A state-of-the-art of physics-informed neural networks in engineering

Técnicas de machine learning vêm ganhando cada vez mais espaço no cenário industrial no intuito de converter o crescente fluxo de informação (data) em melhorias de processos. Entre tais técnicas, as redes neuronais se destacam devido à sua capacidade de aproximador universal de funções, cuja performance pode ser enriquecida ao se fornecer conhecimentos físicos prévios: tem-se, então, o desenvolvimento das Physics-informed neural networks (PINN). Nesse contexto e observando-se um “gap” na produção de trabalhos relacionados ao tema e da difusão dessa temática na grade de formação dos cursos da Escola de Química, esse trabalho se propõe a realizar um estado da arte da técnica mencionada. Observou-se interesse particular das PINN para aplicações em mecânica dos fluidos e transferência de calor. Ademais, as PINN se mostram ferramentas importantes tanto para a resolução de problemas ditos “diretos” quanto “indiretos”. Por fim, através de exemplos práticos, constatou-se a capacidade de se aproximar funções de interesse particular na indústria química usando-se redes neurais sem nenhuma informação física do problema (obtenção do fator de atrito) e utilizando-se a equação diferencial que descreve o problema (resolução da equação de difusão em 1D)

Analysis of Climate-Oriented Researches in Building

This research was supported by the Agencia Nacional de Investigación y Desarrollo (ANID) of Chile, through the projects: ANID FONDECYT 1201052; ANID PFCHA/DOCTORADO BECAS CHILE/2019—21191227; and research group TEP-968 Tecnologías para la Economía Circular of the University of Granada, Spain.The following are available online at https://www.mdpi.com/article/10 .3390/app11073251/s1, Table S1: Studies of Cluster 1; Table S2: Studies of Cluster 3; Table S3: Studies of Cluster 4; Table S4: Studies of Cluster 9; Table S5: Studies without cluster.Many factors and aspects of the construction and operation of buildings depend on climatic parameters and climatic zones, so these will be fundamental for adapting and mitigating the effects of climate change. For this reason, the number of climate-oriented publications in building is increasing. This research presents an analysis on the most-cited climate-oriented studies in building in the period 1979-2019. The main themes, the typologies of these investigations and the principal types of climatic zoning used in these studies were analysed through bibliographic and manual analysis. A broad spectrum of themes directly and indirectly related to climate and climatic zones and buildings was demonstrated. It was found that 88% of all climate-oriented investigations, to one degree or another, are within the scope of the general topic of energy conservation. A thorough understanding of all climate-dependent aspects will help in designing dwellings appropriately in different climate zones. In addition, a methodology that facilitates the establishment of a typology of climate-oriented research is presented. This typology can be used in future research in different scientific areas. It was also revealed that the climate zones of the National Building Codes of China, the USA and Turkey prevailed in the studies analysed.Agencia Nacional de Investigacion y Desarrollo (ANID) of Chile ANID FONDECYT 1201052 ANID PFCHA/DOCTORADO BECAS CHILE/2019-21191227research group TEP-968 Tecnologias para la Economia Circular of the University of Granada, Spai

Using Optimized Features for Modified Optical Backpropagation Neural Network Model in Online Handwritten Character Recognition System

One major problem encountered by researchers in developing character recognition system is selection of efficient features (optimal features). In this paper, Particle Swarm Optimization (PSO) is proposed for feature selection. However, backpropagation algorithm has been reported to be an effective and most widely used supervised training algorithm for multi-layered feedforward neural networks but has the shortcomings of longer training time and entrapment into a local minimal. Several research works have been proposed to improve this algorithm but some of these research works were based on ‘learning parameter’ which in some cases slowed down the training process. Hence, this paper has focused on alleviating the problem of standard backpropagation algorithm based on ‘error adjustment’. To this effect, PSO is integrated with a ‘Modified Optical Backpropagation (MOBP)’ neural network to enhancement the performance of the classifier in terms of recognition accuracy and recognition time.  Experiments were conducted on MOBP neural network and PSO-based MOBP classifiers using 6,200 handwritten character samples (uppercase (A-Z), lowercase (a-z) English alphabet and 10 digits (0-9)) collected from 100 subjects using G-Pen 450 digitizer and the system was tested with 100 character samples written by people who did not participate in the initial data acquisition process. Experimental results show promising results for the PSO-based MOBP classifier in terms of the performance measures. Keywords: Artificial Neural Network, Feature Extraction, Feature Selection, Particle Swarm Optimization, Modified Optical Backpropagation

CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication

Handwriting authentication is a valuable tool used in various fields, such as fraud prevention and cultural heritage protection. However, it remains a challenging task due to the complex features, severe damage, and lack of supervision. In this paper, we propose a novel Contrastive Self-Supervised Learning framework for Robust Handwriting Authentication (CSSL-RHA) to address these issues. It can dynamically learn complex yet important features and accurately predict writer identities. Specifically, to remove the negative effects of imperfections and redundancy, we design an information-theoretic filter for pre-processing and propose a novel adaptive matching scheme to represent images as patches of local regions dominated by more important features. Through online optimization at inference time, the most informative patch embeddings are identified as the "most important" elements. Furthermore, we employ contrastive self-supervised training with a momentum-based paradigm to learn more general statistical structures of handwritten data without supervision. We conduct extensive experiments on five benchmark datasets and our manually annotated dataset EN-HA, which demonstrate the superiority of our CSSL-RHA compared to baselines. Additionally, we show that our proposed model can still effectively achieve authentication even under abnormal circumstances, such as data falsification and corruption.Comment: 10 pages, 4 figures, 3 tables, submitted to ACM MM 202

Ensemble learning using multi-objective optimisation for arabic handwritten words

Arabic handwriting recognition is a dynamic and stimulating field of study within pattern recognition. This system plays quite a significant part in today's global environment. It is a widespread and computationally costly function due to cursive writing, a massive number of words, and writing style. Based on the literature, the existing features lack data supportive techniques and building geometric features. Most ensemble learning approaches are based on the assumption of linear combination, which is not valid due to differences in data types. Also, the existing approaches of classifier generation do not support decision-making for selecting the most suitable classifier, and it requires enabling multi-objective optimisation to handle these differences in data types. In this thesis, new type of feature for handwriting using Segments Interpolation (SI) to find the best fitting line in each of the windows with a model for finding the best operating point window size for SI features. Multi-Objective Ensemble Oriented (MOEO) formulated to control the classifier topology and provide feedback support for changing the classifiers' topology and weights based on the extension of Non-dominated Sorting Genetic Algorithm (NSGA-II). It is designated as the Random Subset based Parents Selection (RSPS-NSGA-II) to handle neurons and accuracy. Evaluation metrics from two perspectives classification and Multiobjective optimization. The experimental design based on two subsets of the IFN/ENIT database. The first one consists of 10 classes (C10) and 22 classes (C22). The features were tested with Support Vector Machine (SVM) and Extreme Learning Machine (ELM). This work improved due to the SI feature. SI shows a significant result with SVM with 88.53% for C22. RSPS for C10 at k=2 achieved 91% accuracy with fewer neurons than NSGA-II, and for C22 at k=10, accuracy has been increased 81% compared to NSGA-II 78%. Future work may consider introducing more features to the system, applying them to other languages, and integrating it with sequence learning for more accuracy

Dynamic control of water distribution system based on network partitioning

The availability on the market of remote control valves for water distribution systems allows a more flexible implementation of the “divide and conquer” paradigm, that consists in dividing large networks into smaller district meter areas defining a water network partitioning (WNP), aiming at controlling water balance, pressure levels and water quality protection. The positioning of gate valves is carried out using optimization approaches to guarantee the network reliability that can be significantly reduced by WNP owing to the closure of several pipes by means of gate valves, decreasing topologic and energy redundancy. Anyway, starting from the optimal positioning of remote controlled gate valves, obtained with SWANP software, the paper investigates the effectiveness of dynamic control, in order to face hydraulic failure in fire estinguishment. The proposed methodology, based on heuristic optimization algorithm, finds the optimal layouts minimizing the number of valves to be opened and maximizing the system performance. The study highlights the advantages of adaptively reconfigurable networks starting from a partitioned system, confirming that a dynamic control represents a significant improvement for smart water networks

Digital twin applications in urban logistics:an overview

Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external factors like pollution and congestion. To counter this, smart cities deploy technologies such as digital twins (DT)s to achieve sustainability. Research suggests that DTs can be beneficial in optimizing the physical systems they are linked with. The concept has been extensively studied in many technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics applications. To do this, we survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the identification of key factors in urban logistics, we produce a conceptual model for the general design of an urban logistics DT through a knowledge graph. We provide an illustration on how the conceptual model can be used in solving a relevant problem and showcase the integration of relevant DDO methods. We finish off with a discussion on research opportunities and challenges based on previous research and our practical experience