Hybrid modelling of anaerobic wastewater treatment processes

2001 ◽  
Vol 43 (1) ◽  
pp. 43-50 ◽  
Author(s):  
A. Kamara ◽  
O. Bernard ◽  
A. Genovesi ◽  
D. Dochain ◽  
A. Benhammou ◽  
...  
Keyword(s):  
Neural Network ◽  
A Priori ◽  
Hybrid Approach ◽  
Training Procedure ◽  
Dynamic Representation ◽  
Knowledge Based ◽  
Treatment Processes ◽  
Kinetic Growth ◽  
The Neural Network ◽  
Component Training

This paper presents a hybrid approach for the modelling of an anaerobic digestion process. The hybrid model combines a feedforward network, describing the bacterial kinetics, and the a priori knowledge based on the mass balances of the process components. We have considered an architecture which incorporates the neural network as a static model of unmeasured process parameters (kinetic growth rate) and an integrator for the dynamic representation of the process using a set of dynamic differential equations. The paper contains a description of the neural network component training procedure. The performance of this approach is illustrated with experimental data.

scholarly journals Reconstruction of 3D Object Shape Using Hybrid Modular Neural Network Architecture Trained on 3D Models from ShapeNetCore Dataset

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1553 ◽  
Author(s):  
Audrius Kulikajevas ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius ◽  
Sanjay Misra
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
A Priori ◽  
Three Dimensional ◽  
Qualitative Evaluation ◽  
3D Models ◽  
Depth Sensors ◽  
The Neural Network ◽  
Artificial Neural Network Ann ◽  
Commercial Applications

Depth-based reconstruction of three-dimensional (3D) shape of objects is one of core problems in computer vision with a lot of commercial applications. However, the 3D scanning for point cloud-based video streaming is expensive and is generally unattainable to an average user due to required setup of multiple depth sensors. We propose a novel hybrid modular artificial neural network (ANN) architecture, which can reconstruct smooth polygonal meshes from a single depth frame, using a priori knowledge. The architecture of neural network consists of separate nodes for recognition of object type and reconstruction thus allowing for easy retraining and extension for new object types. We performed recognition of nine real-world objects using the neural network trained on the ShapeNetCore model dataset. The results evaluated quantitatively using the Intersection-over-Union (IoU), Completeness, Correctness and Quality metrics, and qualitative evaluation by visual inspection demonstrate the robustness of the proposed architecture with respect to different viewing angles and illumination conditions.

Analyses of Possible Failure Mechanisms and Root Failure Causes in Power Plant Components Using Neural Networks and Structural Failure Database

1996 ◽  
Vol 118 (2) ◽  
pp. 237-246 ◽  
Author(s):  
S. Yoshimura ◽  
A. S. Jovanovic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Case Studies ◽  
Failure Mechanisms ◽  
Structural Components ◽  
Type B ◽  
Structural Failure ◽  
Knowledge Based ◽  
The Neural Network ◽  
Unknown Case

This paper describes analyses of case studies on failure of structural components in power plants using hierarchical (multilayer) neural networks. Using selected test data about case studies stored in the structural failure database of a knowledge-based system, the network is trained: either to predict possible failure mechanisms like creep, overheating (OH), or overstressing (OS)-induced failure (network of Type A), or to classify a root failure cause of each case study into either a primary or secondary cause (network of Type B). In the present study, the primary root cause is defined as “manufacturing, material or design-induced causes,” while the secondary one as “not manufacturing, material or design-induced causes, e.g., failures due to operation or mal-operation.” An ordinary three-layer neural network employing the back propagation algorithm with the momentum method is utilized in this study. The results clearly show that the neural network is a powerful tool for analyzing case studies of failure in structural components. For example, the trained network of Type A predicts creep-induced failure in unknown case studies with an accuracy of 86 percent, while the network of Type B classifies root failure causes of unknown case studies with an accuracy of 88 percent. It should be noted that, due to a shortage of available case studies, an appropriate selection of case studies and input parameters to be used for network training was necessary in order to attain high accuracy. A collection of more case studies should, however, resolve this problem, and improve the accuracy of the analyses. An analysis module for case studies using the neural network has also been developed and successfully implemented in a knowledge-based system.

A Hybrid Approach to Missing Data

2010 ◽  
pp. 45-70
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Missing Data ◽  
Hybrid Approach ◽  
Principal Component ◽  
Missing Data Imputation ◽  
The Neural Network ◽  
Genetic Algorithm Approach ◽  
Successful Approach

The problem of missing data in databases has recently been dealt with through the use computational intelligence. The hybrid of auto-associative neural networks and genetic algorithms has proven to be a successful approach to missing data imputation. Similarly, two auto-associative neural networks are developed to be used in conjunction with genetic algorithm to estimate missing data, and these approaches are compared to a Bayesian auto-associative neural network and genetic algorithm approach. One technique combines three neural networks to form a hybrid auto-associative network, while the other merges principal component analysis and neural networks. The hybrid of the neural network and genetic algorithm approach proves to be the most accurate when estimating one missing value, while a hybrid of principal component and neural networks is more consistent and captures patterns in the data more efficiently.

Analysis of Symbol Generation and Integration in a Unified Model Based on a Neural Network

2005 ◽  
Vol 9 (3) ◽  
pp. 297-303
Author(s):  
Yukari Yamauchi ◽  
◽  
Shun'ichi Tano ◽  
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Simple Model ◽  
Unified Model ◽  
Symbolic Processing ◽  
Advantages And Disadvantages ◽  
Intelligent Processing ◽  
Knowledge Based ◽  
The Neural Network ◽  
Trained Neural Network

The computational (numerical information) and symbolic (knowledge-based) processing used in intelligent processing has advantages and disadvantages. A simple model integrating symbols into a neural network was proposed as a first step toward fusing computational and symbolic processing. To verify the effectiveness of this model, we first analyze the trained neural network and generate symbols manually. Then we discuss generation methods that are able to discover effective symbols during training of the neural network. We evaluated these through simulations of reinforcement learning in simple football games. Results indicate that the integration of symbols into the neural network improved the performance of player agents.

Traveltime-table compression using artificial neural networks for Kirchhoff-migration processing of microseismic data

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. U121-U128
Author(s):  
Serafim I. Grubas ◽  
Georgy N. Loginov ◽  
Anton A. Duchkov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Training Procedure ◽  
Velocity Models ◽  
Kirchhoff Migration ◽  
Microseismic Data ◽  
The Neural Network ◽  
Artificial Neural

Massive computation of seismic traveltimes is widely used in seismic processing, for example, for the Kirchhoff migration of seismic and microseismic data. Implementation of the Kirchhoff migration operators uses large precomputed traveltime tables (for all sources, receivers, and densely sampled imaging points). We have tested the idea of using artificial neural networks for approximating these traveltime tables. The neural network has to be trained for each velocity model, but then the whole traveltime table can be compressed by several orders of magnitude (up to six orders) to the size of less than 1 MB. This makes it convenient to store, share, and use such approximations for processing large data volumes. We evaluate some aspects of choosing neural-network architecture, training procedure, and optimal hyperparameters. On synthetic tests, we find a reasonably accurate approximation of traveltimes by neural networks for various velocity models. A final synthetic test shows that using the neural-network traveltime approximation results in good accuracy of microseismic event localization (within the grid step) in the 3D case.

Application of Artificial Neural Networks to Concrete Pavement Joint Evaluation

1996 ◽  
Vol 1540 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Anastasios M. Ioannides ◽  
Don R. Alexander ◽  
Michael I. Hammons ◽  
Craig M. Davis
Keyword(s):  
Neural Network ◽  
Dimensional Analysis ◽  
Load Transfer ◽  
A Priori ◽  
The United States ◽  
Concrete Pavement ◽  
Data Generation ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Joint Evaluation

Application of the principles of dimensional analysis has recently led to the development of a robust method for assessing the deflection and stress load transfer efficiencies of concrete pavement joints and for backcalculating joint parameters. The new method eliminates the need to make a priori assumptions since pertinent inputs can now be experimentally determined using the falling weight deflectometer. A data base has been generated using numerical integration of Westergaard-type integrals and has been used to train a backpropagation neural network algorithm for joint evaluation. The resulting computer program is simple, efficient, and precise and can be used on site for immediate results. Its predictions are verified by comparisons with closed-form and finite-element solutions pertaining to data collected at three major civilian airports in the United States, including the new Denver International Airport. Also discussed is the role of dimensional analysis in the generation of the training set for a neural network. It is demonstrated that significant savings can be achieved through reduction of the dimensionality of the problem, which could be reinvested in broadening the range of applicability of the neural network. Comparison of neural network predictions with those from conventional regression analysis and from direct interpolation illustrates the benefits of data generation on the basis of fundamental principles of mechanics.

Fuzzy Control of a Novel Type of Translational Meshing Motors

2010 ◽  
Vol 121-122 ◽  
pp. 1038-1043
Author(s):  
Wei Wang ◽  
Xin Jian Shan ◽  
Shi Min Wei
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Torque Control ◽  
Training Procedure ◽  
Test Results ◽  
Model Reference Control ◽  
The Neural Network ◽  
Control Scheme

Owing to the nonlinear characteristic of a novel type of translational meshing motor with model uncertainties, a model reference control system which consists of a neural network and a fuzzy controller is used. The torque model is identified based on BP neural network, and then Fuzzy controller works as the controller. The description of the control system and training procedure of the neural network are given. The test results obtained for a torque control scheme suitable for the control of the motor are also presented to verify the effectiveness of the proposed nonlinear control scheme. It has been found that the fuzzy control system is able to work reliably.

scholarly journals Application of Genetic Algorithms for the Selection of Neural Network Architecture in the Monitoring System for Patients with Parkinson’s Disease

2021 ◽  
Vol 11 (12) ◽  
pp. 5470
Author(s):  
Yulia Shichkina ◽  
Yulia Irishina ◽  
Elizaveta Stanevich ◽  
Armando de Jesus Plasencia Salgueiro
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Genetic Algorithms ◽  
Monitoring System ◽  
Network Architecture ◽  
Hybrid Approach ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Using Data

This article describes an approach for collecting and pre-processing phone owner data, including their voice, in order to classify their condition using data mining methods. The most important research results presented in this article are the developed approaches for the processing of patient voices and the use of genetic algorithms to select the architecture of the neural network in the monitoring system for patients with Parkinson’s disease. The process used to pre-process a person’s voice is described in order to determine the main parameters that can be used in assessing a person’s condition. It is shown that the efficiency of using genetic algorithms for constructing neural networks depends on the composition of the data. As a result, the best result in the accuracy of assessing the patient’s condition can be obtained by a hybrid approach, where a part of the neural network architecture is selected analytically manually, while the other part is built automatically.

scholarly journals FACADE RECONSTRUCTION FOR TEXTURED LOD2 CITYGML MODELS BASED ON DEEP LEARNING AND MIXED INTEGER LINEAR PROGRAMMING

2019 ◽  
Vol IV-2/W5 ◽  
pp. 37-44 ◽  
Author(s):  
S. Hensel ◽  
S. Goebbels ◽  
M. Kada
Keyword(s):  
Neural Network ◽  
A Priori ◽  
Linear Optimization ◽  
Mixed Integer ◽  
Building Models ◽  
The Neural Network ◽  
Mixed Integer Linear Optimization ◽  
Bounding Boxes ◽  
Wall Openings

<p><strong>Abstract.</strong> The paper describes a workflow for generating LoD3 CityGML models (i.e. semantic building models with structured facades) based on textured LoD2 CityGML models by adding window and door objects. For each wall texture, bounding boxes of windows and doors are detected using “Faster R-CNN”, a deep neural network. We evaluate results for textures with different resolutions on the ICG Graz50 facade dataset. In general, detected bounding boxes match very well with the rectangular shape of most wall openings. Thus, no further classification of shapes is required. Windows are typically aligned to rows and columns, and only a few different types of windows exist for each facade. However, the neural network proposes rectangles of varying sizes, which are not always aligned perfectly. Thus, we use post-processing to obtain a more realistic appearance of facades. Window and door rectangles get aligned by solving a mixed integer linear optimization problem, which automatically leads to a clustering of these openings into few different classes of window and door types. Furthermore, an a-priori knowledge about the number of clusters is not required.</p>

scholarly journals Assignment of Focus Position with Convolutional Neural Networks in Adaptive Lens Based Axial Scanning for Confocal Microscopy

2022 ◽  
Vol 12 (2) ◽  
pp. 661
Author(s):  
Katharina Schmidt ◽  
Nektarios Koukourakis ◽  
Jürgen W. Czarske
Keyword(s):  
Neural Network ◽  
Applied Voltage ◽  
Laser Scanning ◽  
Degrees Of Freedom ◽  
Single Shot ◽  
Training Procedure ◽  
Focus Position ◽  
The Neural Network ◽  
Complex Adaptive ◽  
Axial Scanning

Adaptive lenses offer axial scanning without mechanical translation and thus are promising to replace mechanical-movement-based axial scanning in microscopy. The scan is accomplished by sweeping the applied voltage. However, the relation between the applied voltage and the resulting axial focus position is not unambiguous. Adaptive lenses suffer from hysteresis effects, and their behaviour depends on environmental conditions. This is especially a hurdle when complex adaptive lenses are used that offer additional functionalities and are controlled with more degrees of freedom. In such case, a common approach is to iterate the voltage and monitor the adaptive lens. Here, we introduce an alternative approach which provides a single shot estimation of the current axial focus position by a convolutional neural network. We use the experimental data of our custom confocal microscope for training and validation. This leads to fast scanning without photo bleaching of the sample and opens the door to automatized and aberration-free smart microscopy. Applications in different types of laser-scanning microscopes are possible. However, maybe the training procedure of the neural network must be adapted for some use cases.

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