International Business Machines Corporation

Abb Schweiz Ag

General Electric Company

Kedar Kulkarni

Padmanabha Venkatagiri Seshadri

Satyam Dwivedi

Amith Singhee

Smitkumar Narotambhai Marvaniya

Jagabondhu Hazra

Shantanu Ravindra Godbole

Umamaheswari C Devi

Carsten Franke

Adrian Vasile Timbus

Jan Poland

Silvia Mastellone

Yan Zhang

Benjamin Grisz

Jacob T Griffith

Nicholas Ettlinger

Komminist S Weldemariam

Pankaj Satyanarayan Dayama

Vikas Raykar

Nitin Singh

Ranjini Bangalore Guruprasad

Jitendra Singh

Komminist Weldemariam

Abhishek Bansal

Sumanta Mukherjee

Geeth Ranmal De Mel

Nupur Aggarwal

Kushagra Manglik

Richard J Tomsett

Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Auto-discovery of reasoning knowledge graphs in supply chains

One embodiment provides a method, including: receiving information for each of a plurality of products; for each of the plurality of products: predicting a demand for the product; calculating a sustainability score, wherein the sustainability score is based upon a material composition of the product; and computing a similarity score, wherein the computing comprises comparing pairs of products within the plurality of products; generating a plurality of assortment choices from the plurality of products, wherein each of the plurality of assortment choices has a corresponding assortment demand value, assortment sustainability score, and assortment similarity score, wherein the generating comprises utilizing a multi-objective formulation based upon the predicted demand, the calculated sustainability score, and the computed similarity score; and providing the plurality of assortment choices to a user for selection.

Retail product assortment generation and recommendation

Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of code datasets prior to a data migration; analyzing the identified code datasets for a plurality of parameters; dynamically predicting a carbon footprint associated with the analyzed code datasets based on the plurality of parameters for each analyzed code dataset; and automatically optimizing the analyzed code datasets based on the predicted carbon footprint for data migration.

Carbon-aware code optimization

One embodiment provides a method, including: receiving a multi-variate time-series dataset comprising a plurality of time-dependent datasets; for each of the plurality of time-dependent datasets, segmenting each of the plurality of time-dependent datasets at a transition point; clustering segments of the plurality of time-dependent datasets into clusters having similar lengths of segments; for each cluster (i) selecting a representative segment length and (ii) identifying a feature subset in that cluster; identifying, across the feature subsets, subset transition points, wherein each of the subset transition points corresponds to a change in value that meets a predetermined threshold within its corresponding feature subset; and determining, by applying a threshold test to the subset transition points, a segment length to be used in segmenting the entire multi-variate time-series dataset.

Change-point driven feature selection for multi-variate time series clustering

Methods, systems, and computer program products for anomaly and mode inference from time series data are provided herein. A computer-implemented method includes receiving time-series sensor data for each one of a group of devices; extracting a set of states for each device in the group from the time-series sensor data; constructing a state-transition graph for each of the devices, wherein each of the state-transition graphs comprises nodes corresponding to each state in the set and edges corresponding to a probability of transition between the extracted states over time; identifying, for each set, a given state as one of: a mode, a normal state and an anomalous state based on the state-transition graph; and detecting one or more anomalous devices in the group by computing similarities between different devices in the group, based at least in part on the determined state-transition graphs.

Anomaly and mode inference from time series data

Methods, systems, and computer program products for contextual anomaly detection across assets are provided herein. A method includes obtaining time-series data frames corresponding to assets; clustering the assets into one or more cohorts based on the time-series data frames, each cohort comprising assets having statistically similar time-series data frames; for each given asset within each cohort: applying a time-context window to the portion of the time-series data frames corresponding to the given asset to generate at least one transformed data frame, and determining an asset distribution for the given asset based on the at least one transformed data frame; determining one or more of that at least one of the assets within at least one of the cohorts is anomalous and that at least one of the cohorts is anomalous; and causing at least one remediation action to be performed based on the determining.

Contextual anomaly detection across assets

Methods, systems, and computer program products for anomaly comparison across multiple assets and time-scales are provided herein. A computer-implemented method includes obtaining time-series data for each one of a plurality of assets; determining, for each of a plurality of time scales, one or more segments of the time-series data for each one of the plurality of assets; generating a graph for each one of the time-scales, the graph representing similarities between the one or more segments of the plurality of assets for the respective time-scale; computing an isolation metric for each of the generated graphs for at least one of the plurality of assets; aggregating the computed isolation metrics for the at least one asset; and determining whether the at least one asset is anomalous, based at least in part on a comparison of the aggregated isolation metric to a threshold.

Anomaly comparison across multiple assets and time-scales

Embodiments of the present invention disclose a method, a computer program product, and a computer system for predictive maintenance of refrigeration cases. A computer collects a temperature time series for a refrigeration case and, based on the temperature time series, learns a refrigeration case signature for both non-frost and defrost cycles. The computer generates features based on the refrigeration case signature and compares the refrigeration case signature to real time, or observed, temperatures and features using a rule-based and/or machine learning framework. Based on determining that the real time data varies beyond a threshold from the refrigeration case signature, the computer identifies a failure symptom of the refrigeration case and diagnoses a root cause of the symptom or failure. In addition, the computer may activate an alarm and open a work order corresponding to the root cause of the symptom or failure.

Predictive maintenance of refrigeration cases

An infant care apparatus cooler facilitates the cooling of components with elevated temperatures such as a convective heater in an infant care apparatus. The infant care apparatus includes an energy storage device and a fan. In the presence of an external source of power, the fan operates to circulate air to be heated by the heating coil and in the absence of external power, the fan is operable with energization from the energy storage device to circulate air past the heating coil, cooling the heating coil.

Infant care apparatus cooler

The present invention relates to a method of operating a wind farm comprising an upstream and a downstream turbine, wherein the upstream turbine is operated with current operation parameters under current wind conditions, wherein the method comprises the steps of: receiving future wind conditions for a time period for the wind farm, and evaluating required operation parameters for minimising wake effect of the downstream turbine under the future wind conditions, and determining a cost coefficient for changing the operation parameters to required operation parameters under consideration of fatigue effect of the wind turbine, calculating power productions Pand P, in the predetermined time period, by the wind farm if operated with the current operation parameters under the current wind conditions and if operated with the required operation parameters under the future wind conditions, respectively, and operating the upstream turbine with the required operation parameters if the cost coefficient is lower than a cost that would be obtained by a power production increment P-P.

Optimal wind farm operation

The present invention is concerned with an operation of a wind farm with a plurality of wind turbines in view of a dynamic frequency response. According to the invention, dynamic frequency support and power production for all wind turbines in a wind farm are handled concurrently in a single optimization step and taking into account wake effects within the wind farm as well as optional wind forecast information. The dynamic frequency support capability of the entire wind farm is planned in advance according to grid requirements and power system condition changes. While existing methods de-load wind turbines with a static percentage in order to supply additional power when needed, the proposed method incorporates the dynamic frequency support into the optimal operation system of wind farm.

Growing community of inventors

Bengaluru, India

Kedar Kulkarni