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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Joint Expansion Planning of Power and Water Distribution Networks

This research considers the joint expansion planning of power and water distribution networks, which are interdependent at various levels. We consider the dependency arising through the power consumption of pumps and develop models for integrating new components into existing networks. Then, we formulate the joint expansion planning as a Mixed Integer Nonlinear Program (MINLP). Applying this MINLP to a small-scale test network, we illustrate the advantages offered by joint expansion planning, such as increased flexibility and reduced costs and redundancy, over independently expanding power and water distribution networks.

expansion planning↗
DOE OSTI2023

ML-Based Power System Stability Assessment Considering Network Topology Changes: WECC 20,000+ Bus System Case Study

Modern power grids are fast-changing and thus require real-time monitoring and online stability assessment. With the rapid development of machine learning (ML) techniques, using data-driven models to provide fast and accurate estimations of power system stability marginal information, such as frequency nadir for frequency stability and critical clearing time (CCT) for transient stability, have become possible. However, despite the numerous research on ML-based methods for frequency nadir and CCT prediction, there is limited work on the impact of different network topology changes. Furthermore, most previous studies only focused on small or synthetic systems, and there is a lack of research on actual large power system models. In this paper, the above issues are addressed by studying the actual U.S. Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. Massive simulations are conducted in PowerWorld Simulator to study the impact of various topology change scenarios on both frequency stability and transient stability. System operating information is extracted from the success dispatch cases of various network topologies to generate a comprehensive dataset for ML-based models. Two ML methods, random forest (RF) and multilayer perceptron (MLP) neural network, are trained and tested for both frequency nadir prediction and CCT prediction. Test results have proven the models are capable of online stability assessment for large power networks such as the WECC system with sufficient accuracy.

critical clearing time↗
DOE OSTI2021

Operation of Natural Gas Pipeline Networks With Storage Under Transient Flow Conditions

Here, we formulate a nonlinear optimal control problem for intraday operation of a natural gas pipeline network that includes storage reservoirs. The dynamics of compressible gas flow through pipes, compressors, reservoirs, and wells are considered. In particular, a reservoir is modeled as a rigid, hollow container that stores gas under isothermal conditions and uniform density, and a well is modeled as a vertical pipe. For each pipe, flow dynamics are described by a coupled partial differential equation (PDE) system in density and mass flux variables, with momentum dissipation modeled using the Darcy–Wiesbach friction approximation. Compressors are modeled as scaling up the pressure of gas between the inlet and outlet. The governing equations for all network components are spatially discretized and assembled into a nonlinear differential-algebraic equation (DAE) system, which synthesizes above-ground pipeline and subsurface reservoir dynamics into a single reduced-order model. We seek to maximize an objective function that quantifies economic profit and network efficiency subject to the flow equations and inequalities that represent operating limitations. The problem is solved using a primal–dual interior point solver, and the solutions are validated in computational experiments and simulations on several pipeline test networks to demonstrate the effectiveness of the proposed methodology.

03 NATURAL GAS↗
DOE OSTI2023

Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization

This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures.

42 ENGINEERING↗
DOE OSTI2019

A Novel Equivalent Model of Active Distribution Networks Based on LSTM

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. Here, it reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.

42 ENGINEERING↗
DOE OSTI2026

Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Large-scale data↗
DOE OSTI2021

Dynamic Interference Management for UAV-Assisted Wireless Networks

Here, we investigate a transmission mechanism aiming to improve the data rate between a base station (BS) and a user equipment (UE) through deploying multiple relaying UAVs. We consider the effect of interference incurred by another established communication network, which makes our problem challenging and different from the state of the art. We aim to design the 3D trajectories and power allocation for the UAVs to maximize the data flow of the network while keeping the interference on the existing communication network below a threshold. We utilize the mobility feature of the UAVs to evade the (un)-intended interference caused by (un)-intentional interferers. To this end, we propose an alternating-maximization approach to jointly obtain the 3D trajectories and the UAVs transmission powers. We handle the 3D trajectory design by resorting to spectral graph the power allocation through convex optimization techniques. We also approach the problem from the intentional interferer’s perspective where smart jammers chase the UAVs to effectively degrade the data flow of the network. We also extend our work to the case for multiple UEs. Finally, we demonstrate the efficacy of our proposed method through extensive simulations.

42 ENGINEERING↗
DOE OSTI2022

Uncoordinated Spectrum Sharing in Millimeter Wave Networks Using Carrier Sensing

Here, we propose using Carrier Sensing (CS) for distributed interference management in millimeter-wave (mmWave) cellular networks where spectrum is shared by multiple operators that do not coordinate among themselves. In addition, even the base station sites can be shared by the operators. We describe important challenges in using traditional CS in this setting and propose enhanced CS protocols to address these challenges. Using stochastic geometry, we develop a general framework for downlink coverage probability analysis of our shared mmWave network in the presence of CS and derive the downlink coverage probability expressions for several CS protocols. Our work is the first to investigate and analyze (using stochastic geometry) CS for mmWave networks with spectrum and BS sites shared among non-coordinating operators. We evaluate the downlink coverage probability of our shared mmWave network using simulations as well as numerical examples based on our analysis. Our evaluations show that our proposed approach leads to an improvement in coverage probability, compared to the coverage probability with no CS, for higher values of signal-to-interference and noise ratio (SINR). Interestingly, our evaluations also reveal that for lower values of SINR, not using any CS is the best strategy in terms of the downlink coverage probability.

42 ENGINEERING↗
DOE OSTI2015

Networked Gamma Radiation System for Tactical Deployment

A networked gamma radiation detection system with directional sensitivity and energy spectral data acquisition capability is being developed by the National Security Technologies, LLC, Remote Sensing Laboratory to support the close and intense tactical engagement of law enforcement who carry out counterterrorism missions. In the proposed design, three clusters of 2" × 4" × 16" sodium iodide crystals (4 each) with digiBASE-E (for list mode data collection) would be placed on the passenger side of a minivan. To enhance localization and facilitate rapid identification of isotopes, advanced smart real-time localization and radioisotope identification algorithms like WAVRAD (wavelet-assisted variance reduction for anomaly detection) and NSCRAD (nuisance-rejection spectral comparison ratio anomaly detection) will be incorporated. We will test a collection of algorithms and analysis that centers on the problem of radiation detection with a distributed sensor network. We will study the basic characteristics of a radiation sensor network and focus on the trade-offs between false positive alarm rates, true positive alarm rates, and time to detect multiple radiation sources in a large area. Empirical and simulation analyses of critical system parameters, such as number of sensors, sensor placement, and sensor response functions, will be examined. This networked system will provide an integrated radiation detection architecture and framework with (i) a large nationally recognized search database equivalent that would help generate a common operational picture in a major radiological crisis; (ii) a robust reach back connectivity for search data to be evaluated by home teams; and, finally, (iii) a possibility of integrating search data from multi-agency responders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Sci-Thur AM: YIS – 05: Prediction of lung tumor motion using a generalized neural network optimized from the average prediction outcome of a group of patients

Purpose: In image-guided adaptive radiotherapy systems, prediction of tumor motion is required to compensate for system latencies. However, due to the non-stationary nature of respiration, it is a challenge to predict the associated tumor motions. In this work, a systematic design of the neural network (NN) using a mixture of online data acquired during the initial period of the tumor trajectory, coupled with a generalized model optimized using a group of patient data (obtained offline) is presented. Methods: The average error surface obtained from seven patients was used to determine the input data size and number of hidden neurons for the generalized NN. To reduce training time, instead of using random weights to initialize learning (method 1), weights inherited from previous training batches (method 2) were used to predict tumor position for each sliding window. Results: The generalized network was established with 35 input data (∼4.66s) and 20 hidden nodes. For a prediction horizon of 650 ms, mean absolute errors of 0.73 mm and 0.59 mm were obtained for method 1 and 2 respectively. An average initial learning period of 8.82 s is obtained. Conclusions: A network with a relatively short initial learning time was achieved. Its accuracy is comparable to previous studies. This network could be used as a plug-and play predictor in which (a) tumor positions can be predicted as soon as treatment begins and (b) the need for pretreatment data and optimization for individual patients can be avoided.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Toward a Better Understanding of Species Interactions through Network Biology

Within the last decade, there has been an explosion of multi-omics data generated for several microbial systems. At the same time, new methods of analysis have emerged that are based on inferring networks that link features both within and between species based on correlation in abundance. These developments prompt two important questions. What can be done with network approaches to better understand microbial species interactions? What challenges remain in applying network approaches to query the more complex systems of natural settings? Here, I briefly describe what has been done and what questions still need to be answered. Over the next 5 to 10 years, we will be in a strong position to infer networks that contain multiple kinds of omic data and describe systems with multiple species. These applications will open the door for a better understanding and use of microbiomes across a variety of fields.

Microbiology↗
DOE OSTI2019

Neural Network Astronomy as a New Tool for Observing Bright and Compact Objects

We propose a new method for solving an important problem of astronomy that arises in observations with ultrahigh-angular-resolution interferometers. This method is based on the application of the theory of artificial neural networks. We propose and compute a multiparameter model for a celestial object like Sgr A*. For this model we have numerically constructed a number of probable images for neural network training. After neural network training on these images, the quality of its operation has been tested on another series of images from the same model. We have proven that a neural network can recognize and classify celestial objects (also obtained from interferometers) virtually no worse than can be done by a human.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Identifying Backbones in Three-Dimensional Discrete Fracture Networks: A Bipartite Graph-Based Approach

In this paper, we present a graph-based method to identify primary flow and transport sub-networks in three-dimensional discrete fracture networks (DFNs). The structure of a DFN lends itself to the use of graphs as a coarse-scale representation that retains the multiscale nature of flow and transport through fracture media. We develop a bipartite graph representation that integrates fracture network topology, fracture geometry, and hydraulic properties. We show that the two most common graph-representations of DFNs, vertices representing intersections and vertices representing fractures, are projections of this bipartite graph thereby providing a generalization of previous DFN-graph frameworks. The primary subnetworks in each DFN are identified by running a heuristic algorithm that determines the edge-disjoint shortest paths through the graph which correspond to the regions where the fastest transport occurs. The method does not have any user-defined parameters and terminates in a finite number of steps. The quality of the method is demonstrated by comparing transport simulations on the identified primary subnetwork and full network, which are in good agreement for early and middle times. In conclusion, these estimates of the first passage times can be achieved with close to an order of magnitude reduction of computational expense using the proposed method.

58 GEOSCIENCES↗
DOE OSTI2021

Anonymization of Network Traces Data through Condensation-based Differential Privacy

Network traces are considered a primary source of information to researchers, who use them to investigate research problems such as identifying user behavior, analyzing network hierarchy, maintaining network security, classifying packet flows, and much more. However, most organizations are reluctant to share their data with a third party or the public due to privacy concerns. Therefore, data anonymization prior to sharing becomes a convenient solution to both organizations and researchers. Although several anonymization algorithms are available, few of them allow sufficient privacy (organization need), acceptable data utility (researcher need), and efficient data analysis at the same time. This article introduces a condensation-based differential privacy anonymization approach that achieves an improved tradeoff between privacy and utility compared to existing techniques and produces anonymized network trace data that can be shared publicly without lowering its utility value. Our solution also does not incur extra computation overhead for the data analyzer. A prototype system has been implemented, and experiments have shown that the proposed approach preserves privacy and allows data analysis without revealing the original data even when injection attacks are launched against it. When anonymized datasets are given as input to graph-based intrusion detection techniques, they yield almost identical intrusion detection rates as the original datasets with only a negligible impact.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantifying Negative Effects of Carbon-Binder Networks from Electrochemical Performance of Porous Li-Ion Electrodes

Porous Li-ion electrodes contain active particles, ion transporting electrolyte, and carbon-binder networks. While macrohomogeneous models are often used to predict electrode behavior, accurate predictions remain challenging, owing to the incomplete understanding of the critical role of carbon-binder networks and how they affect the electrochemical response. The present study systematically characterizes these effects in terms of effective properties by utilizing macrohomogeneous models to analyze the measured responses for electrodes with different carbon-binder content, electrode thickness, and porosity but with identical materials. We find that the impact of the carbon-binder network is more severe than previously thought. Even for low carbon-binder content (5 %wt. dry electrode), the presence of the network decreases the reaction area and increases the ion transport resistance, negatively impacting electrode performance. These effects scale with not just porosity or active material volume but also with carbon-binder content. The findings underscore the importance of connecting all effective properties to electrode specifications in a full factorial sense to transform the electrode design paradigm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Prior knowledge driven Granger causality analysis on gene regulatory network discovery

Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, the propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster.

Comparative network analysis↗
DOE OSTI2015

Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury

Objective: Mild traumatic brain injury (mTBI) has high prevalence in the military, among athletes, and in the general population worldwide (largely due to falls). Consequences can include a range of neuropsychological disorders. Unfortunately, such neural injury often goes undiagnosed due to the difficulty in identifying symptoms, so the discovery of an effective biomarker would greatly assist diagnosis; however, no single biomarker has been identified. We identify several body substances as potential components of a panel of biomarkers to support the diagnosis of mild traumatic brain injury. Methods: Our approach to diagnostic biomarker discovery combines ideas and techniques from systems medicine, natural language processing, and graph theory. We create a molecular interaction network that represents neural injury and is composed of relationships automatically extracted from the literature. We retrieve citations related to neurological injury and extract relationships (semantic predications) that contain potential biomarkers. After linking all relationships together to create a network representing neural injury, we filter the network by relationship frequency and concept connectivity to reduce the set to a manageable size of higher interest substances. Results: 99,437 relevant citations yielded 26,441 unique relations. 18,085 of these contained a potential biomarker as subject or object with a total of 6246 unique concepts. After filtering by graph metrics, the set was reduced to 1021 relationships with 49 unique concepts, including 17 potential biomarkers. Conclusion: We created a network of relationships containing substances derived from 99,437 citations and filtered using graph metrics to provide a set of 17 potential biomarkers. We discuss the interaction of several of these (glutamate, glucose, and lactate) as the basis for more effective diagnosis than is currently possible. This method provides an opportunity to focus the effort of wet bench research on those substances with the highest potential as biomarkers for mTBI.

59 BASIC BIOLOGICAL SCIENCES↗