United States

PMW/ISEA teams are dealing with thousands of trouble tickets on a daily basis – currently the process is reliant on sailors or help desk self-assessing issue severity​. CASREP Prediction Model was develop to predict which Trouble Tickets are most likely to escalate into a CASREP or will take longer to resolve. This allows immediate identification of high-risk issues as soon as the trouble ticket is submitted. Enables team to prioritize response actions and resources, and potentially reduce system downtime. CASREP Prediction Model uses NAVWAR Help Desk ITSM data and five different AI/ML algorithms to work together to boost accuracy. 

The Trouble Ticket Duration model builds upon the framework of the CASREP Prediction Model but with the focus on predicting how long until a trouble ticket will be resolved. The Fleet will be potentially able to forecast downtime and adjust operational plans to compensate effectively. The PMW/ISEA teams are potentially able to use the forecast to prioritize tasking in resolving these trouble tickets.



NAML 2021

Using Machine Learning to Predict CASREP Occurrence and Trouble Ticket Duration

stacked ensemble architecture

synthetic minority oversampling (smote)

prediction

supervised machine learning

regression

classification

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

poster

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

<iframe width=1280 height=706 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/creleVVcvRh?v=6&ff=1&title=0&controls=1" allowfullscreen="true"></iframe>

Create synchronized data to train a machine learning algorithm to esimate turbulence in image data.

Atmospheric Turbulence Estimation from Degraded Image Data

We present several algorithms for vehicle routing problems that involve coordinated operations with a ground vehicle and a collection of drones.

Algorithms for drone-assisted mission planning problems

Patent pending for this big data collection and cleaning process. The sample data includes engineered features through various Navy databases. The data is then modeled in several predictive analytics algorithms, to include error metrics, training, and test sets. This then produces a list of submarine equipment needed to deploy that are forecasted to go obsolete in the next five years. These visual analytics are used by system leads and analysts to procure or prevent any shortfall. In turn, this encourages proactive triggers and money-savings for the fleet.

Forecasting Obsolescence Model Optimization

Recognizing that “garbage in” leads to “garbage out,” many organizations that employ machine learning and other types of data analytics strive for a zero-defect data ingestion process. While these efforts are critical, they are not infallible. Therefore, programs should adopt a multi-layered approach to risk management that includes mitigation plans for addressing issues that occur after faulty data has corrupted data analytic efforts and potentially influenced program-level decisions.

The impetus for what would become the Quality Risk Management Framework was a planned change to an analytics environment’s data ingestion process. The authors realized that the upcoming changes would likely increase the risks relating to data management. The authors also realized that these risks might not be discovered until after that faulty data had been used in subsequent analysis and decision support efforts, including those that may impact the safety of personnel. With these potential issues in mind, the authors performed a literature review in the hopes of finding an existing framework that addressed their concerns. While the review did not uncover a suitable framework, it did uncover elements that the authors would later incorporate into their Quality Risk Management Framework.

The authors’ resulting framework takes the form of a matrix where the columns relate to six quality dimensions and the rows relate to the six phases of the intelligence lifecycle. Using this matrix, the authors were able to identify 36 unique areas where quality risks could arise. Then, the authors developed mitigation plans for each area of potential danger. With these mitigation plans established, the authors are confident their program will be able to rapidly respond to the discovery of a quality issue regardless of when the program discovers it.

What if we were wrong? – Creating and Applying a Quality Risk Management Framework

Over the years, the use of data throughout the V-22 Osprey program steadily grew. Initially, automated software analyzed flight data to inform maintenance decisions. A few years later, analysts mined maintenance data to inform program-level decisions. Then, a few years after that, analysts leveraged artificial intelligence and machine learning on dozens of different V-22 data sets to gain deep insights on aircraft readiness. While each of the three program areas described above was successful, their roadmaps and maturation plans were independent until now. This presentation describes how overlaying a decision framework on the “big picture” enabled system-level optimization of the formerly siloed initiatives.

In the 1950s, John Boyd, an instructor at the Air Force’s Fighter Weapons School, realized that the pilot who made decisions faster than his opponent would win the aerial dogfight. Studying this further, Boyd realized that decision-making was a continual progression through four activities: observe, orient, decide, and act. Boyd subsequently named this decision-making process the OODA Loop. When the authors of this presentation took a system-level view of the V-22 maintenance support infrastructure, they discovered those same four elements were also critical factors in their program's success. They also realized, as did Boyd, that their goal should be to get through the decision-making cycle as quickly as possible.

With this system-level view and a goal of optimizing the OODA Loop, the authors examined the existing technologies and capabilities supporting each of the four decision-making activities. Next, they identified what was necessary to accelerate the program’s progression through the OODA Loop. Then, they set out to fill those gaps. As these new capabilities are introduced, the program’s decision-making system will become increasingly optimized. As a result, the V-22 program will gain maximum benefit from its machine learning and other data analytic efforts.

Applying a Decision Framework to Increase Machine Learning Impact on the V-22 Osprey Program

Naval Officers at the Office of Naval Research Reserve Component (ONR-RC) have developed a prototype taxonomy for Artificial Intelligence (AI), focused on Naval and DoD audiences and applications. Our taxonomy establishes 4 pillars of AI: Data, Mechanism, Function, and Naval Task. Sub-taxonomies within these pillars are based on authoritative resources from organizations such as NASA and the Association for Computing Machinery (ACM). We posit that any AI system can be decomposed and classified according our structure, which mitigates problems of mutual exclusivity that other taxonomies suffer. Organizations like ONR can use taxonomy to organize and shape their AI research portfolio. Furthermore, we hypothesize that concrete proposals for future AI systems can be developed by constructing their Data, Mechanism, Function, and Task components. This hypothesis is currently being tested by students at the Naval War College, who will write such proposals for the ongoing course AI for Strategic Leaders. We offer this prototype taxonomy for feedback and criticism, so that we may continue to improve its utility for wider DoD audiences.

Taxonomy of Artificial Intelligence

The objective of Self-Localization and Intelligent Mapping (SLIM) research is to expand the Navy's AUV capabilities through artificial intelligence (AI) to static undersea sensors and/or dynamic undersea groups, improve autonomous perception and data fusion, and generate world models from individual sensing while localizing the sensors and UUV swarm within the model. In the current study, we present the performance of the proposed method in a networked robotic scenario using robot operating system (ROS) Gazebo for simulation of physical environment.

A Novel Hybrid Method for Self-Localization and Intelligent Mapping of AUVs

NSWC Corona is developing the Perceptive Engine with Natural Language Processing (peNLP) application with the vision of enabling the digital transformation of documentation and processes, and bringing advanced data analytic capabilities to analysts’ fingertips. Its intuitive interface will include: displaying all documentation in one searchable wiki-like location with insightful metadata, tracing of concepts through related files, comparing and contrasting within the corpus to reduce redundancy and discover conflicts, and a better overall understanding of the documents through in-depth analyses and pattern recognition algorithms (i.e. clustering, changing word meaning over time, metrics) for future integration across different data sources (i.e. logistics, specifications, maintenance, RMA, funding). 

The peNLP application has made rapid advancements in its user interface in integrating NLP capabilities with automated Python scripts. With both “Document Search” and “Document Knowledge Graph” views, peNLP utilizes in-house, Corona-developed Python scripts to derive relevant metadata for documentation (i.e. top keywords, action items, date, section). Use of the BM25 search algorithm allows in-depth search capabilities of not only the documentation metadata but also of sections of interest within the documents. Additional information for users is provided by advanced NLP capabilities such as abstractive and extractive summarization as a quick read for end users, topic modeling, hypothesis testing between requirements and documents, Named Entity Recognition (NER) and syntax parsing for identifying roles and responsibilities, and textual entailment using AllenNLP for understanding overlap of similar language between different documents. All of peNLP’s information is integrated together through its extensive Document Knowledge Graph, which not only behaves as a visual aid in finding connections between documents but also serves as the backing database for tracing concepts and requirements through corpora.


Perceptive Engine with NLP (peNLP)

The task of classifying objects at range in low resolution imagery is challenging due to a lack of clarity of the object and spatial information surrounding the object. Deep learning methods, while state-of-the-art when using high-volume, high-resolution data, often fail in this case. Additionally, it has been shown that these deep learning methods are highly susceptible to variations to the input data (e.g. illumination) and therefore can fail to generalize to new data.

In this presentation, we use the tracks of objects derived from video data to study the dynamics of the objects motion to distinguish between targets and confusers. Using a topological approach of embedding the motion tracks using a time-delay embedding, and computing persistent homology, we can identify targets and confusers with high accuracy. For comparison, we utilize deep learning approaches on images of the objects as well as the motion track data. We find the topological approaches are better able to differentiate the different types of objects at a variety of track lengths. In particular we see the most significant performance gains for the shortest track lengths. Moreover, we show that the topological approaches presented are better able to generalize to new data from tracks collected separately, and under different conditions, from those used to train the model. These results build on work showcased at the 2020 NAML and will also describe our first foray into applying these techniques to maritime activity classification from AIS data.

I Spy in the Sea and Sky: Classification of Maritime and Aerial Targets
from Tracking Data

The process of unsupervised anomaly detection, which aims to identify outliers in data sets without the use of labeled training data, is critically important across a variety of domains including medicine, security, finance, and defense. Here we describe a new learning paradigm that combines batch stochastic gradient descent algorithms and loss functions tailored for the anomaly detection problem to reliably identify outliers regardless of the underlying data set or modality. We describe data models that can leverage manifold learning techniques, autoencoders, and generative adversarial networks and show how the training paradigm may be used to learn the unknown parameters that govern the underlying models such that outliers may be reliably identified without relying on previously labeled data. We show here that our framework demonstrates a clear improvement over state-of-the-art techniques on established benchmark data sets for unsupervised multivariate anomaly detection and further demonstrate value within Navy-relevant scenarios.

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Using Machine Learning to Predict CASREP Occurrence and Trouble Ticket Duration

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