Artificial neural networks application in modal analysis of tires

The paper deals with the application of artificial neural networks (ANN) to tires’ own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.Web of Science13527827

ARTIFICIAL NEURAL NETWORKS APPLICATION TO PREDICTION OF ELECTRICITY CONSUMPTION

In this paper, we will present an artificial neural network (ANN) model trained to forecast hourly electricity consumption of energy in industry for a day-ahead. We will start with a brief analysis of the global electricity market with a special reference to the Serbian market. Next, the daily electricity consumption amounts between August 1st and December 19th 2019 will be analyzed using statistical tools. According to the obtained results, we will give predictions of our models, based on different number of previous days

Artificial neural networks application in thermography

In this paper, we present an application of artificial neural network (ANN) analysis in the thermovision identification of the studied thermal fields. Precise thermal field identification plays an important role in distinguished technological processes, for instance in metallurgy. Our efforts were focused in this direction. Thermovision outputs are usually thermograms with a form of a quasi-coloured imaging record of an observed temperature field. A thermogram is usually registered and presented in a form of an electronic or printed image. The character of such a document is informational only, and real temperature values are difficult to detect. The exploitation of neural networks is advantageous, if it is necessary to express complex mutual relations among sensor-based data. More accurate results of the predictions of different metallurgical parameters with the exploitation of neural networks are based on the fact that the application of neural networks enables the assignment of relations among process parameters which cannot be traced using common methods due to their mutual interactions, the considerable amount of data, dynamics and the thus ensuing time demands.Papers presented at the 13th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Portoroz, Slovenia on 17-19 July 2017 .International centre for heat and mass transfer.American society of thermal and fluids engineers

A Review of Artificial Neural Networks Application to Stock Market Predictions

The purpose of this paper is to review artificial neural network applications used in the field of stock price forecasting. The field of stock price forecasting has increasingly grown to be an important subject matter for researchers, everyday investors and practitioners in the finance domain as it aids financial decision making. This study brings to attention some of the neural network applications used in stock price forecasting focusing on application comparisons on different stock market data and the gaps that can be worked on in the foreseeable future. This work makes an introduction of neural network applications to those novels in the field of artificial intelligence. Keywords: Neural Networks, Forecasting Stock Price. Financial Markets, Complexity, Error Measures, Decision Makin

Gapped sequence alignment using artificial neural networks: application to the MHC class I system

Motivation: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8–11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. Availability and implementation: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped sequence alignment is publicly available at: http://www.cbs.dtu.dk/ services/NetMHC-4.0.Fil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentin

Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil

Olive oil is an important commodity in the world, and its demand has grown substantially in recent years. As of today, the determination of olive oil quality is based on both chemical analysis and organoleptic evaluation from specialized laboratories and panels of experts, thus resulting in a complex and time-consuming process. This work presents a new compact and low-cost sensor based on fluorescence spectroscopy and artificial neural networks that can perform olive oil quality assessment. The presented sensor has the advantage of being a portable, easy-to-use, and low-cost device, which works with undiluted samples, and without any pre-processing of data, thus simplifying the analysis to the maximum degree possible. Different artificial neural networks were analyzed and their performance compared. To deal with the heterogeneity in the samples, as producer or harvest year, a novel neural network architecture is presented, called here conditional convolutional neural network (Cond- CNN). The presented technology is demonstrated by analyzing olive oils of different quality levels and from different producers: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). The sensor classifies the oils in the three mentioned classes with an accuracy of 82%. These results indicate that the Cond-CNN applied to the data obtained with the low-cost luminescence sensor, can deal with a set of oils coming from multiple producers, and, therefore, showing quite heterogeneous chemical characteristics.project Innosuisse - Swiss Innovation Agency 36761.1 INNO-LSproject "SUSTAINABLE" - European Union's Horizon 2020 H2020-MSCA-RISE-2020 program 101007702project "PARENT" - European Union's Horizon 2020 H2020-MSCA-ITN-2020 program 956394Junta de Andalucia-FEDER-Fondo de Desarrollo Europeo 2018 P18-H0-470

Прогнозирование технологических параметров методами искусственных нейронных сетей

В роботі розглядається питання використання штучних нейронних мереж для прогнозування вихідних технологічних параметрів процесу різання. Наведено приклади прогнозування шорсткості обробленої поверхні та періоду стійкості різального інструменту за допомогою штучних нейронних мереж модуля Neural Network Toolbox системи MatLab.Problems of artificial neural networks’ application for the prediction of output technological parameters in the time of a cutting process are considered in this work. Examples of the prediction of a processed surface’s roughness and a cutting instrument’s period of durability by the means of artificial neural networks of the Neural Network Toolbox module of the MatLab system are given.В работе рассматриваются вопросы применения искусственных нейронных сетей для прогнозирования выходных технологических параметров процесса резания. Приведены примеры прогнозирования шероховатости обработанной поверхности и периода стойкости режущего инструмента с помощью штучных нейронных сетей модуля Neural Network Toolbox системы MatLab

Інтелектуальна система класифікаційного прогнозування успішності соціальних проектів

У статті викладено науково-методичний підхід до оцінювання ефективності реалізації, успішності впровадження, доцільності реорганізації, актуальності злиття соціально-орієнтованих кампаній, стартапів, проектів, програм тощо. Реалізовано інтелектуальну систему класифікації стартапів за рівнем успішності. При цьому розглянуто основні аспекти формування вхідного математичного опису системи, особливості її функціонування в режимі навчання та екзамену, а також основні критерії оцінки ефективності інтелектуальної системи в інформаційному розумінні. Виконано класифікаційне прогнозування успішності страхових стартапів на основі застосування парадигми штучних нейронних мереж за алгоритмом зворотного поширення помилки. Для підвищення достовірності класифікації оптимізовано структуру нейронної мережі. При цитуванні документа, використовуйте посилання http://essuir.sumdu.edu.ua/handle/123456789/36840The article presents the scientific and methodical approach to performance evaluation of, implementation, successful application, reorganization feasibility, merging relevance of social oriented companies, startups, projects, programs, etc. The intelligence system of startup classification according to the level of success is implemented. Herewith the basic aspects of formation of the input mathematical representation of a system, specifics of its functioning in the mode of study and examination, and basic criteria of intelligent system performance evaluation in the information concept are considered. The forecasting classification of the successfulness of the insurance startups was carried out on the basis of the paradigm of artificial neural networks’ application under the back propagation of error algorithm. To improve the reliability of the classification the structure of neural network is optimized.The article presents the scientific and methodical approach to performance evaluation of, implementation, successful application, reorganization feasibility, merging relevance of social oriented companies, startups, projects, programs, etc. The intelligence system of startup classification according to the level of success is implemented. Herewith the basic aspects of formation of the input mathematical representation of a system, specifics of its functioning in the mode of study and examination, and basic criteria of intelligent system performance evaluation in the information concept are considered. The forecasting classification of the successfulness of the insurance startups was carried out on the basis of the paradigm of artificial neural networks’ application under the back propagation of error algorithm. To improve the reliability of the classification the structure of neural network is optimized. При цитировании документа, используйте ссылку http://essuir.sumdu.edu.ua/handle/123456789/3684

Avaliação de aplicação de redes neurais artificiais em métodos de medição-correlação-predição

In this study a single artificial neural network (ANN) model was developed to predict the short term mean hourly wind speed and wind direction at target sites using short term mean hourly reference wind data. Standard multi-layered, feed-forward, backpropagation neural networks with single hidden layer architecture was designed using neural network toolbox for MATLAB. The hidden layers and output layer of the network consist of tangent sigmoid transfer function (tansig) and linear transfer function (purelin) as an activation function. Five different sites from Japan, Saudi Arabia, Jordan, France and Russia with different terrain complexity, completely different weather conditions, and different correlation coefficient between reference and target sites were tested. Single model was constructed, and two different approaches were experimented. Approach 1 made use of entire concurrent period dataset, the output values from the model was compared against the three methods: regression, matrix and neural network. Second approach was built on certain period of data and tested on unused data. The purpose behind the fabrication of this approach is to try and understand the neural network model. The results of approach 1 was that the neural network model is able to statistically perform better than other methods and equally well in predicting wind direction sectors. The maximum mean absolute percentage error for NN MATLAB model was found to be 62.5% in Japan to 23.7% in France. The model suffers in predicting the lower wind speeds which explains the distortion in wind frequency distribution and resulting in Power density deviation. The maximum deviation was -18.1% in Jordan and -7.9% in France. The sites in Japan, Saudi Arabia, France and Russia were considered for approach 2. The results were interesting, in case of japan the first month was better than the last month result. Overall the performance of the model was better in case of France followed by Russia site. The maximum deviation of Power density was noticed in case of Japan’s last month scenario -26.6% to minimum of about 3.2% in France and -5.2% was observed in case of Russia. In Saudi Arabia site, the only case where the concurrent period extends over a period of one year, the performance of the model was statistically good but suffers from same problem of previous cases. The deviation in power density was spotted around -21.4%.Neste estudo, foi desenvolvido um modelo de rede neural artificial (RNA) para prever a velocidade média do vento de curto prazo e a direção do vento em locais-alvo, usando dados de vento de referência de curto prazo. Foram projetadas redes neurais padrão multi-camadas, feed-forward, de propagação reversa com arquitetura de camada oculta única usando a caixa de ferramentas de rede neural para o MATLAB. As camadas ocultas e a camada de saída da rede consistem na função de transferência sigmóide tangente (tansig) e na função de transferência linear (purelin) como uma função de ativação. Foram testados cinco locais diferentes, Japão, Arábia Saudita, Jordânia, França e Rússia, com diferentes complexidades de terreno, condições climáticas completamente diferentes e diferentes coeficientes de correlação entre os locais de referência e os de destino. Foram testadas duas abordagens diferentes com o modelo construído. Na abordagem foi usado todo o conjunto de dados do período concorrente e os valores de saída do modelo foram comparados com três métodos em estudo: regressão, matriz e rede neural. A segunda abordagem foi construída usando apenas um determinado período de dados e o modelo foi testado em dados não utilizados. O objetivo desta segunda abordagem foi tentar entender o modelo de rede neural. Os resultados obtidos com a abordagem 1 aplicada aos 5 sítios em estudo permitiram verificar que o modelo de rede neural desenvolvido se apresenta estatisticamente melhor do que os outros métodos testados. Verifica-se que é capaz de prever bem a direção do vento por setores. Foi obtido um erro percentual médio absoluto máximo com o modelo NN MATLAB de 62,5% no Japão e de 23,7% na França. O modelo desenvolvido apresenta uma limitação na previsão das velocidades de vento mais baixas, o que explica a distorção na distribuição da frequência do vento e resulta no desvio da densidade de potência. O desvio máximo obtido para a densidade de potência foi de -18,1% na Jordânia e de -7,9% na França. Na abordagem 2 foram utilizados os dados do Japão, Arábia Saudita, França e Rússia. Os resultados foram interessantes. Verificou-se que no caso do Japão foi possível obter melhores resultados para o primeiro mês do que para o último mês. No geral, o desempenho do modelo foi melhor no caso da França, seguido pela Rússia. O desvio máximo da densidade de potência foi observado no caso do cenário do último mês do Japão -26,6% e foram observados desvios mínimos de cerca de 3,2% na França e -5,2% na Rússia. No site da Arábia Saudita, o único caso em que o período concorrente se estende por um período de um ano, o desempenho do modelo foi estatisticamente bom, verificando-se a mesma dificuldade de previsão de velocidades baixas. O desvio na densidade de potência foi de cerca de -21,4%