Adapting Thermal Energy Networks to Site Conditions for Improved Cost and Energy Performance
Overview of the analysis of thermal energy networks (TENs) for two U.S. DOD installations.
25 ENERGY STORAGE↗
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Results for “NETWORK ANALYSIS”
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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Spiking Neurons for Analysis of Patterns
Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.
Huntsberger, Terrance↗
Computationally efficient characterization of potential energy surfaces based on fingerprint distances
An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows us to understand important characteristics like thermodynamic, dynamic, and structural properties. Unfortunately computing the transition states and reaction pathways in addition to the significant energetically low-lying local minima is a computationally demanding task. We here introduce a computationally efficient method that is based on a combination of the minima hopping global optimization method and the insight that uphill barriers tend to increase with increasing structural distances of the educt and product states. This method allows us to replace the exact connectivity information and transition state energies with alternative and approximate concepts. Without adding any significant additional cost to the minima hopping global optimization approach, this method allows us to generate an approximate network of the minima, their connectivity, and a rough measure for the energy needed for their interconversion. This can be used to obtain a first qualitative idea on important physical and chemical properties by means of a disconnectivity graph analysis. Besides the physical insight obtained by such an analysis, the gained knowledge can be used to make a decision if it is worthwhile or not to invest computational resources for an exact computation of the transition states and the reaction pathways. Furthermore it is demonstrated that the here presented method can be used for finding physically reasonable interconversion pathways that are promising input pathways for methods like transition path sampling or discrete path sampling.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics
Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on earth at high geospatial resolution relative to global-scale analyses. The results are captured as networks, in which edges between geolocations are defined if their historical climate similarities exceed a threshold. We apply Markov clustering and our novel Correlation of Correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37x10 18 operations/sec) and one of the largest (168.7x10 21 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Our climatic analysis reveals areas of the world experiencing rapid environmental changes, which can have important implications for global carbon fluxes and zoonotic spillover events. Correlation and network analyses of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.
59 BASIC BIOLOGICAL SCIENCES↗
A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network
Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization.
36 MATERIALS SCIENCE↗
Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network
Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.
59 BASIC BIOLOGICAL SCIENCES↗
Spatial-temporal analysis of groundwater well features from neural network prediction of hexavalent chromium concentration
Not Available
De La Noval, Alejandro J.↗
Markov Game Analysis for Attack-Defense of Power Networks Under Possible Misinformation
Explore the source record for details and available documents.
Game Theory↗
Flow and Elastic Networks on the $\mathcal{n}$-Torus: Geometry, Analysis, and Computation
Not provided.
Mathematics↗
3D electron tomographic and biochemical analysis of ER, Golgi and trans Golgi network membrane systems in stimulated Venus flytrap (Dionaea muscipula) glandular cells
The insect-trapping leaves of Dionaea muscipula provide a model for studying the secretory pathway of an inducible plant secretory system. The leaf glands were induced with bovine serum albumin to secrete proteases that were characterized via zymogram activity gels over a 6-day period. The accompanying morphological changes of the endoplasmic reticulum (ER) and Golgi were analyzed using 3D electron tomography of glands preserved by high-pressure freezing/freeze substitution methods. Secretion of multiple cysteine and aspartic proteases occurred biphasically. The majority of the Golgi was organized in clusters consisting of 3–6 stacks surrounded by a cage-like system of ER cisternae. In these clusters, all Golgi stacks were oriented with their cis-most C1 cisterna facing an ER export site. The C1 Golgi cisternae varied in size and shape consistent with the hypothesis that they form de novo. Following induction, the number of ER-bound polysomes doubled, but no increase in COPII vesicles was observed. Golgi changes included a reduction in the number of cisternae per stack and a doubling of cisternal volume without increased surface area. Polysaccharide molecules that form the sticky slime cause swelling of the trans and trans Golgi network (TGN) cisternae. Peeling of the trans-most cisternae gives rise to free TGN cisternae. One day after gland stimulation, the free TGNs were frequently associated with loose groups of oriented actin-like filaments which were not seen in any other samples. These findings suggest that the secretory apparatus of resting gland cells is “overbuilt” to enable the cells to rapidly up-regulate lytic enzyme production and secretion in response to prey trapping.
59 BASIC BIOLOGICAL SCIENCES↗
U.S. Wind Energy Manufacturing & Supply Chain: A Competitive Analysis
The Global Wind Network (GLWN) assessed the key factors that determine wind energy component manufacturing costs and pricing on a global basis in order to provide a better understanding of the factors that will help enhance the competitiveness of U.S. manufacturers, and reduce installed system costs.
17 WIND ENERGY↗
Analysis of Pre-Trained Deep Neural Networks for Large-Vocabulary Automatic Speech Recognition
Abstract not provided
97 MATHEMATICS AND COMPUTING↗
Performance Analysis of TCP Enhancements in Satellite Data Networks
This research examines two proposed enhancements to the well-known Transport Control Protocol (TCP) in the presence of noisy communication links. The Multiple Pipes protocol is an application-level adaptation of the standard TCP protocol, where several TCP links cooperate to transfer data. The Space Communication Protocol Standard - Transport Protocol (SCPS-TP) modifies TCP to optimize performance in a satellite environment. While SCPS-TP has inherent advantages that allow it to deliver data more rapidly than Multiple Pipes, the protocol, when optimized for operation in a high-error environment, is not compatible with legacy TCP systems, and requires changes to the TCP specification. This investigation determines the level of improvement offered by SCPS-TP's Corruption Mode, which will help determine if migration to the protocol is appropriate in different environments. As the percentage of corrupted packets approaches 5 %, Multiple Pipes can take over five times longer than SCPS-TP to deliver data. At high error rates, SCPS-TP's advantage is primarily caused by Multiple Pipes' use of congestion control algorithms. The lack of congestion control, however, limits the systems in which SCPS-TP can be effectively used.
Broyles, Ren H.↗
A Diagnostic Analysis of the Kennedy Space Center LDAR Network
The performance characteristics of the Kennedy Space Center Lightning Detection and Ranging (LDAR) network are investigated at medium-far range (50-300 km). A 19 month noise-filtered sample of LDAR observations is examined, from which it is determined that the "climatological" VHF source density as observed by LDAR falls off approximately 10 dB every 71 km of ground range from the network centroid. The underlying vertical distribution of LDAR sources is approximately normally distributed with a mean of 9 km and a standard deviation of 2.7 km, implying that loss of below-horizon sources has a negligible effect on column-integrated source densities within 200 km ground range. At medium to far ranges, location errors are primarily radial and have a slightly asymmetric distribution whose first moment increases as r(exp 2). A range calibration derived from these results is used to normalize source density maps on monthly, daily and hourly time scales and yields significant improvements in correlation with NLDN ground strike densities.
Boccippio, Dennis J.↗
A Diagnostic Analysis of the Kennedy Space Center LDAR Network: Data Characteristics - 1
An analytical framework is developed in which to analyze climatological VHF (66 MHz) radiation measurements taken by the Kennedy Space Center Lightning Detection and Ranging (LDAR) network. A 19-month noise-filtered sample of LDAR observations is examined using this framework. It is found that the climatological VHF source density as observed by LDAR falls off approximately 10 dB every 71 km of ground range away from the network centroid (a 31 km e-folding scale). From this framework it is inferred that the underlying source distribution is likely inverse exponential in amplitude, with a log-slope of 126-147 mW(sup -1/2). The underlying vertical distribution of VHF sources is approximately normally distributed with a mean altitude of 9 km and a standard deviation of 2.7 km; this implies that the loss of below-horizon sources has a negligible effect on column-integrated source densities within 200 km ground range. At medium to far ranges, location errors are primarily radial and have a slightly asymmetric distribution whose first moment increases as range squared. Error moments estimated from observed lightning are significantly higher than those from aircraft-based signal generator or analytic solution estimates, suggesting that timing errors arising from poor signal identification and discrimination may dominate over timing errors arising from nominal sensor resolution. The VHF source properties of individual LDAR-observed flashes are computed and an analytic expression for flash detection efficiency vs. range is derived. This reveals nearly constant flash detection efficiency to 80-94 km range from the network centroid.
Boccippio, D. J.↗
A Diagnostic Analysis of the Kennedy Space Center LDAR Network: Cross-Sensor Studies - 2
Range dependencies in total (intracloud and cloud-to-ground) lightning observed by the Kennedy Space Center Lightning Detection and Ranging (LDAR) network are established through cross-comparison with other lightning sensors. Using total lightning observed by the Lightning Imaging Sensor (LIS), LDAR flash detection efficiency is shown to remain above 90% out to 90-100 km range, and to be below 25% at 200 km range. LDAR VHF source location error distributions are also determined as a function of range, and are found to be asymmetric with first moments increasing roughly as range squared. Range normalization schemes for total VHF source density are tested and shown to yield significant (up to 50%) skill improvements over uncorrected data, when compared with National Lightning Detection Network (NLDN) ground flash counts at hourly, daily, monthly and climatological time scales.
Boccippio, D. J.↗
A Diagnostic Analysis of the Kennedy Space Center LDAR Network 2. Cross-Sensor Studies
Range dependencies in total (intracloud and cloud to ground) lightning observed by the Kennedy Space Center Lightning Detection and Ranging (LDAR) network are established through cross comparison with other lightning sensors. Using total lightning observed from space by the Lightning Imaging Sensor (LIS), MAR flash detection efficiency is shown to remain above 90% out to 90-100 km range, and to be below 25% at 200 km range. MAR VHF source location error distributions are also determined as a function of range and are found to be asymmetric with standard deviation increasing roughly as r 2 . Range normalization schemes for total VHF source density are tested and shown to yield significant improvements in correlation with National Lightning Detection Network (NLDN) ground flash density at hourly, daily, monthly, and climatological timescales (up to 50% over uncorrected source densities using an exponential-in-range correction factor with 40-50 km e-folding scale).
Boccippio, D. J.↗
A Diagnostic Analysis of the Kennedy Space Center LDAR Network: Data Characteristics - 1
An analytic framework is developed in which to analyze climatological VHF (66 MHz) radiation measurements taken by the Kennedy Space Center Lightning Detection and Ranging (LDAR) network. A 19 month noise-filtered sample of LDAR observations is examined using this framework. It is found that the climatological impulsive VHF source density as observed by LDAR falls off approximately 10 dB every 71 km of ground range away from the network centroid (a 31 km e-folding scale). The underlying vertical distribution of impulsive VHF sources is approximately normally distributed with a mean altitude of 9 km and a standard deviation of 2.7 km; this implies that the loss of below-horizon sources has a negligible effect on column-integrated source densities within a 200 km ground range. At medium to far ranges, location errors are primarily radial and have a slightly asymmetric distribution whose standard deviation increases as r(exp 2). Error moments estimated from observed lightning are significantly higher than those from aircraft-based signal generator or analytic estimates. LDAR bulk flash detection efficiency is predicted to be above 90% to 94-113 km range from the network centroid and to fall below 10% at ranges greater than 200-240 km.
Boccippio, D. J.↗