Neural Networks Approach to Optimization of Steel Alloys Composition

Part 16: Multi Layer ANNInternational audienceThe paper presents modeling of steels strength characteristics in dependence from their alloying components quantities using neural networks as nonlinear approximation functions. Further, for optimization purpose the neural network models are used. The gradient descent algorithm based on utility function backpropagation through the models is applied. The approach is aimed at synthesis of steel alloys compositions with improved strength characteristics by solving multi-criteria optimization task. The obtained optimal alloying compositions fall into martenzite region of steels. They will be subject of further experimental testing in order to synthesize new steels with desired characteristics

Intelligent Optimization of a Mixed Culture Cultivation Process

In the present paper a neural network approach called "Adaptive Critic Design" (ACD) was applied to optimal tuning of set point controllers of the three main substrates (sugar, nitrogen source and dissolved oxygen) for PHB production process. For approximation of the critic and the controllers a special kind of recurrent neural networks called Echo state networks (ESN) were used. Their structure allows fast training that will be of crucial importance in on-line applications. The critic network is trained to minimize the temporal difference error using Recursive Least Squares method. Two approaches - gradient and heuristic - were exploited for training of the controllers. The comparison is made with respect to achieved improvement of the utility function subject of optimization as well as with known expert strategy for control the PHB production process

Hybrid Modeling and Optimization of Yogurt Starter Culture Continuous Fermentation

The present paper presents a hybrid model of yogurt starter mixed culture fermentation. The main nonlinearities within a classical structure of continuous process model are replaced by neural networks. The new hybrid model accounts for the dependence of the two microorganisms' kinetics from the on-line measured characteristics of the culture medium - pH. Then the model was used further for calculation of the optimal time profile of pH. The obtained results are with agreement with the experimental once

A forecasting model based on time series analysis applied to electrical energy consumption

One of the main objectives of the European Union (EU) is the reduction of energy consumption and the elimination of energy wastage. These two issues are extremely important, especially in large energy demanding areas, such as transportation, manufacturing, etc.. Electricity consumption prediction is a basic tool for energy management system. Precise prediction of transportation companies helps the energy providers to make right decision for proper distribution of electricity. In this paper, the authors present a Time Series Analysis Model and its application to the electricity consumption of public transportation in Sofia (Bulgaria) in 2011, 2012 and 2013. This technique is based on the dataset analysis and is able to arise the trend slope, the periodic pattern and the random component as a function of time. The innovation of the presented model is in the multiple seasonality and in its ability in following the monthly oscillations. The dataset analysed will show a strongly periodic pattern that will be reconstructed with three different seasonal coefficients. The adoption of statistical tests for linearity and stationarity will show that the series under study is nonlinear and stationary. Comparison between models with two and three seasonalities will be performed in terms of error analysis. A validation on the January 2013 dataset for the triple seasonality model will show interesting results in terms of very low mean error and standard deviation. In addition, a proper interpretation of the model coefficients will open the way to the implementation of improved energy management strategies

Neural Network and Time Series Analysis Approaches in Predicting Electricity Consumption of Public Transportation Vehicles

Public transportation is a relevant issue to be considered in urban planning and in network design, thus efficient management of modern electrical transport systems is a very important but difficult task. Tram and trolley-bus transport in Sofia, Bulgaria, is largely developed. It is one of the largest consumers of electricity in the city, which makes the question of electricity prediction very important for its operation. In fact, they are required to notify the energy provider about the expected energy consumption for a given time range. In this paper, two models are presented and compared in terms of predictive performances and error distributions: one is based on Artificial Neural Networks (ANN) and the other on Time Series Analysis (TSA) methods. They will be applied to the energy consumption related to public transportation, observed in Sofia, during 2011, 2012 and 2013. The main conclusion will be that the ANN model is much more precise but requires more preliminary information and computational efforts, while the TSA model, against some errors, shows a low demanding input entries and a lower power of calculation. In addition, the ANN model has a lower time range of prediction, since it needs many recent inputs in order to produce the output. On the contrary, the TSA model prediction, once the model has been calibrated on a certain time range, can be extended at any time period

Time Series Analysis and Forecast of the Electricity Consumption of Local Transportation

Electricity consumption due to transportation systems is a very important parameter to be monitored and studied in large cities, in order to optimize the energy management. Additional economic and environmental benefits can be obtained if a proper and reliable description and forecast of energy absorption is available. In this paper, a Time Series Analysis Model is presented and applied to the electricity consumption of public transportation in Sofia (Bulgaria). This method is able to consider the trend, the periodic and the random components of a certain set of data varying over the time, with the aim of forecasting future slope of the data. The strong periodic feature of the dataset will allow to build a good predictive model, thanks to the implementation of multiple seasonality in charge to reconstruct the daily, weekly and monthly periodicities. The triple seasonality model will show better performances with respect to the double seasonality one, in terms of error statistics, distribution and randomness. In addition, a proper interpretation of the model coefficients will open the way to the implementation of improved energy management processes

Public Transportation Energy Consumption Prediction by means of Neural Network and Time Series Analysis Approaches

Effective management of modern electrical transport systems is a very important and difficult task. Much of the transport of passengers in cities is based on electric vehicles. Tram and trolley transport in Sofia is quite largely developed. It is one of the largest consumers of electricity in the city, which makes the question of electricity prediction very important for its operation. The paper presents two models predicting electricity consumption. One model is based on Artificial Neural Networks (ANN), and the other on Time Series Analysis (TSA). The purpose of this paper is to compare the two models in certain indicators to determine and to identify their advantages and disadvantages. The main conclusion will be that the ANN model is much more precise but requires more inputs and computational efforts, while the TSA model, against some errors, shows a low demanding input entries and an easy computational approach. In addition, the ANN model has a lower range of prediction, since it needs many recent inputs in order to produce the output. On the contrary, the TSA model prediction, once the model has been calibrated on a certain time range, can be extended to any time, of course losing precision in the forecast

Evaluation of Anti-HBs Antibody Immune Response against Hepatitis B virus in Vaccinated People in а North-eastern Bulgaria Region

Introduction: Hepatitis B virus (HBV) is one of the most significant human pathogens responsible for a huge number of acute and chronic liver infectious diseases worldwide.Aim: To find the duration of post-vaccination immune response in individuals allocated to five age groups from 6 months to 20 years.Materials and methods: All tested subjects were born between 1999 and 2018 and therefore covered by the compulsory vaccination program against hepatitis B. For the serological marker anti-HBs Ab we investigated 449 serum samples taken from ambulatory people and patients of St Marina University Hospital in Varna.Results: A positive antibody response (anti-HBs Ab > 10 mIU/ml) was reported in 79.7% (n = 51) of the group of subjects up to one year old, in 70.0% (n = 196) of the subjects in the age range 1 year/1 month to 15 years, and in 39.3% (n = 33) of the subjects 15 years/1 month to 20 years old. Female sex had a better post-vaccination response than male sex with statistically significant relationship between sex and anti-HBs Ab titer (χ2 = 24.76, p <0.01).Conclusions: Regardless of the mass immunization against HBV in Bulgaria, the relative share of chronic HBV infections does not show a downward trend. Therefore, it is very important to study the duration of the post-vaccination immune response by demonstrating the anti-HBs antibodies and to apply a booster dose from the vaccine if needed