Operational Noise Data for OH-58D Army Helicopters

1992 ◽  
Author(s):  
L. J. Benson ◽  
Micheal J. White ◽  
Kevin J. Murphy
Keyword(s):  
Noise Data

scholarly journals Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine

2021 ◽  
Vol 11 (9) ◽  
pp. 3867
Author(s):  
Zhewei Liu ◽  
Zijia Zhang ◽  
Yaoming Cai ◽  
Yilin Miao ◽  
Zhikun Chen
Keyword(s):  
Extreme Learning Machine ◽  
Supervised Classification ◽  
Structured Data ◽  
Model Complex ◽  
Euclidean Domain ◽  
Noise Data ◽  
Data Points ◽  
Learning Machine ◽  
Random Hypergraph ◽  
Special Case

Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on structured data and poor robustness on noise data. This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random hypergraph convolutional layer followed by a hypergraph convolutional regression layer, enabling it to model complex intraclass variations. We show that the traditional ELM is a special case of the HGCELM model in the regular Euclidean domain. Extensive experimental results show that HGCELM remarkably outperforms eight competitive methods on 26 classification benchmarks.

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

scholarly journals A state-space model to derive motorboat noise effects on fish movement from acoustic tracking data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Margarida Barcelo-Serra ◽  
Sebastià Cabanellas ◽  
Miquel Palmer ◽  
Marta Bolgan ◽  
Josep Alós
Keyword(s):  
State Space ◽  
Marine Pollution ◽  
State Space Model ◽  
Movement Pattern ◽  
Noise Pollution ◽  
Individual Variability ◽  
Fish Movement ◽  
Space Model ◽  
Noise Data ◽  
Close Range

AbstractMotorboat noise is recognized as a major source of marine pollution, however little is known about its ecological consequences on coastal systems. We developed a State Space Model (SSM) that incorporates an explicit dependency on motorboat noise to derive its effects on the movement of resident fish that transition between two behavioural states (swimming vs. hidden). To explore the performance of our model, we carried out an experiment where free-living Serranus scriba were tracked with acoustic tags, while motorboat noise was simultaneously recorded. We fitted the generated tracking and noise data into our SSM and explored if the noise generated by motorboats passing at close range affected the movement pattern and the probability of transition between the two states using a Bayesian approach. Our results suggest high among individual variability in movement patterns and transition between states, as well as in fish response to the presence of passing motorboats. These findings suggest that the effects of motorboat noise on fish movement are complex and require the precise monitoring of large numbers of individuals. Our SSM provides a methodology to address such complexity and can be used for future investigations to study the effects of noise pollution on marine fish.

scholarly journals Deep electrical resistivity tomography for the prospection of low- to medium-enthalpy geothermal resources

2019 ◽  
Vol 219 (3) ◽  
pp. 2056-2072
Author(s):  
A Carrier ◽  
F Fischanger ◽  
J Gance ◽  
G Cocchiararo ◽  
G Morelli ◽  
...  
Keyword(s):  
Electrical Potential ◽  
Bouguer Anomaly ◽  
Industrial Area ◽  
Gravity Models ◽  
Efficient Technology ◽  
Geothermal Resources ◽  
Reflection Seismic ◽  
Resistivity Model ◽  
Noise Data ◽  
Geoelectrical Methods

SUMMARY The growth of the geothermal industry sector requires innovative methods to reduce exploration costs whilst minimizing uncertainty during subsurface exploration. Until now geoelectrical prospection had to trade between logistically complex cabled technologies reaching a few hundreds meters deep versus shallow-reaching prospecting methods commonly used in hydro-geophysical studies. We present a recent technology for geoelectrical prospection, and show how geoelectrical methods may allow the investigation of medium-enthalpy geothermal resources until about 1 km depth. The use of the new acquisition system, which is made of a distributed set of independent electrical potential recorders, enabled us to tackle logistics and noise data issues typical of urbanized areas. We acquired a 4.5-km-long 2-D geoelectrical survey in an industrial area to investigate the subsurface structure of a sedimentary sequence that was the target of a ∼700 m geothermal exploration well (Geo-01, Satigny) in the Greater Geneva Basin, Western Switzerland. To show the reliability of this new method we compared the acquired resistivity data against reflection seismic and gravimetric data and well logs. The processed resistivity model is consistent with the interpretation of the active-seismic data and density variations computed from the inversion of the residual Bouguer anomaly. The combination of the resistivity and gravity models suggest the presence of a low resistivity and low density body crossing Mesozoic geological units up to Palaeogene–Neogene units that can be used for medium-enthalpy geothermal exploitation. Our work points out how new geoelectrical methods may be used to identify thermal groundwater at depth. This new cost-efficient technology may become an effective and reliable exploration method for the imaging of shallow geothermal resources.

Transient Analysis and Simulation of Pitting Corrosion for the Estimation of Noise Resistance

CORROSION ◽  
2000 ◽  
Vol 56 (9) ◽  
pp. 928-934
Author(s):  
G. Miramontes de León ◽  
D. C. Farden ◽  
D. E. Tallman
Keyword(s):  
Computer Simulation ◽  
Pitting Corrosion ◽  
Transient Analysis ◽  
Electrochemical Noise ◽  
Electrochemical Impedance ◽  
Transient Behavior ◽  
Noise Resistance ◽  
New Approach ◽  
Noise Data ◽  
Current Transients

Abstract A new approach for the measurement of noise resistance based on the transient behavior of pitting corrosion is presented. Potential noise and current transients have been recognized as a characteristic behavior of pitting corrosion. This new approach uses the transient information present during corrosion as a way to estimate the noise resistance of coated metals directly. Computer simulation and analytical results are presented, indicating that the new technique can be applied to the problem of noise resistance estimation. This new approach was applied to experimental electrochemical noise data obtained with commercial electrochemical impedance spectroscopy (EIS)/electrochemcial noise measurement (ENM) equipment.

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