Low False Alarm Probability Research Articles

Monte Carlo Simulations of Spent Nuclear Fuel Dry Storage Cask Measurements with a New External Remote Monitoring System for Developing Safeguards Approaches

Until a long-term solution for the disposal of spent nuclear fuel (SNF) is available, interim dry casks will be increasingly used for the storage of SNF discharged from civilian nuclear power reactors. Dry casks containing commercial SNF may hold several significant quantities of plutonium, so appropriate nuclear material safeguards monitoring is needed. An external remote monitoring system (RMS) has been developed to advance dry cask safeguards monitoring beyond the current method of containment and surveillance used to maintain continuity of knowledge. In this study, neutron transport simulations of SNF assemblies in a dry cask were performed for several special nuclear material diversion scenarios. The simulations considered various loading patterns and fuel storage durations as long as 100 years. For each fuel loading pattern and storage time investigated, the simulation results were used to calculate the required measurement time to achieve a nondetection probability β ≤ 10% for the diversion of any single fuel assembly in the cask. The calculations were performed for false alarm probabilities α as low as 0.0001% (or 10−6). A Monte Carlo postprocessing approach was developed to consider the impact on the required measurement time of uncertainty in the burnup of fuel assemblies. The study found that the external RMS is well suited for the surveillance of SNF in dry cask storage for nuclear safeguards or other purposes and is able to detect the diversion of a single SNF assembly even after decades of storage and with a very low false alarm probability.

Continuity risk evaluation of the Bayesian posterior integrity monitoring against multiple faults

Compared to the prior integrity monitoring of global navigation satellite system (GNSS), the posterior integrity monitoring incorporates the used GNSS measurements into the computation of integrity risk, thereby providing a more accurate representation of the reliability of position solution. However, the continuity risk evaluation in posterior integrity monitoring remains an unresolved issue. After formulating the posterior integrity risk of each event hypothesis as a parity function, we propose a method to evaluate the posterior continuity risk by computing the cumulative probability of false alarm within a sphere constructed in the parity space. This approach guarantees a conservative estimate of the probability of false alarm, thereby establishing an upper bound for posterior continuity risk. Experiment and analysis based on positioning cases involving GPS, Galileo, and BeiDou satellites suggest that the posterior integrity monitoring can achieve a lower probability of false alarm compared to prior integrity monitoring under certain integrity monitoring configurations. Furthermore, the evaluation accuracy of probability of false alarm inversely correlates with the severity of the differences in solution separation variances.

A novel prognostics solution for accurate identification of degradation patterns in turbo machines with variable observation window

The degradation of a system is a time bound phenomenon, which leads to the deterioration of turbomachinery, in terms of performance and reliability. If undetected and not acted upon in time, this could also lead to sudden system failure, resulting in unplanned unit downtime and maintenance. Unplanned downtime of a turbomachine leads to severe production loss for the end customer and consequent economic damages. Early detection of a degradation pattern would provide the customer with the opportunity to timely carry out corrective actions, preventing an unscheduled down time. The paper evaluates degradation identification methodology currently known from literature and finds them not accurate enough for general purpose application required by the solution. The paper discusses a novel methodology which can accurately detect degradation patterns of timeseries data. Critical features of this methodology are novel time-based correlation enabled regression model with variable observation window, autonomous training, and automatic adjusting capability to incorporate operating behavior change or physical system replacement. This leads to high accuracy, high generalization, and domain agnostic application capability. Moreover, particular focus is given to achieving high probability of detection and a low probability of false alarm. The paper demonstrates the performance achieved by the methodology when applied to the field of prognostics and diagnostics of IoT connected turbomachines through 50+ real application cases.

A novelty detection method for efficient data storage in smart grids

This work presents a new novelty detection method for efficiently storing electrical power system data. The proposed method makes innovative use of the Cycle-by-Cycle Difference (CCD) and the Mathematical Morphology (MM) techniques to identify novelties in the voltage of an electrical power system. Once the novelties are identified, the frames where they occur are extracted from the signal, and the other frames are discarded. Among the extracted frames, those containing power quality (PQ) disturbances are fully stored, while the frames that represent stationary states have only their main parameters stored, contributing to efficient storage. The proposed method exhibited low computational complexity in the operational phase, which was evaluated via a field-programmable gate array (FPGA) platform implementation using a customized embedded processor. The method was evaluated with real and simulated signals. The results indicate that the developed method attained high detection probabilities alongside low false alarm probabilities, resulting in a competitive Area Under the Curve (AUC) exceeding 0.976 (except for interharmonics). Comparison with other methods revealed that our approach yielded competitive results with the lowest computational cost. We concluded that the proposed method may be useful for monitoring PQ disturbances in smart grids, where the data volumes are too large.

Strong Clutter Suppression Using Spatial and Signal Similarity for Multi-Channel SAR Ground-Moving-Target Indication

This paper presents a new two-stage approach for suppressing strong clutter and detecting moving targets using scatterers’ spatial structure and signal similarity. Compared with the traditional strong clutter suppression methods, the proposed method considers both the spatial similarity and the channel correlation of the scatterers, effectively alleviating the false alarm probability and avoiding the missed detection problem caused via identifying strong moving targets as strong stationary clutter. Additionally, a detector is presented based on the linear degree of the radial velocity interferometric phase (LDRVP) to eliminate false alarms from isolated strong scatter points and the edges of strong scatterers. The experimental results of the X-band radar indicate the presented approach’s lower false alarm probability and superior robustness.

Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array

To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of a sample covariance matrix to construct a feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM) and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with the massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers.

Blockchain‐oriented location privacy preserving for cooperative spectrum sensing in 6G wireless networks

AbstractIn this article, the authors proposed a novel blockchain‐oriented location privacy‐preserving (BoLPP) for the Cooperative Spectrum Sensing (CSS) in 6G networks. In order to attain the sustainability of privacy and security for 6G wireless networks, it is a great challenge in this sensing as it faces various malicious attacks while the secondary user (SU) is active. To tackle these issues, the authors proposed a novel framework for blockchain‐oriented Cognitive Radio Networks (CRNs) for CSS using an energy detection technique. Moreover, the authors implemented another novel paradigm BoLPP, to attain the privacy of SUs location for CSS in 6G networks. This approach preserves the SUs’ location and makes the BoLPP framework immune to all malicious attackers. The simulation results have been undergone based on the performance metrics such as response time, consistency, probability of false alarm, frame loss (%), average network throughput, energy efficiency, and security. The outcomes reveal that the proposed scheme achieves high security, privacy, energy efficiency, average network throughput, and low probability of false alarm and frame loss (%) when compared with the existing frameworks such as Friend or Foe (FoF) and Tidal Trust Algorithm (TTA) mechanisms. It is observed that the proposed BoLPP mechanism provides better security and privacy in 6G wireless networks.

RFI Detection and Mitigation for Advanced Correlators in Interferometric Radiometers

This work presents the first RFI detection and mitigation algorithm for the interferometric radiometers that will be implemented in its correlator unit. The algorithm operates in the time and frequency domains, applying polarimetric and statistical tests in both domains, and exhibiting a tunable and arbitrary low probability of false alarm. It is scalable to a configurable number of receivers, and it is optimized in terms of quantization bits and the implementation of the cross-correlations in the time or frequency domains for hardware resource saving. New features of this algorithm are the computation of the Stokes parameters per frequency bin in the Short-Time Fourier Transform and a new parameter called Polarimetric Kurtosis. If RFI is detected in one domain or in both, it is removed using the calculated blanking masks. The optimum algorithm parameters are computed, such as length of the FFTs, the threshold selection for a given probability of false alarm, and the selection of the blanking masks. Last, an important result refers to the application of Parseval’s theorem for the computation of the cross-correlations in the frequency domain, instead of in the time domain, which is more efficient and leads to smaller errors even when using moderate quantization levels. The algorithm has been developed in the framework of the ESA’s technology preparation for a potential L-band radiometer mission beyond SMOS. However, it is also applicable to (polarimetric) real aperture radiometers, and its performance would improve if more than one bit is used in the signal quantization.

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning.

Cooperative network is a promising concept for achieving a high-accuracy decision of spectrum sensing in cognitive radio networks. It enables a collaborative exchange of the sensing measurements among the network users to monitor the primary spectrum occupancy. However, the presence of malicious users leads to harmful interferences in the system by transmitting incorrect local sensing observations.To overcome this security related problem and to improve the accuracy decision of spectrum sensing in cooperative cognitive radio networks, we proposed a new approach based on two machine learning solutions. For the first solution, a new stacking model-based malicious users detection is proposed, using two innovative techniques, including chaotic compressive sensing technique-based authentication for feature extraction with a minimum of measurements and an ensemble machine learning technique for users classification. For the second solution, a novel deep learning technique is proposed, using scalogram images as inputs for the primary user spectrum’s classification. The simulation results show the high efficiency of both proposed solutions, where the accuracy of the new stacking model reaches 97% in the presence of 50% of malicious users, while the new scalogram technique-based spectrum sensing is fast and achieves a high probability of detection with a lower number of epochs and a low probability of false alarm.

A dynamic hierarchical Bayesian approach for forecasting vegetation condition

Abstract. Agricultural drought, which occurs due to a significant reduction in the moisture required for vegetation growth, is the most complex amongst all drought categories. The onset of agriculture drought is slow and can occur over vast areas with varying spatial effects, differing in areas with a particular vegetation land cover or specific agro-ecological sub-regions. These spatial variations imply that monitoring and forecasting agricultural drought require complex models that consider the spatial variations in a given region of interest. Hierarchical Bayesian models are suited for modelling such complex systems. Using partially pooled data with sub-groups that characterise spatial differences, these models can capture the sub-group variation while allowing flexibility and information sharing between these sub-groups. This paper's objective is to improve the accuracy and precision of agricultural drought forecasting in spatially diverse regions with a hierarchical Bayesian model. Results showed that the hierarchical Bayesian model was better at capturing the variability for the different agro-ecological zones and vegetation land covers compared to a regular Bayesian auto-regression distributed lags model. The forecasted vegetation condition and associated drought probabilities were more accurate and precise with the hierarchical Bayesian model at 4- to 10-week lead times. Forecasts from the hierarchical model exhibited higher hit rates with a low probability of false alarms for drought events in semi-arid and arid zones. The hierarchical Bayesian model also showed good transferable forecast skills over counties not included in the training data.

Jamming Attack Detection using Machine Learning Algorithms in Wireless Network

Due to technological improvements, a tremendous amount of data is generated every second. In the world of computing, data is being gathered constantly, from mouse clicks. In the system network, secure lines and servers must will be monitored. Such system were known as Intruder Detection System(IDS). A wireless network is the main target of such attacks, which result an undesirable denial of service. Fuzzy logic, game theory, channel surfing, and time series are several jamming detection methods that have been suggested over the past ten years. The majority of these methods are ineffective in locating intelligent jammers. Thus, effective, high-accurate and quick jamming detection approaches are highly required. For the purpose of detecting jamming attacks, a detailed analysis of certain ML algorithms including Random Forest, SVM, and NN, was performed and evaluate how well several machine learning algorithms detect jamming signals. In order to identify jamming signals, we looked into various signal characteristic types and produce a significant dataset and it will trained, analyzed, and tested using this dataset. The probability of detection, the probability of false alarm, the chance of miss detection, and accuracy were used to study and evaluate the performance of various algorithms. The simulation findings indicate that the random forest method used for jamming detection can identify jammers with high accuracy and detection probability, and low false alarm probability.

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms.

Study on the sequential Bayesian approach for a CsI(Tl)-based radioactive material detection system

Traditional algorithms based on energy spectra are widely used to detect radioactive materials in the field of nuclear security. However, it takes a long time to collect photons to obtain accurate energy spectra, which limits the warning time and increases false-alarm probability. The sequential Bayesian algorithm, which takes both photon energies and their interarrival time into account, could help improve the performance of radioactive detection. In this study, we propose a theoretical optimization method of the traditional sequential Bayesian algorithm to suit the parameters of the CsI(Tl) detector. Based on simulation data, dynamic environmental background parameters and repeated tests were added to the algorithm to further optimize its performance. Finally, with the improved sequential Bayesian algorithm, radioactive sources can be quickly detected by the CsI(Tl) detector with low false-alarm probability (less than 1.1 times per day).

CNN-aided multiple PU spectrum sensing in uncalibrated massive antennas SU system

This paper deploys the Convolutional Neural Network (CNN) to learn and set the statistical test in Spectrum Sensing (SS) task of multiple primary user (PU) sources in massive uncalibrated antennas of secondary users (SU) sharing the same spectrum resources. The proposed deep learning-based SS method (DL-SS) is based on the CNN architecture that has the capability of extracting features of the sample covariance matrices (SCMs) that are given as the network input, improving the overall performance and robustness. The proposed CNN-SS method is compared with nine recent multiple-antennas SS methods, namely the arithmetic–geometric detector (AGM), John’s detector (JD), sphericity detector (SD), generalized likelihood test (GLRT), locally most powerful invariant test (LMPIT), maximum–minimum eigenvalue detector (MME), covariance detector (CAV), Hadamard detector (HD) and volume detector (VD) methods; besides, the proposed method is also compared with five recent state-of-art CNN-based SS methodologies. Performance-complexity trade-off of the proposed and reference SS methods are corroborated via Monte Carlo Simulations (MCS). The proposed CNN-SS method under uncalibrated massive antennas reveals substantial benefits w.r.t. the reference methods and is competitive with others CNN-SS methodologies, both in terms of complexity and performance, achieving detection probability of Pd=0.9(@SNR=−20dB) under very low false alarm probability Pf=0.1. Under different figures of merit, the performance of the CNN-based SS detector has revealed to be indubitably superior regarding the state-of-art SS detectors. However, the proposed CNN-based SS detector presents relative computational complexity increases. Hence, to be effective, such a superior operational performance requires a very efficient processing structure in the SU base stations.

Automatic Detection of Underwater Small Targets Using Forward-Looking Sonar Images

Forward-looking sonar is one of the essential imaging equipment employed in exploring underwater targets. However, it is always challenging to detect targets from sonar images considering the complex environment. This paper represents an automatic underwater target detection method using clustering, segmentation, and feature discrimination. Firstly, we combine the Fuzzy C-means Clustering (FCM) and K-means to cluster the sonar image globally to obtain as many Regions of Interests (ROIs) as possible. Secondly, the Pulse Coupled Neural Network (PCNN) is used to locally segment the target boundary from the ROIs. Finally, multiple features are extracted from the target area as the feature vector, which is inputted into the nonlinear converter to enlarge the features’ distance. Then we use Fisher discriminant to estimate the classification threshold, which realizes the underwater target detection. The experimental results show that the proposed method has low detection error and good real-time performance under low false alarm probability, which is not inferior to the popular deep learning approaches at present.

Spectrum Sensing Method Based on Residual Cellular Network

The traditional spectrum sensing method based on convolutional neural network (CNN) has the single-branch convolutional network structure and the shallow network structure which limits the ability of extracting the Primary User (PU) feature. Aiming at these problems, a spectrum sensing method based on the residual cellular network (ResCelNet) is proposed in this work, involving a structure of "dual-branch convolution plus summation operation plus residual learning plus dual-branch convolution plus summation operation". Specifically, the dual-branch convolution improves the feature extraction ability, the addition operation enhances the micro-feature information, and the residual learning is adopted to facilitate training the deep spectrum sensing network. This method transforms the spectrum sensing problem into the image binary classification problem. Firstly, the received signals are reshaped into a matrix and normalized to gray levels, which is used as the input of the network. Secondly, the feature information of gray-scale images is extracted and the network is trained through dual-branch convolution and residual learning. Finally, the test data is input into the trained model and spectrum sensing based on image classification is implemented. The experimental results demonstrate that the proposed method exhibits a higher detection probability and a lower false alarm probability as well as better generalization ability as compared with the traditional methods under low signal to noise ratio (SNR) circumstance.

ADS-B Crowd-Sensor Network and Two-Step Kalman Filter for GNSS and ADS-B Cyber-Attack Detection.

Automatic Dependent Surveillance-Broadcast is an Air Traffic Control system in which aircraft transmit their own information (identity, position, velocity, etc.) to ground sensors for surveillance purposes. This system has many advantages compared to the classical surveillance radars: easy and low-cost implementation, high accuracy of data, and low renewal time, but also limitations: dependency on the Global Navigation Satellite System, a simple unencrypted and unauthenticated protocol. For these reasons, the system is exposed to attacks like jamming/spoofing of the on-board GNSS receiver or false ADS-B messages’ injection. After a mathematical model derivation of different types of attacks, we propose the use of a crowd sensor network capable of estimating the Time Difference Of Arrival of the ADS-B messages together with a two-step Kalman filter to detect these attacks (on-board GNSS/ADS-B tampering, false ADS-B message injection, GNSS Spoofing/Jamming). Tests with real data and simulations showed that the algorithm can detect all these attacks with a very high probability of detection and low probability of false alarm.

A Novel Weighted Cluster Based Co-operative Spectrum Sensing Scheme for Cognitive Radio Network

Background: Cognitive radio can be described as a radio system where it familiarizes to the situations of the environs by some procedure like analyzing, observing and learning. Cognitive radio can basically recycle the unused white space in the license spectrum through sensing the network thus the method can achieve the maximum implementation of the radio bandwidth. Objective: The motivation of this research is to improve the performance of spectrum sensing of a group of users in every cluster where various channel environment will be presented in comparison with the conventional weighted sensing. Methods: With the intention of lessening the fading and shadowing, co-operative detection is favored over independent detection. Clustered based cooperation has been employed to expand the sensing enactment moderately. Sensing of co-operative spectrum which is weighted has been used to boost the cluster cooperation scheme. To crack the critical problem this research proposed a weighted cluster based co-operative sensing in which the weight calculation is performed at FC reducing all available redundancies. Results: From the simulation it is found that our proposed algorithm has better probability of detection (i.e, at -6 dB of SNR, clustered, Weighted Clustered and Novel Weighted Clustered probability of detections are 67%,84% and 95% respectively). Even if Novel Weighted Clustered has the lower False Alarm probability. Conclusion: Proposed novel weighted co-operative spectrum sensing algorithm for cognitive radio network spectrum sensing scheme can attain better performance under different fading environments.

Aggregate Preamble Sequence Design and Detection for Massive IoT With Deep Learning

Massive Internet of Things (mIoT) is a major use case of the fifth generation (5G) wireless systems. mIoT aims to support a large number of connection requests from IoT devices. However, the conventional Long Term Evolution (LTE) random access procedure hinders the support of mIoT due to the limited number of available preambles. In this paper, we propose to aggregate two Zadoff-Chu preamble sequences from two different roots to obtain a larger set of preambles by considering all possible combinations of preamble sequence pairs. Decoding the aggregate preambles is challenging because the receiver needs to decode two preamble sequences where each one is allocated half of the transmit power. We propose two receiver architectures for preamble decoding. The first one is a threshold-based receiver which only requires minor changes to the LTE preamble receiver architecture. The second proposed preamble decoder architecture exploits a deep neural network. Simulations show that the proposed aggregate preamble design results in a lower service time for backlogged IoT devices compared to existing collision avoidance techniques. Moreover, the proposed receiver architectures can decode the aggregate preambles with low probabilities of misdetection and false alarm (less than 11%), especially in the high signal-to-noise ratio (SNR) regime.

A statistics-based threshold for the RMS-energy oscillation detector

Power system operators and reliability coordinators currently rely on extensive baselining studies to set thresholds for their oscillation detectors. The resulting thresholds are based largely on engineering judgment and may vary significantly between organizations. In this paper, statistical distribution theory is used to derive a threshold for the widely deployed root-mean-square (RMS)-energy oscillation detector. This expression provides a theoretical basis for the detector’s configuration, simplifies the process of selecting the threshold, and enables improved consistency among organizations that need to coordinate during system-wide oscillations. Three methods for calculating the threshold using synchrophasor measurements are also proposed. These methods ensure that the threshold can be calculated reliably for various applications. Tests with simulated and field-measured data demonstrate that the statistics-based threshold provides consistent detection of oscillations while maintaining a low probability of false alarm.