Pub Date : 2022-03-08DOI: 10.1177/15485129231166140
T. Rockwood, G. Steeger, Matthew D. Stein
As space becomes increasingly populated with new satellites and systems, modeling and simulation of existing and future systems becomes more important. The two-line element set has been the standard format for sharing data about a satellite’s orbit since the 1960s, and well-developed algorithms can predict the future location of satellites based on these data. In order to simulate potential future systems, especially when mixed with existing systems, data must be generated to represent the desired orbits. We present a means to create two-line element sets with parameters that closely resemble real satellite behavior and rely on a novel approach to calculate the mean motion for even greater accuracy.
{"title":"Generating realistic two-line element sets for notional space vehicles and constellations","authors":"T. Rockwood, G. Steeger, Matthew D. Stein","doi":"10.1177/15485129231166140","DOIUrl":"https://doi.org/10.1177/15485129231166140","url":null,"abstract":"As space becomes increasingly populated with new satellites and systems, modeling and simulation of existing and future systems becomes more important. The two-line element set has been the standard format for sharing data about a satellite’s orbit since the 1960s, and well-developed algorithms can predict the future location of satellites based on these data. In order to simulate potential future systems, especially when mixed with existing systems, data must be generated to represent the desired orbits. We present a means to create two-line element sets with parameters that closely resemble real satellite behavior and rely on a novel approach to calculate the mean motion for even greater accuracy.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91124324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-03DOI: 10.1177/15485129221080399
Larry C. Llewellyn, M. Grimaila, D. Hodson, Scott Graham
Modeling and simulation is a proven cost-efficient means for studying the behavioral dynamics of modern systems of systems. Our research is focused on evaluating the ability of neural networks to approximate multivariate, nonlinear, complex-valued functions. In order to evaluate the accuracy and performance of neural network approximations as a function of nonlinearity (NL), it is required to quantify the amount of NL present in the complex-valued function. In this paper, we introduce a metric for quantifying NL in multi-dimensional complex-valued functions. The metric is an extension of a real-valued NL metric into the k-dimensional complex domain. The metric is flexible as it uses discrete input–output data pairs instead of requiring closed-form continuous representations for calculating the NL of a function. The metric is calculated by generating a best-fit, least-squares solution (LSS) linear k-dimensional hyperplane for the function; calculating the L2 norm of the difference between the hyperplane and the function being evaluated; and scaling the result to yield a value between zero and one. The metric is easy to understand, generalizable to multiple dimensions, and has the added benefit that it does not require a closed-form continuous representation of the function being evaluated.
{"title":"A metric for quantifying nonlinearity in k-dimensional complex-valued functions","authors":"Larry C. Llewellyn, M. Grimaila, D. Hodson, Scott Graham","doi":"10.1177/15485129221080399","DOIUrl":"https://doi.org/10.1177/15485129221080399","url":null,"abstract":"Modeling and simulation is a proven cost-efficient means for studying the behavioral dynamics of modern systems of systems. Our research is focused on evaluating the ability of neural networks to approximate multivariate, nonlinear, complex-valued functions. In order to evaluate the accuracy and performance of neural network approximations as a function of nonlinearity (NL), it is required to quantify the amount of NL present in the complex-valued function. In this paper, we introduce a metric for quantifying NL in multi-dimensional complex-valued functions. The metric is an extension of a real-valued NL metric into the k-dimensional complex domain. The metric is flexible as it uses discrete input–output data pairs instead of requiring closed-form continuous representations for calculating the NL of a function. The metric is calculated by generating a best-fit, least-squares solution (LSS) linear k-dimensional hyperplane for the function; calculating the L2 norm of the difference between the hyperplane and the function being evaluated; and scaling the result to yield a value between zero and one. The metric is easy to understand, generalizable to multiple dimensions, and has the added benefit that it does not require a closed-form continuous representation of the function being evaluated.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81552345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/15485129221078519
J. Cohn, E. Vorm, Erin Baker
The fields of Artificial Intelligence (AI) and Education & Training (E&T) are experiencing an unprecedented resur-gence. This is due in no small part to recent advances in the science and technology that drive discovery and inno-vation in these fields. The development of ever more pow-erful and efficient processing systems, a renaissance in allied fields like neuroscience, data analytics and visualiza-tion, cognitive science, cognitive computing, and advances in materials science have collectively enabled the solution of challenges to these fields which, only a decade ago, appeared insurmountable. Consequently, it is timely to explore the possibilities and potential benefits to be accrued when these two fields intersect. The goals of this special issue are threefold: (1) to promote understanding of AI for education and training applications, (2) to gain awareness of research and development activities in AI that are appli-cable to education and training applications, and (3) to characterize the reciprocal benefits that advances in education and training have on the further advancement of AI. The issue begins with two ‘‘perspective pieces’’ that set the stage for understanding different approaches viewing the between AI a in a that unique of to frame the discussion of aligning AI with E&T to learning
{"title":"The whole is greater than the sum of its parts: possibility and potential at the intersection between artificial intelligence and education & training","authors":"J. Cohn, E. Vorm, Erin Baker","doi":"10.1177/15485129221078519","DOIUrl":"https://doi.org/10.1177/15485129221078519","url":null,"abstract":"The fields of Artificial Intelligence (AI) and Education & Training (E&T) are experiencing an unprecedented resur-gence. This is due in no small part to recent advances in the science and technology that drive discovery and inno-vation in these fields. The development of ever more pow-erful and efficient processing systems, a renaissance in allied fields like neuroscience, data analytics and visualiza-tion, cognitive science, cognitive computing, and advances in materials science have collectively enabled the solution of challenges to these fields which, only a decade ago, appeared insurmountable. Consequently, it is timely to explore the possibilities and potential benefits to be accrued when these two fields intersect. The goals of this special issue are threefold: (1) to promote understanding of AI for education and training applications, (2) to gain awareness of research and development activities in AI that are appli-cable to education and training applications, and (3) to characterize the reciprocal benefits that advances in education and training have on the further advancement of AI. The issue begins with two ‘‘perspective pieces’’ that set the stage for understanding different approaches viewing the between AI a in a that unique of to frame the discussion of aligning AI with E&T to learning","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83059877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-13DOI: 10.1177/15485129221074352
A. Frank
War games have played an essential role in the development of military force structures, strategies, operational concepts, and more. Military organizations are currently confronting uncertainties over the ways in which Artificial Intelligence (AI) may affect warfare at nearly every level, from combat tactics to operational concepts to force structure to deterrence and national and international security. This paper explores how game designers and players can approach questions regarding how AI may be employed in alternative contexts allowing for insight into how emerging and imagined technologies may affect warfare at many different levels of analysis. It identifies six application areas of AI technologies that games should consider—(1) principal–agent relations, (2) organizational and operational complexity, (3) attention management, (4) exploratory analysis, (5) information exploitation and model validation, and (6) adaptive behavior in open-ended systems—and suggests conceptual and practical strategies for investigating them in games that can be played in the absence of real-world systems and algorithms that perform these functions.
{"title":"Gaming AI without AI","authors":"A. Frank","doi":"10.1177/15485129221074352","DOIUrl":"https://doi.org/10.1177/15485129221074352","url":null,"abstract":"War games have played an essential role in the development of military force structures, strategies, operational concepts, and more. Military organizations are currently confronting uncertainties over the ways in which Artificial Intelligence (AI) may affect warfare at nearly every level, from combat tactics to operational concepts to force structure to deterrence and national and international security. This paper explores how game designers and players can approach questions regarding how AI may be employed in alternative contexts allowing for insight into how emerging and imagined technologies may affect warfare at many different levels of analysis. It identifies six application areas of AI technologies that games should consider—(1) principal–agent relations, (2) organizational and operational complexity, (3) attention management, (4) exploratory analysis, (5) information exploitation and model validation, and (6) adaptive behavior in open-ended systems—and suggests conceptual and practical strategies for investigating them in games that can be played in the absence of real-world systems and algorithms that perform these functions.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81310911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-08DOI: 10.1177/15485129211073126
P. Davis, P. Bracken
In this paper, we discuss how artificial intelligence (AI) could be used in political-military modeling, simulation, and wargaming of conflicts with nations having weapons of mass destruction and other high-end capabilities involving space, cyberspace, and long-range precision weapons. AI should help participants in wargames, and agents in simulations, to understand possible perspectives, perceptions, and calculations of adversaries who are operating with uncertainties and misimpressions. The content of AI should recognize the risks of escalation leading to catastrophe with no winner but also the possibility of outcomes with meaningful winners and losers. We discuss implications for the design and development of families of models, simulations, and wargames using several types of AI functionality. We also discuss decision aids for wargaming, with and without AI, informed by theory and exploratory work using simulation, history, and earlier wargaming.
{"title":"Artificial intelligence for wargaming and modeling","authors":"P. Davis, P. Bracken","doi":"10.1177/15485129211073126","DOIUrl":"https://doi.org/10.1177/15485129211073126","url":null,"abstract":"In this paper, we discuss how artificial intelligence (AI) could be used in political-military modeling, simulation, and wargaming of conflicts with nations having weapons of mass destruction and other high-end capabilities involving space, cyberspace, and long-range precision weapons. AI should help participants in wargames, and agents in simulations, to understand possible perspectives, perceptions, and calculations of adversaries who are operating with uncertainties and misimpressions. The content of AI should recognize the risks of escalation leading to catastrophe with no winner but also the possibility of outcomes with meaningful winners and losers. We discuss implications for the design and development of families of models, simulations, and wargames using several types of AI functionality. We also discuss decision aids for wargaming, with and without AI, informed by theory and exploratory work using simulation, history, and earlier wargaming.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82503206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-07DOI: 10.1177/15485129211067767
A. Vasso, R. Cobb, J. Colombi, Bryan D. Little, David W. Meyer
The US Government is the world’s de facto provider of space object cataloging data, but it is challenged to maintain pace in an increasingly complex space environment. This work advances a multi-disciplinary approach to better understand and evaluate an underexplored solution recommended by national policy in which current collection capabilities are augmented with non-traditional sensors. System architecting techniques and extant literature identified likely needs, performance measures, and potential contributors to a conceptualized Augmented Network (AN). Multiple hypothetical architectures of ground- and space-based telescopes with representative capabilities were modeled and simulated on four separate days throughout the year, then evaluated against performance measures and constraints using Multi-Objective Optimization. Decision analysis and Pareto optimality identified a small, diverse set of high-performing architectures while preserving design flexibility. Should decision-makers adopt the AN approach, this research effort indicates (1) a threefold increase in average capacity, (2) a 55% improvement in coverage, and (3) a 2.5-h decrease in the average maximum time a space object goes unobserved.
{"title":"Multi-day evaluation of space domain awareness architectures via decision analysis and multi-objective optimization","authors":"A. Vasso, R. Cobb, J. Colombi, Bryan D. Little, David W. Meyer","doi":"10.1177/15485129211067767","DOIUrl":"https://doi.org/10.1177/15485129211067767","url":null,"abstract":"The US Government is the world’s de facto provider of space object cataloging data, but it is challenged to maintain pace in an increasingly complex space environment. This work advances a multi-disciplinary approach to better understand and evaluate an underexplored solution recommended by national policy in which current collection capabilities are augmented with non-traditional sensors. System architecting techniques and extant literature identified likely needs, performance measures, and potential contributors to a conceptualized Augmented Network (AN). Multiple hypothetical architectures of ground- and space-based telescopes with representative capabilities were modeled and simulated on four separate days throughout the year, then evaluated against performance measures and constraints using Multi-Objective Optimization. Decision analysis and Pareto optimality identified a small, diverse set of high-performing architectures while preserving design flexibility. Should decision-makers adopt the AN approach, this research effort indicates (1) a threefold increase in average capacity, (2) a 55% improvement in coverage, and (3) a 2.5-h decrease in the average maximum time a space object goes unobserved.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82138573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1177/15485129211070058
H. Çam
Recent strides in cyber operations, including description of the threat lifecycle, and component threat models, are currently limited only by the ability to estimate current system state, in terms of vulnerability and subsequent risk. Therefore, it is highly desirable to lay down a testable, repeatable, set of rules, policies, machine learning (ML) and artificial intelligence techniques for modeling and estimating cyber risk, vulnerabilities, and exploits in systems and networks. Recent improvements in learning models, deep learning, and big data analytics have the potential to capture the relationships among the security features and adversary activities to enhance cybersecurity defense and estimation of risk and vulnerabilities. This special issue is composed of six papers that provide insight into cyber risk and vulnerability from various perspectives, including modeling a cybersecurity environment, leveraging ML capabilities, assessing cybersecurity attacks and vulnerabilities, optimizing limited resources of cybersecurity security operation centers, and agent-based target evaluation in an air defense simulation environment. The paper by Dasari, Im, and Geerhart presents an approach to accomplishing mission computation goals and resource requirements for the time-sensitive data processing tasks in tactical computing platforms that are mostly mobile, with limited computing and communication resources. To optimize the computation platforms and algorithms for the mission requirements such as performing computation in mission time, the paper describes a socalled mission class with deterministic polynomial time complexity, wherein the computations must complete in mission time within an environment with limited resources. The paper also investigates feasible models that can minimize energy and maximize memory, efficiency, and computational power. The paper by Shah, Farris, Ganesan, and Jajodia investigates various optimization methods of vulnerability selection against some constraints (e.g., personnel-hour allocations, as well as vulnerability age, severity, and persistence score requirements) of Cyber-Security Operations Centers. The paper presents two different mathematical models and approaches to vulnerability selection for mitigation with either single attribute value selection or multiple attribute value selection in decision-making process. The empirical results indicate that the multiple attribute value optimization policy performs better in satisfying all vulnerability attribute requirements. The paper by Werth, Griffith, Hairston, and Morris focuses on the development of a virtual, modular testbed to provide a high-fidelity model of the cyber and physical components of a networked generator system. A highfidelity model of the generator was included to allow the evaluation of more types of threat models. Supply chain attacks with simulated hardware and software trojans are examined in case studies. The proposed testbed provides an opport
{"title":"Cyber risk and vulnerability estimation","authors":"H. Çam","doi":"10.1177/15485129211070058","DOIUrl":"https://doi.org/10.1177/15485129211070058","url":null,"abstract":"Recent strides in cyber operations, including description of the threat lifecycle, and component threat models, are currently limited only by the ability to estimate current system state, in terms of vulnerability and subsequent risk. Therefore, it is highly desirable to lay down a testable, repeatable, set of rules, policies, machine learning (ML) and artificial intelligence techniques for modeling and estimating cyber risk, vulnerabilities, and exploits in systems and networks. Recent improvements in learning models, deep learning, and big data analytics have the potential to capture the relationships among the security features and adversary activities to enhance cybersecurity defense and estimation of risk and vulnerabilities. This special issue is composed of six papers that provide insight into cyber risk and vulnerability from various perspectives, including modeling a cybersecurity environment, leveraging ML capabilities, assessing cybersecurity attacks and vulnerabilities, optimizing limited resources of cybersecurity security operation centers, and agent-based target evaluation in an air defense simulation environment. The paper by Dasari, Im, and Geerhart presents an approach to accomplishing mission computation goals and resource requirements for the time-sensitive data processing tasks in tactical computing platforms that are mostly mobile, with limited computing and communication resources. To optimize the computation platforms and algorithms for the mission requirements such as performing computation in mission time, the paper describes a socalled mission class with deterministic polynomial time complexity, wherein the computations must complete in mission time within an environment with limited resources. The paper also investigates feasible models that can minimize energy and maximize memory, efficiency, and computational power. The paper by Shah, Farris, Ganesan, and Jajodia investigates various optimization methods of vulnerability selection against some constraints (e.g., personnel-hour allocations, as well as vulnerability age, severity, and persistence score requirements) of Cyber-Security Operations Centers. The paper presents two different mathematical models and approaches to vulnerability selection for mitigation with either single attribute value selection or multiple attribute value selection in decision-making process. The empirical results indicate that the multiple attribute value optimization policy performs better in satisfying all vulnerability attribute requirements. The paper by Werth, Griffith, Hairston, and Morris focuses on the development of a virtual, modular testbed to provide a high-fidelity model of the cyber and physical components of a networked generator system. A highfidelity model of the generator was included to allow the evaluation of more types of threat models. Supply chain attacks with simulated hardware and software trojans are examined in case studies. The proposed testbed provides an opport","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88918173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-29DOI: 10.1177/15485129211064809
K. Stanney, JoAnn Archer, Anna Skinner, Charis K. Horner, C. Hughes, Nicholas P Brawand, E. Martin, Stacey A. Sanchez, Larry Moralez, C. Fidopiastis, R. Perez
While virtual, augmented, and mixed reality technologies are being used for military medical training and beyond, these component technologies are oftentimes utilized in isolation. eXtended Reality (XR) combines these immersive form factors to support a continuum of virtual training capabilities to include full immersion, augmented overlays that provide multimodal cues to personalize instruction, and physical models to support embodiment and practice of psychomotor skills. When combined, XR technologies provide a multi-faceted training paradigm in which the whole is greater than the sum of the constituent capabilities in isolation. When XR applications are adaptive, and thus vary operational stressors, complexity, learner assistance, and fidelity as a function of trainee proficiency, substantial gains in training efficacy are expected. This paper describes a continuum of XR technologies and how they can be coupled with numerous adaptation strategies and supportive artificial intelligence (AI) techniques to realize personalized, competency-based training solutions that accelerate time to proficiency. Application of this training continuum is demonstrated through a Tactical Combat Casualty Care training use case. Such AI-enabled XR training solutions have the potential to support the military in meeting their growing training demands across military domains and applications, and to provide the right training at the right time.
{"title":"Performance gains from adaptive eXtended Reality training fueled by artificial intelligence","authors":"K. Stanney, JoAnn Archer, Anna Skinner, Charis K. Horner, C. Hughes, Nicholas P Brawand, E. Martin, Stacey A. Sanchez, Larry Moralez, C. Fidopiastis, R. Perez","doi":"10.1177/15485129211064809","DOIUrl":"https://doi.org/10.1177/15485129211064809","url":null,"abstract":"While virtual, augmented, and mixed reality technologies are being used for military medical training and beyond, these component technologies are oftentimes utilized in isolation. eXtended Reality (XR) combines these immersive form factors to support a continuum of virtual training capabilities to include full immersion, augmented overlays that provide multimodal cues to personalize instruction, and physical models to support embodiment and practice of psychomotor skills. When combined, XR technologies provide a multi-faceted training paradigm in which the whole is greater than the sum of the constituent capabilities in isolation. When XR applications are adaptive, and thus vary operational stressors, complexity, learner assistance, and fidelity as a function of trainee proficiency, substantial gains in training efficacy are expected. This paper describes a continuum of XR technologies and how they can be coupled with numerous adaptation strategies and supportive artificial intelligence (AI) techniques to realize personalized, competency-based training solutions that accelerate time to proficiency. Application of this training continuum is demonstrated through a Tactical Combat Casualty Care training use case. Such AI-enabled XR training solutions have the potential to support the military in meeting their growing training demands across military domains and applications, and to provide the right training at the right time.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89407907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-14DOI: 10.1177/15485129211062880
C. Arnold, S. Nykl, Scott Graham, R. Leishman
We propose a new algorithm variant for Structure from Motion (SfM) to enable real-time image processing of scenes imaged by aerial drones. Our new SfM variant runs in real-time at 4 Hz equating to an 80× computation time speed-up compared to traditional SfM and is capable of a 90% size reduction of original video imagery, with an added benefit of presenting the original two-dimensional (2D) video data as a three-dimensional (3D) virtual model. This opens many potential applications for a real-time image processing that could make autonomous vision–based navigation possible by completely replacing the need for a traditional live video feed. The 3D reconstruction that is generated comes with the added benefit of being able to generate a spatially accurate representation of a live environment that is precise enough to generate global positioning system (GPS) coordinates from any given point on an imaged structure, even in a GPS-denied environment.
{"title":"Structure from motion with planar homography estimation: a real-time low-bandwidth, high-resolution variant for aerial reconnaissance","authors":"C. Arnold, S. Nykl, Scott Graham, R. Leishman","doi":"10.1177/15485129211062880","DOIUrl":"https://doi.org/10.1177/15485129211062880","url":null,"abstract":"We propose a new algorithm variant for Structure from Motion (SfM) to enable real-time image processing of scenes imaged by aerial drones. Our new SfM variant runs in real-time at 4 Hz equating to an 80× computation time speed-up compared to traditional SfM and is capable of a 90% size reduction of original video imagery, with an added benefit of presenting the original two-dimensional (2D) video data as a three-dimensional (3D) virtual model. This opens many potential applications for a real-time image processing that could make autonomous vision–based navigation possible by completely replacing the need for a traditional live video feed. The 3D reconstruction that is generated comes with the added benefit of being able to generate a spatially accurate representation of a live environment that is precise enough to generate global positioning system (GPS) coordinates from any given point on an imaged structure, even in a GPS-denied environment.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88030106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-03DOI: 10.1177/15485129211061688
R. Wallace
Across military Zweikampf and public health, error, blindness, and incompetence carry singular burden. Here, we adapt methods developed for the analysis of pandemic mismanagement to the study of armed conflict. Stability of control during such conflict depends on prompt recognition of, and response to, rapidly changing events. In addition to “conventional” Clausewitzian fog and friction, there are almost always inherent or induced delays to threat recognition. For a system to be stable without such delay, there will be a critical lag at which control fails, as it similarly does if institutional cognition sufficiently degrades. In such cases, tactical thrashing becomes manifest. In a military context, there is no way around such dynamics, which are routinely—often brilliantly—exploited.
{"title":"On Maskirovka: the dynamics of delay in threat recognition","authors":"R. Wallace","doi":"10.1177/15485129211061688","DOIUrl":"https://doi.org/10.1177/15485129211061688","url":null,"abstract":"Across military Zweikampf and public health, error, blindness, and incompetence carry singular burden. Here, we adapt methods developed for the analysis of pandemic mismanagement to the study of armed conflict. Stability of control during such conflict depends on prompt recognition of, and response to, rapidly changing events. In addition to “conventional” Clausewitzian fog and friction, there are almost always inherent or induced delays to threat recognition. For a system to be stable without such delay, there will be a critical lag at which control fails, as it similarly does if institutional cognition sufficiently degrades. In such cases, tactical thrashing becomes manifest. In a military context, there is no way around such dynamics, which are routinely—often brilliantly—exploited.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90709764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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