Lateral Graphene p–n Junctions Realized by Nanoscale Bipolar Doping Using Surface Electric Dipoles and Self‐Organized Molecular Anions
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Yong Zhang↗
SEARCH · Engineering Papers
Results for “self organization”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Galaxy and mass assembly (GAMA): Self-Organizing Map application on nearby galaxies
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Benne W Holwerda↗
Transit shapes and self-organizing maps as a tool for ranking planetary candidates: application to Kepler and K2
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D. J. Armstrong↗
Autonomic Computing: Panacea or Poppycock?
Autonomic Computing arose out of a need for a means to cope with rapidly growing complexity of integrating, managing, and operating computer-based systems as well as a need to reduce the total cost of ownership of today's systems. Autonomic Computing (AC) as a discipline was proposed by IBM in 2001, with the vision to develop self-managing systems. As the name implies, the influence for the new paradigm is the human body's autonomic system, which regulates vital bodily functions such as the control of heart rate, the body's temperature and blood flow-all without conscious effort. The vision is to create selfivare through self-* properties. The initial set of properties, in terms of objectives, were self-configuring, self-healing, self-optimizing and self-protecting, along with attributes of self-awareness, self-monitoring and self-adjusting. This self-* list has grown: self-anticipating, self-critical, self-defining, self-destructing, self-diagnosis, self-governing, self-organized, self-reflecting, and self-simulation, for instance.
Sterritt, Roy↗
Innovation and application of ANN in Europe demonstrated by Kohonen maps
One of the most important contributions to neural networks comes from Kohonen, Helsinki/Espoo, Finland, who had the idea of self-organizating maps in 1981. He verified his idea by an algorithm of which many applications make use of. The impetus for this idea came from biology, a field where the Europeans have always been very active at several research laboratories. The challenge was to model the self-organization found in the brain. Today one goal is the development of more sophisticated neurons which model the biological neurons more exactly. They should come to a better performance of neural nets with only a few complex neurons instead of many simple ones. A lot of application concepts arise from this idea: Kohonen himself applied it to speech recognition, but the project did not overcome much more than the recognition of the numerals one to ten at that time. A more promising application for self-organizing maps is process control and process monitoring. Several proposals were made which concern parameter classification of semiconductor technologies, design of integrated circuits, and control of chemical processes. Self-organizing maps were applied to robotics. The neural concept was introduced into electric power systems. At Dortmund we are working on a system which has to monitor the quality and the reliability of gears and electrical motors in equipment installed in coal mines. The results are promising and the probability to apply the system in the field is very high. A special feature of the system is that linguistic rules which are embedded in a fuzzy controller analyze the data of the self-organizing map in regard to life expectation of the gears. It seems that the fuzzy technique will introduce the technology of neural networks in a tandem mode. These technologies together with the genetic algorithms start to form the attractive field of computational intelligence.
Goser, Karl↗
New Propagating Mode near the Superfluid Transition in He-4
We have observed a new temperature-entropy wave that propagates opposite to the direction of a steady heat flux Q when the helium column is heated from above. This propagating mode is due to non-linear thermo-conductance of the helium sample in the self-organized state. Such a mode had been predicted to exist on the self-organized heat transport state for Q less than about 100 nW/sq cm. We confirm that this mode exists in this regime. However, we also observe that it propagates even when the helium is pushed away from the self-organized heat transport state into the normal state.
Sergatskov, Dmitri A.↗
The performance evaluation of a new neural network based traffic management scheme for a satellite communication network
A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.
Ansari, Nirwan↗
Water as a matrix for life
"Follow the water" is the canonical strategy in searching for life in the universe. Conventionally, discussion of this topic is focused on how solvent supports organic chemistry sufficiently rich to seed life. Perhaps more importantly, solvent must promote self-organization of organic matter into functional structures capable of responding to environmental changes. This process is based on non-covalent interactions. They are constantly formed and broken in response to internal and external stimuli. This requires that their strength must be properly tuned. If they were too weak, the system would exhibit undesired, uncontrolled response to natural fluctuations of physical and chemical parameters. If they were too strong kinetics of biological processes would be slow and energetics costly. Non-covalent interactions are strongly mediated by the solvent. Specifically, high dielectric solvents for life are needed for solubility of polar species and flexibility of biological structures stabilized by electrostatic interactions. Water exhibits a remarkable trait that it promotes solvophobic interactions between non-polar species, which are responsible for self-organization phenomena such as the formation of cellular boundary structures, and protein folding and aggregation. Unusual temperature dependence of hydrophobic interactions - they often become stronger as temperature increases - is a consequence of the temperature insensitivity of properties of the liquid water. This contributes to the existence of robust life over a wide temperature range. Water is not the only liquid with favorable properties for supporting life. Other pure liquids or their mixtures that have high dielectric constants and simultaneously support some level of self-organization will be discussed.
Pohorille, Andrew↗
Adaptive parallel logic networks
Adaptive, self-organizing concurrent systems (ASOCS) that combine self-organization with massive parallelism for such applications as adaptive logic devices, robotics, process control, and system malfunction management, are presently discussed. In ASOCS, an adaptive network composed of many simple computing elements operating in combinational and asynchronous fashion is used and problems are specified by presenting if-then rules to the system in the form of Boolean conjunctions. During data processing, which is a different operational phase from adaptation, the network acts as a parallel hardware circuit.
Martinez, Tony R.↗
Assessment of Ice Shape Roughness Using a Self-Orgainizing Map Approach
Self-organizing maps are neural-network techniques for representing noisy, multidimensional data aligned along a lower-dimensional and nonlinear manifold. For a large set of noisy data, each element of a finite set of codebook vectors is iteratively moved in the direction of the data closest to the winner codebook vector. Through successive iterations, the codebook vectors begin to align with the trends of the higher-dimensional data. Prior investigations of ice shapes have focused on using self-organizing maps to characterize mean ice forms. The Icing Research Branch has recently acquired a high resolution three dimensional scanner system capable of resolving ice shape surface roughness. A method is presented for the evaluation of surface roughness variations using high-resolution surface scans based on a self-organizing map representation of the mean ice shape. The new method is demonstrated for 1) an 18-in. NACA 23012 airfoil 2 AOA just after the initial ice coverage of the leading 5 of the suction surface of the airfoil, 2) a 21-in. NACA 0012 at 0AOA following coverage of the leading 10 of the airfoil surface, and 3) a cold-soaked 21-in.NACA 0012 airfoil without ice. The SOM method resulted in descriptions of the statistical coverage limits and a quantitative representation of early stages of ice roughness formation on the airfoils. Limitations of the SOM method are explored, and the uncertainty limits of the method are investigated using the non-iced NACA 0012 airfoil measurements.
Icing↗
Is Water a Universal Solvent for Life?
There are strong reasons to believe that the laws, principles and constraints of physics and chemistry are universal. It is much less clear how this universality translates into our understanding of the origins of life. Conventionally, discussions of this topic focus on chemistry that must be sufficiently rich to seed life. Although this is clearly a prerequisite for the emergence of living systems, I propose to focus instead on self-organization of matter into functional structures capable of reproduction, evolution and responding to environmental changes. In biology, most essential functions are largely mediated by noncovalent interactions (interactions that do not involve making or breaking chemical bonds). Forming chemical bonds is only a small part of what living systems do. There are specific implications of this point of view for universality. I will concentrate on one of these implications. Strength of non-covalent interactions must be properly tuned. If they were too weak, the system would exhibit undesired, uncontrolled response to natural fluctuations of physical and chemical parameters. If they were too strong kinetics of biological processes would be slow and energetics costly. This balance, however, is not a natural property of complex chemical systems. Instead, it has to be achieved with the aid of an appropriate solvent for life. In particular, potential solvents for life must be characterized by a high dielectric constant to ensure solubility of polar species and sufficient flexibility of biological structures stabilized by electrostatic interactions. Among these solvents, water exhibits a remarkable trait that it also promotes solvophobic (hydrophobic) interactions between non-polar species, typically manifested by a tendency of these species to aggregate and minimize their contacts with the aqueous solvent. Hydrophobic interactions are responsible, at least in part, for many self-organization phenomena in biological systems, such as the formation of cellular boundary structures or protein folding. Strengths of electrostatic and hydrophobic interactions are similar and can be balanced over a wide range of temperatures, which considerably increases the repertoire of interactions that can be used to modulate biological functions. Some properties of water, e.g. its chemical activity against polymerization reactions, are considered as unfavorable to life. In actuality, this might be a favorable trait because life requires a balance between constructive and destructive processes. For example, molecules synthesized in response to specific conditions must be degraded once these conditions change. Otherwise regulation of biological processes would be virtually impossible. Water might not be the only liquid with favorable properties for supporting life. It has been proposed that formamide, which might be present elsewhere in the universe in sufficient quantities to warrant interest, could be a potential alternative to water for the origin of life. However, this will remain highly hypothetical until it is demonstrated in further studies on its physical, chemical and biological properties it is capable of mediating self-organization of matter and providing proper balance between different types of non-covalent interactions.
Pohorill, Andrew↗
The Future of Bio-technology
Hosts of technologies, most notably in electronics, have been on the path of miniaturization for decades and in 2005 they have crossed the threshold of the nano-scale. Crossing the nano-scale threshold is a milestone in miniaturization, setting impressive new standards for component-packing densities. It also brings technology to a scale at which quantum effects and fault tolerance play significant roles and approaches the feasible physical limit form many conventional "top-down" manufacturing methods. I will suggest that the most formidable manufacturing problems in nanotechnology will be overcome and major breakthroughs will occur in a host of technologies, when nanotechnology converges with bio-technology; i.e. I will argue that the future of bio-technology is in nanotechnology. In 2005, methods in molecular biology, microscopy, bioinformatics, biochemistry, and genetic engineering have focused considerable attention on the nano-scale. On this scale, biology is a kind of recursive chemistry in which molecular recognition, self-assembly, self-organization and self-referencing context-control lead to the emergence of the complexity of structures and processes that are fundamental to all life forms. While we are still far from understanding this complexity, we are on the threshold of being able to use at least some of these biological properties for .technology. I will discuss the use of biomolecules, such as DNA, RNA, and proteins as "tools" for the bio-technologist of the future. More specifically, I will present in some detail an example of how we are using a genetically engineered 60-kDa protein (HSP60) from an organism living in near boiling sulfuric acid to build nano-scale templates for arranging metallic nanoparticles. These "extremophile" HSP60s self-assemble into robust double-ring structures called "chaperonins," which further assemble into filaments and arrays with nanometer accuracy. I will discuss our efforts to use chaperonins to organize quantum dots, electronic and magnetic nano-particles for electronic and photonic applications.
Trent, Jonathan↗
Autonomous Information Unit: Why Making Data Smart Can also Make Data Secured?
In this paper, we introduce a new fine-grain distributed information protection mechanism which can self-protect, self-discover, self-organize, and self-manage. In our approach, we decompose data into smaller pieces and provide individualized protection. We also provide a policy control mechanism to allow 'smart' access control and context based re-assembly of the decomposed data. By combining smart policy with individually protected data, we are able to provide better protection of sensitive information and achieve more flexible access during emergency conditions. As a result, this new fine-grain protection mechanism can enable us to achieve better solutions for problems such as distributed information protection and identity theft.
data security↗
Communications and control for electric power systems: Power flow classification for static security assessment
This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.
Niebur, D.↗
Intelligent manipulation technique for multi-branch robotic systems
New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system.
Chen, Alexander Y. K.↗
Photospheric Current Spikes and Their Possible Association with Flares - Results from an HMI Data Driven Model
A data driven, near photospheric magnetohydrodynamic model predicts spikes in the horizontal current density, and associated resistive heating rate per unit volume Q. The spikes appear as increases by orders of magnitude above background values in neutral line regions (NLRs) of active regions (ARs). The largest spikes typically occur a few hours to a few days prior to M or X flares. The spikes correspond to large vertical derivatives of the horizontal magnetic field. The model takes as input the photospheric magnetic field observed by the Helioseismic & Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. This 2.5 D field is used to determine an analytic expression for a 3 D magnetic field, from which the current density, vector potential, and electric field are computed in every AR pixel for 14 ARs. The field is not assumed to be force-free. The spurious 6, 12, and 24 hour Doppler periods due to SDO orbital motion are filtered out of the time series of the HMI magnetic field for each pixel using a band pass filter. The subset of spikes analyzed at the pixel level are found to occur on HMI and granulation scales of 1 arcsec and 12 minutes. Spikes are found in ARs with and without M or X flares, and outside as well as inside NLRs, but the largest spikes are localized in the NLRs of ARs with M or X flares. The energy to drive the heating associated with the largest current spikes comes from bulk flow kinetic energy, not the electromagnetic field, and the current density is highly non-force free. The results suggest that, in combination with the model, HMI is revealing strong, convection driven, non-force free heating events on granulation scales, and that it is plausible these events are correlated with subsequent M or X flares. More and longer time series need to be analyzed to determine if such a correlation exists. Above an AR dependent threshold value of Q, the number of events N(Q) with heating rates greater than or equal to Q obeys a scale invariant power law distribution for each AR given by N(Q) varies Q(sup -s), where 0.40 less than or equal to S less than or equal to 0.53, with a mean and standard deviation across the 14 ARs of 0.47 and 0.045, showing there is little variation of S from one AR to another. These properties of N(Q) are in close agreement with those of the distribution N(E) for the total energy E of solar flares, determined from observations to be N(E) = constant x E(sup -alpha). From observations of nanoflares in the 0.7 to 4 MK range, and from observations of flares in hard X-rays, it is found that 0.51 less than or equal to alpha less than or equal to 0.57, and 0.4 less than or equal to alpha less than or equal to 0.6, respectively (Crosby et al. 1993, Sol. Phys., 143, 275; Aschwanden & Parnell 2002, ApJ, 572, 1048). Observations also show that, as is found here for the exponent S, there is little variation of alpha with AR (Wheatland 2000, ApJ, 532, 1209), indicating N(E) and N(Q) are largely independent of individual properties of ARs such as area, total magnetic flux, and distribution of current density (i.e. non-potentiality). Therefore the power law scaling of the photospheric heating rate Q computed here on granulation scales is essentially identical to that found for coronal observations of flare energies on scales 1-2 orders of magnitude larger. This suggests the physical mechanisms that cause Q and coronal flares are closely related. It seems likely that Q is the signature of a magnetic reconnection process in an energy range and volume orders of magnitude smaller than those of flares. In this context, at least the larger spikes in Q might be signatures of UV photospheric or lower chromospheric bombs in which plasma is heated to temperatures approximately 10(exp -5) K (Peter et al. 2014, Science 346, 1255726; Judge 2015, ApJ, 808, 116). In addition, lattice based avalanche simulations of flare energy release predict 0.4 less than or equal to alpha less than or equal to 0.5, while analytic, fractal-diffusive self-organized criticality models predict 0.4 less than or equal to alpha less than or equal to 0.67, in excellent agreement with observations, and the results presented here (Aschwanden & Parnell 2002, ApJ, 572, 1048; Aschwanden 2012, A&A, 539, A2; Aschwanden 2013, in "Self Organized Criticality Systems"; Aschwanden et al. 2016, SSR, 198, 47).
Goodman, Michael↗
Locomotion training of legged robots using hybrid machine learning techniques
In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible patent by NASA, Johnson Space Center. An alternative modular approach is also developed which uses separate controllers for each stage of the running stride. A self-organizing fuzzy-neural controller controls the height, distance and angular momentum of the stride. A CMAC-based controller controls the movement of the leg from the time the foot leaves the ground to the time of landing. Because the leg joints are controlled at each time step during flight, movement is smooth and obstacles can be avoided. Initial results indicate that this approach can yield fast, accurate results.
Simon, William E.↗
Molecular Simulations in Astrobiology
One of the main goals of astrobiology is to understand the origin of cellular life. The most direct approach to this problem is to construct laboratory models of protocells. Such efforts, currently underway in the NASA Astrobiology Program, are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures that are capable of performing protocellular functions. Many of these functions, such as importing nutrients, capturing energy and responding to changes in the environment, are carried out by proteins bound to membranes. We use computer simulations to address the following questions about these proteins: (1) How do small proteins self-organize into ordered structures at water-membrane interfaces and insert into membranes? (2) How do peptides form membrane-spanning structures (e.g. channels)? (3) By what mechanisms do such structures perform their functions? The simulations are performed using the molecular dynamics method. In this method, Newton's equations of motion for each atom in the system are solved iteratively. At each time step, the forces exerted on each atom by the remaining atoms are evaluated by dividing them into two parts. Short-range forces are calculated in real space while long-range forces are evaluated in reciprocal space, using a particle-mesh algorithm which is of order O(NInN). With a time step of 2 femtoseconds, problems occurring on multi-nanosecond time scales (10(exp 6)-10(exp 8) time steps) are accessible. To address a broader range of problems, simulations need to be extended by three orders of magnitude, which requires algorithmic improvements and codes scalable to a large number of processors. Work in this direction is in progress. Two series of simulations are discussed. In one series, it is shown that nonpolar peptides, disordered in water, translocate to the nonpolar interior of the membrane and fold into helical structures (see Figure). Once in the membrane, the peptides exhibit orientational flexibility with changing conditions, which may have provided a mechanism of transmitting signals between the protocell and its environment. In another series of simulations, the mechanism by which a simple protein channel efficiently mediates proton transport across membranes was investigated. This process is a key step in cellular bioenergetics. In the channel under study, proton transport is gated by four histidines that occlude the channel pore. The simulations identify the mechanisms by which protons move through the gate.
Pohorille, Andrew↗