scholarly journals Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 407
Author(s):  
Vitor B. Furlong ◽  
Luciano J. Corrêa ◽  
Fernando V. Lima ◽  
Roberto C. Giordano ◽  
Marcelo P. A. Ribeiro
Keyword(s):  
Tracking Error ◽  
Fuzzy Rule ◽  
Soft Sensor ◽  
Training Data ◽  
Data Sets ◽  
State Variables ◽  
Reactor Performance ◽  
Biomass Hydrolysis ◽  
Free Carbohydrates ◽  
Artificial Neural Network Ann

Second generation ethanol faces challenges before profitable implementation. Biomass hydrolysis is one of the bottlenecks, especially when this process occurs at high solids loading and with enzymatic catalysts. Under this setting, kinetic modeling and reaction monitoring are hindered due to the conditions of the medium, while increasing the mixing power. An algorithm that addresses these challenges might improve the reactor performance. In this work, a soft sensor that is based on agitation power measurements that uses an Artificial Neural Network (ANN) as an internal model is proposed in order to predict free carbohydrates concentrations. The developed soft sensor is used in a Moving Horizon Estimator (MHE) algorithm to improve the prediction of state variables during biomass hydrolysis. The algorithm is developed and used for batch and fed-batch hydrolysis experimental runs. An alteration of the classical MHE is proposed for improving prediction, using a novel fuzzy rule to alter the filter weights online. This alteration improved the prediction when compared to the original MHE in both training data sets (tracking error decreased 13%) and in test data sets, where the error reduction obtained is 44%.

scholarly journals ANN Synthesis Model of Single-Feed Corner-Truncated Circularly Polarized Microstrip Antenna with an Air Gap for Wideband Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhongbao Wang ◽  
Shaojun Fang
Keyword(s):  
Computer Aided Design ◽  
Microstrip Antenna ◽  
Microstrip Antennas ◽  
Air Gap ◽  
Training Data ◽  
Data Sets ◽  
Circularly Polarized ◽  
Artificial Neural Network Ann ◽  
Aided Design ◽  
Lm Algorithm

A computer-aided design model based on the artificial neural network (ANN) is proposed to directly obtain patch physical dimensions of the single-feed corner-truncated circularly polarized microstrip antenna (CPMA) with an air gap for wideband applications. To take account of the effect of the air gap, an equivalent relative permittivity is introduced and adopted to calculate the resonant frequency andQ-factor of square microstrip antennas for obtaining the training data sets. ANN architectures using multilayered perceptrons (MLPs) and radial basis function networks (RBFNs) are compared. Also, six learning algorithms are used to train the MLPs for comparison. It is found that MLPs trained with the Levenberg-Marquardt (LM) algorithm are better than RBFNs for the synthesis of the CPMA. An accurate model is achieved by using an MLP with three hidden layers. The model is validated by the electromagnetic simulation and measurements. It is enormously useful to antenna engineers for facilitating the design of the single-feed CPMA with an air gap.

Implementation Issues in Artificial Neural Network Based Thermal Analysis

2000 ◽  
Author(s):  
K. T. Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Exchangers ◽  
Training Data ◽  
Data Sets ◽  
Two Phase ◽  
Numerical Experimentation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Abstract It is now known the generally it can be demonstrated that artificial neural network (ANN), particularly the fully-connected feedforward configuration with backward propagation error-correction routine, can be a rather effective and accurate tool to correlate performance data of thermal devices such as heat exchangers (Sen and Yang, 2000; Kalogirou, 1999). Good examples are the recent demonstrations for the compact fin-tube heat exchangers (Diaz et al., 1999a; Yang et al., 2000; Pacheco-Vega et al., 1999) including those with complex geometries and also two-phase evaporators (Pacheco-Vega et al., 2000) as well as the dynamic modeling of such heat exchangers and their adaptive control (Diaz et al., 1999b; Diaz et al., 2000). Unfortunately, despite such successes, there are still implementation issues of the ANN analysis which lead to uncertainties in its applications and the achieved results. The present paper discusses such issues and the current practices in dealing with them. Those that will be discussed include the number of hidden layers, the number of nodes in each hidden layer, the range within which the input-output data are normalized, the initial assignment of weights and biases, the selection of training data sets, and the training rate. As will be shown, the specific choices are by no means trivial, and yet are rather important in achieving good ANN results in any given application. Since there are no general sound theoretical basis for such choices at the present time, past experience and numerical experimentation are often the best guides. However, many of these choices and issues relating to them involve optimization. As a result. Some of the existing optimization algorithms may prove to be useful and highly desirable in this regard. The current on-going research to provide some rational basis in these issues will also be discussed. Finally, it will also be mentioned that successfully implemented ANNs have many additional uses in practice. Examples include parameter sensitivity analysis, training, design of new experiments, and clustering of data sets.

scholarly journals A Robust Observer—Based Adaptive Control of Second—Order Systems with Input Saturation via Dead-Zone Lyapunov Functions

Computation ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 82
Author(s):  
Alejandro Rincón ◽  
Gloria M. Restrepo ◽  
Fredy E. Hoyos
Keyword(s):  
Lyapunov Functions ◽  
Dead Zone ◽  
Tracking Error ◽  
Input Saturation ◽  
Chemical Reactor ◽  
Second Order ◽  
Control Law ◽  
Compact Set ◽  
State Variables ◽  
Robust Observer

In this study, a novel robust observer-based adaptive controller was formulated for systems represented by second-order input–output dynamics with unknown second state, and it was applied to concentration tracking in a chemical reactor. By using dead-zone Lyapunov functions and adaptive backstepping method, an improved control law was derived, exhibiting faster response to changes in the output tracking error while avoiding input chattering and providing robustness to uncertain model terms. Moreover, a state observer was formulated for estimating the unknown state. The main contributions with respect to closely related designs are (i) the control law, the update law and the observer equations involve no discontinuous signals; (ii) it is guaranteed that the developed controller leads to the convergence of the tracking error to a compact set whose width is user-defined, and it does not depend on upper bounds of model terms, state variables or disturbances; and (iii) the control law exhibits a fast response to changes in the tracking error, whereas the control effort can be reduced through the controller parameters. Finally, the effectiveness of the developed controller is illustrated by the simulation of concentration tracking in a stirred chemical reactor.

MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble

2021 ◽  
Vol 16 (1) ◽  
pp. 1-24
Author(s):  
Yaojin Lin ◽  
Qinghua Hu ◽  
Jinghua Liu ◽  
Xingquan Zhu ◽  
Xindong Wu
Keyword(s):  
Empirical Studies ◽  
Feature Space ◽  
Training Data ◽  
Data Sets ◽  
Learning Framework ◽  
Feature Spaces ◽  
Public Data ◽  
Margin Distribution ◽  
Label Correlations ◽  
Label Correlation

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, mu lti- l abel-specific f eature space e nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.

scholarly journals Experiments of Image Classification Using Dissimilarity Spaces Built with Siamese Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1573
Author(s):  
Loris Nanni ◽  
Giovanni Minchio ◽  
Sheryl Brahnam ◽  
Gianluca Maguolo ◽  
Alessandra Lumini
Keyword(s):  
Vector Space ◽  
Image Classification ◽  
Ad Hoc ◽  
Feature Space ◽  
Medical Data ◽  
Training Data ◽  
Data Sets ◽  
Large Set ◽  
Clustering Methods ◽  
Siamese Networks

Traditionally, classifiers are trained to predict patterns within a feature space. The image classification system presented here trains classifiers to predict patterns within a vector space by combining the dissimilarity spaces generated by a large set of Siamese Neural Networks (SNNs). A set of centroids from the patterns in the training data sets is calculated with supervised k-means clustering. The centroids are used to generate the dissimilarity space via the Siamese networks. The vector space descriptors are extracted by projecting patterns onto the similarity spaces, and SVMs classify an image by its dissimilarity vector. The versatility of the proposed approach in image classification is demonstrated by evaluating the system on different types of images across two domains: two medical data sets and two animal audio data sets with vocalizations represented as images (spectrograms). Results show that the proposed system’s performance competes competitively against the best-performing methods in the literature, obtaining state-of-the-art performance on one of the medical data sets, and does so without ad-hoc optimization of the clustering methods on the tested data sets.

scholarly journals Predicting the liquefaction potential of soil layers in Tabriz city via artificial neural network analysis

2021 ◽  
Vol 3 (7) ◽  
Author(s):  
Mohammad Alizadeh Mansouri ◽  
Rouzbeh Dabiri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Liquefaction Potential ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Prediction Approach ◽  
Empirical Approaches

AbstractSoil liquefaction is a phenomenon through which saturated soil completely loses its strength and hardness and behaves the same as a liquid due to the severe stress it entails. This stress can be caused by earthquakes or sudden changes in soil stress conditions. Many empirical approaches have been proposed for predicting the potential of liquefaction, each of which includes advantages and disadvantages. In this paper, a novel prediction approach is proposed based on an artificial neural network (ANN) to adequately predict the potential of liquefaction in a specific range of soil properties. To this end, a whole set of 100 soil data is collected to calculate the potential of liquefaction via empirical approaches in Tabriz, Iran. Then, the results of the empirical approaches are utilized for data training in an ANN, which is considered as an option to predict liquefaction for the first time in Tabriz. The achieved configuration of the ANN is utilized to predict the liquefaction of 10 other data sets for validation purposes. According to the obtained results, a well-trained ANN is capable of predicting the liquefaction potential through error values of less than 5%, which represents the reliability of the proposed approach.

scholarly journals Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3373
Author(s):  
Ludek Cicmanec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Regression Function ◽  
Large Data ◽  
Soil Strength ◽  
Data Sets ◽  
Bearing Strength ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

Application of the C4.5 Algorithm to Predict the Types of Disease in Pigs Based on Android

2021 ◽  
Vol 10 (1) ◽  
pp. 105
Author(s):  
I Gusti Ayu Purnami Indryaswari ◽  
Ida Bagus Made Mahendra
Keyword(s):  
Programming Language ◽  
Test Data ◽  
Training Data ◽  
Data Sets ◽  
Android Application ◽  
C4.5 Algorithm ◽  
Sqlite Database

Many Indonesian people, especially in Bali, make pigs as livestock. Pig livestock are susceptible to various types of diseases and there have been many cases of pig deaths due to diseases that cause losses to breeders. Therefore, the author wants to create an Android-based application that can predict the type of disease in pigs by applying the C4.5 Algorithm. The C4.5 algorithm is an algorithm for classifying data in order to obtain a rule that is used to predict something. In this study, 50 training data sets were used with 8 types of diseases in pigs and 31 symptoms of disease. which is then inputted into the system so that the data is processed so that the system in the form of an Android application can predict the type of disease in pigs. In the testing process, it was carried out by testing 15 test data sets and producing an accuracy value that is 86.7%. In testing the application features built using the Kotlin programming language and the SQLite database, it has been running as expected.

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