Knowledge of the signal-in-space (SIS) anomaly probability is important for the integrity monitoring of satellite navigation. An efficient SIS anomaly detection method is indispensable for characterizing the probabili...
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Knowledge of the signal-in-space (SIS) anomaly probability is important for the integrity monitoring of satellite navigation. An efficient SIS anomaly detection method is indispensable for characterizing the probability of SIS anomalies with sufficient confidence. The traditional GPS anomaly detection method based on the zero-mean Gaussian distribution assumption of the instantaneous signal-in-space user range error (IURE) is not suitable for the emerging BeiDou navigation satellite system (BDS) because of the nonzero-mean, asymmetric distribution of the BDS IURE. By deliberately extracting the time series trend terms of the satellite orbit and clock errors, an SIS anomaly detection method with the worst user location protection principle is proposed based on 6 years of BDS data from March 2013 to March 2019. The detection results have shown that the probability of single-satellite SIS anomalies is at the 10(-3) level and the probability of multiple-satellite SIS concurrent anomalies is at the 10(-4) level. Meanwhile, the operational service performance provided by the BDS gradually improves over time.
Environmental impact of pollutants can be analyzed effectively by acquiring fish behavioral signals in water with biological behavior sensors. However, a variety of factors, such as the complexity of biological organi...
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Environmental impact of pollutants can be analyzed effectively by acquiring fish behavioral signals in water with biological behavior sensors. However, a variety of factors, such as the complexity of biological organisms themselves, the device error and the environmental noise, may compromise the accuracy and timeliness of model predictions. The current methods lack prior knowledge about the fish behavioral signals corresponding to characteristic pollutants, and in the event of a pollutant invasion, the fish behavioral signals are poorly discriminated. Therefore, we propose a novel method based on Bayesian sequential,which utilizes multi-channel prior knowledge to calculate the outlier sequence based on wavelet feature followed by calculating the anomaly probability of observed values. Furthermore, the relationship between the anomaly probability and toxicity is analyzed in order to achieve forewarning effectively. At last, our algorithm for fish toxicity detection is verified by integrating the data on laboratory acceptance of characteristic pollutants. The results show that only one false positive occurred in the six experiments, the present algorithm is effective in suppressing false positives and negatives, which increases the reliability of toxicity detections, and thereby has certain applicability and universality in engineering applications.
Uncertainty quantification of cybersecurity anomaly detection results provides critical guidance for decision makers on whether or not to accept the results. Improving the trustworthiness of anomaly predictions can re...
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Uncertainty quantification of cybersecurity anomaly detection results provides critical guidance for decision makers on whether or not to accept the results. Improving the trustworthiness of anomaly predictions can reduce the amount of alert false positives that security teams have to process. In this work we investigate the use of Bayesian Autoencoder (BAE) models for uncertainty quantification in anomaly detection. A novel heteroscedastic aleatoric uncertainty modelling method is explored that jointly considers aleatoric and epistemic uncertainty. Heteroscedastic aleatoric uncertainty is modelled on the latent layer of the BAE and further explored through considering the variational lower bound. An uncertainty quantification framework for cybersecurity is designed and verified on UNSW-NB15 and CIC-IDS-2017 data sets. This research enhances the modelling of uncertainty in the BAE model and expands its application in cybersecurity.
Event detection is an essential issue for wireless sensor networks research. Energy saving and reliable detection are major challenges for the resource constraints on sensor nodes. To give attention to both of them, t...
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Event detection is an essential issue for wireless sensor networks research. Energy saving and reliable detection are major challenges for the resource constraints on sensor nodes. To give attention to both of them, this paper presents a distributed event detection approach using self-learning threshold to fully exploit the energy-reliability tradeoff in wireless sensor networks. In the proposed approach, a stream of real-valued sensor readings is mapped into symbol sequences in order to reduce data dimensionality and simplify event description. A dynamic conversion granularity is adopted to improve the effectiveness of symbolic representation. Then the anomaly probabilities of symbol sequences are estimated through Markov model, and sensor nodes participating in the event detection make local decisions in a distributed manner based on the learned anomaly detection threshold. A timer-based node sleep scheduling is developed to prolong network lifetime during the detection process. Subsequently, the final detection decision is made by a bitwise voting based on the local decisions. A comprehensive set of simulations demonstrate that the proposed approach achieves considerable energy conservation while maintaining fast and accurate event detection.
To reduce the risk to nearby aircraft during space vehicle launches, the FAA shuts down a large column of airspace, often for hours at a time. These airspace restrictions lead to many rerouted aircraft. Recent researc...
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ISBN:
(纸本)9781479989409
To reduce the risk to nearby aircraft during space vehicle launches, the FAA shuts down a large column of airspace, often for hours at a time. These airspace restrictions lead to many rerouted aircraft. Recent research has explored ways to make airspace closures dynamic throughout the launch process and limit their geographic extent. This paper models the problem as a Markov decision process whose solution defines when an aircraft should be rerouted, minimizing inconvenience and cost while maintaining safety. The model captures the launch vehicle trajectory, probability of anomaly, potential debris trajectories, and air traffic. An optimization approach known as backwards induction value iteration results in a policy that recommends aircraft maneuvers. The scenario investigated in this work includes a two-stage-to-orbit launch vehicle from Cape Canaveral Air Force Base, commercial aircraft at 35,000 ft, and potential debris trajectories. This approach yields policies that are safe and that cause minimal disruption to the airspace.
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