scholarly journals Neural Network Inverse Model for Quality Monitoring - Application to a High Quality Lackering Process

2017 ◽  
Author(s):  
Philippe Thomas ◽  
Marie-Christine Suhner ◽  
Emmanuel Zimmermann ◽  
Hind Bril El Haouzi ◽  
André Thomas ◽  
...  
Keyword(s):  
Neural Network ◽  
Inverse Model ◽  
Quality Monitoring ◽  
High Quality ◽  
Monitoring Application

Force Control of a Hydraulic Actuator With a Neural Network Inverse Model

2021 ◽  
Vol 6 (2) ◽  
pp. 2814-2821
Author(s):  
Sung-Woo Kim ◽  
Buyoun Cho ◽  
Seunghoon Shin ◽  
Jun-Ho Oh ◽  
Jemin Hwangbo ◽  
...  
Keyword(s):  
Neural Network ◽  
Force Control ◽  
Inverse Model ◽  
Hydraulic Actuator

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

A general inverse control model of a magneto-rheological damper based on neural network

2021 ◽  
pp. 107754632098638
Author(s):  
Yaya Yan ◽  
Longlei Dong ◽  
Yi Han ◽  
Weishuo Li
Keyword(s):  
Neural Network ◽  
Fuzzy Controller ◽  
Back Propagation ◽  
Inverse Model ◽  
Damping Force ◽  
Inverse Control ◽  
Nonlinear Hysteresis ◽  
Road Excitation ◽  
Magneto Rheological ◽  
Vehicle Suspension System

Because of the nonlinear hysteresis characteristics of the magneto-rheological damper, the damper’s inverse model has disadvantages of low fitting accuracy and poor practicality. Therefore, in this study, an optimized genetic algorithm has been proposed to optimize the back propagation neural network’s initial weights and threshold. Compared with other damper controllers, the proposed inverse model improves the control current’s prediction accuracy and tracks the desired damping force in real time. Moreover, the proposed inverse model and designed fuzzy controller are applied to the 1/4 vehicle suspension system simulation. The obtained results show that the optimized neural network model can be applied to a practical control. The root mean square value of body acceleration of semi-active suspension is lower than that of passive suspension under different road excitation. This method provides a foundation for the accurate modeling and semi-active control of the magneto-rheological damper.

An IOTA-Based Micropayment System for Air Quality Monitoring Application

2021 ◽  
Author(s):  
Ryota Nakada ◽  
Zhetao Li ◽  
Tingrui Pei ◽  
Kien Nguyen ◽  
Hiroo Sekiya
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Application

scholarly journals Improved Empirical Identification of the Inverse Model of a Squeeze-Film Damper Bearing Based on a Recurrent Neural Network

2018 ◽  
Author(s):  
Ghaith Ghanim Al-Ghazal ◽  
Philip Bonello ◽  
Sergio G. Torres Cedillo
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Structural Model ◽  
Solution Process ◽  
Time History ◽  
Inverse Model ◽  
Training Data ◽  
Squeeze Film ◽  
Squeeze Film Damper ◽  
History Of

Most recently proposed techniques for inverse rotordynamic problems seek to identify the unbalance on a rotor using a known structural model and measurements from externally mounted sensors only. Such non-intrusive techniques are important for balancing rotors that cannot be accessed under operational conditions because of temperature or space restrictions. The presence of nonlinear bearings, like squeeze-film damper (SFD) bearings used in aero-engines, complicates the solution process of the inverse rotordynamic problem. In certain practical aero-engine configurations, the solution process requires a substitute for internal instrumentation to quantify the SFD journal vibration. This can be provided by an inverse model of the SFD bearing which outputs the time history of the relative vibration of the SFD journal relative to its housing, for a given input time history of the SFD force. This paper focuses on the inverse model of the SFD and presents an improved methodology for its identification via a Recurrent Neural Network (RNN) trained using experimental data from a purposely designed rig. The novel application of chirp excitation via two orthogonal shakers considerably improves both the quality of the training data and the efficiency of its generation, relative to an earlier preliminary work. Validation test results show that the RNNs can predict the journal displacement time history with reasonable accuracy. It is therefore expected that such an inverse SFD model would serve as a reliable component in the solution of the wider inverse problem of a rotordynamic system.

scholarly journals Reconstruction of flow conditions from 2004 Indian Ocean tsunami deposits at the Phra Thong island using a deep neural network inverse model

2021 ◽  
Vol 21 (5) ◽  
pp. 1667-1683
Author(s):  
Rimali Mitra ◽  
Hajime Naruse ◽  
Shigehiro Fujino
Keyword(s):  
Neural Network ◽  
Grain Size ◽  
Indian Ocean ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Deep Neural Network ◽  
Inverse Model ◽  
Indian Ocean Tsunami ◽  
Flow Conditions ◽  
2004 Indian Ocean Tsunami

Abstract. The 2004 Indian Ocean tsunami caused significant economic losses and a large number of fatalities in the coastal areas. The estimation of tsunami flow conditions using inverse models has become a fundamental aspect of disaster mitigation and management. Here, a case study involving the Phra Thong island, which was affected by the 2004 Indian Ocean tsunami, in Thailand was conducted using inverse modeling that incorporates a deep neural network (DNN). The DNN inverse analysis reconstructed the values of flow conditions such as maximum inundation distance, flow velocity and maximum flow depth, as well as the sediment concentration of five grain-size classes using the thickness and grain-size distribution of the tsunami deposit from the post-tsunami survey around Phra Thong island. The quantification of uncertainty was also reported using the jackknife method. Using other previous models applied to areas in and around Phra Thong island, the predicted flow conditions were compared with the reported observed values and simulated results. The estimated depositional characteristics such as volume per unit area and grain-size distribution were in line with the measured values from the field survey. These qualitative and quantitative comparisons demonstrated that the DNN inverse model is a potential tool for estimating the physical characteristics of modern tsunamis.

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