The measuring apparatus according to the present invention searches each of subbands divided from a target band set with respect to the Frequency Distribution Information (FDI) obtained periodically to find a unit band corresponding to a maximum frequency component among unit bands pertaining to said each subband; marks the found unit band as a Dominant Unit Band (DUB); organizes a DUB distribution table by collecting a plural pieces of the FDI as much as a predetermined time; checks a distribution locus of DUBs on the organized DUB distribution table. And, if it is confirmed that the DUB distribution table shows a specific distribution pattern corresponding to an acceleration sound, etc., the measuring apparatus makes a measured intensity to be identified as an intensity measured at a start position of a platform or a tunnel section, based on the time when the specific distribution pattern appears.

Systems and methods include reception, for each of a first plurality of consecutive time periods, of an acceleration value associated with a first location of a structure, determination of a first value of a first indicator based on absolute values of the acceleration values, determination of a second value of a second indicator based on absolute values of differences of consecutive one of the acceleration values, determination of a first value of an index based on the first value and the second value, determination of a physical characteristic of the structure based on the first value of the first indicator and the first value of the index, and transmission of an alert based on the physical characteristic.

Systems and methods include reception, for each of a first plurality of consecutive time periods, of an acceleration value associated with a first location of a structure, determination of a first value of a first indicator based on absolute values of the acceleration values, determination of a second value of a second indicator based on absolute values of differences of consecutive one of the acceleration values, determination of a first value of an index based on the first value and the second value, determination of a physical characteristic of the structure based on the first value of the first indicator and the first value of the index, and transmission of an alert based on the physical characteristic.

A road surface state determination device includes a tire-side device and a vehicle-body-side system. The tire-side device is attached to each of a plurality of tires included in a vehicle. The vehicle-body-side system is included in a body of the vehicle. The tire-side device may output a detection signal corresponding to a magnitude of vibration applied to the tire. The tire-side device may extract data items of a road surface state indicative of the vibration of the tire during one rotation of the tire from the detection signal. The tire-side device may generate road surface data. The tire-side device may transmit the road surface data. The vehicle-body-side system may receive the road surface data. The vehicle-body-side system may determine the road surface state based on the road surface data.

A road surface state determination device includes a tire-side device and a vehicle-body-side system. The tire-side device is attached to each of a plurality of tires included in a vehicle. The vehicle-body-side system is included in a body of the vehicle. The tire-side device may output a detection signal corresponding to a magnitude of vibration applied to the tire. The tire-side device may extract data items of a road surface state indicative of the vibration of the tire during one rotation of the tire from the detection signal. The tire-side device may generate road surface data. The tire-side device may transmit the road surface data. The vehicle-body-side system may receive the road surface data. The vehicle-body-side system may determine the road surface state based on the road surface data.

A distributed computing system for artificial intelligence in autonomously appreciating a circumstance context of a smart device. Raw context data is detected by sensors associated with the smart device. The raw context data is pre-processed by the smart device and then provided to a cloud based server for further processing. At the cloud based server, various sets of feature data are obtained from the pre-processed context data. The various sets of feature data are compared with corresponding classification parameters to determine a classification of a continuous event and/or a classification of transient event, if any, which occur in the context. The determined classification of the continuous event and the transient event will be used to autonomously configure the smart device or another related smart device to fit the context.

A platform system receives sensor data describing the state and orientation of a tracked object and models the pose of the tracked object to determine user interactions with the platform system. To ensure that incorrect sensor data due to a saturation event or connection loss does not impact user experience, the platform system identifies regions for correction in sensor data streams based on the sensor data being at or above a saturation limit or not being received. The platform system predicts sensor data for an identified region of correction by applying a fit corresponding to points adjacent to the region for correction and determining predicted sensor data using the applied fit. The predicted sensor data is used to correct the modeled pose for the tracked object.

A platform system receives sensor data describing the state and orientation of a tracked object and models the pose of the tracked object to determine user interactions with the platform system. To ensure that incorrect sensor data due to a saturation event or connection loss does not impact user experience, the platform system identifies regions for correction in sensor data streams based on the sensor data being at or above a saturation limit or not being received. The platform system predicts sensor data for an identified region of correction by applying a fit corresponding to points adjacent to the region for correction and determining predicted sensor data using the applied fit. The predicted sensor data is used to correct the modeled pose for the tracked object.

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for receiving an indication that a vehicle has begun accelerating from a stationary state. A computing system sets, in response to having received the indication that the vehicle has begun accelerating from the stationary state, an orientation value generated using a gyroscope to a default orientation value. The computing system repeatedly updates the orientation value generated using the gyroscope, based on changes in gyroscope orientation that occurred after the computing system set the orientation value to the default orientation value. The computing system determines that the updated orientation value satisfies criteria that indicates that the vehicle is likely to encounter or has encountered a dangerous situation. The computing system outputs a signal to cause the vehicle to employ a safety measure.

Described herein are systems, methods, and other techniques for determining a period during which an implement of a construction machine is interacting with a ground surface. A vibration signal that is indicative of a movement of the implement is captured. One or more features are extracted from the vibration signal. The one or more features are provided to a machine-learning model to generate a model output. An implement-on-ground (IOG) start time and an IOG end time are predicted based on the model output, the IOG start time and the IOG end time forming the period.