scholarly journals Observation and Maneuver Detection for Cislunar Vehicles

2021 ◽  
Author(s):  
Jesse A. Greaves ◽  
Daniel J. Scheeres
Keyword(s):  
Optimal Control ◽  
Situation Awareness ◽  
Hypothesis Test ◽  
Control Policy ◽  
Detection Methods ◽  
Sufficient Information ◽  
Data Sets ◽  
Sustained Development ◽  
Statistical Framework ◽  
Future Work

AbstractInternational interest in the sustained development of cislunar space will generate traffic and debris in the region which requires monitoring; similar to how current space situation awareness is necessary for the traffic and debris near Earth. There are many challenges associated with developing a cislunar situation awareness program, but 2 primary issues addressed by this paper are observational strategies and maneuver detection methods. This work proposes an observational strategy that utilizes the ballistic Optimal Control Based Estimator (OCBE) to filter measurements from cislunar optical observers. To reduce numerical issues associated with filtering, new modifications to the ballistic Optimal Control Based Estimator (OCBE) are introduced that preserve the OCBE update equations in Square Root Information (SRI) space. This new derivation produces a more stable version of the ballistic OCBE which is beneficial for filtering larger data sets with non-linear motion and measurements. Applying the SRI OCBE to the estimation problem it was found that only a single L2 observer with angle and angle-rate measurements provided sufficient information for consistent estimation. Then a newly developed maneuver detection method is presented to statistically identify maneuvers. The method applies a binary hypothesis test to the optimal control policy of the ballistic OCBE to quantify mismodeling. This method was tested given a impulsive maneuver policy with a mean of 50 mm/s and standard deviation of 15 mm/s, and 194 out of 200 tests correctly identified if a maneuver occurred. The OCBE control policy also provided appropriate impulse estimates of mismodeling, which may be used to reconstruct maneuvers in future work. Together, the proposed observation and maneuver detection methodology yields reliable tracking and provides a statistical framework to detect maneuvers.

scholarly journals Early Detection Methods for Silicosis in Australia and Internationally: A Review of the Literature

2021 ◽  
Vol 18 (15) ◽  
pp. 8123
Author(s):  
Emma K. Austin ◽  
Carole James ◽  
John Tessier
Keyword(s):  
Early Detection ◽  
Literature Review ◽  
Detection Method ◽  
Detection Methods ◽  
X Rays ◽  
Review Of The Literature ◽  
Occupational Lung Disease ◽  
Work Related ◽  
Growing Body ◽  
Future Work

Pneumoconiosis, or occupational lung disease, is one of the world’s most prevalent work-related diseases. Silicosis, a type of pneumoconiosis, is caused by inhaling respirable crystalline silica (RCS) dust. Although silicosis can be fatal, it is completely preventable. Hundreds of thousands of workers globally are at risk of being exposed to RCS at the workplace from various activities in many industries. Currently, in Australia and internationally, there are a range of methods used for the respiratory surveillance of workers exposed to RCS. These methods include health and exposure questionnaires, spirometry, chest X-rays, and HRCT. However, these methods predominantly do not detect the disease until it has significantly progressed. For this reason, there is a growing body of research investigating early detection methods for silicosis, particularly biomarkers. This literature review summarises the research to date on early detection methods for silicosis and makes recommendations for future work in this area. Findings from this review conclude that there is a critical need for an early detection method for silicosis, however, further laboratory- and field-based research is required.

scholarly journals Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3536
Author(s):  
Jakub Górski ◽  
Adam Jabłoński ◽  
Mateusz Heesch ◽  
Michał Dziendzikowski ◽  
Ziemowit Dworakowski
Keyword(s):  
Novelty Detection ◽  
Feature Space ◽  
Detection Methods ◽  
Data Sets ◽  
Similar Distribution ◽  
Detection Algorithms ◽  
Rotary Machinery ◽  
Detection Approach ◽  
Input Reconstruction ◽  
Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

Peak detection in sediment - charcoal records: impacts of alternative data analysis methods on fire-history interpretations

2010 ◽  
Vol 19 (8) ◽  
pp. 996 ◽  
Author(s):  
Philip E. Higuera ◽  
Daniel G. Gavin ◽  
Patrick J. Bartlein ◽  
Douglas J. Hallett
Keyword(s):  
Data Analysis ◽  
Fire History ◽  
Threshold Value ◽  
Decomposition Methods ◽  
Peak Detection ◽  
Detection Methods ◽  
Statistical Framework ◽  
Analysis Methods ◽  
Decomposition Models ◽  
Data Analysis Methods

Over the past several decades, high-resolution sediment–charcoal records have been increasingly used to reconstruct local fire history. Data analysis methods usually involve a decomposition that detrends a charcoal series and then applies a threshold value to isolate individual peaks, which are interpreted as fire episodes. Despite the proliferation of these studies, methods have evolved largely in the absence of a thorough statistical framework. We describe eight alternative decomposition models (four detrending methods used with two threshold-determination methods) and evaluate their sensitivity to a set of known parameters integrated into simulated charcoal records. Results indicate that the combination of a globally defined threshold with specific detrending methods can produce strongly biased results, depending on whether or not variance in a charcoal record is stationary through time. These biases are largely eliminated by using a locally defined threshold, which adapts to changes in variability throughout a charcoal record. Applying the alternative decomposition methods on three previously published charcoal records largely supports our conclusions from simulated records. We also present a minimum-count test for empirical records, which reduces the likelihood of false positives when charcoal counts are low. We conclude by discussing how to evaluate when peak detection methods are warranted with a given sediment–charcoal record.

scholarly journals Trust between Humans and Learning Machines: Developing the Gray Box

2017 ◽  
Vol 139 (06) ◽  
pp. S9-S13 ◽  
Author(s):  
James C. Christensen ◽  
Joseph B. Lyons
Keyword(s):  
Situation Awareness ◽  
Task Allocation ◽  
Intelligent Agent ◽  
Semantic Space ◽  
Learning Systems ◽  
Learning System ◽  
Sufficient Information ◽  
Learning Technology ◽  
Learning Machines ◽  
Potential Impact

This article explores the notion of the ‘Gray Box’ to symbolize the idea of providing sufficient information about the learning technology to establish trust. The term system is used throughout this article to represent an intelligent agent, robot, or other form of automation that possesses both decision initiative and authority to act. The article also discusses a proposed and tested Situation Awareness-based Agent Transparency (SAT) model, which posits that users need to understand the system’s perception, comprehension, and projection of a situation. One of the key challenges is that a learning system may adopt behavior that is difficult to understand and challenging to condense to traditional if-then statements. Without a shared semantic space, the system will have little basis for communicating with the human. One of the key recommendations of this article is that there is a need to provide learning systems with transparency as to the state of the human operator, including their momentary capabilities and potential impact of changes in task allocation and teaming approach.

scholarly journals WFMHT Method of Orbit Maneuver Detection Based on Space-Based Bearing-Only Measurement

2018 ◽  
Vol 36 (6) ◽  
pp. 1185-1192
Author(s):  
Lei Liu ◽  
Jianfeng Cao ◽  
Ye Liu
Keyword(s):  
Real Time ◽  
Situation Awareness ◽  
Detection System ◽  
Hypothesis Test ◽  
Test Methods ◽  
Time Motion ◽  
Weighted Fusion ◽  
Innovation Characteristics ◽  
Detection Window ◽  
Time Awareness

The method of orbit maneuver detection of space targets is investigated using the space-based bearing-only measurement, which aims to acquire a real-time or nearly real-time awareness of orbit maneuver in the space situation awareness. First, the model for estimating real-time motion of a space target is presented, which only uses the space-based bearing-only measurements. The innovation characteristics of the normal orbit and orbit maneuver are analyzed and compared. Second, based on the hypothesis test methods of the distribution characteristic of the stochastic sequence, the WFMHT (i.e., weighted fusion of multi hypothesis tests) method with the innovation is put forward to detect the orbit maneuver. Furthermore, the criterion of determining the weight coefficients is studied. Finally, the method is validated by numeric simulations. The results show that the highest gained success rate is up to 36% with the WFMHT method than the prevalent Chi2 method. With the WFMHT method, the detection system achieves a strengthened robustness with greatly shortened detection window. The research will be beneficial to construction of our space situation awareness system.

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