Identification of Modified Starches Using Infrared Spectroscopy and Artificial Neural Network Processing

1998 ◽  
Vol 52 (3) ◽  
pp. 329-338 ◽  
Author(s):  
Ludmila Dolmatova ◽  
Cyril Ruckebusch ◽  
Nathalie Dupuy ◽  
Jean-Pierre Huvenne ◽  
Pierre Legrand
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectra ◽  
Corn Starch ◽  
Raw Materials ◽  
Attenuated Total Reflection ◽  
Novel Approach ◽  
Modified Starches ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The authentication of food is a very important issue both for the consumers and for the food industry with respect to all levels of the food chain from raw materials to finished products. Corn starch can be used in a wide variety of food preparation as bakery cream fillings, sauce, or dry mixes. There are many modifications of the corn starch in connection with its use in the agrofood industry. This paper describes a novel approach to the classification of modified starches and the recognition of their modifications by artificial neural network (ANN) processing of attenuated total reflection Fourier transform spectroscopy (ATR/FT-IR) spectra. Using the self-organizing artificial neural network of the Kohonen type, we can obtain natural groupings of similarly modified samples on a two-dimensional plane. Such mapping provides the expert with the possibility of analyzing the distribution of samples and predicting modifications of unknown samples by using their relative position with respect to existing clusters. On the basis of the available information in the infrared spectra, a feedforward artificial neural network, trained with the intensities of the derivative infrared spectra as input and the starch modifications as output, allows the user to identify modified starches presented as prediction samples.

A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network

2009 ◽  
Author(s):  
Magnus Fast ◽  
Thomas Palme´ ◽  
Magnus Genrup
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Sequential Analysis ◽  
Ann Model ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Operational Data

Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine’s performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine.

Prediction of the Density & Thermal Conductivity of Light Bricks on the Effect of Aluminium Elements Using Artificial Neural Network (ANN)

2019 ◽  
Vol 964 ◽  
pp. 270-279
Author(s):  
Zulkifli ◽  
Gede Panji
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Raw Materials ◽  
Simulation Method ◽  
Acoustic Properties ◽  
Main Element ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Element Variation

Indonesia with abundant limestone raw materials, lightweight brick is the most important component in building construction, so it needs a light brick product that qualifies in thermal, mechanical and acoustic properties. In this paper raised the lightweight brick domains that qualify on the properties of thermal conductivity as building wall components.The advantage of low light density brick (500-650 kg/m3), more economical, suitable for high rise building can reduce the weight of 30-40% in compared to conventional brick (clay brick). To obtain AAC type lightweight brick product that qualifies for low thermal and density properties to the effect of Aluminum (Al) additive element variation using artificial neural network (ANN). The composition of the main elements of lightweight brick O (29-45 % wt), Si (25-35% wt) and Ca (20-40 % wt). Mixing ratio of the main element of light brick (Ca, O and Si) with Aluminum additive element (Al), is done by simulation method of artificial neural network (ANN), Al additive element as a porosity regulator is formed. The simulation of thermal conductivity to the influence of main element variation: Ca (22-32 % wt), Si (12-33 % wt). Simulation of thermal conductivity to effect of additive Al variation (1-7 % wt). Simulation of thermal conductivity to density variation (500-1200 kg/m3). The simulated results of four AAC brick samples showed the thermal conductivity (0.145-0.192 W/m.K) to the influence of qualified Aluminum additives (2.10-6.75 % wt). Additive Al the higher the lower density value (higher porosity) additive Al smaller than 2.10 % wt does not meet the requirements in the simulation.Thermal conductivity of AAC light brick sample (0.184 W/m.K) the influence of the main elements that qualify Ca (20.32-30.35 % wt) and Si (26.57 % wt). Simulation of artificial neural network (ANN) of light brick shows that maximum allowable Si content of 26.57 % wt, Ca content is in the range 20.32-30.35 % wt, and the minimum content of aluminum in brick is light at 2.10 % wt. ANN tests performed to predict the thermal conductivity of light brick samples obtained results of the average AAC light brick thermal conductivity of 0.151 W/m.K. The best performance with Artificial Neural Network (ANN) characteristics has a validation MSE of 0.002252.

scholarly journals SEIRDQ: A COVID-19 case projection modeling framework using ANN to model quarantine

2021 ◽  
Author(s):  
Harish Chandra ◽  
Xianwei Meng ◽  
Arman Margaryan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Sources ◽  
Modeling Framework ◽  
Novel Approach ◽  
Out Of Sample ◽  
Artificial Neural ◽  
Model Versus ◽  
Artificial Neural Network Ann ◽  
The Impact

We propose and implement a novel approach to model the evolution of COVID-19 pandemic and predict the daily COVID-19 cases (infected, recovered and dead). Our model builds on the classical SEIR-based framework by adding additional compartments to capture recovered, dead and quarantined cases. Quarantine impacts are modeled using an Artificial Neural Network (ANN), leveraging alternative data sources such as the Google mobility reports. Since our model captures the impact of lockdown policies through the quarantine functions we designed, it is able to model and predict future waves of COVID-19 cases. We also benchmark out-of-sample predictions from our model versus those from other popular COVID-19 case projection models.

scholarly journals A Novel Approach to Damage Assessment in Structures Based on Artificial Neural Network Working Parallel With a Hybrid Stochastic Optimization

2021 ◽  
Author(s):  
H. Tran-Ngoc ◽  
S Khatir ◽  
T. Le-Xuan ◽  
H. Tran - Viet ◽  
G. De Roeck ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Models ◽  
Global Search ◽  
Computer Algorithms ◽  
Local Minima ◽  
Starting Position ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Artificial neural network (ANN) is the study of computer algorithms that can learn from experience to improve performance. ANN employs backpropagation (BP) algorithms using gradient descent (GD)-based learning methods to reduce the discrepancies between predicted and real targets. Even though these differences are considerably decreased after each iteration, the network may still face major risks of being entrapped in local minima if complex error surfaces contain too numerous the best local solutions. To overcome those drawbacks of ANN, numerous researchers have come up with solutions to local minimum prevention by choosing a beneficial starting position that relies on the global search capability of other algorithms. This strategy possibly assists the network in avoiding the first local minima. However, a network often has many local bests widely distributed. Hence, the solution of choosing good starting points may no further be beneficial because the particles are probably entrapped in other local optimal solutions throughout the process of looking for the global best. Therefore, in this work, a novel ANN working parallel with the stochastic search capacity of evolutionary algorithms, is proposed. Additionally, to increase the efficiency of the global search capacity, a hybrid of particle swarm optimization and genetic algorithm (PSOGA) is applied during the process of seeking the best solution, which effectively guarantees to assist the network of ANN in escaping from local minima. This strategy gains both benefits of GD techniques as well as the global search capacity of PSOGA that possibly solves the local minima issues thoroughly. The effectiveness of ANNPSOGA is assessed using both numerical models consisting of various damage cases (single and multiple damages) and a free-free steel beam with different damage levels calibrated in the laboratory. The results demonstrate that ANNPSOGA provides higher accuracy than traditional ANN, PSO, and other hybrid ANNs (even a higher level of noise is employed) and also considerably decreases calculational cost compared with PSO.

Application of Artificial Neural Network Based Gas Path Diagnostics on Gas Pipeline Compressors

2020 ◽  
Author(s):  
Suleiman M. Suleiman ◽  
Yi-Guang Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Field Measurements ◽  
Discharge Temperature ◽  
Novel Approach ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Compressor Map ◽  
Map Data

Abstract This paper presents the development of an artificial neural network (ANN) Gas Path Diagnostics (GPD) technique applied to pipeline compression system for fault detection and quantification. The work detailed the various degradation mechanisms and the effect of such degradations on the performance of natural gas compressors. The data used in demonstrating the ANN diagnostics is so derived using an advanced thermodynamic performance simulation model of integrated pipeline and compressor systems, which has embedded empirical compressor map data and pipeline resistance model. Implantation of faults within the model is in such a way to account for faults degradations caused by fouling, erosion and corrosion, of various degrees of severities, to obtain wide range of corresponding simulated “true” measurements. In order to account for uncertainties normally encountered in field measurements, Gaussian noise distribution was combined with simulated true measurements, which depends on the instrument’s tolerances. Furthermore, since judicious measurements selection are crucial in ensuring flawless GPD predictions, a sensitivity and correlation analysis of the available measurements revealed that discharge temperature, rotational speed and torque are the most effective measurements for the diagnostics with acceptable degrees of accuracies. The measurements observability technique is a novel approach in pipeline compressor diagnostics. Analytical case studies of the developed method show that, a selected ANN architecture can detect and quantify faults related to degradation in efficiency and flow capacities in the presence of instrument error, varied operational and environmental conditions.

scholarly journals A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Aminaton Marto ◽  
Mohsen Hajihassani ◽  
Danial Jahed Armaghani ◽  
Edy Tonnizam Mohamad ◽  
Ahmad Mahir Makhtar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Imperialist Competitive Algorithm ◽  
Ann Model ◽  
Competitive Algorithm ◽  
Maximum Charge ◽  
Flyrock Distance ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann
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