Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS最新文献

{"title":"Explainable artificial intelligence for education and training","authors":"K. Fiok, F. Farahani, W. Karwowski, T. Ahram","doi":"10.1177/15485129211028651","DOIUrl":"https://doi.org/10.1177/15485129211028651","url":null,"abstract":"Researchers and software users benefit from the rapid growth of artificial intelligence (AI) to an unprecedented extent in various domains where automated intelligent action is required. However, as they continue to engage with AI, they also begin to understand the limitations and risks associated with ceding control and decision-making to not always transparent artificial computer agents. Understanding of “what is happening in the black box” becomes feasible with explainable AI (XAI) methods designed to mitigate these risks and introduce trust into human-AI interactions. Our study reviews the essential capabilities, limitations, and desiderata of XAI tools developed over recent years and reviews the history of XAI and AI in education (AIED). We present different approaches to AI and XAI from the viewpoint of researchers focused on AIED in comparison with researchers focused on AI and machine learning (ML). We conclude that both groups of interest desire increased efforts to obtain improved XAI tools; however, these groups formulate different target user groups and expectations regarding XAI features and provide different examples of possible achievements. We summarize these viewpoints and provide guidelines for scientists looking to incorporate XAI into their own work.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86008142","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}

{"title":"Autonomous underwater vehicle challenge: design and construction of a medium-sized, AI-enabled low-cost prototype","authors":"Dimitrios Paraschos, N. Papadakis","doi":"10.1177/15485129211027236","DOIUrl":"https://doi.org/10.1177/15485129211027236","url":null,"abstract":"The design of an autonomous underwater vehicle (AUV) with physical dimensions of 1100 mm × 700 mm × 330 mm, and weight of 55 kg, is introduced herein. This paper describes the design, materials, hydrodynamics, and system architecture of an AUV prototype named Synoris, developed as a low-cost and medium-scale testbed platform. Synoris moves via six brushless motors, can reach up to 200 m depth, has an autonomy estimated around 6 hours and a modular design for multiple payload options. Stability control, autonomous movement, obstacle avoidance temperature/pressure sensing, and video/image capturing are simultaneously performed by exploiting a set of onboard computers that are described briefly in Section 4. The whole platform is built on top of the open source software called ROS (robotic operating system) that provides a flexible framework for writing robot software by providing services such as low-level device control, message parsing, data fusion, and system integration. Synoris is ideal for underwater applications and missions, involving machine learning and computer vision features. AUV development in general meets high-cost solutions due to the complexity and harshness of the operational environment. Even the most cost-effective solutions demand plentiful resources. This paper describes the entire process of development and how a relatively low-cost approach can provide a reliable AUV for many underwater applications, involving AI and machine-learning capabilities.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83162818","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}

{"title":"Analysis of the energy harvesting non-orthogonal multiple access technique for defense applications over Rayleigh fading channel conditions","authors":"Manoj Kumar Beuria, Ravi Shankar, S. Singh","doi":"10.1177/15485129211021168","DOIUrl":"https://doi.org/10.1177/15485129211021168","url":null,"abstract":"The energy harvesting (EH) technique has ignited a rising research interest due to its ability to enhance the battery power of user equipment by harvesting energy by using a simple radio frequency circuit. In this paper, the simultaneous wireless information and power transfer non-orthogonal multiple access (NOMA) technique is investigated over Rayleigh fading channel conditions. EH NOMA improves the power efficiency, and it results in better throughput as compared to conventional NOMA techniques. In this paper, we investigate the achievable data rate and the outage probability performance of the near user, also called the relay user (RU), and the far user (FU). It can clearly be shown that the RU data rate is saturated and at 1 bits/s/Hz will become constant, while the FU data rate increases with an increase in transmitted power. It is seen by analysis that the RU data rate would saturate due to EH. It is further shown that with a transmitted power of 18 dBm, the net output of the FUs is readily shown to be based around a value of 2.80 bits/s/Hz. In comparison, it is shown that the FU’s instantaneous information rate dropped below the target data rate.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85170240","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}

{"title":"Artificial intelligence for defense applications","authors":"Nathaniel D. Bastian","doi":"10.1177/15485129211009072","DOIUrl":"https://doi.org/10.1177/15485129211009072","url":null,"abstract":"Artificial intelligence (AI) is a set of algorithmic techniques, tools, and technologies that provide machines with the ability to perform tasks that normally require human intelligence – to perceive the world, learn from experience, reason about information, represent knowledge, act, and adapt. Given the multitude of rapid technological advancements in AI, the defense community has emphasized the importance of leveraging these very technologies to be prepared to fight and win the wars of the future. As one of the ways to modernize key capabilities, the defense community has specifically mentioned the need to invest broadly in the military application of AI, including rapid application of commercial breakthroughs, to gain competitive military advantages. To solve some of the most critical problems facing the defense community, the future force requires the ability to converge capabilities from across multiple domains at speeds and scales beyond human cognitive abilities. This special issue is composed of six papers that promote an understanding of AI for defense applications, as well as providing awareness into some of the state-of-theart research and development activities in AI that are applicable to defense applications spanning fraud detection for national security, computer vision for satellite imagery analysis, hidden Markov modeling for the maritime domain, deep learning for radio frequency systems, representation learning for militarily relevant graphs, and robot swarms for military reconnaissance and surveillance. First, the paper by Kerwin and Bastian investigates the national security challenge of predicting fraud, as criminals continually exploit the electronic financial system to defraud consumers and businesses by finding weaknesses in the system, including in audit controls. Their work uses stacked generalizations via meta-learning combined with a resampling methodology particularly useful for the imbalanced fraud data structure to improve fraud detection for national security. Second, the paper by Humphries, Parker, Jonas, Adams, and Clark investigates the problem of quickly and accurately identifying building and road infrastructure via satellite imagery for the execution of tactical military operations in an urban environment. Their work uses an object detection algorithm powered by convolutional neural networks to predict both buildings and road intersections present in an image, as well as use of a contourfinding algorithm for data labeling. Third, the paper by Caelli, Mukerjee, McCabe, and Kirszenblat tackles the problem of integrated sensor and tactical information fusion from a number of sources to enable rapid decision throughput based upon situation awareness for maritime surveillance missions. Their work develops a method using a hidden Markov model to objectively encode, summarize, and analyze airborne maritime surveillance crew activities to gain insights into probabilistic relationships between the attention switching","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72636715","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}

{"title":"Optimizing fire allocation in a Network Centric Warfare-type model","authors":"N. H. Nguyen, M. A. Vu, A. N. Ta, D. V. Bui, M. Hy","doi":"10.1177/15485129211022861","DOIUrl":"https://doi.org/10.1177/15485129211022861","url":null,"abstract":"In this paper, we introduce a non-linear Lanchester’s model of Network Centric Warfare type and investigate an optimization problem for this model, where only the Red force is supplied by several supply agents. Optimal fire allocation of the Blue force is sought in the form of a piece-wise constant function of time. A “threatening rate” is computed for the Red force and each of its supply agents at the beginning of each stage of the combat. These rates can be used to derive the optimal decision for the Blue force to focus its firepower to the Red force itself or one of its supply agents. This optimal fire allocation is derived and proved by considering an optimization problem of the number of Blue force troops. Numerical experiments are included to demonstrate the theoretical results.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86067397","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}

{"title":"Design and simulation research of the double-spin folding mechanism based on a suspended missile","authors":"Zhao Yuan, Sy-Jye Guo, SN Zhang, Jq Zhao, Wj Lu, H. Zhao","doi":"10.1177/15485129211023169","DOIUrl":"https://doi.org/10.1177/15485129211023169","url":null,"abstract":"Based on the suspension of a missile using folding rotary wings and airbags, in order to improve the basic parameters and motion characteristics of the rotor during the unfolding process and analyze the aerodynamic characteristics of the entire device in the suspension state, after proposing a scheme of double-spin mechanism, the main folding and unfolding mechanism, initial driving device, rotating driving device, and locking mechanism were designed, and the simulation research is studied by the Automatic Dynamic Analysis of Mechanical System and Ansys Fluent Fluid Simulation software, respectively. The results show that the rotation rate was controlled at 41.8 mm/s, the various motion parameters are reasonable, and the operation process is relatively smooth, with high reliability. The speed and pressure value at the tip of the rotor are higher and the aerodynamic disturbance is obvious, which has a great influence on the aerodynamic performance. The speed and pressure distribution of the surrounding flow field is stable, the lift provided is 46 N, and the lift coefficient is 0.55, which can ensure the long-time suspension state of the missile. This paper puts forward a valuable design idea and has practical reference value for the research of the suspended missile.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80267153","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}

{"title":"Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning","authors":"Ravisankar Malladi, Manoj Kumar Beuria, Ravi Shankar, S. Singh","doi":"10.1177/15485129211022857","DOIUrl":"https://doi.org/10.1177/15485129211022857","url":null,"abstract":"In modern wireless communication scenarios, non-orthogonal multiple access (NOMA) provides high throughput and spectral efficiency for fifth generation (5G) and beyond 5G systems. Traditional NOMA detectors are based on successive interference cancellation (SIC) techniques at both uplink and downlink NOMA transmissions. However, due to imperfect SIC, these detectors are not suitable for defense applications. In this paper, we investigate the 5G multiple-input multiple-output NOMA deep learning technique for defense applications and proposed a learning approach that investigates the communication system’s channel state information automatically and identifies the initial transmission sequences. With the use of the proposed deep neural network, the optimal solution is provided, and performance is much better than the traditional SIC-based NOMA detectors. Through simulations, the analytical outcomes are verified.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80644550","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}

{"title":"Shipborne moving target indicator radar versus incoming sea-skimming missile mathematical modeling and simulation","authors":"T. G. Kostis","doi":"10.1177/15485129211023982","DOIUrl":"https://doi.org/10.1177/15485129211023982","url":null,"abstract":"Anti-ship missiles are a primary threat to naval targets because sea-skimming tactics enable the missile to approach the vessel while being obscured by sea clutter. In order to counteract this serious danger, the ship’s radar employs moving target indicator processing in order to detect and subsequently start the tracking of the incoming missile. In this paper, we set up a relevant simulation in order to examine the moving target indicator process for a naval scenario at horizon range which gives the greatest counter-reaction time, which usually is for very small grazing angles in a sea cluttered environment. Finally, the results of the simulator are beneficial to radar designers and educational professionals.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87229226","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}

{"title":"Challenges and opportunities for artificial intelligence in surgery","authors":"P. Andreatta, Christopher S. Smith, J. Graybill, M. Bowyer, E. Elster","doi":"10.1177/15485129211022855","DOIUrl":"https://doi.org/10.1177/15485129211022855","url":null,"abstract":"Surgery is an exceptionally complex domain where multi-dimensional expertise is developed over an extended period of time, and mastery is maintained only through ongoing engagement in surgical contexts. Expert surgeons integrate perceptual information through both conscious and subconscious awareness, and respond to the environment by leveraging their deep understanding of surgical constructs. However, their ability to utilize these deep knowledge structures can be complicated by continuous advances in technology, medical science, pharmacology, technique, materials, operative environments, etc. that must be routinely accommodated in professional practice. The demands on surgeons to perform perfectly in ever-changing contexts increases cognitive load, which could be reduced through judicious use of accurate and reliable artificial intelligence (AI) systems. AI has great potential to support human performance in complex environments such as surgery; however, the foundational requirements for the rules governing algorithmic development of performance requirements necessitate the active involvement of surgeons to precisely model the quantitative measures of performance along the continuum of expertise. Providing the AI development community with these data will help assure that accurate and reliable systems are designed to supplement human performance in applied surgical contexts. The Military Health System’s Clinical Readiness Program is developing these types of metrics to support military medical readiness.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83997742","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}

{"title":"Using Monte Carlo simulations to translate military and law enforcement training results to operational metrics","authors":"Adam T. Biggs, D. A. Hirsch","doi":"10.1177/15485129211021159","DOIUrl":"https://doi.org/10.1177/15485129211021159","url":null,"abstract":"There are numerous challenges comparing research initiatives due to methodological differences and scenario-specific problems. Military and law enforcement issues present an extreme variant of this challenge. Specifically, assessment and training scenarios strive for realism, but operators cannot engage one another with live rounds or induce the full spectrum of environmental stressors for obvious safety reasons. Instead, particular factors are evaluated in a given scenario via experimental statistics despite the inherent difficulty in communicating inferential statistics to the intended audience of military and law enforcement professionals. The current investigation explores how Monte Carlo simulations can use probabilistic distribution sampling to convert statistical inferences into concrete operational outcomes. Using this type of distribution sampling, statistical inferences can be translated into operational metrics such as the probability of winning a gunfight. Describing these statistical values and effect sizes in terms of survival provides a more appreciable operational metric that military and law enforcement personnel can use when evaluating the advantages of various training platforms or equipment. Several approaches are examined that each accomplish this general goal, including circumstances outside of marksmanship and lethal force decision-making.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90976926","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}