Готові списки джерел за темами / Military modeling

Добірка наукової літератури з теми "Military modeling"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Military modeling"

1

Eller, A. "Military modeling." IEEE Journal of Oceanic Engineering 11, no. 1 (January 1986): 135. http://dx.doi.org/10.1109/joe.1986.1145136.

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2

Penev, Valentine. "Simulation Modeling in Military Affairs: Status and Perspectives." Information & Security: An International Journal 1, no. 1 (1998): 91–102. http://dx.doi.org/10.11610/isij.0107.

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3

Kolodinskaya, V. I., and V. Yu Nefyodova. "THE EXPERIENCE OF TEACHING THE THEME “MODELLING” BASED ON SOLVING APPLIED MILITARY CASES." Informatics in school, no. 9 (December 18, 2019): 7–15. http://dx.doi.org/10.32517/2221-1993-2019-18-9-7-15.

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The article is dealing with the theme “Modeling” basing it on applied military case studies. The universal educational actions were formulated, as well as educational outcomes. Math modeling tasks are reviewed using the available aerial photographs, and the modelling task for armed military confrontation is given.
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4

Cohen, Eliot A., and Wayne P. Hughes. "Military Modeling for Decision Making." Foreign Affairs 76, no. 6 (1997): 159. http://dx.doi.org/10.2307/20048303.

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5

Feighner, Brian H., Stephen Eubank, Robert J. Glass, Victoria J. Davey, Jean-Paul Chrétien, and Joel C. Gaydos. "Infectious Disease Modeling and Military Readiness." Emerging Infectious Diseases 15, no. 9 (September 2009): e1-e1. http://dx.doi.org/10.3201/eid1509.090702.

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6

Shliakhovoi, Dmitrii A. "Modeling of a Generalized Speech Portrait of a Blogger (as Exemplified in the German Military Blogosphere)." RUDN Journal of Language Studies, Semiotics and Semantics 10, no. 4 (December 15, 2019): 879–92. http://dx.doi.org/10.22363/2313-2299-2019-10-4-879-892.

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The study investigates the modeling of a generalized speech portrait of a blog-discourse subject in the speech space of the German military blogosphere. The relevance of the work lies in the development of a methodology for modeling a speech portrait of a virtual linguistic personality, behind the mask of which is a real person, actively participating in the life of his Internet community, united by professional activities and nationality. In this work, the author uses examples from the blog discourse of the German military blogosphere, however, the method of constructing a generalized speech portrait of an online personality, based on the works of Professor Boris Boyko, we apply to the description of any linguistic personality in the communicative space of the global network. The purpose of this study is to highlight the main features for constructing a generalized speech portrait of a German military blogger as a subject of online communication. We were able to identify typical features of the speech of a German military blogger and his virtual linguistic personality, which include units of military vocabulary and terminology, jargon, professionalisms, abbreviations, stable units of verbal communication of military personnel, specific hashtags and non-verbal signs that carry concepts of duty, militancy, patriotism, mourning, etc., associated with the values of military personnel. As a result of the study, we are approaching the speech portrayal of smaller groups within the social group of German military bloggers, for example, creating a speech portrait of a military medic, paratrooper or tanky. The results of our research are of particular interest to military translators, specialists engaged in the study of the theory and practice of Internet communication, blog-discourse, social group dialectology. Knowing the specifics of the speech manifestations of certain social-group communities will allow the recognition of separate subject of online communication by speech characteristics.
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7

Smelik, Ruben M., Tim Tutenel, Klaas Jan de Kraker, and Rafael Bidarra. "Declarative Terrain Modeling for Military Training Games." International Journal of Computer Games Technology 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/360458.

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Анотація:
Military training instructors increasingly often employ computer games to train soldiers in all sorts of skills and tactics. One of the difficulties instructors face when using games as a training tool is the creation of suitable content, including scenarios, entities, and corresponding terrain models. Terrain plays a key role in many military training games, as for example, in our case game Tactical Air Defense. However, current manual terrain editors are both too complex and too time-consuming to be useful for instructors; automatic terrain generation methods show a lot of potential, but still lack user control and intuitive editing capabilities. We present a novel way for instructors to model terrain for their training games: instead of constructing a terrain model using complex modeling tools, instructors can declare the required properties of their terrain using an advanced sketching interface. Our framework integrates terrain generation methods and manages dependencies between terrain features in order to automatically create a complete 3D terrain model that matches the sketch. With our framework, instructors can easily design a large variety of terrain models that meet their training requirements.
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8

POPA, Brîndușa Maria. "Organisational Culture – Modeling Factor of Military Leadership." Romanian Military Thinking 2022, no. 1 (February 2022): 198–213. http://dx.doi.org/10.55535/rmt.2022.1.11.

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"Organisational culture embodies not only a system of beliefs, values, and attitudes of the people forming the organisation, it is also a formative or shaping factor of its leadership as leadership, in its turn, shapes culture. Leaders are responsible for creating systems on which culture develops and norms are reinforced in the workplace. Culture, in response, influences the organisational environment and the strategies built to fulfil the organisational vision and mission, the policies and processes that allow this process. An organisational culture that promotes excellence, fairness, team spirit, characteristics underlain by open, two-way communication, induces the people formed in such an environment to embrace and promote its characteristics. Inevitably, the military organisation is subject to these functional principles, its members being shaped by the organisation specifics, and military education, as part of this organisation, can make a major contribution to the development of culture and the formation of leaders. We will try to demonstrate these ideas based on the analysis of the specialised studies mentioned in the article, but also on personal empirical observations."
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9

Tsybulko, Alexey Alexandrovich, and Alexander Nikolaevich Dakhin. "Modeling of Formation of Readiness of Cadets of Military Institutions of the Troops of the National Guard of the Russian Federation for Command Activity." Siberian Pedagogical Journal, no. 6 (December 27, 2021): 101–14. http://dx.doi.org/10.15293/1813-4718.2106.11.

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Анотація:
Problem and Aim. The article considers the problem of modeling such a pedagogical phenomenon as the cadet’s readiness for command activity. The military professional experience of the future officer was formalized and the correlation of “readiness” with the traditional competence of the military man was validated. The essential components of the modeling process of the cadet’s readiness for command activity are established, the criteria for the effectiveness of such modeling are determined. The purpose of the article is establish the essential components of the process of modeling the readiness of the cadet for command activity, determine the criteria for the effectiveness of such modeling. Methodology. Research methods: comparative analysis of military-vocational training technologies, highlighting the essential intentions of the cognitive plan in them, followed by designing a criteria base for the effectiveness of using such technologies in the formation of cadets’ readiness for command activities. The article considers the problem of modeling such a pedagogical phenomenon as the cadet’s readiness for command activity. The military professional experience of the future officer was formalized and the correlation of “readiness” with the traditional competence of the military man was validated. The essential components of the modeling process of the cadet’s readiness for command activity are established, the criteria for the effectiveness of such modeling are determined.
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10

Song, Guo Jun, Xiao Dong Mu, and Rui Hua Chang. "Research on Entity Modeling Method in Agent-Based CGF Simulation." Applied Mechanics and Materials 121-126 (October 2011): 2185–89. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2185.

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This paper studies the entity modeling method in Agent-based TBM combat simulation system. The architecture of the TBM combat simulation by using HLA/RTI platform has been build. According to the command hierarchy features of military operations, Agent entities are divided into three levels, and a semi-autonomous Agent model has been build. We present a Semantic-based multiple-layered military scenario development framework and implement a Semantic-based Military Scenario Development platform, which can support military commander to semi-automatically generate a military mission plan with MSDL document. Finally gives the flow chart of TBM combat simulation system.
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Дисертації з теми "Military modeling"

1

Kaptan, Varol. "MODELING AUTONOMOUS AGENTS IN MILITARY SIMULATIONS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3825.

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Simulation is an important tool for prediction and assessment of the behavior of complex systems and situations. The importance of simulation has increased tremendously during the last few decades, mainly because the rapid pace of development in the field of electronics has turned the computer from a costly and obscure piece of equipment to a cheap ubiquitous tool which is now an integral part of our daily lives. While such technological improvements make it easier to analyze well-understood deterministic systems, increase in speed and storage capacity alone are not enough when simulating situations where human beings and their behavior are an integral part of the system being studied. The problem with simulation of intelligent entities is that intelligence is still not well understood and it seems that the field of Artificial Intelligence (AI) has a long way to go before we get computers to think like humans. Behavior-based agent modeling has been proposed in mid-80's as one of the alternatives to the classical AI approach. While used mainly for the control of specialized robotic vehicles with very specific sensory capabilities and limited intelligence, we believe that a behavior-based approach to modeling generic autonomous agents in complex environments can provide promising results. To this end, we are investigating a behavior-based model for controlling groups of collaborating and competing agents in a geographic terrain. In this thesis, we are focusing on scenarios of military nature, where agents can move within the environment and adversaries can eliminate each other through use of weapons. Different aspects of agent behavior like navigation to a goal or staying in group formation, are implemented by distinct behavior modules and the final observed behavior for each agent is an emergent property of the combination of simple behaviors and their interaction with the environment. Our experiments show that while such an approach is quite efficient in terms of computational power, it has some major drawbacks. One of the problems is that reactive behavior-based navigation algorithms are not well suited for environments with complex mobility constraints where they tend to perform much worse than proper path planning. This problem represents an important research question, especially when it is considered that most of the modern military conflicts and operations occur in urban environments. One of the contributions of this thesis is a novel approach to reactive navigation where goals and terrain information are fused based on the idea of transforming a terrain with obstacles into a virtual obstacle-free terrain. Experimental results show that our approach can successfully combine the low run-time computational complexity of reactive methods with the high success rates of classical path planning. Another interesting research problem is how to deal with the unpredictable nature of emergent behavior. It is not uncommon to have situations where an outcome diverges significantly from the intended behavior of the agents due to highly complex nonlinear interactions with other agents or the environment itself. Chances of devising a formal way to predict and avoid such abnormalities are slim at best, mostly because such complex systems tend to be be chaotic in nature. Instead, we focus on detection of deviations through tracking group behavior which is a key component of the total situation awareness capability required by modern technology-oriented and network-centric warfare. We have designed a simple and efficient clustering algorithm for tracking of groups of agent suitable for both spatial and behavioral domain. We also show how to detect certain events of interest based on a temporal analysis of the evolution of discovered clusters.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science
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2

Muehl, Timothy John. "Modeling civilians and the civil-military interactions." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA288646.

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3

VILLARMOSA, ALEXANDRE DE MENEZES. "COMPUTATIONAL MODELING AGENTS BASED ON MILITARY DOCTRINE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25293@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Desde o início de do século XIX, simulações de combate são utilizadas em treinamentos militares. Para que um treinamento ocorra de forma confiável é necessário envolver uma grande quantidade de militares nos adestramentos. No final de 1940, a ideia de agentes computacionais em inteligência artificial se mostrou uma excelente ferramenta, contribuindo para reduzir esta quantidade de pessoas envolvidas nas simulações de combate. Agentes capazes de perceber o ambiente em que estão inseridos e tomar decisões, agindo sobre ele, seguindo um conjunto de regras podem representar o comportamento de um soldado. Agentes inseridos numa simulação militar devem então, perceber o campo de batalha e tomar uma série de ações com base em uma doutrina militar. Logo, o objetivo deste trabalho é apresentar, através da modelagem de agentes computacionais uma definição do comportamento destes baseados na doutrina militar, para que estes agentes possam substituir parte dos militares evolvidos em uma simulação de combate, sem afetar a confiabilidade desta. Além de tornar os sistemas de simulação mais eficientes reduzindo a quantidade de militares necessária para a sua correta aplicação, este trabalho também ajuda a verificar a consistência lógica das ações descritas nos manuais doutrinários.
Since the beginning of nineteenth century combat simulations are used in military training. It s necessary to involve lots of military to these trainings occur reliably. In the late 1940s the idea of computational agents was developed in artificial intelligence and showed as an excellent tool to reduce the amount of personnel involved in combat simulations. Agents perceive the environment where they are inserted and take actions upon it following a set of rules. That reminds the behavior of a soldier. A soldier, or a group of then, perceive the battlefield and take a series of actions based on military doctrine. Therefore, the scope of this work is to present a viable way to define the behavior of computational agents based on military doctrine, so that they can replace some of the personnel involved in a combat simulation without affecting the reliability of the training in course. In addition making more efficient simulation systems, reducing the amount of required military for its proper implementation, can also help to check the logical consistency of the actions planned in the doctrinal manuals.
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4

Coombs, Aaron. "Modeling Attrition in a Military Selection Context." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/100781.

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Attrition, employee turnover, self-selection, and withdrawal all refer to an employee’s exit from an organization, or from an organization’s recruitment or selection process. When individuals with the desired knowledge, skills, abilities, and other qualities (KSAOs) attrit, it represents lost productivity to an organization (Barrick & Zimmerman, 2009). Therefore, organizations should seek a selection program that screens out unwanted characteristics while minimizing the voluntary withdrawal, or quitting, of those who would be a good organizational fit. A military selection context amplifies these two aims because of the limited number of qualified individuals relative to the organization’s personnel needs, and because of the high potential cost of a bad hire. However, there are few studies of attrition during a selection process, and even fewer in a military context that combines physical, cognitive, and personality components as relevant performance dimensions. The purpose of the study was to model attrition from a military special operations selection through training program to determine what combination of physical abilities, cognitive abilities, and personality scales best predicts success. The study examined archival data from 748 candidate records spanning eight different classes during 2019. Secondary purposes of the study included comparing differences in attrition from the first week of the program to the remaining seven weeks, and comparing the predictive validity of a personality trait profile model to a model using personality scales T-scores. In conducting the analysis and modeling, exploratory factor analysis was conducted on the sample Jackson Personality Inventory-Revised (JPI-R) personality scales, finding both similarities and differences with previous study samples (Detwiler & Ramanaiah, 1996; Paunonen & Jackson, 1996). The result of the study was a logit prediction model with a ROC AUC of .784, and an F1 score of .69, that incorporated three physical predictors, performance IQ, and three personality variables: JPI-R T-score for sociability, and two composites created from the factor analysis—a Conscientiousness Composite and an Openness Composite (negative relationship with candidate success). Models for week 1 attrition and attrition from weeks 2-8 differed from the 8-week attrition model, and from each other in the significance and the importance of the personality variables and of cognitive abilities. Physical predictors: run score, pushups score, and sit-ups score, were significant and strong predictors of success for each of the time periods. Verbal IQ was not significant for any time period, while performance IQ was significant in predicting 8-week success, and for success during the week 2-8 time period. Personality predictors varied the most by timeframe, although some component of Conscientiousness predicted strongly for each timeframe. Whereas Openness-related facets predicted for 8-week success and success from week 1 with a negative relationship, Openness factors were non-significant in weeks 2-8. In contrast, Anxiety, a related sub-facet of Neuroticism, predicted moderately (negative relationship) for success from weeks 2-8, but was non-significant for week 1 and for the 8-week program. Unexpected findings included the sample’s different factor structure on the JPI-R, the dominance of the physical predictors in all models, and the strength of personality predictors relative to cognitive abilities. Implications for military and similar types of selection contexts, where selection through training includes a significant physical component, are discussed.
M.S.
The study analyzed attrition from a military special operations selection program to determine what combination of individual differences measured before the program best predicted attrition during the program. The individual differences measured prior to the program were physical abilities, cognitive abilities, and personality. Archival data from 748 candidate records spanning eight different classes during 2019 was analyzed. Attrition is the departure of an individual from an organization, or from a hiring process. This study dealt with attrition from a hiring, or personnel selection process, which is less commonly studied than attrition from within an organization. Secondary purposes of the study included how attrition from the first week of the program differed from the remaining seven weeks, and determining if a specific broad personality profile best predicted attrition. The study found additional results that were not anticipated, specifically, that the military sample differed meaningfully on important dimensions of the Jackson Personality Inventory-Revised (JPI-R) personality scales, in comparison with previous study samples (Detwiler & Ramanaiah, 1996; Paunonen & Jackson, 1996). The practical result of the study was a mathematical prediction model that incorporated a candidate’s scores on pushups, sit-ups, 2-mile run, performance IQ, and three personality variables, and calculates a candidates’ probability of success. The three personality variables that predicted success were scores for sociability, and two composites—a Conscientiousness Composite and an Openness Composite. Mathematical models for week 1 attrition and attrition from weeks 2-8 differed from the 8-week attrition model, and from each other, suggesting that attrition during different timeframes is due to different reasons. Physical predictors: 2 mile run score, pushups score, and sit-ups score, were strong predictors of success for each of the time periods. Verbal IQ did not predict for any time period, while performance IQ predicted 8-week success, and success during the week 2-8 time period. Personality predictors varied the most by timeframe, although a component of Conscientiousness predicted strongly for each timeframe. Openness-related personality facets predicted for 8-week success and success from week 1 with a negative relationship. In contrast, Anxiety, a related sub-facet of Neuroticism, predicted moderately (negative relationship) for success only from weeks 2-8. Unexpected findings included the military sample’s different factor structure on the JPI-R, the dominance of the physical predictors in all models, and the strength of personality predictors relative to cognitive abilities. Implications for military and similar types of selection contexts, where selection through training includes a significant physical component, such as police or firefighters, are discussed.
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5

Anderson, Colin M. "Generalized weapon effectiveness modeling." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FAnderson.pdf.

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6

Dickie, Alistair James. "Modeling robot swarms using agent-based simulation." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FDickie.pdf.

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7

Hall, Andrew O. "Simulating and optimizing military manpower modeling and mountain range options /." College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9299.

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Анотація:
Thesis (Ph . D.) -- University of Maryland, College Park, 2009.
Thesis research directed by: .University of Maryland (College Park, Md.) College of Business and Management . Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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8

Saltmarsh, Elizabeth. "A modeling trade-off forecasting environment for military aircraft sustainment." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53587.

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Анотація:
One of the overarching goals for military aircraft sustainment is to keep a high proportion of aircraft available despite the need for maintenance. Traditional solutions to this problem require conservative resource estimates, but this is costly. In recent years an overall paradigm shift towards affordability has created pressure to find other options for achieving high values of fleet level metrics. Past efforts at increasing affordability have had mixed success, and as a result such strategies need to be tested early on in the lifetime of a product, ideally before the product is ever fielded. In order to provide the ability to evaluate the effects of sustainment decisions such as different maintenance paradigms and cost goals, this thesis develops a sustainment modeling environment, known as Sustain-ME, to facilitate open analysis based on the best information available. The goal of creating Sustain-ME is to allow decision makers to define a sustainment scenario and compare different decisions of interest on a common basis. Sustain-ME is a discrete event simulation, which means it efficiently provides a reasonable prediction of operational behavior. This thesis describes the information used to construct Sutain-ME, including the assumptions made for many of the parameters of the modeled sustainment process. It next verifies the behavior of the different elements that make up the sustainment model including operations, maintenance, maintenance paradigms, and the supply chain. Finally a methodology for using SustainME is defined and a demonstration of the types of studies Sustain-ME was built to perform is shown. The demonstration compares three different maintenance paradigms: reactive maintenance, condition based maintenance, and a novel CBM paradigm known as CBM-MiMOSA.
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9

Jungkunz, Patrick. "Modeling human visual perception for target detection in military simulations." Monterey, Calif. : Naval Postgraduate School, 2009. http://handle.dtic.mil/100.2/ADA501666.

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Анотація:
Dissertation (Ph.D. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, June 2009.
Dissertation Advisor(s): Darken, Christian J. "June 2009." Description based on title screen as viewed on July 10, 2009. DTIC Identifiers: Human visual perception, visual attention, eye tracking, human behavior modeling, visual search, semantic relevance, relevance mapa. Author(s) subject terms: Human Visual Perception, Visual Attention, Eye Movements, Eye Tracking, Human Behavior Modeling, Target Detection, Visual Search, Semantic Relevance, Relevance Map. Includes bibliographical references (p. 145-149). Also available in print.
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10

Campbell, Benjamin W. "Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1558024695617708.

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Книги з теми "Military modeling"

1

Jones, Kim. Modeling military miniatures: Tips, tools & techniques. Atglen, PA: Schiffer Publishing, 1995.

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2

Jones, Kim. Modeling weapons & accessories for military miniatures. Atglen, PA: Schiffer Pub., 1996.

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3

Modeli v sisteme prini︠a︡tii︠a︡ voenno-strategicheskikh resheniĭ v SSSR. Moskva: Imperium Press, 2005.

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4

Jones, Kim. Modeling militaryminiatures: Tips, tools & techniques. Atglen, PA: Schiffer Publishing, 1995.

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5

Elliott, Marc N. Modeling the departure of military pilots from the service. Santa Monica, CA: Rand, 2004.

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6

Elliott, Marc N. Modeling the departure of military pilots from the service. Santa Monica, CA: Rand, 2001.

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7

Khomenko, I︠U︡ P. Matematicheskoe modelirovanie vnutriballisticheskikh prot︠s︡essov v stvolʹnykh sistemakh. Novosibirsk: Izd-vo Sibirskogo otdelenii︠a︡ Rossiĭskoĭ akademii nauk, 1999.

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8

Zaloga, Steve. Modeling US armor of World War 2. Oxford: Osprey, 2009.

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9

Allen, Patrick D. Modeling global positioning system effects in the TLC/NLC model. Santa Monica, CA: Rand, 1994.

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10

L, Adams John. Modeling and forecasting the demand for aircraft recoverable spare parts. Santa Monica, CA: Rand, 1993.

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Частини книг з теми "Military modeling"

1

Despres, John. "Politico-Military Assessment." In Modeling and Analysis of Conventional Defense in Europe, 193–95. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2175-0_11.

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2

Diallo, Saikou Y., and José J. Padilla. "Military Interoperability Challenges." In Handbook of Real-World Applications in Modeling and Simulation, 298–327. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118241042.ch8.

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Leclaire, R. J., D. Pasqualini, J. S. Dreicer, G. L. Toole, N. M. Urban, R. W. Bent, T. N. Mcpherson, and N. W. Hengartner. "Infrastructure Modeling: Status and Applications." In Sustainable Cities and Military Installations, 391–427. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7161-1_19.

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Kraft, Reuben H., Rebecca A. Fielding, Kevin Lister, Allen Shirley, Tim Marler, Andrew C. Merkle, Andrzej J. Przekwas, X. G. Tan, and Xianlian Zhou. "Modeling Skeletal Injuries in Military Scenarios." In Studies in Mechanobiology, Tissue Engineering and Biomaterials, 3–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/8415_2016_191.

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Padilla, José J. "Annex 2: Military Simulation Systems." In Engineering Principles of Combat Modeling and Distributed Simulation, 851–68. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118180310.oth2.

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Lofdahl, Corey. "Applying System Dynamics to Military Operations." In Social Computing, Behavioral - Cultural Modeling and Prediction, 274–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29047-3_33.

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Darken, Rudolph P., and Curtis L. Blais. "The Uniformed Military Modeling and Simulation Professional." In The Profession of Modeling and Simulation, 151–66. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119288091.ch8.

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Loftin, R. Bowen, Mikel D. Petty, Ryland C. Gaskins, and Frederic D. McKenzie. "Modeling Crowd Behavior for Military Simulation Applications." In Organizational Simulation, 471–536. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471739448.ch17.

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Fox, William P., and Robert Burks. "Logistics Network Modeling." In Applications of Operations Research and Management Science for Military Decision Making, 455–83. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20569-0_9.

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Yu, Tian-Li, Scott Santarelli, and David E. Goldberg. "Military Antenna Design Using a Simple Genetic Algorithm and hBOA." In Scalable Optimization via Probabilistic Modeling, 275–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34954-9_12.

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Тези доповідей конференцій з теми "Military modeling"

1

Kuikka, Vesa. "Methods for Modeling Military Capabilities." In 2019 IEEE International Systems Conference (SysCon). IEEE, 2019. http://dx.doi.org/10.1109/syscon.2019.8836719.

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2

Isci, Hakan, Sema Simsek, Ozer Tekisen, and Ilker Suer. "Perception and Cognition Under Military Aggravating Factors." In AIAA Modeling and Simulation Technologies Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-8095.

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Faied, Mariam, and Anouck Girard. "Modeling and optimizing military air operations." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5399926.

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Szymonik, Andrzej, and Tomasz Smal. "Safety Modeling in Military Warehouse Management." In 2019 International Conference on Military Technologies (ICMT). IEEE, 2019. http://dx.doi.org/10.1109/miltechs.2019.8870094.

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Bidyuk, Peter, Aleksandr Gozhvi, and Iryna Kalinina. "Modeling Military Conflicts Using Bayesian Networks." In 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC). IEEE, 2018. http://dx.doi.org/10.1109/saic.2018.8516861.

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Miller, Jennifer, and Syed Mohammad. "Vehicle Level Human Performance Modeling for Military Vehicle Simulation." In 2007 Digital Human Modeling Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2007. http://dx.doi.org/10.4271/2007-01-2508.

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Gindin, Yosef, and Alexander Peleg. "Real avionics in flight simulators for military aircraft." In Modeling and Simulation Technologies Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1999. http://dx.doi.org/10.2514/6.1999-4024.

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Sonnenberg, Christian. "Modeling handheld usability for military-based devices." In MILCOM 2012 - 2012 IEEE Military Communications Conference. IEEE, 2012. http://dx.doi.org/10.1109/milcom.2012.6415737.

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Kang, Haeran, Jong Hyuk Lee, Jang Ryeol Baek, Kyong-Ho Lee, and Young Hoon Lee. "Semantic Conceptual Modeling for Military Simulation Scenario." In Visualization, Imaging and Image Processing / 783: Modelling and Simulation / 784: Wireless Communications. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.783-072.

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Li, Xiong, Yujin Wu, Yukun Cao, and Zhiming Dong. "Cognitive Agents for Modeling Military Command Entities." In 2006 5th IEEE International Conference on Cognitive Informatics. IEEE, 2006. http://dx.doi.org/10.1109/coginf.2006.365694.

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Звіти організацій з теми "Military modeling"

1

Gupta, Pradeep K. Traction Modeling of Military Oils. Fort Belvoir, VA: Defense Technical Information Center, October 1986. http://dx.doi.org/10.21236/ada178303.

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2

Browe, Bryan E., and Gary S. Brown. Advanced Propagation Modeling for Military Applications. Fort Belvoir, VA: Defense Technical Information Center, November 2000. http://dx.doi.org/10.21236/ada391203.

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Cobb, Barry R. Modeling Uncertainty in Military Supply Chain Management Decisions. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada612034.

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Stuhmiller, James H. Mathematical Modeling in Support of Military Operational Medicine. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada458419.

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Girard, P. E., J. W. Ulvilla, and M. F. O'Connor. Modeling Choice Under Uncertainty in Military Systems Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1991. http://dx.doi.org/10.21236/ada244040.

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Stuhmiller, James H. Modeling for Military Operational Medicine Scientific and Technical Objectives. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada395911.

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Stuhmiller, James H. Modeling for Military Operational Medicine Scientific and Technical Objectives. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada406110.

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Stuhmiller, James H., Weixin Shen, Eugene Niu, and Adam Fournier. Modeling for Military Operational Medicine Scientific and Technical Objectives. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada430253.

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Stuhmiller, James H., Weixin Shen, and Eugene Niu. Modeling for Military Operational Medicine Scientific and Technical Objectives. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada420424.

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Honneywell, Richard G. Resource Allocation Modeling for Military Systems and Technology Acquisition. Fort Belvoir, VA: Defense Technical Information Center, June 1988. http://dx.doi.org/10.21236/ada208131.

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