Abstract: A system for exchanging data in an automation environment is provided. The system includes at least one programmable logic controller (PLC A) containing program instructions executable by the at least one programmable logic controller and a queue block (50) configured to dynamically exchange data between the program instructions of the PLC and a data consumer (PLC C, PLC D).

Abstract: A system for managing physical assets in a manufacturing system includes a plurality of product controllers corresponding to physical assets in the manufacturing system. Each product controller is configured to store a desired product state for a physical asset and collect sensor data received from other digital companions. Furthermore, each product controller determines an actual product state based on the collected sensor data, as well as one or more actions to be performed on one or more physical assets in the manufacture system to yield the desired product state. Once the actions are determined, the product controller transmits control instructions for performing the one or more actions to one or more operation controllers in the manufacturing system.

Abstract: At least one target area is identified based on a pollution concentration field of a geographic area. At least one candidate site is selected from a plurality of sites within the geographic area based on a relationship between the plurality of sites and pollution sources, where the plurality of sites are potential positions for deploying sensors for monitoring pollution. A target site is determined from the at least one candidate site based on the at least one target area.

Abstract: Embodiments of the invention are directed to a computer-implemented method that includes using a reinforcement learning (RL) system to generate a first set of displayed region candidates based on inputs received from online users while watching video. A recommendation system is used to rank the first set of displayed region candidates based on inputs received from a local user watching video. The recommendation system is further used to select a first highest ranked one of the first set of displayed region candidates. Based on the first highest ranked one of the first set of displayed region candidates, a first section of a first raw video frame is fetched that matches the first highest ranked one of the first set of displayed candidate regions, wherein the first section of the first raw video frame includes a first predicted display region of the video frame.

Abstract: A method can include obtaining, from a video having a first resolution, a set of frames having a second resolution. The first resolution can be higher than the second resolution. The set of frames can include a first frame and a second frame. The method can include generating a difference feature map. The method can include obtaining a third frame having the first resolution. The method can include detecting, based on the difference feature map, a first location of a first object in the third frame. The method can include cropping, from the third frame, a first cropped area. The first cropped area can be smaller than a third frame area. The method can include generating, based on a feature map and the difference feature map, a spatial attention layer. The method can include detecting, by the spatial attention layer, the first object in the first cropped area.

Abstract: A lossy compression algorithm is described for performing data compression of high-frequency floating point time-series data, for example. The compression algorithm utilizes a prediction engine that employs at least one of a linear prediction model or a non-linear prediction model to calculate one-step-ahead prediction of a current data value at current sampling time t using N previous quantized data values, where N is the model order. A prediction error is determined between the predicted value and an actual value, and the prediction error is quantized. A quantized current data value is determined from the predicted value and the quantized prediction error. The quantized prediction error is sent from an edge device to a data decompressor on a cloud device. The decompressor reconstructs the quantized current data value using the received quantized prediction error and by generating the same predicted value as the compressor.

Abstract: An automation system and a method of injecting transactional services in automation is provided. The method performed by the automation system comprises providing a gatekeeper for deployment access control to determine which components can be deployed by an abstraction layer. The method further comprises providing a plugin interface between a consumer plugin associated with a first automation function and a provider plugin associated with a second automation function. The method further comprises injecting an interceptor plugin associated with a transactional service of the transactional services between the consumer plugin and the provider plugin based on an interceptor design pattern.

Abstract: Current approaches to integrating industrial ecosystems, for instance integrating automation functions across different vendors, lack efficiencies and capabilities. For example, system integrators are often required to develop special software that functions as a proxy or adaptor between different systems. In such cases, the proxy or adaptor is often specific to a particular set of equipment or vendors, and which can limit reusability, among other technical drawbacks. Embodiments described herein overcome e one or more of the described-herein shortcomings or technical problems by providing methods, systems, and apparatuses for automatically generating interfaces, for instance glue code, that enables interoperability between different ecosystems in automated industrial systems.

Abstract: An abnormal area is detected using an initial spatial weights matrix between pairs of air quality sensors in a plurality of air quality sensors distributed across a geographical area and air quality data for each air quality sensor. The spatial weights matrix utilizes a distance between pairs of air quality sensors and wind direction through the geographical area. The initial spatial weights matrix and air quality data are used to calculate a plurality of local moran's indexes, one for each air quality sensor. The plurality of local moran's indexes are used to divide the plurality of air quality sensors into four groups. The groups are classified as proper or improper, and the proper groups are identified as abnormal areas.

Abstract: Correction management techniques are provided. In one embodiment, the techniques involve determining, via a first machine learning model, a first and second data based on a respective first and second raw data obtained from a plurality of sensors, determining, based on a deviation between the first data and the second data, an inaccuracy of the first data, identifying an ambient situation corresponding to the first raw data and the second raw data, selecting, from historical raw data of the plurality of sensors, a group of raw data corresponding to the ambient situation, and correcting, via a second machine learning model, the first data based on the selected group of raw data.

Abstract: A computer implemented method, computer system, and computer program product are provided for air-pollution emission source monitoring. To determine the air-pollution emission of a monitored area, location data and air pollution data are taken from sensor stations positioned along a boundary of the monitored area. The macroscale atmospheric data of a region, where the monitored area is located, is also received. A boundary pollutant distribution can be estimated based on the location data and the air pollution data. Horizontal diffusion and vertical diffusion of the monitored area can be estimated based on the boundary pollutant distribution and the macroscale atmospheric data. To determine an accurate amount of pollution contribution caused by a monitored area, a calculation based on the boundary pollutant distribution, the horizontal diffusion, and the vertical diffusion can be used.

Abstract: A method of operating an intelligent programmable logic controller (PLC) as part of a production process within an automation system includes the intelligent PLC receiving automation system data and a semantic context model comprising a plurality of ontologies providing formal specifications of conceptual entities associated with the automation system. The intelligent PLC creates one or more semantic annotations for the automation system data using the semantic context model. These semantic annotations are stored along with the automation system data in a non-volatile storage medium included in the intelligent PLC.

Abstract: A method, system, and computer program product, include recording high-frequency wave data of local arid adjacent fault curves of a power grid to compare the high-frequency wave data with historical fault data to detect an actual fault region in the power grid, the power grid having a plurality of sensors distributed in the power grid, each sensor being upstream of one adjacent sensor and downstream of an other adjacent sensor.

Abstract: A method for performing security failover in an industrial production environment includes a programmable logic controller (PLC) receiving notification that a function block (FB) or a function (FC) on the programmable logic controller has been maliciously revised. The PLC next determines whether the function block or the function is also maliciously revised on a failover computing device. If the failover computing device is not maliciously revised, a failover operation is performed by the PLC. This operation includes sending a data block comprising one or more input parameters to the function block or the function and receiving an output data resulting from executing the function block or the function with the data block on the failover computing device.

Abstract: A computer-implemented method of identifying federations of manufacturing apps using House of Quality (HoQ) analysis includes receiving user request information comprising a plurality of desired functions and an importance value for each of the plurality of desired functions and generating a correlation matrix between the plurality of apps, wherein each element of the correlation matrix indicates a degree of correlation between a pair of apps. A relationship matrix is generated which comprises numerical values indicating relationships between a plurality of apps and the plurality of desired functions, wherein each column of relationship matrix is associated with an app and each row is associated with a desired function. Next, a weighted matrix is created by multiplying each row in the relationship matrix by the importance value corresponding to the desired function associated with the row.

Abstract: Techniques for monitoring particulate matter (PM) mass concentration using relatively low cost devices are described. A computer-implemented method comprises determining, by a device operatively coupled to a processor, relationships between: first PM mass data determined by a monitor station device for a first atmospheric area over a period of time; first PM count data determined by a reference PM count device for the first atmospheric area over the period of time; and first conditional information comprising first values for defined conditional parameters, wherein the first values are associated with the first atmospheric area over the period of time. The method further includes generating an initial conversion model based on the relationships, wherein the conversion model converts a PM count to a PM mass based on one or more conditional parameters of the defined conditional parameters and features for updating the conversion model.

Abstract: An abnormal area is detected using an initial spatial weights matrix between pairs of air quality sensors in a plurality of air quality sensors distributed across a geographical area and air quality data for each air quality sensor. The spatial weights matrix utilizes a distance between pairs of air quality sensors and wind direction through the geographical area. The initial spatial weights matrix and air quality data are used to calculate a plurality of local moran's indexes, one for each air quality sensor. The plurality of local moran's indexes are used to divide the plurality of air quality sensors into four groups. The groups are classified as proper or improper, and the proper groups are identified as abnormal areas.

Abstract: A model-based human machine interface (HMI) system is provided. The HMI system includes a plurality of output devices each having output capabilities that include at least one unique output capability level and a semantics library that is configured to receive SUC component models each having an output format and data. The semantics library is configured to analyze the data relative to the SUC component models and to transmit each of the SUC component models to one or more of the plurality of output devices. The one or more of the plurality of output devices are selected based on a correlation between the output capabilities of the plurality of output devices and the output format of the SUC component models.

Abstract: A method, a device and a computer program product for abnormal air pollution emission prediction are proposed. In the method, a first set of features characterizing air condition in a zone is obtained. Whether the zone is subject to abnormal air pollution emission in a future first time period is determined based on the first set of features and using a first prediction classifier. In response to determining that the zone is subject to abnormal air pollution emission in the first time period, a second set of features characterizing air condition in the zone is obtained. A future second time period in which the zone is subject to abnormal air pollution emission is determined based on the second set of features and using a second prediction classifier. The second time period is included in the first time period. In this way, the abnormal air pollution emission in the zone can be accurately and efficiently predicted.

Abstract: Techniques for monitoring particulate matter (PM) mass concentration using relatively low cost devices are described. A computer-implemented method comprises determining, by a device operatively coupled to a processor, relationships between: first PM mass data determined by a monitor station device for a first atmospheric area over a period of time; first PM count data determined by a reference PM count device for the first atmospheric area over the period of time; and first conditional information comprising first values for defined conditional parameters, wherein the first values are associated with the first atmospheric area over the period of time. The method further includes generating an initial conversion model based on the relationships, wherein the conversion model converts a PM count to a PM mass based on one or more conditional parameters of the defined conditional parameters and features for updating the conversion model.