An edge computing platform with machine learning capability is provided between a local network with a plurality of sensors and a remote network. A machine learning model is created and trained in the remote network using aggregated sensor data and deployed to the edge platform. Before being deployed, the model is edge-converted (“edge-ified”) to run optimally with the constrained resources of the edge device and with the same or better level of accuracy. The “edge-ified” model is adapted to operate on continuous streams of sensor data in real-time and produce inferences. The inferences can be used to determine actions to take in the local network without communication to the remote network. A closed-loop arrangement between the edge platform and remote network provides for periodically evaluating and iteratively updating the edge-based model.

An edge computing platform with machine learning capability is provided between a local network with a plurality of sensors and a remote network. A machine learning model is created and trained in the remote network using aggregated sensor data and deployed to the edge platform. Before being deployed, the model is edge-converted (“edge-ified”) to run optimally with the constrained resources of the edge device and with the same or better level of accuracy. The “edge-ified” model is adapted to operate on continuous streams of sensor data in real-time and produce inferences. The inferences can be used to determine actions to take in the local network without communication to the remote network. A closed-loop arrangement between the edge platform and remote network provides for periodically evaluating and iteratively updating the edge-based model.

Provided is a learning support system which can enhance a learning effect by sharing a matter of interest among learners viewing a learning content. The learning support system includes: a learning content display unit configured to play and display a teaching material video; a learner information reporting unit configured to acquire biological information of a learner during play of the teaching material video and report the biological information as learner information; a region-of-attention identification unit configured to identify a region-of-attention of the learner based on the biological information; a learner information display creation unit configured to generate a screen in which the region-of-attention is superimposed on the teaching material video of another learner belonging to the same cluster as the learner; and a learner information transmission unit configured to transmit the screen to the another learner.

A vehicle-mounted device includes a data collecting device that collects sensor data from a vehicle sensor, a data transmitting device that transmits the sensor data to a server, a data receiving device that receives data about an outside-vehicle status, and an inside-vehicle/outside-vehicle cooperation device that, in response to receipt of the data about the outside-vehicle status, cooperates with a device outside the vehicle and controls an operation inside the vehicle. The server includes a map creating device that maintains a traffic status overview map on the basis of received sensor data, a vehicle selecting device that selects a vehicle capable of transmitting optimum sensor data for maintaining the traffic status overview map in view of a line status among vehicle-mounted devices present within a specific area, and a transmission permission/prohibition signal transmitting device that transmits an instruction of permitting transmission of sensor data to the vehicle.

An apparatus and method for parameter comprehensive monitoring and troubleshooting of power transformation and distribution are disclosed. The apparatus includes a data acquisition unit, an on-site CPU, a main CPU, an operation and maintenance control center, a UPS and an energy storage breaking mechanism. Each on-site CPU compares the relevant state values of equipment line collected by a data acquisition unit with a threshold set by fiber Bragg grating sensor nodes and sums up to a main CPU. The main CPU stores and display the relevant state values through a display screen. The node represents each distribution point. A link represents a data transmission path. An attached table displays all state parameters. A working state of the distribution equipment is determined according to a color of the node and the link.

An approach for diagnosing degradations in performance and malfunctions in sensor networks is disclosed. This approach is based on so-called “fault signatures”. Such fault signatures are generated for known fault conditions through a statistical analysis process that results in each known fault having a unique fault signature. Such unique fault signatures can then point to the root cause of a problem.

An approach for diagnosing degradations in performance and malfunctions in sensor networks is disclosed. This approach is based on so-called “fault signatures”. Such fault signatures are generated for known fault conditions through a statistical analysis process that results in each known fault having a unique fault signature. Such unique fault signatures can then point to the root cause of a problem.

According to certain embodiments, an electronic device comprises a communication module; a plurality of sensors and configured to obtain sensing data; at least one processor operatively connected to the plurality of sensors and the communication module; and a memory operatively connected to the at least one processor, wherein the memory stores instructions that, when executed, cause the at least one processor to perform a plurality of operations comprising: transmitting the sensing data to a server through the communication module; receiving, from the server, information on a similarity between the sensing data and a first cluster among a plurality of clusters clustering data related to user activities, through the communication module, wherein the similarity is identified based on a center similarity score between the sensing data and the first cluster, a score that is a function of a variance of the first cluster, a score that is a function a distance between the first cluster and other clusters, and an intersection score between the first cluster and a second cluster adjacent to the first cluster; and executing a function corresponding to the sensing data based on the similarity.

According to certain embodiments, an electronic device comprises a communication module; a plurality of sensors and configured to obtain sensing data; at least one processor operatively connected to the plurality of sensors and the communication module; and a memory operatively connected to the at least one processor, wherein the memory stores instructions that, when executed, cause the at least one processor to perform a plurality of operations comprising: transmitting the sensing data to a server through the communication module; receiving, from the server, information on a similarity between the sensing data and a first cluster among a plurality of clusters clustering data related to user activities, through the communication module, wherein the similarity is identified based on a center similarity score between the sensing data and the first cluster, a score that is a function of a variance of the first cluster, a score that is a function a distance between the first cluster and other clusters, and an intersection score between the first cluster and a second cluster adjacent to the first cluster; and executing a function corresponding to the sensing data based on the similarity.

A system and method for managing sensors including determining health operation states of the sensors correlative with sensor accuracy, classifying the sensors by their respective health operation state, and teaming two sensors each having a health operation state that is intermediate to give a team having a health operation state that is healthy. The sampling frequency of the sensors to determine sensor accuracy may be dynamic.