NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

MACHINE LEARNING OPERATIONS (MLOPS) ARCHITECTURE CONSIDERATIONS FOR DEEP LEARNING WITH A PASSIVE ACOUSTIC VECTOR SENSOR

As machine learning augmented decision-making becomes more prevalent, defense applications for these techniques are needed to prevent being outpaced by peer adversaries. One area that has significant potential is deep learning applications to classify passive sonar acoustic signatures, which would accelerate tactical, operational, and strategic decision-making processes in one of the most contested and difficult warfare domains. Convolutional Neural Networks have achieved some of the greatest success in accomplishing this task; however, a full production pipeline to continually train, deploy, and evaluate acoustic deep learning models throughout their lifecycle in a realistic architecture is a barrier to further and more rapid success in this field of research. Two main contributions of this thesis are a proposed production architecture for model lifecycle management using Machine Learning Operations (MLOps) and evaluation of the same on live passive sonar stream. Using the proposed production architecture, this work evaluates model performance differences in a production setting and explores methods to improve model performance in production. Through documenting considerations for creating a platform and architecture to continuously train, deploy, and evaluate various deep learning acoustic classification models, this study aims to create a framework and recommendations to accelerate progress in acoustic deep learning classification research.Los Alamos National LabLieutenant, United States NavyApproved for public release. Distribution is unlimited

On the automation of RAN slicing provisioning: solution framework and applicability examples

Network slicing is a fundamental feature of 5G systems that allows the partitioning of a single network into a number of segregated logical networks, each optimized for a particular type of service, or dedicated to a particular customer or application. While support for network slicing (e.g. identifiers, functions, signalling) is already defined in the latest 3GPP Release 15 specifications, solutions for efficient automated management of network slicing (e.g. automatic provisioning of slices) are still at a much more incipient stage, especially for what concerns the next-generation Radio Access Network (NG-RAN). In this context, and consistently with the new service-based management architecture defined by 3GPP for 5G systems, this paper presents a functional framework for the management of network slicing in a NG-RAN infrastructure, delineating the interfaces and information models necessary to support the dynamic and automatic deployment of RAN slices. A discussion on the complexity of such automation follows together with an illustrative description of the applicability of the overall framework and information models in the context of a neutral host provider scenario that offers RAN slices to third party service providers.Peer ReviewedPostprint (published version

Classification of Acoustic Emission Signals from an Aluminum Pressure Vessel Using a Self-Organizing Map

Acoustic emission nondestructive testing has been used for real-time monitoring of complex structures. All of the structures were made of materials at least 0.070 inch thick. The purpose of this research was to demonstrate the feasibility of using neural networks to classify acoustic emission signals gathered from a pressure vessel made of 2024-T3 aluminum 0.040 inches thick, i.e. thin aluminum sheet. AE waveforms were recorded during fatigue cycling of one pressure vessel using a wide band transducer and a digital oscilloscope connected to a computer. The source for each signal was determined using two narrow band transducers and a LOCAN-AT data acquisition system. The power spectrum was calculated for each waveform. A Kohonen self-organizing map (SOM) was used to cluster the spectra. The network clustered the data on a two-dimensional feature space according to the source of the signal. A total of 3,600 power spectra were used to train the neural network, and 1,800 were used to test the network. Initially there was overlap between the clusters on the two-dimensional feature space; however, this was found to be due to human error. The SOM itself correctly classified all of the signals

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Navigation and Control of Automated Guided Vehicle using Fuzzy Inference System and Neural Network Technique

Automatic motion planning and navigation is the primary task of an Automated Guided Vehicle (AGV) or mobile robot. All such navigation systems consist of a data collection system, a decision making system and a hardware control system. Artificial Intelligence based decision making systems have become increasingly more successful as they are capable of handling large complex calculations and have a good performance under unpredictable and imprecise environments. This research focuses on developing Fuzzy Logic and Neural Network based implementations for the navigation of an AGV by using heading angle and obstacle distances as inputs to generate the velocity and steering angle as output. The Gaussian, Triangular and Trapezoidal membership functions for the Fuzzy Inference System and the Feed forward back propagation were developed, modelled and simulated on MATLAB. The reserach presents an evaluation of the four different decision making systems and a study has been conducted to compare their performances. The hardware control for an AGV should be robust and precise. For practical implementation a prototype, that functions via DC servo motors and a gear systems, was constructed and installed on a commercial vehicle