Abstract: Pre-trained models for Named Entity Recognition (NER) come with static NE classes, limited in number, and remain same irrespective of domain of the input text. Thus, domain specific training is required. Embodiments of the present disclosure provide a method and system for enhanced NER using a custom-built REGEX matcher and a heuristic entity ruler. The invention helps in discovering the NE's of the given text with pipeline-based approach with combination of models of NLP transformer, custom-built REGEX, and heuristic entity rules. The method automatically handles class resolution based on the heuristic entity ruler. The method enables a user to customize or add any new heuristic rules for entity ruler or custom regex as a knowledgebase to train the model with automatic relearning and unlearning. The extracted NEs are provided for further processing or masking in a structured format.

Abstract: In industrial inspection scenarios, early detection of machine malfunction is extremely essential as it helps in preventing any significant damage and the associated economic losses. Embodiments herein provide a method and system for an acoustic based anomaly detection in industrial machines using a beamforming and a sequential transform learning. Herein, the system employs two-stage multi-channel source separation technique that uses the well-known delay and sum beamforming followed by a recent data-driven sequential transform learning (STL) approach to obtain clean sources. The STL is a solution to linear state-space model where operators/matrices are learnt from data and is used here to model the dynamics of time-varying source signals for source separation. Subsequently, a reference template matching is employed on each separated source to detect an anomaly.

Abstract: This disclosure relates to non-destructive estimation of coating layer thickness based on sweep frequency photo acoustic guided wave technique. Coating of a substrate/surface protects it from wear, corrosion and serves the cosmetic aspect, hence making coating technology is an essential part industrial process. The existing techniques for coating thickness determination are either destructive or requires a prior knowledge of the refractive index of the surface under investigation or use of sophisticated instrumentation, complicated procedure and harmful radiation during industrial deployment. The disclosure utilizes an intensity modulated Continuous Wave (CW) laser diode to excite a sample thus, making the technique a partially contact based method. Further a calibration curve is plotted by determining a frequency spectrum and resonance frequency. The calibration curve is used for estimation of a coating layer thickness.

Abstract: Existing systems for fault detection and classification have the disadvantage that they have limited or no capability for fault localization and root cause identification, probably due to the challenges associated with modeling the nonlinear interactions among process variables and capturing the nonstationary behavior that is typical of most industrial processes. The disclosure herein generally relates to industrial manufacturing systems, and, more particularly, to method and system for localization of faults in an industrial manufacturing plant. The system uses a perturbation based approach for fault localization, in which the system determines variables having dominant effect on identified faults, in terms of a perturbation score calculated for each of the variables.

Abstract: Existing multistatic configurations of Radar systems requires a direct LoS signal and/or time synchronization among the Radar transmitter and the multistatic distributed Radar receivers. The present disclosure provides a phaseless frequency-modulated continuous-wave multistatic Radar (PFMR) imaging that relaxes requirement of the direct LoS signal and only requires a plurality of parameters of a FMCW signal comprising a chirp signal rate, a carrier frequency and, a period of chirp to be known. Further, it also removes condition of the time synchronization among a plurality of FMCW multistatic distributed Radar receivers. However, because of absence of the time synchronization among a plurality of FMCW multistatic distributed Radar receivers, an unknown random phase offset appears after deramping.

Abstract: Traditional food quality monitoring systems fail to monitor the variation of food quality in real-time scenarios. Existing machine learning approaches require dedicated data models for different classes of food items due to differences in characteristics of different food items. Also, to generate such data models, a lot of annotated data is required per food item, which are expensive. The disclosure herein generally relates to monitoring and shelf-life prediction of food items, and, more particularly, to system and method for real-time monitoring and shelf-life prediction of food items. The system generates a data model using a knowledge graph indicative of a hierarchical taxonomy for a plurality of categories of the plurality of food items, which in turn contains metadata representing similarities in physio-chemical degradation pattern of different classes of the food items. This data model serves as a generic data model for real-time shelf-life prediction of different food items.

Abstract: Pathogens invade and infect humans. Understanding the infection mechanism is essential for determining targets for new therapeutics. Existing methods provide too many false positive results. A method and system for predicting protein-protein interaction between a host and a pathogen has been provided. The disclosure provides a pipeline for predicting HPIs, which is a combination of biological knowledge-based filters, domain-based filter and sequence-based predictions. Biologically feasible interactions are only possible when both the proteins share common localization and overlapping expression profiles. This observation was used as the first filter to remove biologically irrelevant HPIs. Proteins interact with each other through domains. Both interacting and non-interacting protein pairs provide valuable information about the probability of protein-protein interactions and hence both were used to derive statistical inferences to remove improbable HPIs.

Abstract: It is important to know the flow rates of oil and gas from individual wells in connected oil and gas wells. The existing methods for multiphase flow measurement are prohibitively expensive and used infrequently. The system is configured to ingest real-time and non-real-time data from a plurality of well data sources. Utilizing this data, a plurality of physics-guided data-driven well surveillance models run in real-time for forecasting a plurality of parameters including the flow rates of oil, gas and brine from individual wells, computing the health of well assets and performing fault detection and localization in well assets. The system is also configured to automatically compose a well performance optimization problem based on the current performance of the wells and health of well assets and solve the problem to identify optimal process settings for improving the operation of connected oil and gas wells.

Abstract: Considering the dependency of a flight hold time on multitude of dynamically varying factors, determining an optimal hold time balancing between passenger utility and airline utility is challenging. State of art approaches are limited to use of only deterministic approaches with limited ML assistance that require huge labelled training data. Embodiments disclosed herein provide a method and system for computing and recommending optimal hold time for every flight of an airline so as to minimize passenger misconnects in airline operations through Reinforcement Learning (RL). The method disclosed utilizes RL, which is trained to make decision at a flight level considering local factors while still adhering to the global objective based on global factors. Further method introduces business constants in the rewards to the RL agents bringing in airline specific flexibility in reward function.

Abstract: A processor implemented method of detecting a concentration of plurality of chemical residue in an agricultural produce is provided. The method include (a) receiving, by a hyper spectral device, a data set associated with one or more reflectance measurements of the agricultural produce; (b) determining, data associated with a plurality of bands; (c) dynamically reiterating, the steps (a) and (b) at predetermined time interval to obtain a trained dataset; (d) determining, relevant wavelengths among the selected trained data sets based on a feature selection technique to form an array of emitters; (e) calibrating, by the identified array of emitters, to emit light on the detecting region of one or more sample of the agricultural produce to obtain data associated with reflectance and transmittance; and (f) calculating, a calibration index with a de-multiplication flag to detect presence or absence of the plurality of chemical residue in the agricultural produce.

Abstract: Disadvantage of state-of-the-art scheduling mechanisms for Electric Vehicle (EV) charging is that they fail to accommodate dynamic requirements in terms of charging needs. The disclosure herein generally relates to EV fleet charging, and, more particularly, to a method and system for Electric Vehicle (EV) fleet charging by accommodating one or more dynamic requirements. The system initially generates a base charging plan for a fleet of EVs. Further, the system checks if the base charging plan is to be modified to accommodate one or more dynamic charging requirements obtained. Upon determining that the base charging plan is to be modified, the system modifies the base charging plan till a) no more vehicles are left to charge, or b) all of a plurality of chargers have an assignment.

Abstract: This disclosure relates generally to method and system for incremental functional approach-based dataflow analysis. Static dataflow analysis can take hours to days depending on size and complexity of the code. In today's agile development environment faster analysis is required which can handle incremental changes to the code in an efficient manner. The method includes by performing a static dataflow analysis over a set of functions of a source code. Further, obtains a set of impacted functions from the source code and executes a dataflow analysis over the set of impacted functions of the source code. The method performs an incremental functional approach-based dataflow analysis over the set of impacted functions including an incremental bottom-up analysis and an incremental top-down analysis. The method efficiently updates results of dataflow analysis in response to incremental changes which is fast and scalable and minimizes the number of procedures by comparing summaries across the versions.

Abstract: This disclosure relates generally to identifying candidate genome sequences. Next generation sequencing (NGS) is a massively parallel sequencing technique for identifying candidate genome sequences. The current state-of-the-art techniques for identifying candidate genome sequences does not efficiently address the problem of distributing abundance values across several related strains that are present in the reference under the same species. The disclosed technique proposes a technique for identifying candidate genome sequences by estimating coverage. The disclosed technique includes a local search-based optimization to compute maximum likelihood-based estimates using constrains on coverage/cardinality thresholds for identifying candidate genome sequences.

Abstract: Synthetic data is an annotated information that computer simulations or algorithms generate as an alternative to real-world data. synthetic data is created in digital worlds rather than collected from or measured in the real world. Embodiments herein provide a method and system for generating synthetic data with domain adaptable features using a neural network. The system is configured to receive seed data from a source domain as an input data. The seed data is considered as a normal state of a machine. The normal state, which is an initial stage of the source domain, consists of a set of features with a certain range of values. Further, a neural network based model is used to generate high quality data with adaptation of the domain specific features. To obtain large amount data for training robust deep learning models to adapt domains emphasizing set of features/providing higher importance selectively.

Abstract: Conventionally, root cause analysis and process documentation in process industries has been manually performed resulting in time consuming effort, cost, and human resources. Moreover, in the event of failure, looking at such document and searching for possible root causes is practically impossible in the interest of time and cost associated. Systems and methods of the present disclosure systematically curate knowledge of industrial process(es) from various sources and generate process ontology via meta model(s). Root cause graph (RCG) is created wherein the RCG corresponds to process and root cause and failure modes in the process. The RCG is then transformed to machine instructions which are executed for root cause analysis in real time. The created graphs/knowledge also help in identifying conflicting knowledge or redundant knowledge. Present disclosure enables root cause analysis as soon as a failure occurs or as the systems show or indicate a tendency towards failure.

Abstract: Conventional transport mode detection relies either on GPS data or uses supervised learning for transport mode detection, requiring labelled data with hand crafted features. Embodiments of the present disclosure provide a method and system for identification of transport modes of commuters via unsupervised learning implemented using a multistage learner. Unlabeled time series data received from accelerometer of commuters mobiles from a diversified population is processed using a unique journey segment detection technique to eliminate redundant data corresponding to stationary segments of commuter or user. The non-stationary journey segments are represented using domain generalizable Invariant Auto-Encoded Compact Sequence (I-AECS), which is a learned compact representation encompassing the encoded best diversity and commonality of latent feature representation across diverse users and cities.

Abstract: Metal corrosion is a ubiquitous phenomenon costing the global economy, trillions of dollars annually. Conventional corrosion inhibitor compounds are either not so effective or require huge amount of inhibitor for adequate protection. The present disclosure addresses the problem of acid corrosion of iron and iron alloys which is relevant for a wide variety of industries such as oil and gas production and acid pickling etc. The technical solution provided in the present disclosure is a new corrosion inhibitor composition including Naphthalene-1-thiocarboxamide for iron and iron alloys in HCl media. This is specifically relevant for acid well stimulation in the oil and gas industries.

Abstract: This disclosure relates to a system and method for at source data masking and discovery of unique identifier for at-source masking. The method reads a table of production database comprising sensitive column from a source database for at source data masking. A unique identifier column is identified, and a temporary table is created which has three or more columns. Columns of temporary table comprises a sensitive column from the table of production database, a column for masked data of sensitive column and a unique identifier column. Sensitive column of the temporary table is masked using a known masking technique and the original data of the sensitive column and the masked data of the sensitive column is inserted into the temporary table. Finally, the production database is updated with the masked data of the sensitive column.

Abstract: Embodiments of the present disclosure provide a method and system for co-operative and cascaded inference on the edge device using an integrated Deep Learning (DL) model for object detection and localization, which comprises a strong classifier trained on largely available datasets and a weak localizer trained on scarcely available datasets, and work in coordination to first detect object (fire) in every input frame using the classifier, and then trigger a localizer only for the frames that are classified as fire frames. The classifier and the localizer of the integrated DL model are jointly trained using Multitask Learning approach. Works in literature hardly address the technical challenge of embedding such integrated DL model to be deployed on edge devices. The method provides an optimal hardware software partitioning approach for components or segments of the integrated DL model which achieves a tradeoff between latency and accuracy in object classification and localization.

Abstract: This disclosure relates generally to root zone moisture estimation for vegetation cover using remote sensing. Conventionally, it is challenging to estimate root zone soil moisture using only satellite data. Moreover, estimation of soil moisture under vegetation cover based on bare surface soil moisture and vegetation parameters is not available. The disclosed method and system facilitate estimation of an ensemble of soil moisture under vegetation cover and root zone soil moisture using process based soil water balance for spatial estimation of root zone soil moisture. The system estimates bare surface soil moisture for different soil types/textures using the baseline bare surface model and soil properties derived from satellite data and in-situ sensors. The method further provides temporal spatially distributed soil moisture inputs to an intelligent irrigation management/information system which is very important to reduce and regulate water consumption.