mismatch effects and soft sensor failures. Application to an eighth order model of the QCSEE turbofan engine is discussed.
Leininger, G. G.↗
Testability enhancement in digital system design
A new way of employing hardware redundancy to reduce the number of tests for fault detection in both combinational and synchronous sequential circuits is investigated. An approach is presented for utilizing systematic redundancy to simplify design work. Models for PLM (programmable logic module) and CMM (controllable memory module) are depicted. Systematic design and detection procedures are described. Using these procedures, an easily testable circuit (for stuck faults) can be designed. In contrast to the earlier results on fault detection in logic circuit, two tests are needed to detect any stuck faults of combinational logic circuit. Four tests are necessary and sufficient to detect any stuck faults of elementary logic gates and the malfunction of flip-flops of synchronous sequential circuit using Delay flip-flops or trigger flip-flops.
Chuang, C. S.↗
Linear array optical edge sensor
A series of independent parallel pairs of light emitting and detecting diodes for a linear pixel array, which is laterally positioned over an edge-like discontinuity in a workpiece to be scanned, is disclosed. These independent pairs of light emitters and detectors sense along intersecting pairs of separate optical axes. A discontinuity, such as an edge in the sensed workpiece, reflects a detectable difference in the amount of light from that discontinuity in comparison to the amount of light that is reflected on either side of the discontinuity. A sequentially sychronized clamping and sampling circuit detects that difference as an electrical signal which is recovered by circuitry that exhibits an improved signal-to-noise capability for the system.
Bejczy, Antal K.↗
Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems
Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. Here this paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated subregion. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.
42 ENGINEERING↗
Bayesian design of decision rules for failure detection
The formulation of the decision making process of a failure detection algorithm as a Bayes sequential decision problem provides a simple conceptualization of the decision rule design problem. As the optimal Bayes rule is not computable, a methodology that is based on the Bayesian approach and aimed at a reduced computational requirement is developed for designing suboptimal rules. A numerical algorithm is constructed to facilitate the design and performance evaluation of these suboptimal rules. The result of applying this design methodology to an example shows that this approach is potentially a useful one.
Chow, E. Y.↗
A Meteorological (humidity, temperature, aerosols)) mobile dial system: Concepts and design
Since 1982 a program was conducted to develop a mobile meteorological (humidity, temperature, aerosols) Differential Absorption Lidar (DIAL) devoted to the studies of the nuclear power plant atmospheric surroundings. The measurement objectives are defined according to the user needs and the lidar feasibility. The concepts and design adopted to meet both the requirement and the measurement objectives are described. Each sub-system is addressed sequentially: transmitting system, receiving system, detection system, and post detection.
Cahen, C.↗
Enceladus Vent Explorer Concept
Our concept, Enceladus Vent Explorer (EVE), is a robotic pathfinder mission to enter these doors. EVE's goals are to descend into erupting conduits up to ~2 km deep, characterize the unknown interior structure of the vent-conduit system, assess the accessibility to the subsurface ocean through the vent-conduit system, potentially reach the liquid interface, and perform astrobiology and volcanology observations in the vent-conduit system. EVE sends two types of modules: Surface Module (SM) and Descent Module (DM). SM is a lander that stays on the surface, while tens of small (~3 kg, 10 cm in width and 30 cm in length) DMs separate from SM, move to a vent, and descend into it. DMs rely on a power and communication link provided by SM through a cable. As the payload volume of DM is extremely limited, each DM can carry only a single miniaturized instrument. This limitation is complemented by heterogeneity. There are several types of DMs, all of which share the common mobility system but carry different instruments. For example, a "scout DM" creates a 3-D map of the geyser system with its stereo cameras and structured light. A "sample return DM" collects particles and ice cores in the vent and deliver them to the mass spectrometer in the SM. An "in-situ science DM" carries science instruments, such as a microscopic imager and a microfluidics chip for biosignature detection. DMs are sent either sequentially or in parallel.
Enceladus↗