59 results about "Time series processing" patented technology

A data processing apparatus includes a plurality of image processing units which execute different processes on input image data and an encoding unit which encodes the image data. A generation unit selects an image processing unit to be used from among a plurality of image processing units based on requests from a plurality of output destinations to process image data of each frame input. The selected image processing unit processes the image data in time-series and the encoding unit encodes the processed image data. Thus, a plurality of image data corresponding to requests from a plurality of output destinations are generated.

An object of the present invention is to provide an ultrasonic urinary volume sensor capable of estimating a urinary volume in the bladder accurately corresponding to any particular individuals and / or conditions thereof by incorporating a space time series processing method allowing for an accurate estimation of the urinary volume in the bladder in association with the particular individuals and / or conditions thereof. An ultrasonic urinary volume sensor of the present invention comprises: a probe 1 having a plurality of ultrasonic oscillators 3 for oscillating ultrasonic waves toward a wall surface of the bladder, which is adhesively placed over a body surface in an abdominal section via an ultrasonic wave transmission medium interposed therebetween; and a processing section 2 for detecting and processing reflective echoes of the ultrasonic waves from the wall surface of the bladder, which have been oscillated by the plurality of ultrasonic oscillators 3 of the probe 1, wherein the plurality of ultrasonic oscillators 3 is disposed along a direction of expansion of the bladder.

A system and related device emulates the behavior of mobile networks independently from the manner of collecting data about the behavior of the network obtained through a simulation of field measurements. The system emulates the behavior of the network during the exchange of information between terminals and uses an emulation device comprising two circuit blocks. The first is able to process the information according to time sequences corresponding to those of mobile networks. The second one exchanges information between the terminals connected to suitable interfaces. This architecture makes it possible to emulate uplink and downlink functions of the mobile network, displaying on the terminals the effects of the behavior of the network under various operating conditions.

A data processing apparatus includes a plurality of image processing units which execute different processes on input image data and an encoding unit which encodes the image data. A generation unit selects an image processing unit to be used from among a plurality of image processing units based on requests from a plurality of output destinations to process image data of each frame input. The selected image processing unit processes the image data in time-series and the encoding unit encodes the processed image data. Thus, a plurality of image data corresponding to requests from a plurality of output destinations are generated.

The invention discloses a personnel flow monitoring method and apparatus based on a mobile network. The method comprises: acquiring time point position information of a mobile terminal and geographical position information of a base station; performing time sequence processing based on the position information of the mobile terminal at each time point, and obtaining the position information of the mobile terminal in each time period; matching the geographical position information of the base station with the preset geographical position information of a monitoring point, and obtaining a base station within the monitoring scope of the monitoring point; and analyzing the position information of the mobile terminal at each time point, the position information of the mobile terminal in each time period and the base station within the monitoring scope of the monitoring point, and obtaining the flow information of the mobile terminal within the monitoring scope of the monitoring point as personnel flow information. By using the method and apparatus provided by the invention, the problems of large resource investment and incapability of tracing back a personnel flow direction during conventional personnel flow monitoring can be solved.

The invention discloses a moving point target detection method and system based on high-time-phase detection, and the method comprises the steps: obtaining an image sequence shot by a high-frame-frequency camera, and dividing the image sequence into a plurality of basic processing units; in each basic processing unit, detecting a pixel with a target by using time domain transient fluctuation information caused when the target passes through the pixel to obtain a track of the target; and associating the target tracks of all the basic processing units through a track association method to obtaina target overall track. According to the method provided by the invention, a high-frame-frequency detection data and time sequence processing method is utilized, so that the detection accuracy of thelow-signal-to-noise-ratio moving point target can be improved, and the false alarm rate is reduced.

The invention provides an object detector for a vehicle that can ensure sufficient S / N ratio and avoid error detection even if structure of a time sequence filter for receiving signal does not become complex. The detector has a L / D apparatus (3) that control light emitting of laser and control light receiving of reflector light, and an object detection part that detects object by receiving signal from the L / D apparatus (3). The object detection part includes: an accumulative data memorizer (43) that received signals are cumulatively added up for each of areas into which the target area is divided; a region measurement data storage memorizer (44) that measures data according with each region storage region; a time series processor (45) that noise is removed from obtained data by time series filtering; the object detector (47), if the value of a peak in data is less than that of the corresponding peak obtained in the previous scan by more than a specified value, such data are considered as an after image and not detected as an object.

The invention discloses a time series unsupervised anomaly detection method based on a conditional regularization flow model. The time series unsupervised anomaly detection method comprises the following steps of: (1) preprocessing a time series, and constructing a training data set; (2) constructing a recurrent neural network for processing a historical time series into implicit representation; (3) constructing the conditional regularization flow model for modeling probability density of an observation window by taking a historical observation series as a condition, wherein the conditional regularization flow model is used for calculating conditional logarithm likelihood of a time series in the observation window; (4) learning optimization model parameters based on a maximum likelihood principle; (5) selecting a threshold value according to the conditional logarithm likelihood of all the samples under the conditional regularization flow model with determined parameters; (6) and calculating the conditional logarithm likelihood of the time series in the observation window on line by using the recurrent neural network and the conditional regularization flow model with the determinedparameters, and reporting the observation window to be abnormal when the conditional logarithm likelihood is lower than a specified threshold value. The time series unsupervised anomaly detection method can effectively reduce the false alarm rate of anomaly detection.

An optical scanner has an oscillation-type optical scanning mechanism which includes a movable member which resonates at a predetermined resonance frequency, a drive signal generator which generates a drive signal for allowing the movable member to resonate at the resonance frequency, and an oscillation signal generator which generates an oscillation signal in response to a change of the radiation direction of the light beams. Here, the oscillation signal generator includes a dot clock generator which generates a dot clock which becomes the reference of time-series processing by setting a frequency of the oscillation signal as a reference frequency.

The invention relates to a stock index prediction method and device based on a neural network model and a time sequence, and the method comprises the steps: extracting time sequence data of a to-be-predicted stock index, and obtaining feature data; preprocessing the feature data to obtain first data; performing time sequence processing on the first data to obtain second data; and training the second data through a neural network model, and predicting the price of the stock finger to be predicted, thereby improving the accuracy and length of stock finger prediction.

The invention discloses an unstructured grid significant wave height prediction system based on deep learning, which comprises a long short-term memory (LSTM) model, and the unstructured grid significant wave height prediction system comprises a time sequence processing model composed of two LSTMs and is a recurrent neural network with an input gate, a forget gate and an output gate. The input of the model is the effective wave height data of the unstructured grid in 10000 hours provided by the FVCOM mode, and the final output is the predicted wave height in the future 1 hour, 6 hours, 12 hours and 24 hours.