Abstract: Health monitoring of complex industrial assets remains the most critical task for avoiding downtimes, improving system reliability, safety and maximizing utilization. Recent advances in time-series synthetic data generation have several inherent limitations for realistic applications. A method and system have been provided for generating mixed variable type multivariate temporal synthetic data. The system provides a framework for condition and constraint knowledge-driven synthetic data generation of real-world industrial mixed-data type multivariate time-series data. The framework consists of a generative time-series model, which is trained adversarially and jointly through a learned latent embedding space with both supervised and unsupervised losses. The system addresses the key desideratum in diverse time dependent data fields where data availability, data accuracy, precision, timeliness, and completeness are of prior importance in improving the performance of the deep learning models.

Abstract: Existing privacy-preserving techniques suffer from inherent drawbacks to retain characteristics of observed, and original industrial time series data for utility in the downstream tasks such as process modelling, control, optimization and etc. The embodiments herein provide a method and system for privacy preserving generative mechanism for data-disclosure of the industrial multivariate mixed-variable time series data. The system fuses an industrial time series data with a random gaussian noise to preserve the privacy of the industrial time series data and trades-off the privacy with the utility of synthetic-private data. Further, the system presents the privacy-preserving synthetic industrial data generative mechanism for data disclosure with minimal risk of AI technique and strong privacy guarantees.

Abstract: Recent advances and techniques in missing data imputation suffer from inherent limitations of preserving the relationship among the input feature attributes and the target variable and temporal relations between observations spanning across timeframes because of which it is also challenging to reconcile missing data for any downstream tasks. Present disclosure provides system and method that implement for congeniality-preserving Generative Adversarial Networks (cpGAN) for imputing low-dimensional incomplete multivariate industrial time-series data. The method minimizes the rubric based on the information theory for Machine Learning (ML) between the empirical probability distributions of the reconcile data and the non-linear original data to preserve the temporal dependencies and retain the input feature-attributes and target-variable relationship and probability distributions of the original data.

Abstract: This disclosure relates generally to system and method for molecular property prediction. The method utilizes a set-pooling aggregation operator to derive a graph-level representation of a complete input molecular graphs to assist in inductive learning tasks. The method includes iteratively down-sampling the molecular graph into a coarsened molecular graph, and determining adjacency matrix and feature matrix of the coarsened molecular graph. The method then includes computing an average of the hidden state node attributes of the coarsened graph obtained after preforming the iterations to obtain a graph level representation vector of the molecular graph. Using a linear layer from the graph level representation vector the molecular properties are determined.

Abstract: This disclosure relates generally to system and method for molecular property prediction. The disclosed method includes mapping node embeddings of a molecular graph to a graph-level embedding characterizing the molecular graph. The graph level representation is acquired by pooling characteristics of hidden states of the nodes in the molecular graph by performing an iterative content based attention in a plurality of iterations. The content based attention is performed by considering an edge information fused transformed hidden state vector of the nodes of the molecular graph. The graph level embedding is fed through the linear projection to predict the molecular properties of the molecular graphs.

Abstract: This disclosure relates generally to system and method for molecular property prediction. The conventional methods for molecular property prediction suffer from inherent limitation to effectively encapsulate the characteristics of the molecular graph. Moreover, the known methods are computationally intensive, thereby leading to non-performance in real-time scenarios. The disclosed method overcomes the limitations of typical dynamic neighborhood aggregation (DNA) method by fusing the static edge attributes in determining the self-attention coefficients. In an embodiment, the disclosed method transforms the hidden state of a sink node by utilizing a neural-net function, which takes as input an aggregated single-message vector obtained by the self-attention mechanism and the self-attention mechanism transformed hidden state of the node.

Abstract: This disclosure relates generally to Error! Reference source not found.system and method for molecular property prediction. The conventional methods for molecular property prediction suffer from inherent limitation to effectively encapsulate the characteristics of the molecular graph. Moreover, the known methods are computationally intensive, thereby leading to non-performance in real-time scenarios. The disclosed method includes performing self-attention on the nodes of a molecular graph of different sized neighborhood, and further performing a shared attention mechanism across the nodes of the molecular graphs to compute attention coefficients using an Edge-conditioned graph attention neural network (EC-GAT). The EC-GAT effectively utilizes the edge characteristics in the molecular graph for molecular property prediction.

Abstract: This disclosure relates generally to system and method for molecular property prediction. Typically, message-pooling mechanism employed in molecular property prediction using conventional message passing neural networks (MPNN) causes over smoothing of the node embeddings of the molecular graph. The disclosed system utilizes edge conditioned identity mapping convolution neural network for the message passing phase. In message passing phase, the system computes an incoming aggregated message vector for each node of the plurality of nodes of the molecular graph based on encoded message received from neighboring nodes such that encoded message vector is generated by fusing a node information and an connecting edge information of the set of neighboring nodes of the node. The incoming aggregated message vector is utilized for computing updated hidden state vector of each node.

Abstract: This disclosure relates generally to system and method for molecular property prediction using hypergraph message passing neural network (HMPNN). Typical MPNN architectures used for chemical graphs representation learning have limitations, including, inefficiency to learn long-range dependencies for homogeneous graphs, ineffectiveness to model topological properties of graphs taking into consideration the multiscale representations, and lack of anti-smoothing weighting mechanism to address graphs random walk limit distribution. Disclosed method and system HyperGraph attention-driven Hypergraph Convolution. The Hypergraph attention driven convolution, on molecular hypergraph results in learning efficient embeddings on the high-order molecular graph-structured data. By taking into account the transient incidence matrix, the induced inductive bias augments the scope of molecular hypergraph representation learning.

Abstract: State of the art techniques used for Flue Gas Desulpharization (FGD) process monitoring fail to comprehend the relationship between various process parameters, which is crucial in determining the performance of a FGD process being monitored. The disclosure herein generally relates to industrial process monitoring, and, more particularly, to a method and system for performance optimization of Flue Gas Desulphurization (FGD) Unit. The system identifies Key Performance Indicators (KPIs) associated with the process being monitored, and identifies parameters associated with each KPI. This information is used to generate several predictive models, from which a predictive model having the highest value of composite model score amongst the predictive models is selected as the predictive model for processing the input data, which is then used to perform optimization of the FGD process.