scholarly journals A Hybrid Approach Enabling Large-Scale Glycomic Analysis of Post-Golgi Vesicles Reveals a Transport Route for Polysaccharides

2019 ◽  
Vol 31 (3) ◽  
pp. 627-644 ◽  
Author(s):  
Thomas Wilkop ◽  
Sivakumar Pattathil ◽  
Guangxi Ren ◽  
Destiny J. Davis ◽  
Wenlong Bao ◽  
...  
Keyword(s):  
Large Scale ◽  
Hybrid Approach ◽  
Golgi Vesicles

scholarly journals Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Meysam Goodarzi ◽  
Darko Cvetkovski ◽  
Nebojsa Maletic ◽  
Jesús Gutiérrez ◽  
Eckhard Grass
Keyword(s):  
Bayesian Approach ◽  
Large Scale ◽  
Hybrid Approach ◽  
Position Estimation ◽  
5G Networks ◽  
Clock Skew ◽  
Depth Analysis ◽  
The Impact ◽  
Mean Square Errors ◽  
Clock Offset

AbstractClock synchronization has always been a major challenge when designing wireless networks. This work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Furthermore, we provide an in-depth analysis of the impact of the proposed approach on a synchronization-sensitive service, i.e., localization. Specifically, we expose the substantial benefit of belief propagation (BP) running on factor graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian recursive filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into local synchronization domains and applying the most suitable synchronization algorithm (BP- or BRF-based) on each domain. The performance of the hybrid approach is then evaluated in terms of the root mean square errors (RMSEs) of the clock offset, clock skew, and the position estimation. According to the simulations, in spite of the simplifications in the hybrid approach, RMSEs of clock offset, clock skew, and position estimation remain below 10 ns, 1 ppm, and 1.5 m, respectively.

scholarly journals Development of PM<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality model

2015 ◽  
Vol 8 (7) ◽  
pp. 2153-2165 ◽  
Author(s):  
C. E. Ivey ◽  
H. A. Holmes ◽  
Y. T. Hu ◽  
J. A. Mulholland ◽  
A. G. Russell
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Large Scale ◽  
Major Ions ◽  
Current Knowledge ◽  
Direct Method ◽  
Hybrid Approach ◽  
Sensitivity Analyses ◽  
Adjustment Factors ◽  
The Impact

Abstract. An integral part of air quality management is knowledge of the impact of pollutant sources on ambient concentrations of particulate matter (PM). There is also a growing desire to directly use source impact estimates in health studies; however, source impacts cannot be directly measured. Several limitations are inherent in most source apportionment methods motivating the development of a novel hybrid approach that is used to estimate source impacts by combining the capabilities of receptor models (RMs) and chemical transport models (CTMs). The hybrid CTM–RM method calculates adjustment factors to refine the CTM-estimated impact of sources at monitoring sites using pollutant species observations and the results of CTM sensitivity analyses, though it does not directly generate spatial source impact fields. The CTM used here is the Community Multiscale Air Quality (CMAQ) model, and the RM approach is based on the chemical mass balance (CMB) model. This work presents a method that utilizes kriging to spatially interpolate source-specific impact adjustment factors to generate revised CTM source impact fields from the CTM–RM method results, and is applied for January 2004 over the continental United States. The kriging step is evaluated using data withholding and by comparing results to data from alternative networks. Data withholding also provides an estimate of method uncertainty. Directly applied (hybrid, HYB) and spatially interpolated (spatial hybrid, SH) hybrid adjustment factors at withheld observation sites had a correlation coefficient of 0.89, a linear regression slope of 0.83 ± 0.02, and an intercept of 0.14 ± 0.02. Refined source contributions reflect current knowledge of PM emissions (e.g., significant differences in biomass burning impact fields). Concentrations of 19 species and total PM2.5 mass were reconstructed for withheld observation sites using HYB and SH adjustment factors. The mean concentrations of total PM2.5 at withheld observation sites were 11.7 (± 8.3), 16.3 (± 11), 8.59 (± 4.7), and 9.2 (± 5.7) μg m−3 for the observations, CTM, HYB, and SH predictions, respectively. Correlations improved for concentrations of major ions, including nitrate (CMAQ–DDM (decoupled direct method): 0.404, SH: 0.449), ammonium (CMAQ–DDM: 0.454, SH: 0.492), and sulfate (CMAQ–DDM: 0.706, SH: 0.730). Errors in simulated concentrations of metals were reduced considerably: 295 % (CMAQ–DDM) to 139 % (SH) for vanadium; and 1340 % (CMAQ–DDM) to 326 % (SH) for manganese. Errors in simulated concentrations of some metals are expected to remain given the uncertainties in source profiles. Species concentrations were reconstructed using SH results, and the error relative to observed concentrations was greatly reduced as compared to CTM-simulated concentrations. Results demonstrate that the hybrid method along with a spatial extension can be used for large-scale, spatially resolved source apportionment studies where observational data are spatially and temporally limited.

Simulation study of a constant time hybrid approach for large scale terrain mapping using satellite stereo imagery

2016 ◽  
Vol 79 ◽  
pp. 132-146
Author(s):  
A. Sarkar ◽  
R. Reiger ◽  
D. Chatterjee ◽  
S. Patranabis ◽  
H. Singh ◽  
...  
Keyword(s):  
Simulation Study ◽  
Large Scale ◽  
Hybrid Approach ◽  
Constant Time ◽  
Terrain Mapping ◽  
Stereo Imagery

Detecting Pressure Anomalies While Drilling Using a Machine Learning Hybrid Approach

2021 ◽  
Author(s):  
Aurore Lafond ◽  
Maurice Ringer ◽  
Florian Le Blay ◽  
Jiaxu Liu ◽  
Ekaterina Millan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Large Scale ◽  
Hybrid Approach ◽  
Physical Models ◽  
Training Data ◽  
Digital Data ◽  
Machine Learning Techniques ◽  
New System

Abstract Abnormal surface pressure is typically the first indicator of a number of problematic events, including kicks, losses, washouts and stuck pipe. These events account for 60–70% of all drilling-related nonproductive time, so their early and accurate detection has the potential to save the industry billions of dollars. Detecting these events today requires an expert user watching multiple curves, which can be costly, and subject to human errors. The solution presented in this paper is aiming at augmenting traditional models with new machine learning techniques, which enable to detect these events automatically and help the monitoring of the drilling well. Today’s real-time monitoring systems employ complex physical models to estimate surface standpipe pressure while drilling. These require many inputs and are difficult to calibrate. Machine learning is an alternative method to predict pump pressure, but this alone needs significant labelled training data, which is often lacking in the drilling world. The new system combines these approaches: a machine learning framework is used to enable automated learning while the physical models work to compensate any gaps in the training data. The system uses only standard surface measurements, is fully automated, and is continuously retrained while drilling to ensure the most accurate pressure prediction. In addition, a stochastic (Bayesian) machine learning technique is used, which enables not only a prediction of the pressure, but also the uncertainty and confidence of this prediction. Last, the new system includes a data quality control workflow. It discards periods of low data quality for the pressure anomaly detection and enables to have a smarter real-time events analysis. The new system has been tested on historical wells using a new test and validation framework. The framework runs the system automatically on large volumes of both historical and simulated data, to enable cross-referencing the results with observations. In this paper, we show the results of the automated test framework as well as the capabilities of the new system in two specific case studies, one on land and another offshore. Moreover, large scale statistics enlighten the reliability and the efficiency of this new detection workflow. The new system builds on the trend in our industry to better capture and utilize digital data for optimizing drilling.

A Hybrid Approach of Heuristic and Neural Network for Packing Problems

1994 ◽  
Author(s):  
Zuo Dai ◽  
Jianzhong Cha
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Hybrid Approach ◽  
Two Dimensions ◽  
Packing Problems ◽  
Combinatorial Optimization Problems ◽  
Complete Problems ◽  
Np Complete

Abstract Artificial Neural Networks, particularly the Hopfield-Tank network, have been effectively applied to the solution of a variety of tasks formulated as large scale combinatorial optimization problems, such as Travelling Salesman Problem and N Queens Problem [1]. The problem of optimally packing a set of geometries into a space with finite dimensions arises frequently in many applications and is far difficult than general NP-complete problems listed in [2]. Until now within accepted time limit, it can only be solved with heuristic methods for very simple cases (e.g. 2D layout). In this paper we propose a heuristic-based Hopfield neural network designed to solve the rectangular packing problems in two dimensions, which is still NP-complete [3]. By comparing the adequacy and efficiency of the results with that obtained by several other exact and heuristic approaches, it has been concluded that the proposed method has great potential in solving 2D packing problems.

A Hybrid Approach for Model Order Reduction and Controller Design of Large-Scale Power Systems

2021 ◽  
pp. 211-226
Author(s):  
Rishabh Singhal ◽  
Yashonidhi Srivastava ◽  
Shini Agarwal ◽  
Abhimanyu Kumar ◽  
Souvik Ganguli
Keyword(s):  
Power Systems ◽  
Model Order Reduction ◽  
Large Scale ◽  
Controller Design ◽  
Hybrid Approach ◽  
Order Reduction ◽  
Model Order
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