Abstract: Owing to their stochastic nature, Distributed Energy Resources (DERs) are more suited to participate in short-term or intraday electricity markets. However, it is very difficult for an asset owner to manage their operation when interacting with markets and create operation schedules. Present disclosure provides systems and methods that for the trades/bids placed earlier to be corrected based on the revised forecasts of demand and generation in the DER pool. The system models an optimal bidding problem of aggregators in an intraday market as a mixed-integer non-linear programming (MINLP) problem. The MINLP problem is converted to an NLP problem by an optimization model and integer variables are relaxed to solve the NLP problem and obtain (i) an optimal intraday operating schedule for one or more DERs, and (ii) an intraday bid associated with the DERs for a plurality of delivery slots to be traded in an intraday market.

Abstract: A widening gap between the emerging technology curve and technology adoption curve of businesses constitute the technology debt for an enterprise. Embodiments herein provide a method and system to identify technology debts and propose recommendations which may remediate the risk associated with technology debts. The system enables enterprises to take stock of their IT landscape across application and infrastructure that are running the risk of becoming obsolete. The system analyses various critical dimensions of an IT environment across various infrastructure and application components to arrive at the technology debts associated with each domain. The system provides a risk scoring mechanism that combines risk scoring parameters, past impact due to the identified technology debt, obsolescence component percentage in every technology area, security vulnerabilities present in the technology landscape, and critically of the application workload running on obsolescent technology component.

Abstract: Enterprises need assurance that their internal documents like policies, procedures, controls, standard operating procedures (SOPS) are adherent to the regulatory obligations. In conventional practice this requires a manual effort where legal, business and IT experts collaborate for assuring completeness and consistency in enterprise documents with respect to regulatory obligations, thereby establishing regulatory hygiene. Governance risk and compliance (GRC) frameworks help experts with collaboration but do not provide automation aids necessary to reduce the analysis and synthesis burden. Present disclosure provides system and method for determining hygiene in the plurality of enterprise documents with respect to the plurality of regulatory obligations by extracting concept ontology models from multiple enterprise documents and multiple regulations and enabling navigation across multiple documents via the ontology.

Abstract: This disclosure relates generally to methods and systems for designing an optimal dual-band metamaterial polarization converter for refractive index sensing applications. Most of the existing techniques for designing the metamaterial-based polarization converters operating at very high frequency range limits the sensing performance and increases fabrication complexity. In the design of the optimal dual-band metamaterial polarization converter, first a circular split-ring resonator (SRR) as a unit cell is designed. Secondly, the two capacitive gaps of the top layer, are aligned at 180 degrees with respect to each other and at 45 degrees with respect to X-axis and Y-axis. Lastly, step-by-step tuning the one or more key design parameters of the SRR, is performed until an optimum frequency response is obtained, to obtain the optimal dual-band metamaterial polarization converter.

Abstract: State of art techniques hardly provide data balancing for multi-label multi-class data. Embodiments of the present disclosure provide a method and system for identifying cardiac abnormality in multi-lead ECGs using a Hybrid Neural Network (HNN) with fulcrum based data re-balancing for data comprising multiclass-multilabel cardiac abnormalities. The fulcrum based dataset re-balancing disclosed enables maintaining natural balance of the data, control the re-sample volume, and still support the lowly represented classes there by aiding proper training of the DL architecture. The HNN disclosed by the method utilizes a hybrid approach of pure CNN, a tuned-down version of ResNet, and a set of handcrafted features from a raw ECG signal that are concatenated prior to predicting the multiclass output for the ECG signal. The number of features is flexible and enables adding additional domain-specific features as needed.

Abstract: Quality of weld images in with bad lighting condition and specific image color formats add constraints to existing automated weld inspection systems. Embodiments herein provide a method and system based on Domain Knowledge Infused Adaptive-Network-based Fuzzy Inference System (DKI-ANFIS) for weld quality inspection. The DKI-ANFIS inspects the quality of weld joint using domain driven quality inspection techniques. A segmentation algorithm is used to extract the weld joint in form of fractals followed by an unsupervised technique to extract useful geometrical features from the fractals. These geometrical features are used for quality index generation. A weld inspection model comprising the DKI-ANFIS is used for determining the quality of the weld joint. DKI-ANFIS modifies layers of ANFIS by infusing layer of domain knowledge to give better results even if there is a class imbalance in the data or the data is skewed or there is only a short corpus of data available.

Abstract: The disclosure relates generally to knowledge retrieval using ontology-based context matching. Conventional knowledge retrieval methods and systems are based on generative context that is modeled loosely as some meta data annotations. The meta data annotations do not provide sufficient semantic basis for meaningful context matching, compromising safety aspect. Other typical systems are designed such that the context is encoded into knowledge elements themselves, thus matching is too strict and the knowledge can only be used in contexts that match exactly, compromising the reuse aspect. The disclosed method and system explicitly models the context in which knowledge is generated, and matches the problem context with the generative context of knowledge, with respect to the underlying domain ontology, to assess the degree of similarity between the two, which then provides the basis for ranking the suitability of different knowledge elements for the problem context.

Abstract: Embodiments herein provide a method and system for a real-time allocation of a preferred room type based on one or more preferences of a customer. Every hotel maintains its own reservation database, the system gathers some of property management system (PMS) attributes of that day from the hotel administrator. The PMS attributes includes customer's booked room type, number of rooms booked, number of days customers will stay. Further, the system verifies all the preferences of the hotel and the different room type that are available for offering to customers on the day of check-in. A Check-in Check-Out model takes the attributes such as customers' reservation details, weather, arrival, and departure patterns of the customers from the transportation data store to predict the check-in and check-out time of the customers using pre-trained machine learning prediction models. A looping technique maps the rooms to the customers according to the preferred room types.

Abstract: This disclosure relates generally to in-silico modeling of hemodynamic patterns of physiologic blood flow. Conventional cardiovascular hemodynamic models depend on neuromodulation schemes (baroreflex autoregulation) and threshold parameters of neuromodulation correlate with physical activities. Thus these models may not work practically for a large set of people due to dependency on prior knowledge of these parameters. The present disclosure enables estimating blood pressure of a subject by estimating cardiac parameters based on the morphology of ECG signal associated with the subject and hence activation delays in cardiac chambers of the in-silico model is reproduced purposefully. In accordance with the present disclosure, the blood pressure of the subject can be estimated using only the ECG signal even if the signal is missed for some time instance(s) or is noisy.

Abstract: Migrating application from on premise HPC cluster to serverless platform is tedious task and involves significant amount of human efforts as cloud infrastructure needs to be created, data along with libraries and application code need to be copied from on-premise to cloud, and application need to be made compliant for execution on cloud. Present disclosure provides method and system for performing automated migration of high performance computing application to serverless platform. The system first check cloud readiness of application based on operation qualification parameters of application. In case application is found to be cloud ready, the system determines whether application can be executed on serverless platform based on execution time of the application and permissible limits defined for application in service level agreements.

Abstract: The embodiments herein provide a method and system for an augmented reality based indoor navigation. The system creates an augmented reality (AR) based fingerprinting to perform the dynamic routing. A floor wise plan of the building is uploaded. The floor plan is converted to a floor plan graph. Then cloud anchors are created and placed across the floor. An anchor is a fixed position and orientation in the real world as recognized by any Augmented Reality (AR) device. Each time an anchor is placed, its position information is fingerprinted on the floorplan graph and that fingerprinting is saved in the database. When user scans for nearest QR code to identify their current position, the user location is identified and the planogram details, fingerprinting and pre-processed floorplan matrix are obtained from database. When user selects his destination, dynamic routing is performed to obtain shortest/optimized path viewed through augmented reality fingerprinting.

Abstract: The present disclosure predicts a delay associated with a vehicle. Conventional methods are mainly mathematical based and machine learning based networks are not predicting delay accurately. Initially, the present disclosure Initially, the system receives a user query comprising an expected delay of a target vehicle in at least one target station. Further, a real time data associated with the user query in a predefined horizon is obtained. Further, a spatial feature vector, a temporal feature vector and spatiotemporal features are extracted based on the real time data using a feature extraction technique. Finally, the expected is predicted based on the plurality of features using a trained adversarial regression model, wherein the trained adversarial regression model comprises a critic network and a regressor network. The regressor network is trained with a plurality of architectures and a best architecture with minimum Mean Absolute Error (MAE) is selected for delay prediction.

Abstract: Conventional fork type autonomous mobile robots (AMRs) are suitable to handle pallets and are typically designed with two forks. Such AMRs are very bulky and designed for a cart handling application, and usually have large openings and less suitable for lifting roller carts. Present disclosure provides chassis with integrated single fork assembly for AMRs/Autonomous Guided Vehicles (AGVs) for transporting roller cages/carts within warehouses. Chassis with integrated single fork assembly enables performing tasks given by end users. Chassis carries steer and drive wheel and swivel wheels to increase stability of the AMR wherein a fork mechanism is provided which includes fork wheels and lifting mechanism.

Abstract: This disclosure relates to a method and system for multi-sensor fusion in the presence of missing and noisy labels. Prior methods for multi-sensor fusion do not estimate and correct labels for learning effective models in semi-supervised learning methods. Embodiments of the present disclosure provides a method for learning robust sensor-specific autoencoder based fusion model by utilizing a graph structure to perform label propagation and correction. In the disclosed Graph regularized AutoFuse (GAF) method latent representation for each sensor is learnt using the sensor-specific autoencoders. Further these latent representations are combined and fed to a classifier for multi-class classification. The disclosure presents a joint optimization formulation for multi-sensor fusion where label propagation and correction, sensor-specific learning and classification are executed together.

Abstract: Temperature measurement is an important part of many potential applications in process industry. Conventional temperature measurement methods require manual intervention for process monitoring and fail to provide accurate and precise measurement of temperature of an enclosed mixed fluid chamber. The present disclosure provides artificial intelligence based temperature measurement in mixed fluid chamber. A plurality of inputs pertaining to the mixed fluid chamber are received to build a fluid based model. The fluid based model is used to generate one or more fluid parameters. The one or more fluid parameters are used along with a ground truth temperature data and the received plurality of inputs for training an artificial intelligence (AI) based model. However, the AI based model is trained with and without knowledge of fluid flow. The trained AI based model is further used to accurately estimate temperature of the mixed fluid chamber for a plurality of test input data.

Abstract: Conventional fork-type autonomous mobile robots (AMRs) have been suited to handle pallets and are typically designed with two forks. Such AMRs are very bulky in nature and specifically designed for a cart handling application, and usually have large openings and less suitable for lifting roller carts. Present disclosure provides a fork assembly for AMRs/Autonomous Guided Vehicles (AGVs) for transporting roller cages/carts within warehouses. The fork assembly when integrated with AMR enables performing various tasks. More specifically, the fork assembly includes a first plate and a second plate. The fork assembly further include roller movement enabler blocks that are driven by respective fork motors. Movement of the roller movement enabler blocks enable rolling of rollers on respective roller guides within tapered region thereby enable lowering and rising of the second plate with reference to the first plate for lifting a payload and movement thereof to a desired location.

Abstract: Organizations are constantly flooded with questions, ranging from mundane to the unanswerable. It is therefore respective department that actively looks for automated assistance, especially to alleviate the burden of routine, but time-consuming tasks. The embodiments of the present disclosure provide BiLSTM-Siamese Network based Classifier for identifying target class of queries and providing responses to queries pertaining to the identified target class, which acts as an automated assistant that alleviates burden of answering queries in well-defined domains. Siamese Model (SM) is trained for a epochs, and then the same Base-Network is used to train Classification Model (CM) for b epochs iteratively until best accuracy is observed on validation test, wherein SM ensures it learns which sentences are similar/dissimilar semantically while CM learns to predict target class of every user query. Here a and b are assumed to be hyper parameters and are tuned for best performance on the validation set.

Abstract: Data is prone to various attacks such as cyber-security attacks, in any industry. State of the art systems in the domain of data security fail to identify adversarial attacks in real-time, and this leads to security issues, as well as results in the process/system providing unintended results. The disclosure herein generally relates to data security analysis, and, more particularly, to a method and system for assessing impact of adversarial attacks on time series data and providing defenses against such attacks. The system performs adversarial attacks on a selected data-driven model to determine impact of the adversarial attacks on the selected data model, and if the impact is such that performance of the selected data model is less than a threshold, then the selected data model is retrained.

Abstract: Embodiments herein provide a method and system for recommending cost optimization options for a cloud resource. The system and method employ a predefined library of optimization models to determine a best cost optimization options for a cloud resource. The one or more cloud optimization models will be available for different cloud resource type. Each cloud optimization model includes one or more cost optimization levers that can be applied to a cloud resource type. Each of the cost optimization lever includes one or more criteria to check for a condition based on the inputs provided. This criteria will generally be different for business critical and non-mission critical applications. Usage patterns, where each usage pattern is a combination of one or more optimization levers that can be applied together, including the sequence in which to apply. Herein, each criteria to shortlist the best cost saving options.