Contributions to Improving Detection Capabilities for Global Seismic Networks, Characterization of Socorro Region Seismicity, and Modeling of Seismic Wave Propagation
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Geosciences↗
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YOLO for Radio Frequency Signal Classification
Radio frequency signal classification plays a pivotal role in various applications, including spectrum management, wireless security, and cognitive radio. Extant signal classification methods require significant data throughput and are not multilabel. We propose a novel approach to radio frequency signal classification by leveraging the You Only Look Once (YOLO) object detection method. YOLO is a state-of-the-art deep learning model renowned for its real-time object detection capabilities in computer vision applications. We adapt YOLO for signal classification to enable the automatic and efficient identification of various signal types within a power spectral density image. Index Terms—radio-frequency analysis, object detection, neural networks, machine learning, deep learning.
42 ENGINEERING↗
IDENTIFYING ELUSIVE ELECTROMAGNETIC COUNTERPARTS TO GRAVITATIONAL WAVE MERGERS: AN END-TO-END SIMULATION
Combined gravitational wave (GW) and electromagnetic (EM) observations of compact binary mergers should enable detailed studies of astrophysical processes in the strong-field gravity regime. This decade, ground-based GW interferometers promise to routinely detect compact binary mergers. Unfortunately, networks of GW interferometers have poor angular resolution on the sky and their EM signatures are predicted to be faint. Therefore, a challenging goal will be to unambiguously pinpoint the EM counterparts of GW mergers. We perform the first comprehensive end-to-end simulation that focuses on: (1) GW sky localization, distance measures, and volume errors with two compact binary populations and four different GW networks; (2) subsequent EM detectability by a slew of multiwavelength telescopes; and (3) final identification of the merger counterpart amidst a sea of possible astrophysical false positives. First, we find that double neutron star binary mergers can be detected out to a maximum distance of 400 Mpc (or 750 Mpc) by three (or five) detector GW networks, respectively. Neutron-star-black-hole binary mergers can be detected a factor of 1.5 further out; their median to maximum sky localizations are 50-170 deg{sup 2} (or 6-65 deg{sup 2}) for a three (or five) detector GW network. Second, by optimizing depth, cadence, and sky area, we quantify relative fractions of optical counterparts that are detectable by a suite of different aperture-size telescopes across the globe. Third, we present five case studies to illustrate the diversity of scenarios in secure identification of the EM counterpart. We discuss the case of a typical binary, neither beamed nor nearby, and the challenges associated with identifying an EM counterpart at both low and high Galactic latitudes. For the first time, we demonstrate how construction of low-latency GW volumes in conjunction with local universe galaxy catalogs can help solve the problem of false positives. We conclude with strategies that would best prepare us for successfully identifying the elusive EM counterpart of a GW merger.
79 ASTRONOMY AND ASTROPHYSICS↗
Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds
Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.
5G↗
Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds
Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.
5G↗
Deep anomaly detection for industrial systems: a case study
We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation are used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are discrete in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.
anomaly detection, deep neural network↗
Inter-Comparison of Lightning Trends from Ground-Based Networks During Severe Weather: Applications Toward GLM
The planned GOES-R Geostationary Lightning Mapper (GLM) will provide total lightning data on the location and intensity of thunderstorms over a hemispheric spatial domain. Ongoing GOES-R research activities are demonstrating the utility of total flash rate trends for enhancing forecasting skill of severe storms. To date, GLM total lightning proxy trends have been well served by ground-based VHF systems such as the Northern Alabama Lightning Mapping Array (NALMA). The NALMA (and other similar networks in Washington DC and Oklahoma) provide high detection efficiency (> 90%) and location accuracy (< 1 km) observations of total lightning within about 150 km from network center. To expand GLM proxy applications for high impact convective weather (e.g., severe, aviation hazards), it is desirable to investigate the utility of additional sources of continuous lightning that can serve as suitable GLM proxy over large spatial scales (order 100 s to 1000 km or more), including typically data denied regions such as the oceans. Potential sources of GLM proxy include ground-based long-range (regional or global) VLF/LF lightning networks such as the relatively new Vaisala Global Lightning Dataset (GLD360) and Weatherbug Total Lightning Network (WTLN). Before using these data in GLM research applications, it is necessary to compare them with LMAs and well-quantified cloud-to-ground (CG) lightning networks, such as Vaisala s National Lightning Detection Network (NLDN), for assessment of total and CG lightning location accuracy, detection efficiency and flash rate trends. Preliminary inter-comparisons from these lightning networks during selected severe weather events will be presented and their implications discussed.
Carey, Lawrence D.↗
Detection and Identification of Human Locomotion by Distributed Acoustic Sensing with Deep Neural Networks
This paper reports intrusion detection using distributed acoustic sensors based on phase- sensitive enhanced Rayleigh scattering optical time-domain reflectometry. Vibration data induced by human locomotion was analyzed by convolution neural networks and achieve over 76.25% accuracy on human identification.
Peng, Zhaoqiang↗
Context-aided analysis of community evolution in networks
Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure based on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.
97 MATHEMATICS AND COMPUTING↗
SIGNATURES OF ILLICIT NUCLEAR PROCUREMENT NETWORKS: AN OVERVIEW OF PRELIMINARY APPROACHES AND RESULTS
The illicit trafficking of strategic nuclear commodities (defined here as the goods needed for a covert nuclear program excluding special nuclear materials) poses a significant challenge to the international nuclear nonproliferation community. Export control regulations, both domestically and internationally, seek to inhibit the spread of strategic nuclear commodities by restricting their sale to parties that may use them for nefarious purposes. However, export controls alone are not sufficient for preventing the illicit transfer of strategic nuclear goods. There are two major pitfalls to relying solely on export control regulations for the deterrence of proliferation of strategic goods. First, export control enforcement today relies heavily on the honesty and willingness of participants to adhere to the legal framework already in place. Secondly, current practices focus on the evaluation of single records which allow for the necessary goods to be purchased separately and hidden within the thousands of legitimate commerce transactions that occur each day, disregarding strategic information regarding several purchases. Our research presents two preliminary data-centric approaches for investigating procurement networks of strategic nuclear commodities. Pacific Northwest National Laboratory (PNNL) has been putting significant effort into nonproliferation activities as an institution, both in terms of the classical nuclear material focused approach and in the examination of other strategic goods necessary to implement a nuclear program. In particular, the PNNL Signature Discovery Initiative (SDI) has codified several scientific methodologies for the detection, characterization, and prediction of signatures that are indicative of a phenomenon of interest. The methodologies and tools developed under SDI have already been applied successfully to problems in bio-forensics, cyber security and power grid balancing efforts and they have now made the nonproliferation of strategic goods into a challenge problem for testing their methodology and tools. As a first step towards the detection and characterization of illicit procurement networks, our research examines procurement networks as defined by a system of entities (people or companies) that enter into transactions of specific items with one another. Once we have defined such networks, we are interested in answering questions about the behavior and characterization of such networks. The questions we wish to answer regarding procurement networks are, first, “Can we detect networks within large, noisy datasets?” and second, “To what extent can we compare multiple networks and identify their similarities?” As procurement networks can be naturally viewed as a graph, we have employed several graph analytic tools to aid in these tasks. In particular, Graphscape, an SDI tool, uses a novel method to approximate edit distance, a graph distance measure based on the number of changes needed to transform one graph into another, in order to measure how similar two given graphs are to each other. Given a set of graphs where vertices represent companies and edges represent a shipment from company A to company B, we can calculate an all-for-all comparison of graphs. In this way, we are able to determine which graphs are most similar, and which require more changes to transform one into the other. The set of graphs to be compared can be further specialized to provide more insight, e.g., using different time periods to explore events in a company life cycle.
graph analysis↗
Investigating the Efficacy of CubeSats for Asteroid Detection
A simulation to examine the potential of a network of CubeSats for detecting Near Earth Objects is discussed, in terms of goals, methods used and initial results obtained. By designing a basic optical system and the orbital parameters of the satellites in this network, their effectiveness for detecting asteroids is examined, with a small sample of cataloged asteroids considered.The conditions to be satisfied for detection cover both the geometrical aspects of astronomy such as field of view and line of sight, along with more technical optics-based conditions such as resolution and sensitivity of our telescopes. Of special interest to us in this work is the region of the sky between 45 deg. and 90 deg. from the Sun, as seen from the Earth. This part of the sky is currently unobservable by ground-based surveys and so provides the primary reason to consider a space-based one. There exist a number of issues with the simulation which call these results into question, but an eort has been made to remove those results which exceed the possible capabilities of the satellite network, and identify those aspects of the mission which should be examined in order to provide an in-depth assessment of it's performance. With these filters applied to the overall data, a tentative result of 1458 total detections over an 85 year period has been obtained, with 14 of the 22 asteroids in the sample being detected at least once. A number of ways in which the simulation could be improved are also proposed, both in-terms of addressing the aforementioned issues, as well as how to improve on the accuracy of the simulation and capture as many aspects of a space-based optical astronomy mission as possible,with the possible nal form of the simulation being a tool for assessing the performance of any space-based optical mission to detect asteroids.
Near Earth Objects↗
A New Approach to Multivariate Network Traffic Analysis
© 2019, Springer Science+Business Media, LLC & Science Press, China. Network traffic analysis is one of the core functions in network monitoring for effective network operations and management. While online traffic analysis has been widely studied, it is still intensively challenging due to several reasons. One of the primary challenges is the heavy volume of traffic to analyze within a finite amount of time due to the increasing network bandwidth. Another important challenge for effective traffic analysis is to support multivariate functions of traffic variables to help administrators identify unexpected network events intuitively. To this end, we propose a new approach with the multivariate analysis that offers a high-level summary of the online network traffic. With this approach, the current state of the network will display patterns compiled from a set of traffic variables, and the detection problems in network monitoring (e.g., change detection and anomaly detection) can be reduced to a pattern identification and classification problem. In this paper, we introduce our preliminary work with clustered patterns for online, multivariate network traffic analysis with the challenges and limitations we observed. We then present a grid-based model that is designed to overcome the limitations of the clustered pattern-based technique. We will discuss the potential of the new model with respect to the technical challenges including streaming-based computation and robustness to outliers.
Kim, Jinoh↗
Error correction for deep space network teletype circuits
Error detection codes and correction devices for Deep Space Network /DSN/ teletypewriter systems
Fredricksen, H.↗
Feature detection in satellite images using neural network technology
A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.
Augusteijn, Marijke F.↗
Detection of Synchrophasor False Data Injection Attack using Feature Interactive Network
The synchrophasor data recorded by Phasor Measurement Units (PMUs) plays an increasingly critical role in the regulation and situational awareness of power systems. However, the widely installed PMUs are vulnerable to multiple malicious attacks from cyber hackers during data transmission and storage. To address this problem, a Modified Ensemble Empirical Mode Decomposition (MEEMD) is proposed first to extract the intrinsic mode functions of each Synchrophasor Data Attacks (SDA). The frequency-based adaptive screening criterion embedded in MEEMD is used to eliminate the false intrinsic mode functions. Next, a Multivariate Convolutional Neural Network (MCNN) is proposed to identify multiple SDA by utilizing the extracted intrinsic mode functions and original SDA as input vectors. A fusion block as the main structure of MCNN is also leveraged to increase the diversity of features and compress the model parameters. Integrating MEEMD and MCNN, a framework with automatic feature extraction and multi-source information fusion capability, referred to as Feature Interactive Network (FIN), is proposed to detect multiple SDA. Based on the proposed FIN framework, six types of SDA are explored for the first time using actual synchrophasor data in FNET/Grideye that was collected from different locations in the U.S. Eastern Interconnection. Finally, a large quantity of experiments with different attack strengths are used to evaluate the adaptability and classification performance of the proposed FIN.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Model-Trained Neural Networks and Electronic Holography Demonstrated to Detect Damage in Blades
Detect Damage in Blades Electronic holography can show damaged regions in fan blades at 30 frames/sec. The electronic holograms are transformed by finite-element-model-trained artificial neural networks to visualize the damage. The trained neural networks are linked with video and graphics to visualize the bending-induced strain distribution, which is very sensitive to damage. By contrast, it is very difficult to detect damage by viewing the raw, speckled, characteristic fringe patterns. For neural-network visualization of damage, 2 frames or 2 fields are used, rather than the 12 frames normally used to compute the displacement distribution from electronic holograms. At the NASA Lewis Research Center, finite element models are used to compute displacement and strain distributions for the vibration modes of undamaged and cracked blades. A model of electronic time-averaged holography is used to transform the displacement distributions into finite-element-resolution characteristic fringe patterns. Then, a feedforward neural network is trained with the fringe-pattern/strain-pattern pairs, and the neural network, electronic holography, and video are implemented on a workstation. Now that the neural networks have been tested successfully at 30 frames/sec on undamaged and cracked cantilevers, the electronic holography and neural-network processing are being adapted for onsite damage inspection of twisted fan blades and rotormounted blades. Our conclusion is that model-trained neural nets are effective when they are trained with good models whose application is well understood. This work supports the aeromechanical testing portion of the Advanced Subsonic Technology Project.
Decker, Arthur J.↗
Flowzilla: A Methodology for Detecting Data Transfer Anomalies in Research Networks
© 2018 IEEE. Research networks are designed to support high volume scientific data transfers that span multiple network links. Like any other network, research networks experience anomalies. Anomalies are deviations from profiles of normality in a research network's traffic levels. Diagnosing anomalies is critical both for network operators and users (e.g., scientists). In this paper we present Flowzilla, a general framework for detecting and quantifying anomalies on scientific data transfers of arbitrary size. Flowzilla incorporates Random Forest Regression(RFR) for predicting the size of data transfers and utilizes an adaptive threshold mechanism for detecting outliers. Our results demonstrate that our framework achieves up to 92.5% detection accuracy. Furthermore, we are able to predict data transfer sizes up to 10 weeks after training with accuracy above 90%.
Giannakou, Anna↗
Adaptive packet transmission in response to anomaly detection in software defined smart meter networks
In this paper, we examine a basic smart meter network topology in mininet and address the issue of congestion over a commodity network, proposing an adaptive algorithm to cope with varying grid data delivery latencies.
adaptive algorithm↗