Artificial Intelligence and Machine Learning: A Perspective on Integrated Systems Opportunities and Challenges for Multi-Domain Operations

This paper provides a perspective on historical background, innovation and applications of Artificial Intelligence (AI) and Machine Learning (ML), data successes and systems challenges, national security interests, and mission opportunities for system problems. AI and ML today are used interchangeably, or together as AI/ML, and are ubiquitous among many industries and applications. The recent explosion, based on a confluence of new ML algorithms, large data sets, and fast and cheap computing, has demonstrated impressive results in classification and regression and used for prediction, and decision-making. Yet, AI/ML today lacks a precise definition, and as a technical discipline, it has grown beyond its origins in computer science. Even though there are impressive feats, primarily of ML, there still is much work needed in order to see the systems benefits of AI, such as perception, reasoning, planning, acting, learning, communicating, and abstraction. Recent national security interests in AI/ML have focused on problems including multidomain operations (MDO), and this has renewed the focus on a systems view of AI/ML. This paper will address the solutions for systems from an AI/ML perspective and that these solutions will draw from methods in AI and ML, as well as computational methods in control, estimation, communication, and information theory, as in the early days of cybernetics. Along with the focus on developing technology, this paper will also address the challenges of integrating these AI/ML systems for warfare

Image Segmentation in a Remote Sensing Perspective

Image segmentation is generally defined as the process of partitioning an image into suitable groups of pixels such that each region is homogeneous but the union of two adjacent regions is not, according to a homogeneity criterion that is application specific. In most automatic image processing tasks, efficient image segmentation is one of the most critical steps and, in general, no unique solution can be provided for all possible applications. My thesis is mainly focused on Remote Sensing (RS) images, a domain in which a growing attention has been devoted to image segmentation in the last decades, as a fundamental step for various application such as land cover/land use classification and change detection. In particular, several different aspects have been addressed, which span from the design of novel low-level image segmentation techniques to the de?nition of new application scenarios leveraging Object-based Image Analysis (OBIA). More specifically, this summary will cover the three main activities carried out during my PhD: first, the development of two segmentation techniques for object layer extraction from multi/hyper-spectral and multi-resolution images is presented, based on respectively morphological image analysis and graph clustering. Finally, a new paradigm for the interactive segmentation of Synthetic Aperture Radar (SAR) multi-temporal series is introduced

Unsupervised methods in multilingual and multimodal semantic modeling

In the first part of this project, independent component analysis has been applied to extract word clusters from two Farsi corpora. Both word-document and word-context matrices have been considered to extract such clusters. The application of ICA on the word-document matrices extracted from these two corpora led to the detection of syntagmatic word clusters, while the utilization of word-context matrix resulted in the extraction of both syntagmatic and paradigmatic word clusters. Furthermore, we have discussed some potential benefits of this automatically extracted thesaurus. In such a thesaurus, a word is defined by some other words without being connected to the outer physical objects. In order to fill such a gap, symbol grounding has been proposed by philosophers as a mechanism which might connect words to their physical referents. From their point of view, if words are properly connected to their referents, their meaning might be realized. Once this objective is achieved, a new promising horizon would open in the realm of artificial intelligence. In the second part of the project, we have offered a simple but novel method for grounding words based on the features coming from the visual modality. Firstly, indexical grounding is implemented. In this naïve symbol grounding method, a word is characterized using video indexes as its context. Secondly, such indexical word vectors have been normalized according to the features calculated for motion videos. This multimodal fusion has been referred to as the pattern grounding. In addition, the indexical word vectors have been normalized using some randomly generated data instead of the original motion features. This third case was called randomized grounding. These three cases of symbol grounding have been compared in terms of the performance of translation. Besides that, word clusters have been excerpted by comparing the vector distances and from the dendrograms generated using an agglomerative hierarchical clustering method. We have observed that pattern grounding exceled the indexical grounding in the translation of the motion annotated words, while randomized grounding has deteriorated the translation significantly. Moreover, pattern grounding culminated in the formation of clusters in which a word fit semantically to the other members, while using the indexical grounding, some of the closely related words dispersed into arbitrary clusters

Automatic vision based fault detection on electricity transmission components using very highresolution

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations. Fault identification is one of the most significant bottlenecks faced by Electricity transmission and distribution utilities in developing countries to deliver credible services to customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial resolution to monitor four major Electric power transmission network (EPTN) components condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks (CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage object detection model on the electric transmission power line imagery to localize, classify and inspect faults present. The components fault considered include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision of 89.61%. All the developed SSD based models achieve a high precision rate and low recall rate in detecting the faulty components, thus achieving acceptable balance levels F1-score and representation. Finally, comparable to other works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood

Terrain classification using machine learning algorithms in a multi-temporal approach A QGIS plug-in implementation

Land cover and land use (LCLU) maps are essential for the successful administration of a nation’s topography, however, conventional on-site data gathering methods are costly and time-consuming. By contrast, remote sensing data can be used to generate up-to-date maps regularly with the help of machine learning algorithms, in turn, allowing for the assessment of a region’s dynamics throughout time. The present dissertation will focus on the implementation of an automated land use and land cover classifier based on remote sensing imagery provided by the mod ern sentinel-2 satellite constellation. The project, with Portugal at its focus, will expand on previous approaches by utilizing temporal data as an input variable in order to harvest the contextual information contained in the vegetation cycles. The pursued solution investigated the implementation of a 9-class classifier plug-in for an industry standard, open-source geographic information system. In the course of the testing procedure, various processing techniques and machine learning algorithms were evaluated in a multi-temporal approach. Resulting in a final overall accuracy of 65,9% across the targeted classes.Mapas de uso e ocupação do solo são cruciais para o entendimento e administração da topografia de uma nação, no entanto, os métodos convencionais de aquisição local de dados são caros e demorados. Contrariamente, dados provenientes de métodos de senso riamento remoto podem ser utilizados para gerar regularmente mapas atualizados com a ajuda de algoritmos de aprendizagem automática. Permitindo, por sua vez, a avaliação da dinâmica de uma região ao longo do tempo. Utilizando como base imagens de sensoriamento remoto fornecidas pela recente cons telação de satélites Sentinel-2, a presente dissertação concentra-se na implementação de um classificador de mapas de uso e ocupação do solo automatizado. O projeto, com foco em Portugal, irá procurar expandir abordagens anteriores através do aproveitamento de informação contextual contida nos ciclos vegetativos pela utilização de dados temporais adicionais. A solução adotada investigou a produção e implementação de um classificador geral de 9 classes num plug-in de um sistema de informação geográfico de código aberto. Durante o processo de teste, diversas técnicas de processamento e múltiplos algoritmos de aprendizagem automática foram avaliados numa abordagem multi-temporal, culminando num resultado final de precisão geral de 65,9% nas classes avaliadas

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

Integrated helicopter survivability

A high level of survivability is important to protect military personnel and equipment and is central to UK defence policy. Integrated Survivability is the systems engineering methodology to achieve optimum survivability at an affordable cost, enabling a mission to be completed successfully in the face of a hostile environment. “Integrated Helicopter Survivability” is an emerging discipline that is applying this systems engineering approach within the helicopter domain. Philosophically the overall survivability objective is ‘zero attrition’, even though this is unobtainable in practice. The research question was: “How can helicopter survivability be assessed in an integrated way so that the best possible level of survivability can be achieved within the constraints and how will the associated methods support the acquisition process?” The research found that principles from safety management could be applied to the survivability problem, in particular reducing survivability risk to as low as reasonably practicable (ALARP). A survivability assessment process was developed to support this approach and was linked into the military helicopter life cycle. This process positioned the survivability assessment methods and associated input data derivation activities. The system influence diagram method was effective at defining the problem and capturing the wider survivability interactions, including those with the defence lines of development (DLOD). Influence diagrams and Quality Function Deployment (QFD) methods were effective visual tools to elicit stakeholder requirements and improve communication across organisational and domain boundaries. The semi-quantitative nature of the QFD method leads to numbers that are not real. These results are suitable for helping to prioritise requirements early in the helicopter life cycle, but they cannot provide the quantifiable estimate of risk needed to demonstrate ALARP. The probabilistic approach implemented within the Integrated Survivability Assessment Model (ISAM) was developed to provide a quantitative estimate of ‘risk’ to support the approach of reducing survivability risks to ALARP. Limitations in available input data for the rate of encountering threats leads to a probability of survival that is not a real number that can be used to assess actual loss rates. However, the method does support an assessment across platform options, provided that the ‘test environment’ remains consistent throughout the assessment. The survivability assessment process and ISAM have been applied to an acquisition programme, where they have been tested to support the survivability decision making and design process. The survivability ‘test environment’ is an essential element of the survivability assessment process and is required by integrated survivability tools such as ISAM. This test environment, comprising of threatening situations that span the complete spectrum of helicopter operations requires further development. The ‘test environment’ would be used throughout the helicopter life cycle from selection of design concepts through to test and evaluation of delivered solutions. It would be updated as part of the through life capability management (TLCM) process. A framework of survivability analysis tools requires development that can provide probabilistic input data into ISAM and allow derivation of confidence limits. This systems level framework would be capable of informing more detailed survivability design work later in the life cycle and could be enabled through a MATLAB® based approach. Survivability is an emerging system property that influences the whole system capability. There is a need for holistic capability level analysis tools that quantify survivability along with other influencing capabilities such as: mobility (payload / range), lethality, situational awareness, sustainability and other mission capabilities. It is recommended that an investigation of capability level analysis methods across defence should be undertaken to ensure a coherent and compliant approach to systems engineering that adopts best practice from across the domains. Systems dynamics techniques should be considered for further use by Dstl and the wider MOD, particularly within the survivability and operational analysis domains. This would improve understanding of the problem space, promote a more holistic approach and enable a better balance of capability, within which survivability is one essential element. There would be value in considering accidental losses within a more comprehensive ‘survivability’ analysis. This approach would enable a better balance to be struck between safety and survivability risk mitigations and would lead to an improved, more integrated overall design

The Russian National Security Strategy : shaping perceptions and coordinating actions

Dr. Katri Pynnöniemi’s review of Russian strategy documents in The Russian National Security Strategy: shaping perceptions and coordinating actions is revealing. Russian national strategy is consistent across multiple organs of the Russian government and focused on several main themes. Dr. Pynnöniemi rarely mentions Putin, but his hand is evident in the presence of the same themes that he has stressed publically for years. The strategy documents show Russia competing globally for “power and prestige” and locally for national sovereignty. Russia is painted as on the defensive against the West, which continues its Cold War policy of containment and is the instigator of all instability areas of Russian influence. These documents largely apply both internationally and domestically, as maintaining stability is a key theme. This justifies Russian actions as self-defense against Western instigated aggression. The documents stress the multi-domain aspects of competition with the West, reaffirming the US Army’s emphasis on Multi-Domain Operations. Overall, the insights into Russian strategic thinking in relation to the West provides a view to how Russia will pursue its interests and therefore what the Army may face within the Russian sphere of influence and why

Reference Model for Interoperability of Autonomous Systems

This thesis proposes a reference model to describe the components of an Un-manned Air, Ground, Surface, or Underwater System (UxS), and the use of a single Interoperability Building Block to command, control, and get feedback from such vehicles. The importance and advantages of such a reference model, with a standard nomenclature and taxonomy, is shown. We overview the concepts of interoperability and some efforts to achieve common refer-ence models in other areas. We then present an overview of existing un-manned systems, their history, characteristics, classification, and missions. The concept of Interoperability Building Blocks (IBB) is introduced to describe standards, protocols, data models, and frameworks, and a large set of these are analyzed. A new and powerful reference model for UxS, named RAMP, is proposed, that describes the various components that a UxS may have. It is a hierarchical model with four levels, that describes the vehicle components, the datalink, and the ground segment. The reference model is validated by showing how it can be applied in various projects the author worked on. An example is given on how a single standard was capable of controlling a set of heterogeneous UAVs, USVs, and UGVs