18,112 research outputs found
A two-level structure for advanced space power system automation
The tasks to be carried out during the three-year project period are: (1) performing extensive simulation using existing mathematical models to build a specific knowledge base of the operating characteristics of space power systems; (2) carrying out the necessary basic research on hierarchical control structures, real-time quantitative algorithms, and decision-theoretic procedures; (3) developing a two-level automation scheme for fault detection and diagnosis, maintenance and restoration scheduling, and load management; and (4) testing and demonstration. The outlines of the proposed system structure that served as a master plan for this project, work accomplished, concluding remarks, and ideas for future work are also addressed
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Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.
The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The circuit's high performance is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit's noise, intrinsic and/or extrinsic to the memory cell array. The dynamic scaling of weights, enabled by analog memory devices, allows for efficient realization of different annealing approaches to improve functionality. The proposed approach is experimentally verified for two representative applications, namely by implementing neural network for solving a four-node graph-partitioning problem, and a Boltzmann machine with 10-input and 8-hidden neurons
Perspective: Organic electronic materials and devices for neuromorphic engineering
Neuromorphic computing and engineering has been the focus of intense research
efforts that have been intensified recently by the mutation of Information and
Communication Technologies (ICT). In fact, new computing solutions and new
hardware platforms are expected to emerge to answer to the new needs and
challenges of our societies. In this revolution, lots of candidates
technologies are explored and will require leveraging of the pro and cons. In
this perspective paper belonging to the special issue on neuromorphic
engineering of Journal of Applied Physics, we focus on the current achievements
in the field of organic electronics and the potentialities and specificities of
this research field. We highlight how unique material features available
through organic materials can be used to engineer useful and promising
bioinspired devices and circuits. We also discuss about the opportunities that
organic electronic are offering for future research directions in the
neuromorphic engineering field
NASA SBIR abstracts of 1991 phase 1 projects
The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included
Machine learning plasma-surface interface for coupling sputtering and gas-phase transport simulations
Thin film processing by means of sputter deposition inherently depends on the
interaction of energetic particles with a target surface and the subsequent
particle transport. The length and time scales of the underlying physical
phenomena span orders of magnitudes. A theoretical description which bridges
all time and length scales is not practically possible. Advantage can be taken
particularly from the well-separated time scales of the fundamental surface and
plasma processes. Initially, surface properties may be calculated from a
surface model and stored for a number of representative cases. Subsequently,
the surface data may be provided to gas-phase transport simulations via
appropriate model interfaces (e.g., analytic expressions or look-up tables) and
utilized to define insertion boundary conditions. During run-time evaluation,
however, the maintained surface data may prove to be not sufficient. In this
case, missing data may be obtained by interpolation (common), extrapolation
(inaccurate), or be supplied on-demand by the surface model (computationally
inefficient). In this work, a potential alternative is established based on
machine learning techniques using artificial neural networks. As a proof of
concept, a multilayer perceptron network is trained and verified with sputtered
particle distributions obtained from transport of ions in matter based
simulations for Ar projectiles bombarding a Ti-Al composite. It is demonstrated
that the trained network is able to predict the sputtered particle
distributions for unknown, arbitrarily shaped incident ion energy
distributions. It is consequently argued that the trained network may be
readily used as a machine learning based model interface (e.g., by
quasi-continuously sampling the desired sputtered particle distributions from
the network), which is sufficiently accurate also in scenarios which have not
been previously trained
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
1992 NASA/ASEE Summer Faculty Fellowship Program
For the 28th consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama and MSFC during the period June 1, 1992 through August 7, 1992. Operated under the auspices of the American Society for Engineering Education, the MSFC program, was well as those at other centers, was sponsored by the Office of Educational Affairs, NASA Headquarters, Washington, DC. The basic objectives of the programs, which are the 29th year of operation nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers
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