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Journal articles on the topic 'Autonomous distributed systems'

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Published: 7 October 2023

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1

ITO, Masami, and Hideo YUASA. "Autonomous Distributed Systems." Journal of the Robotics Society of Japan 10, no. 4 (1992): 464–67. http://dx.doi.org/10.7210/jrsj.10.464.

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2

Fujita, Hiroyuki. "Autonomous Distributed Micro Systems." Journal of the Society of Mechanical Engineers 97, no. 905 (1994): 298–301. http://dx.doi.org/10.1299/jsmemag.97.905_298.

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3

Khalgui, Mohamed. "Distributed Reconfigurations of Autonomous IEC61499 Systems." ACM Transactions on Embedded Computing Systems 12, no. 1 (January 2013): 1–23. http://dx.doi.org/10.1145/2406336.2406354.

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4

Ras, Z. "Ontology-based distributed autonomous knowledge systems." Information Systems 29, no. 1 (March 2004): 47–58. http://dx.doi.org/10.1016/s0306-4379(03)00033-4.

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5

Ota, Jun. "Multi-agent robot systems as distributed autonomous systems." Advanced Engineering Informatics 20, no. 1 (January 2006): 59–70. http://dx.doi.org/10.1016/j.aei.2005.06.002.

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6

YANO, Masafumi. "Design Principle for Biological Autonomous Distributed Systems." Journal of the Robotics Society of Japan 10, no. 4 (1992): 468–73. http://dx.doi.org/10.7210/jrsj.10.468.

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7

KUMAGAI, Sadatoshi. "A Paradigm Shift toward Autonomous Distributed Systems." IEICE ESS FUNDAMENTALS REVIEW 3, no. 1 (2009): 4–5. http://dx.doi.org/10.1587/essfr.3.1_4.

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8

Dam, Koen H. Van, Zofia Verwater-Lukszo, Jaap A. Ottjes, and Gabriel Lodewijks. "Distributed intelligence in autonomous multi-vehicle systems." International Journal of Critical Infrastructures 2, no. 2/3 (2006): 261. http://dx.doi.org/10.1504/ijcis.2006.009442.

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9

Oike, Shunsuke, Tomohisa Tanaka, Jiang Zhu, and Yoshio Saito. "Robust Production Scheduling Using Autonomous Distributed Systems." Key Engineering Materials 516 (June 2012): 166–69. http://dx.doi.org/10.4028/www.scientific.net/kem.516.166.

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This research proposes a method of production scheduling using autonomous distributed systems. A concrete message protocol is proposed to realize the production scheduling which includes not only Machine but also Human and AGV scheduling. Moreover this method realizes real time scheduling and parallel scheduling. Therefore, a new structure of production scheduling is proposed, which can realize a change of the type of production scheduler to correspond with a type of production system.
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10

Suguri, Toshihiko, Hiroyuki Yamashita, Shingo Kinoshita, and Yasushi Okada. "Load balancing in distributed autonomous cooperative systems." Systems and Computers in Japan 31, no. 6 (June 2000): 74–89. http://dx.doi.org/10.1002/(sici)1520-684x(200006)31:6<74::aid-scj8>3.0.co;2-z.

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11

NAGAO, Yoichi, Hideaki OHTA, Hironobu URABE, Shin-ichi NAKANO, and Sadatoshi KUMAGAI. "Net-Based Cooperative Control for Autonomous Distributed Systems." Transactions of the Society of Instrument and Control Engineers 32, no. 6 (1996): 967–74. http://dx.doi.org/10.9746/sicetr1965.32.967.

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12

Pandiaraj, K., P. Taylor, N. Jenkins, and C. Robb. "Distributed load control of autonomous renewable energy systems." IEEE Transactions on Energy Conversion 16, no. 1 (March 2001): 14–19. http://dx.doi.org/10.1109/60.911397.

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13

Yasuda, Keiichiro, Yoshihisa Tabuchi, and Tsunayoshi Ishii. "Decentralized Autonomous Control of Super Distributed Energy Systems." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2005 (May 5, 2005): 297–302. http://dx.doi.org/10.5687/sss.2005.297.

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14

Griswold, Victor Jon. "Core algorithms for autonomous monitoring of distributed systems." ACM SIGPLAN Notices 26, no. 12 (December 1991): 36–45. http://dx.doi.org/10.1145/127695.122762.

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15

Oike, Shunsuke, Tomohisa Tanaka, Jiang Zhu, and Yoshio Saito. "215 Robust Production Scheduling Using Autonomous Distributed Systems." Proceedings of Manufacturing Systems Division Conference 2011 (2011): 85–86. http://dx.doi.org/10.1299/jsmemsd.2011.85.

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16

Satzger, Benjamin, Andreas Pietzowski, and Theo Ungerer. "Autonomous and scalable failure detection in distributed systems." International Journal of Autonomous and Adaptive Communications Systems 4, no. 1 (2011): 61. http://dx.doi.org/10.1504/ijaacs.2011.037749.

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17

Nicolescu, Razvan. "Emerging regimes of value in distributed autonomous systems." Ubiquity: The Journal of Pervasive Media 6, no. 1 (November 1, 2019): 73–81. http://dx.doi.org/10.1386/ubiq_00009_1.

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Abstract This position article brings together perspectives from social sciences, computer science and economy to interrogate the emerging meanings of value produced by Distributed Autonomous Organizations (DAO). We explore this process in the context of the wider political economy enabled by Distributed Ledger Technologies (DLT) and Smart Contracts (SC). The article then questions the ways in which the current implementations of DAO reflect the various regimes of value and the emergent possibilities to rethink the social contract.
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18

Chapin, Steve J., and Eugene H. Spafford. "Dissemination of state information in distributed autonomous systems." Computer Communications 21, no. 11 (August 1998): 969–79. http://dx.doi.org/10.1016/s0140-3664(98)00168-6.

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19

A., Botchkaryov. "STRUCTURAL ADAPTATION OF DATA COLLECTION PROCESSES IN AUTONOMOUS DISTRIBUTED SYSTEMS USING REINFORCEMENT LEARNING METHODS." Computer systems and network 2, no. 1 (March 23, 2017): 13–26. http://dx.doi.org/10.23939/csn2020.01.013.

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A method of structural adaptation of data collection processes has been developed based on reinforcement learning of the decision block on the choice of actions at the structural and functional level subordinated to it, which provides a more efficient distribution of measuring and computing resources, higher reliability and survivability of information collection subsystems of an autonomous distributed system compared to methods of parametric adaptation. In particular, according to the results of experimental studies, the average amount of information collected in one step using the method of structural adaptation is 23.2% more than in the case of using the methods of parametric adaptation. At the same time, the amount of computational costs for the work of the structural adaptation method is on average 42.3% more than for the work of parametric adaptation methods. The reliability of the work of the method of structural adaptation was studied using the efficiency preservation coefficient for different values of the failure rate of data collection processes. Using the recovery rate coefficient for various values of relative simultaneous sudden failures, the survivability of a set of data collection processes organized by the method of structural adaptation has been investigated. In terms of reliability, the structural adaptation method exceeds the parametric adaptation methods by an average of 21.1%. The average survivability rate for the method of structural adaptation is greater than for methods of parametric adaptation by 18.4%. Key words: autonomous distributed system, data collection process, structural adaptation, reinforcement learning
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20

Samoylenko, H. T., and A. V. Selivanova. "Distributed information systems in e-commerce." Mathematical machines and systems 2 (2023): 69–74. http://dx.doi.org/10.34121/1028-9763-2023-2-69-74.

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The article discusses the basic requirements for electronic commerce information systems that support business. The features of the modular design of electronic trade information systems are characterized and the advantages and disadvantages of independently developed information-but-computational resources are determined. The expediency of using distributed information systems for electronic trade tasks is justified. The concept of distributed information systems involves the use of various technologies and protocols to ensure the availability, reliability, and scalability of the system. The architecture of a distributed information system involves the creation of a system with distributed components that interact using standard interfaces and use various technologies for communications. The prospects for the use of distributed information systems are determined and the advantages of using a distributed architecture are analyzed. The article studies the stages of building the architecture of a distributed information system and defines its main components. The architecture of distributed systems can consist of such components as database servers, web servers, applications, security tools, and network equipment, and may vary depending on the specific system and its needs. The types of architectures of distributed information systems and the specifics and features of their application are determined. The article discusses microservices-oriented architecture (Microservices-Oriented Architecture, MOSA), the basic idea of which is that software is divided into small, autonomous microservices that interact with each other using APIs. The use of MOSA for electronic trade information systems allows for increasing the speed of development and implementation of additional functions and ensures scalability and resistance to failures.
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21

Kaga, Tomoyuki, and Toshio Fukuda. "Generation of Observation Arrangement in Distributed Autonomous Robotic Systems." Journal of Robotics and Mechatronics 15, no. 1 (February 20, 2003): 96–104. http://dx.doi.org/10.20965/jrm.2003.p0096.

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Distributed Autonomous Robotic System (DARS) copes with dynamic environments with generation of metalevel functions. This paper addresses such functional generation in distributed sensing. In distributed sensing, dynamically adaptable spatial distribution for observation is one of the most important capabilities of DARS. To realize this, we propose a method for dynamical generation of observation arrangement in a distributed manner. The proposed method is verified with computational simulation.
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22

Asama, Hajime. "Special Issue on Distributed Robotic Systems." Journal of Robotics and Mechatronics 8, no. 5 (October 20, 1996): 395. http://dx.doi.org/10.20965/jrm.1996.p0395.

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Distributed Robotic Systems are focused on as a new strategy to realize flexible, robust and fault-tolerant robotic systems. In conferences and symposia held recently, the number of papers related to the Distributed Robotic Systems has increased rapidly1,2,3) which shows this area has become one of the most interesting subjects in robotics. The Distributed Robotic Systems require a broad area of interdisciplinary technologies related not only to robotics and computer engineering (especially distributed artificial intelligence and artificial life), but also to biology and psychology. Distributed Robotic Systems can be defined as robot systems which are composed of various types and levels of units, such as cells, modules, agents and robots. One category of papers included in this volume is a robot with a distributed architecture, where modular structure is adopted and/or the robot system is controlled by many CPUs in a distributed manner. Cellular robotic systems are included in this category4). Another category of the papers is cooperative motion control of multiple robots. Coordinated control of multiple manipulators and cooperative motion control by multiple mobile robots using communication are discussed in these papers. The new elemental technologies are also presented, which are required for realization of advanced cooperative motion control of multiple autonomous mobile robots in this volume. The last category of the papers is self-organization of distributed robotic systems. Though the Journal of Robotics and MecharQnics has already published the special issues on the self-organization system,5,6) the latest progress is also presented in this volume. The papers belonging to this category are directed to swarm/collective intelligence in multi-robot cooperation issues. I believe this special issue will inspire the reader's interests in the Distributed Robotic Systems and accelerate the growth of this new arising interdisciplinary research area. References: 1)H.Asama, T.Fukuda, T.Arai and I.Endo eds., Distributed Autonomous Robotic Systems, Springer-Verlag, Tokyo, (1994). 2) H.Asama, T.Fukuda, T.Arai and I.Endo eds.,Distributed Autonomous Robotic Systems 2 , Springer-Verlag, Tokyo, (1996). 3) Robotics Society of Japan, Advanced Robotics 10,6, (1996). 4) T.Fukuda and T.Ueyama, Cellullar Robotics and Micro Robotic Systems,World Scientific, Singapore, (1994). 5) Fuji Technology Press Ltd., Journal of Robotics and Mechatronics,4,2,(1992). 6) Fuji Technology Press Ltd., Journal of Robotics and Mechatronics,4,3,(1992).
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23

YUASA, Hideo, Yoshiteru ITO, and Masami ITO. "Autonomous Distributed Systems which Generate Various Patterns Using Bifurcation." Transactions of the Society of Instrument and Control Engineers 27, no. 11 (1991): 1307–14. http://dx.doi.org/10.9746/sicetr1965.27.1307.

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24

Kim, Ho-Duck, Han-Ul Yoon, and Kwee-Bo Sim. "Strategy of Object Search for Distributed Autonomous Robotic Systems." International Journal of Fuzzy Logic and Intelligent Systems 6, no. 3 (September 1, 2006): 264–69. http://dx.doi.org/10.5391/ijfis.2006.6.3.264.

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25

Baldoni, R., F. Quaglia, and P. Fornara. "An index-based checkpointing algorithm for autonomous distributed systems." IEEE Transactions on Parallel and Distributed Systems 10, no. 2 (1999): 181–92. http://dx.doi.org/10.1109/71.752783.

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26

Kortenkamp, David, Reid Simmons, Tod Milam, and Joaquín L. Fernández. "A Suite of Tools for Debugging Distributed Autonomous Systems." Formal Methods in System Design 24, no. 2 (March 2004): 157–88. http://dx.doi.org/10.1023/b:form.0000017720.64153.57.

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27

Wong, Johnny S. K., and Armin R. Mikler. "Intelligent mobile agents in large distributed autonomous cooperative systems." Journal of Systems and Software 47, no. 2-3 (July 1999): 75–87. http://dx.doi.org/10.1016/s0164-1212(99)00027-8.

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28

Pentaris, Fragkiskos, and Yannis Ioannidis. "Query optimization in distributed networks of autonomous database systems." ACM Transactions on Database Systems 31, no. 2 (June 2006): 537–83. http://dx.doi.org/10.1145/1138394.1138397.

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29

Ra?, Zbigniew W. "Reducts-driven query answering for distributed autonomous knowledge systems." International Journal of Intelligent Systems 17, no. 2 (January 29, 2002): 113–24. http://dx.doi.org/10.1002/int.10011.

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30

Yasuda, Keiichiro, and Tsunayoshi Ishii. "Hierarchical decentralized autonomous control in super-distributed energy systems." IEEJ Transactions on Electrical and Electronic Engineering 2, no. 1 (2006): 63–71. http://dx.doi.org/10.1002/tee.20099.

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31

Ishii, Tsunayoshi, and Keiichiro Yasuda. "Hierarchical decentralized autonomous control of super-distributed energy systems." IEEJ Transactions on Electrical and Electronic Engineering 2, no. 1 (2006): v—vi. http://dx.doi.org/10.1002/tee.20112.

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32

Deng, Zhenhua, and Yiguang Hong. "Multi-Agent Optimization Design for Autonomous Lagrangian Systems." Unmanned Systems 04, no. 01 (January 2016): 5–13. http://dx.doi.org/10.1142/s230138501640001x.

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In this paper, distributed optimization control for a group of autonomous Lagrangian systems is studied to achieve an optimization task with local cost functions. To solve the problem, two continuous-time distributed optimization algorithms are designed for multiple heterogeneous Lagrangian agents with uncertain parameters. The proposed algorithms are proved to be effective for those heterogeneous nonlinear agents to achieve the optimization solution in the semi-global sense, even with the exponential convergence rate. Moreover, simulation adequately illustrates the effectiveness of our optimization algorithms.
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33

Daszczuk, Wiktor B. "Graphic modeling in Distributed Autonomous and Asynchronous Automata (DA3)." Software and Systems Modeling 21, no. 1 (November 1, 2021): 363–98. http://dx.doi.org/10.1007/s10270-021-00917-7.

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AbstractAutomated verification of distributed systems becomes very important in distributed computing. The graphical insight into the system in the early and late stages of the project is essential. In the design phase, the visual input helps to articulate the collaborative distributed components clearly. The formal verification gives evidence of correctness or malfunction, but in the latter case, graphical simulation of counterexample helps for better understanding design errors. For these purposes, we invented Distributed Autonomous and Asynchronous Automata (DA3), which have the same semantics as the formal verification base—Integrated Model of Distributed Systems (IMDS). The IMDS model reflects the natural characteristics of distributed systems: unicasting, locality, autonomy, and asynchrony. Distributed automata have all of these features because they share the same semantics as IMDS. In formalism, the unified system definition has two views: the server view of the cooperating distributed nodes and the agent view of the migrating agents performing distributed computations. The automata have two formally equivalent forms that reflect two views: Server DA3 for observing servers exchanging messages, and Agent DA3 for tracking agents, which visit individual servers in their progress of distributed calculations. We present the DA3 formulation based on the IMDS formalism and their application to design and verify distributed systems in the Dedan environment. DA3 formalism is compared with other concepts of distributed automata known from the literature.
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34

Przysowa, Radoslaw. "Special Issue “Technologies for Future Distributed Engine Control Systems”." Aerospace 8, no. 12 (December 6, 2021): 379. http://dx.doi.org/10.3390/aerospace8120379.

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35

Karray, Fakhri, Rogelio Soto, Federico Guedea, and Insop Song. "Integration of Distributed Robotic Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 1 (January 20, 2004): 7–13. http://dx.doi.org/10.20965/jaciii.2004.p0007.

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Creating a distributed and remote operated robotic system is a very challenging and time-consuming task. This paper contains the development issues taking into account when multiple robotic components are integrated to create a distributed robotic application using the standard middleware, Common Object Request Broker Architecture (CORBA) specification. The main idea is to define a set of generic interfaces using the Interface Definition Language (IDL) that can be used with common components in order to facilitate the integration of new components or the modification of them. The generic IDL interfaces are based on wrapper functions, which provide an abstract encapsulated behaviors of the low level components. The approach is shown using two types of arm manipulators and two different pan-tilt model units. Because of the modularity and the abstraction of this approach, this development can be seen as the first stage in constructing more autonomous and complex system.
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36

Nunes, Urbano, José Alberto Fonseca, Luís Almeida, Rui Araújo, and Rodrigo Maia. "Using distributed systems in real-time control of autonomous vehicles." Robotica 21, no. 3 (May 13, 2003): 271–81. http://dx.doi.org/10.1017/s0263574702004770.

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In this paper distributed architectures for autonomous vehicles are addressed, with a special emphasis on its real-time control requirements. The interconnection of the distributed intelligent subsystems is a key factor in the overall performance of the system. To better understand the interconnection requirements, the main techniques and modules of a global navigation system are described. A special focus on fieldbuses properties and major characteristics is made in order to point out some potentialities, which make them attractive in autonomous vehicles real-time applications, either in terms of reliability as in terms of real-time restrictions.
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37

Kolesnikov, Valeriy. "Modelling a Swarm of Delivery Drones for Disaster Relief Utilizing an Organization Approach." Journal of Physics: Conference Series 2224, no. 1 (April 1, 2022): 012115. http://dx.doi.org/10.1088/1742-6596/2224/1/012115.

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Abstract This paper describes an organization-based approach to modelling an autonomous distributed system. This approach simplifies design of such systems and allows for a better understanding of the system overall. Such an approach is applicable for modelling a variety of distributed systems. We demonstrated it for a swarm system of autonomous drones used for disaster relief aid delivery.
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38

Harrison, Robert, Daniel Vera, and Bilal Ahmad. "Towards the realization of dynamically adaptable manufacturing automation systems." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2207 (August 16, 2021): 20200365. http://dx.doi.org/10.1098/rsta.2020.0365.

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The transition from traditional to truly smart dynamically adaptable manufacturing demands the adoption of a high degree of autonomy within automation systems, with resultant changes in the role of the human, in both the manufacturing and logistics functions within the factory. In the context of smart manufacturing, this paper describes research towards the realization of adaptable autonomous automation systems from both the control and information perspectives. Key facets of the approach taken at WMG are described in relation to human–machine interaction, autonomous approaches to assembly and intra-logistics, integration and dynamic system-wide optimization. The progression from simple distributed behavioural components towards autonomous functional entities is described. Effective systems integration and the importance of interoperability in the realization of more distributed and autonomous automation systems are discussed, so that operational information can propagate seamlessly, eliminating the traditional boundary between operational technology and information technology systems, and as an enabler for global knowledge collection, analysis and optimization. This article is part of the theme issue ‘Towards symbiotic autonomous systems'.
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39

Goldstein-Lev, Anat, and Gad Ariav. "Configuring Systems of Massively Distributed, Autonomous and Interdependent Decision Makers." International Journal of Decision Support System Technology 4, no. 2 (April 2012): 17–41. http://dx.doi.org/10.4018/jdsst.2012040102.

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Massively distributed systems in general and massively distributed DSS in particular have become – and will inevitably continue to become – more and more common in the contemporary massively networked world. However, the authors understanding – and corresponding intuitions – of system performance at these scales are only preliminary. This study aimed at the development of a solid approach and modeling platform for the study of Massively Distributed DSS, the MDDSS, an application at the forefront of IS/ICT applications today. The key contribution of this research is that it charts design guidelines with respect to expected MDDSS performance, guidelines which explicitly relate essential and observable attributes of the decision situations to the corresponding preferred MDDSS architecture.
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40

Sullivan, Joshua, Adam W. Koenig, Justin Kruger, and Simone D’Amico. "Generalized Angles-Only Navigation Architecture for Autonomous Distributed Space Systems." Journal of Guidance, Control, and Dynamics 44, no. 6 (June 2021): 1087–105. http://dx.doi.org/10.2514/1.g005439.

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41

Sauter, Dominique, Taha Boukhobza, Hamelin Frédéric, and Didier Theilliol. "Decentralized and Autonomous Design for FDI of Distributed Control Systems." IFAC Proceedings Volumes 42, no. 23 (2009): 213–18. http://dx.doi.org/10.3182/20091014-3-cl-4011.00039.

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42

Olatunbosun, A., and I. Lawal. "Decentralized and Distributed Information Filter for Autonomous Intelligent Multisensor Systems." Journal of Scientific Research and Reports 13, no. 5 (January 10, 2017): 1–10. http://dx.doi.org/10.9734/jsrr/2017/27886.

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43

THARUMARAJAH, A. "A SELF-ORGANIZING MODEL FOR SCHEDULING DISTRIBUTED AUTONOMOUS MANUFACTURING SYSTEMS." Cybernetics and Systems 29, no. 5 (July 1998): 461–80. http://dx.doi.org/10.1080/019697298125588.

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44

HIRAOKA, Hiroyuki, and Tohru IHARA. "Education and Practice on Production Systems Featuring Autonomous Distributed Factory." Journal of Jsee 42, no. 6 (1994): 26–30. http://dx.doi.org/10.4307/jsee1953.42.6_26.

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45

Strasser, T., and R. Froschauer. "Autonomous Application Recovery in Distributed Intelligent Automation and Control Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, no. 6 (November 2012): 1054–70. http://dx.doi.org/10.1109/tsmcc.2012.2185928.

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46

Yasuda, Gen’ichi. "Distributed autonomous control of modular robot systems using parallel programming." Journal of Materials Processing Technology 141, no. 3 (November 2003): 357–64. http://dx.doi.org/10.1016/s0924-0136(03)00288-7.

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47

Moghadam, Rohollah, and Hamidreza Modares. "Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments." IEEE Transactions on Cybernetics 49, no. 11 (November 2019): 3957–67. http://dx.doi.org/10.1109/tcyb.2018.2856089.

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48

Sheth, Amit P., and James A. Larson. "Federated database systems for managing distributed, heterogeneous, and autonomous databases." ACM Computing Surveys 22, no. 3 (September 1990): 183–236. http://dx.doi.org/10.1145/96602.96604.

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49

Thangavel, Kathiravan, Dario Spiller, Roberto Sabatini, Stefania Amici, Nicolas Longepe, Pablo Servidia, Pier Marzocca, Haytham Fayek, and Luigi Ansalone. "Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors." Sensors 23, no. 6 (March 22, 2023): 3344. http://dx.doi.org/10.3390/s23063344.

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Recent developments in Distributed Satellite Systems (DSS) have undoubtedly increased mission value due to the ability to reconfigure the spacecraft cluster/formation and incrementally add new or update older satellites in the formation. These features provide inherent benefits, such as increased mission effectiveness, multi-mission capabilities, design flexibility, and so on. Trusted Autonomous Satellite Operation (TASO) are possible owing to the predictive and reactive integrity features offered by Artificial Intelligence (AI), including both on-board satellites and in the ground control segments. To effectively monitor and manage time-critical events such as disaster relief missions, the DSS must be able to reconfigure autonomously. To achieve TASO, the DSS should have reconfiguration capability within the architecture and spacecraft should communicate with each other through an Inter-Satellite Link (ISL). Recent advances in AI, sensing, and computing technologies have resulted in the development of new promising concepts for the safe and efficient operation of the DSS. The combination of these technologies enables trusted autonomy in intelligent DSS (iDSS) operations, allowing for a more responsive and resilient approach to Space Mission Management (SMM) in terms of data collection and processing, especially when using state-of-the-art optical sensors. This research looks into the potential applications of iDSS by proposing a constellation of satellites in Low Earth Orbit (LEO) for near-real-time wildfire management. For spacecraft to continuously monitor Areas of Interest (AOI) in a dynamically changing environment, satellite missions must have extensive coverage, revisit intervals, and reconfiguration capability that iDSS can offer. Our recent work demonstrated the feasibility of AI-based data processing using state-of-the-art on-board astrionics hardware accelerators. Based on these initial results, AI-based software has been successively developed for wildfire detection on-board iDSS satellites. To demonstrate the applicability of the proposed iDSS architecture, simulation case studies are performed considering different geographic locations.
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50

Miyamoto, Toshiyuki, Shinji Doi, Hiroki Nogawa, and Sadatoshi Kumagai. "Autonomous distributed secret sharing storage system." Systems and Computers in Japan 37, no. 6 (2006): 55–63. http://dx.doi.org/10.1002/scj.20388.

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