Abstract: This disclosure relates generally to methods and systems for automatic extraction of self-reported activities of an individual from a freestyle narrative text. Manual extraction of such self-reported activities of the individual from the freestyle narrative text over the period of time is a complex task and consume a significant amount of time. The present systems and methods utilize a predefined grammar pattern and a natural language processing technique to generate one or more candidate activity phrases, from the pre-processed input text posted by the individual. A deep learning based supervised classification model is utilized to automatically extract the one or more self-reported activities of the individual, from the one or more candidate activity phrases. Manual intervention and efforts of analyzing the freestyle narrative text to extract the self-reported activities are avoided. Longitudinal assessment of the self-reported activities may reveal routines and behavior of the individual.

Abstract: Emotional loneliness is referred as the absence of an attachment figure in one's life and someone to turn to. The existing methods use installation of sensors for tracking the movement, behaviour and activity of the person, but most of the efforts are obtrusive in nature. A non-obtrusive method and system for detection of emotional loneliness of a person have been provided. The disclosure is utilizing multiple varied techniques to understand the emotional loneliness. The multiple techniques comprise room change movement anomalies, living room stay anomalies, correlating the living room stay with the bedroom stay and outdoor movement anomalies. The methodology also ensures reduced variance and false positives, as emotional loneliness is finally determined based on more than two positives of above methods. The detection of person's movement is done using a featured engineered dataset based on collection of raw time series data collected from a plurality of motion sensors.

Abstract: This disclosure relates generally to a system and method for real-time non-invasive estimation of food quality within enclosed package. Existing works utilize invasive methods that require direct contact of the food item with the sensors. In the present disclosure, a potential is applied over a plurality of frequencies through the food item contained the enclosed package which includes a plurality of polyethylene layers and a conducting layer arranged between two adjacent polyethylene layers using electrochemical impedance spectroscopy. Values of electrical voltages and the electrical impedances of the food item are then obtained. A plurality of features is derived from the obtained values of the electrical voltages and the electrical impedances using a trained model. The present disclosure estimates the quality of the food item in real-time by co-relating the plurality of derived features with the quality of the food item contained inside the enclosed package.

Abstract: Vehicle-to-Grid (V2G) technologies are being adopted to reduce peak demand and to take over as energy sources during grid instability. It is necessary to estimate attributes of electric vehicle trips and residual battery charge in order to correctly predict spatio-temporal availability of energy from EVs to form a micro grid. However, it may not be feasible to get the required attributes for all vehicles in a city-scale traffic scenario. Embodiments of the present disclosure and system address the problem of accurately estimating the local energy reserve that is available from parked EVs during a given time of the day. In addition, the system also determines which neighborhoods have the potential to form micro grids using the parked EVs during a given time period. This will help grid operator(s) to plan and design smart grids which can create EV-powered micro grids in neighborhoods during periods of peak demand or during disruptions.

Abstract: This disclosure relates generally to real-time path planning. Planning amidst obstacles in a cluttered indoor environment is a difficult task for a robotic agent. The disclosed method provides semidefinite programming induced free-space based path planning. Free-space is generated by an efficient environment grid resolution independent seeding technique. In the proposed resolution independent seeding technique, initial position of the robotic agent is considered as the first seed. For subsequent seeding, information of the expanded earlier seeds are employed intelligently. This process is followed unto a finite sequence, which naturally results in a contiguous navigable convex free-space. This contiguous navigable convex free-space is employed to create an undirected graph, which is then used for path planning. Path planning is done locally by evaluating the subgoal with respect to a final goal. Local planning cumulatively assists the planner to attain the final goal.

Abstract: Industrial plants involve a large amount of equipment, which generate a large amount of data. By analyzing this data, the operator can diagnose anomaly in the plant. Analyzing this data is difficult and time taking task. A method and system for diagnosing anomaly in an industrial system in a time efficient and convenient manner has been provided. The system is configured to diagnose the anomaly by finding out one or more sensors responsible for the anomaly. The present disclosure treats the anomaly detection model as a score generating function. Whenever for a particular instance the score given by the anomaly detection model crosses a pre-determined threshold, anomaly is reported and the diagnosis algorithm is triggered. The system is configured to diagnose the anomaly predicted in case of time series as well as non-time series data.

Abstract: With the proliferation of data and documents available on the internet and other information sources, analysis of adverse events poses a serious technical challenge on account of associated data volume and variety. This disclosure relates generally to identification and profiling of adverse events. By receiving a set of articles from a plurality of data sources and utilizing a series of Natural Language Processors, NLP techniques are employed to identify implicit and explicit adverse events. Entity statistics and sentiment extraction and analysis is performed. An ontology based adverse event identification framework is proposed for identification and profiling of implicit adverse event. An attention based bi-directional long short term memory network for adverse event identification and classification is proposed.

Abstract: Good health is one of the paramount essentials for any human being. A method and system for assessing and improving wellness of a person using body gestures have been provided. The system is configured to address fitness, wellness and physical movement related problems for both the abled and the differently abled society. The disclosure provides an intelligent and adaptive system that will comprehend the gestures and postures of the person to assess his capability of undertaking wellness activities. The person's movement capabilities and the environment are learnt by the system to track the gestures for the activities. Based on his gestures, the extent of activity achievement is calculated. The system also has the feature of provisioning dynamic activities to the person based on prior attempts and levels of achievements. Additionally, the system also providing the fitness routine in an AR environment by human-to-object mapping through object detection and augmentation.

Abstract: Re-usage of part of object or object is highly important in manufacturing industry as it can drastically reduce cost and time spent on manufacturing. However, lack of proper information about availability of similar parts leads to redesigning of similar part. Existing databases for engineering drawings do not store categorized information due to which performing feature-based search is not possible. Present application provides systems and methods for extracting, digitizing, and using engineering drawing data. The system receives engineering drawing document and extracts text data present in each cell of table provided in document. Once table data is extracted, isometric views and views other than isometric views that are present in document are identified by the system using pretrained machine learning based model. The system further extract view labels and coordinate information from identified views.

Abstract: A trouble ticket may contain various details of an observed issue and information of the actual issue which is hidden in the trouble ticket description along with other information. Embodiments herein describe an algorithm of algorithms (AoA) layer approach for extracting issues from trouble ticket descriptions. The method identifies the type of trouble ticket whether it is machine generated or user generated trouble tickets and accordingly selects the appropriate algorithm for issue extraction. It selects a relevant algorithm for the machine generated trouble tickets, and user generated trouble tickets based on trouble ticket data properties, historical performance, and a user feedback. It identifies right mix of columns from a service catalog to be used for matching based on presence of weighted keywords in the input data and identifies high-weighted keywords from input data descriptions and compares them with selected columns from catalog to generate accurate matches with their similarity score.

Abstract: Wiper is utilized to clean side-view mirror of a vehicle. Existing wiper attached with the side-view mirror obstructs visibility of a driver while cleaning the side-view mirror in a rainy season or in a dusty environment, which leads to an accident. A vehicle mirror wiping unit is provided. The vehicle mirror wiping unit includes a mirror; a mirror shutter assembly; a mirror casing; a mirror mounting unit, consisting of: the mirror, a mirror spring, a spring retaining cap, and a mirror spring holder; a pin pass through a cut-out of the spring retaining cap, and a cut-out in the mirror spring holder; and a cleaning elastomer is triggered by the mirror shutter motor, and the mirror shutter moves from the first end of the mirror to the second end of the mirror. A frontal surface of the mirror shutter is a reflective surface functioning as a mirror.

Abstract: All the model-driven systems may not have capability to perform designing and execution of experiments, which limits functionality of such model-driven systems. The disclosure herein generally relates to Design of Experiments (DOE), and, more particularly, to a model driven sub-system for design and execution of experiments. The sub-system when plugged into the model driven system, uses legacy components as well components of the sub-system to perform designing and execution of the design of experiments.

Abstract: This disclosure relates generally to method and system for predicting batch processes. Conventional batch schedulers provide a single point of control for defining and monitoring background executions in a distributed network. The method of the present disclosure obtains a set of batch jobs from one or more users to generate a set of batch graphs by deriving a metadata. Further, a set of batch models is generated for the set of batch graphs. The set of batch models includes at least one of a forecasting model, a job-job regression model, and a job-workload regression model. Further, a batch job schedule is generated for the set of batch graphs to predict a revised batch job schedule with a real time feed and the set of batch models. Additionally, a proactive notification is sent to each user alarming one or more unexpected delays indicating the revised batch job schedule.

Abstract: Trajectory optimization is process of designing a trajectory of operating variables that optimizes measure of performance while satisfying a set of constraints, when the system moves from one state to another. It is very necessary to achieve optimization in real time. A system and method for real-time trajectory optimization has been provided. The trajectory optimization of a process can be performed in any dynamical automated system. The system is configured to optimize the trajectory in both online and offline mode. In the online mode, the system optimizes the trajectory of the process in real-time. The system has the ability to handle both machine learning and deep learning based time series models along with first principles based models represented by ordinary/partial differential equation or differential algebraic equation based dynamic models of the process to estimate process variables given the disturbance profile and the actuation profile of manipulated variables.

Abstract: Reckless behavior of drivers like, speeding, sudden acceleration and swerving through lanes can cause fatality and financial loss. Conventional methods mainly focus on driving style classification. The conventional methods mainly focus on driver classification and are not able to provide trip classification of a driver. Hence there is a challenge in trip classification of the driver based on acceleration data. The present disclosure for trip classification addresses the problem of end to end trip classification based on the acceleration data. Here, a journey is segmented into a plurality of sub-journey segments and each sub-journey segment is associated with a plurality of driving events. An event score is calculated for each sub-journey and a normalization is performed on the event score. Further, the journey is classified into at least one of good, average or bad based on the normalized data by utilizing a fuzzy based classification.

Abstract: This disclosure generally relates to the field of structural health monitoring, and, more particularly, to a method and system for evaluating residual life of components made of composite materials. Existing methods require performing computational methods such as Finite Element Analysis (FEA) on the results of Non-Destructive Testing (NDT) every time a component is inspected. This makes the process expensive and time-consuming. Thus, embodiments of present disclosure provide a method wherein NDT is performed using different sensing methods such as ultrasound, ultrasound pulse echo, thermography to determine type of defect, location of defect and depth of defect in a test component which are then fed into a pre-trained machine learning model to predict residual life of the component. Testing time is greatly reduced since the pre-trained machine learning model is trained offline using results of the computational methods.

Abstract: Existing systems enable secure storage of encryption keys in the form of digital wallets, however, since the keys are preconfigured, they can be prone to malicious attacks. The embodiments herein provide a method and system for randomizing distribution of cryptographic keys across multiple secure key storage devices. The system generates random storage identities (RSIDs) for secure key storage devices by selecting a random storage device from a device portfolio, assigns the RSIDs randomly to create crypto addresses based on random access and partition the devices by deriving crypto addresses. Further, the system generates a user hash function and maps the user hash function to find an associated RSID hash function. The system identifies a device ID, a partition ID and a business date from a device mapper associated with the RSIDs to regenerate new RSIDs and recommends the regenerated new RSIDs randomly to each of the plurality of devices.

Abstract: Conventionally, biometric template protection has been achieved to improve matching performance with high levels of security by use of deep convolution neural network models. However, such attempts have prominent security limitations mapping information of images to binary codes is stored in an unprotected form. Given this model and access to the stolen protected templates, the adversary can exploit the False Accept Rate (FAR) of the system. Secondly, once the server system is compromised all the users need to be re-enrolled again. Unlike conventional systems and approaches, present disclosure provides systems and methods that implement encrypted deep neural network(s) for biometric template protection for enrollment and verification wherein the encrypted deep neural network(s) is utilized for mapping feature vectors to a randomly generated binary code and a deep neural network model learnt is encrypted thus achieving security and privacy for data protection.

Abstract: The embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN learn edges, arcs, arcs segments and the other shape features of the spectrum. Thus, the method disclosed enables converting a spectral signature to a shape, and then this shape is decomposed using hierarchical features learned at different convolution layers of the disclosed DCNN at different levels.

Abstract: This disclosure relates generally to assessing soil carbon sequestration. One of the driving factors for climate change and global warming is emission of greenhouse gas emissions. Of the possible ways to reduce climate change and global warming adoption, sustainable agricultural practices will enable efficient soil carbon sequestration thereby reducing the greenhouse gases as well as increasing the crop yield. The disclosure is a method and a system for real time assessing soil carbon sequestration of farm based on remote sensing. The soil carbon sequestration of farm is assessed by continuously monitoring the farm at real time based on remote sensing using a plurality of satellite data and a plurality of farming data using several techniques. The several techniques utilized for assessing soil carbon sequestration includes machine learning, a carbon sequestration estimation technique, estimating a crop health index and an adoption index and computing a set of carbon sequestration parameters.