Abstract: Systems and methods for automating process setting to a target factory, which can involve creating templatized business terms, templatized business data configurator logics, and a templatized data profile by machine learning from training data from at least one reference factory; storing the templatized business terms, the templatized business data configurator logics, and the templatized data profile into a knowledge graph; querying the knowledge graph with a data profile of the target factory to obtain corresponding templated business terms; and applying the corresponding templated business terms and corresponding templated business data configurator logics to a data catalogue of the target factory.

Abstract: Example implementations described herein involve systems and methods to select machine learning models that will be executed in a cellular Mobile Edge Computing for cellular enabled applications. In contrast to related art implementations, the example implementations described herein considers different data fidelities of received data due to the cellular wireless channel and also performs service resource allocation accordingly.

Abstract: Example implementations described herein involve systems and methods that can involve extracting features from each of a plurality of time-series sensor data, the plurality of time-series sensor data associated with execution of one or more operations; clustering the extracted features into a plurality of tasks that occur from execution of the one or more operations, each of the plurality of tasks associated with a clustering identifier (ID) from the clustering; and calculating a cycle time of the cycle based on the initiation and end of the cycle recognized by referencing a cycle pattern model, wherein the cycle pattern model comprises configuration information of a cycle including a set from a plurality of the clustering IDs.

Abstract: Example implementations involve a location system, which can involve associating each location of one or more unidentified targets detected from sensor data of the one or more sensors with identifiers corresponding to the transmitter of each of the one or more pairs of electronic devices, by calculating first distance relationships indicative of relationships of distances between the each location of the one or more unidentified targets detected from the sensor data of the one or more sensors and a reference point; calculating second distance relationships indicative of relationships of distances between the transmitter and the receiver of each of the one or more pairs of electronic devices; and associating the identifiers corresponding to the transmitter of the each of the one or more pairs of electronic devices with the each location of the one or more unidentified targets based on the first distance relationships and the second distance relationships.

Abstract: Example implementations described herein involve systems and methods for training and managing machine learning models in an industrial setting. Specifically, by leveraging the similarity across certain production areas, example implementations can group together these areas to train models efficiently that use human pose data to predict human activities or specific task(s) the workers are engaged in. The example implementations do away with previous methods of independent model construction for each production area and takes advantage of the commonality amongst different environments.

Abstract: Example implementations described herein involve systems and methods to select machine learning models that will be executed in a cellular Mobile Edge Computing for cellular enabled applications. In contrast to related art implementations, the example implementations described herein considers different data fidelities of received data due to the cellular wireless channel and also performs service resource allocation accordingly.

Abstract: Example implementations described herein involve systems and methods that can involve extracting features from each of a plurality of time-series sensor data, the plurality of time-series sensor data associated with execution of one or more operations; clustering the extracted features into a plurality of tasks that occur from execution of the one or more operations, each of the plurality of tasks associated with a clustering identifier (ID) from the clustering; and calculating a cycle time of the cycle based on the initiation and end of the cycle recognized by referencing a cycle pattern model, wherein the cycle pattern model comprises configuration information of a cycle including a set from a plurality of the clustering IDs.

Abstract: A method for providing data to a client computing device from an edge computing device is discussed herein. The method may include performing a network proximity check regarding the client computing device associated with a request for data captured by the wideband sensor. The method may further include determining, based on at least one proximity metric associated with the client computing device, a route for data responsive to the request for data associated with the network proximity check, where the route is one of a route including the cloud storage or a route that does not include the cloud storage. The method may also include receiving the request for data captured by the wideband sensor associated with the network proximity check. The method may also include transmitting the data responsive to the request for data captured by the wideband sensor associated with the network proximity check to the client computing device through the determined route.

Abstract: Example implementations involve a location system, which can involve associating each location of one or more unidentified targets detected from sensor data of the one or more sensors with identifiers corresponding to the transmitter of each of the one or more pairs of electronic devices, by calculating first distance relationships indicative of relationships of distances between the each location of the one or more unidentified targets detected from the sensor data of the one or more sensors and a reference point; calculating second distance relationships indicative of relationships of distances between the transmitter and the receiver of each of the one or more pairs of electronic devices; and associating the identifiers corresponding to the transmitter of the each of the one or more pairs of electronic devices with the each location of the one or more unidentified targets based on the first distance relationships and the second distance relationships.

Abstract: Example implementations described herein are directed to systems and methods for extracting signal in the presence of noise for industrial IoT systems. Through the example implementations described herein, the high-frequency band signal of the sensor output can be maintained despite data compression, while also retaining information regarding the condition of the machine. Example implementations can also generate compressed data pairs to synchronized downsampled envelopes and spectrum data.

Abstract: Systems and methods described herein are directed to minimizing the resource requirements for edge and network while keeping the accuracy of machine learning classifier by utilizing simulated test data. Once sufficient measured test data is collected by the server, the server instructs the edge computer to reduce the transmission of data received from the corresponding sensors.

Abstract: Example implementations described herein are directed to systems and methods for extracting signal in presence of strong noise for industrial Internet of Things (IoT) system especially for monitoring systems of consumable items such as lathe machines, coolers and so on. Example implementations can utilize a sawtooth mother Wavelet instead of usual wavelet analysis to cleanse the incoming sensor data, thereby allowing for the converting sensor data to feature values despite having heavy noise interference.

Abstract: Systems and methods described herein are directed to minimizing the resource requirements for edge and network while keeping the accuracy of machine learning classifier by utilizing simulated test data. Once sufficient measured test data is collected by the server, the server instructs the edge computer to reduce the transmission of data received from the corresponding sensors.

Abstract: Example implementations described herein are directed to systems and methods for extracting signal in the presence of noise for industrial IoT systems. Through the example implementations described herein, the high-frequency band signal of the sensor output can be maintained despite data compression, while also retaining information regarding the condition of the machine. Example implementations can also generate compressed data pairs to synchronized downsampled envelopes and spectrum data.

Abstract: In example implementations described herein, the power of time series machine learning is used to extract the statistics of Programmable Logic Controller (PLC) data and external sensor data. The accuracy of time series machine learning is improved by manufacturing context-dependent segmentation of the time series into states which is factory may be in. The invention can capture subtle trends in these time series data and be able to classify them into several outcomes from ICS security attacks to normal anomalies and machine/sensor failures.

Abstract: Example implementations are directed to maximizing the accuracy of command recognition in a noisy environment, such as a factor shop floor, by providing appropriate parameters and configurations to a speech recognition algorithm and a denoising algorithm based on an operator condition, such as the identified user and the location. Through the example implementations described herein, machine processes can be controlled through properly configured speech recognition and denoising algorithms despite having a surrounding noisy environment.

Abstract: Systems and methods described herein are directed to a specialized Internet of Things (IoT) device deploying both acoustic and radio wave signals. In example implementations described herein, camera data and acoustic sensor data is integrated to generate an acoustic sensor heatmap for the holistic sensing systems in an IoT area.

Abstract: Example implementations described herein are directed to systems and methods for extracting signal in presence of strong noise for industrial Internet of Things (IoT) system especially for monitoring systems of consumable items such as lathe machines, coolers and so on. Example implementations can utilize a sawtooth mother Wavelet instead of usual wavelet analysis to cleanse the incoming sensor data, thereby allowing for the converting sensor data to feature values despite having heavy noise interference.

Abstract: The invention relates to a system and method for automated software testing based on ML. The system comprises a software design module 101 which is configured to provide at least one of business requirement, flow document etc. The requirement parser 102 extracts the actionable items from output of the software design module 101. A ML engine 103 uses supervised ML algorithm to map actionable items with the historic test suites. The test suites and test cases are stored in a NoSQL database. Further, a test design module 104 is configured to create automatic test case design based on ML and assign priorities to the test cases using the parser. A human feedback 105 to the system helps to make the system learns or adjusts the decision making to be more precise.

Abstract: Systems and methods involving integrating camera and acoustic sensor data, and automatically capturing the acoustic sensor heatmap for the holistic sensing systems in Internet of Things (IoT) systems. In particular, example implementations described herein capture the local sound noise environment or localized noise profiles (e.g., noise fingerprint) adaptively to the change of noise profiles and automatically apply captured noise profiles to the streaming noise reduction in signal processing for industrial IoT areas.