Predicting Thermoelectric Power Factor of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing

An additive manufacturing (AM) process, like laser powder bed fusion, allows for the fabrication of objects by spreading and melting powder in layers until a freeform part shape is created. In order to improve the properties of the material involved in the AM process, it is important to predict the material characterization property as a function of the processing conditions. In thermoelectric materials, the power factor is a measure of how efficiently the material can convert heat to electricity. While earlier works have predicted the material characterization properties of different thermoelectric materials using various techniques, implementation of machine learning models to predict the power factor of bismuth telluride (Bi2Te3) during the AM process has not been explored. This is important as Bi2Te3 is a standard material for low temperature applications. Thus, we used data about manufacturing processing parameters involved and in-situ sensor monitoring data collected during AM of Bi2Te3, to train different machine learning models in order to predict its thermoelectric power factor. We implemented supervised machine learning techniques using 80% training and 20% test data and further used the permutation feature importance method to identify important processing parameters and in-situ sensor features which were best at predicting power factor of the material. Ensemble-based methods like random forest, AdaBoost classifier, and bagging classifier performed the best in predicting power factor with the highest accuracy of 90% achieved by the bagging classifier model. Additionally, we found the top 15 processing parameters and in-situ sensor features to characterize the material manufacturing property like power factor. These features could further be optimized to maximize power factor of the thermoelectric material and improve the quality of the products built using this material.Comment: 8 pages, 2 figures, 2 tables, accepted at Data Science for Smart Manufacturing and Healthcare workshop (DS2-MH) at SIAM International Conference on Data Mining (SDM23) conferenc

Artificial neural networks for predicting the generation of acetaldehyde in pet resin in the process of injection of plastic packages

The industrial production of preforms for the manufacture of PET bottles, during the plastic injection process, is essential to regulate the drying temperature of the PET resin, to control the generation of Acetaldehyde (ACH), which alters the flavor of carbonated or non-carbonated drinks, giving the drink a citrus flavor and putting in doubt the quality of packaged products. In this work, an Artificial Neural Network (ANN) of the Backpropagation type (Cascadeforwardnet) is specified to support the decision-making process in controlling the ideal drying temperature of the PET resin, allowing specialists to make the necessary temperature regulation decisions  for the best performance by decreasing ACH levels. The materials and methods were applied according to the manufacturer\u27s characteristics on the moisture in the PET resin grain, which may contain between 50 ppm and 100 ppm of ACH. Data were collected for the method analysis, according to temperatures and residence times used in the blow injection process in the manufacture of the bottle preform, the generation of ACH from the PET bottle after solid post-condensation stage reached residual ACH levels below (3-4) ppm, according to the desired specification, reaching levels below 1 ppm. The results found through the Computational Intelligence (IC) techniques applied by the ANNs, where they allowed the prediction of the ACH levels generated in the plastic injection process of the bottle packaging preform, allowing an effective management of the parameters of production, assisting in strategic decision making regarding the use of temperature control during the drying process of PET resin

GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer

This thesis is divided into two parts:Part I: Analysis of Fruits, Vegetables, Cheese and Fish based on Image Processing using Computer Vision and Deep Learning: A Review. It consists of a comprehensive review of image processing, computer vision and deep learning techniques applied to carry out analysis of fruits, vegetables, cheese and fish.This part also serves as a literature review for Part II.Part II: GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer. This part introduces to an end-to-end deep neural network architecture that can predict the degree of acceptability by the consumer for a guava based on sensory evaluation

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%

Neural networks to intrusion detection

Recent research indicates a lot of attempts to create an Intrusion Detection System that is capable of learning and recognizing attacks it faces for the first time. Benchmark datasets were created by the MIT Lincoln Lab and by the International Knowledge Discovery and Data Mining group (KDD). A few competitions were held and many systems developed. The overall preference was given to Expert Systems that were based on Decision Making Tree algorithms. This work is devoted to the problem of Neural Networks as means of Intrusion Detection. After multiple techniques and methodologies are investigated, we show that properly trained Neural Networks are capable of fast recognition and classification of different attacks. The advantage of the taken approach allows us to demonstrate the superiority of the Neural Networks over the systems that were created by the winner of the KDD Cups competition and later researchers due to their capability to recognize an attack, to differentiate one attack from another, i.e. classify attacks, and, the most important, to detect new attacks that were not included into the training set. The results obtained through simulations indicate that it is possible to recognize attacks that the Intrusion Detection System never faced before on an acceptably high level

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

A Novel NSNN Technique for the Estimations of TP Optical Properties

No Abstrac

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work