711 results about "Recurrent neural nets" patented technology

The invention discloses a method for accelerating an LSTM neural network algorithm on an FPGA platform. The FPGA is a field-programmable gate array platform and comprises a general processor, a field-programmable gate array body and a storage module. The method comprises the following steps that an LSTM neural network is constructed by using a Tensorflow pair, and parameters of the neural networkare trained; the parameters of the LSTM network are compressed by adopting a compression means, and the problem that storage resources of the FPGA are insufficient is solved; according to the prediction process of the compressed LSTM network, a calculation part suitable for running on the field-programmable gate array platform is determined; according to the determined calculation part, a softwareand hardware collaborative calculation mode is determined; according to the calculation logic resource and bandwidth condition of the FPGA, the number and type of IP core firmware are determined, andacceleration is carried out on the field-programmable gate array platform by utilizing a hardware operation unit. A hardware processing unit for acceleration of the LSTM neural network can be quicklydesigned according to hardware resources, and the processing unit has the advantages of being high in performance and low in power consumption compared with the general processor.

A method and apparatus for data communication in an oil well environment, wherein the method comprises detecting an acoustic signal transmitted along an acoustic channel, the acoustic signal being distorted from transmission through the acoustic channel, generating a transmitted data signal in response to the acoustic signal, inputting the transmitted data signal to an adaptive equalizer and adaptively equalizing the transmitted data signal to produce an equalized data signal related to the transmitted data signal by a mathematical function. The detecting step may include positioning an acoustic receiver in a communication unit along the acoustic channel. The communication unit may be positioned downhole and the adaptive equalizer may be positioned remotely relative to the communication unit or may be placed in the communication unit. The adaptive equalizer may be a frequency domain filter, a neural net adaptive equalizer or a nonlinear recurrent neural net equalizer. The acoustic signal may comprise a plurality of discrete transmissions which may be a training sequence for training the adaptive equalizer and may comprise a first discrete transmission transmitted repeatedly.The method of data communication in an oil well environment may comprise the steps of transmitting an acoustic signal from a first location along an acoustic channel, detecting the acoustic signal at a second location along the acoustic channel, generating a transmitted data signal in response to the acoustic signal, inputting the transmitted data signal to an adaptive equalizer and adaptively equalizing the transmitted data signal to produce an equalized data signal related to the transmitted data signal by a mathematical function. The transmitting step may further comprise positioning an acoustic transmitter in a first communication unit along the acoustic channel downhole or elsewhere. The method may further comprise acquiring data, generating an original data signal in response to the acquired data and inputting the original data signal to the acoustic transmitter. The acoustic signal may comprise a series of acoustic training signals for training the adaptive equalizer. The acoustic training signals may be transmitted at a predetermined time. A stored training signal may include a series of stored training data signals corresponding to the series of acoustic training signals. At least a portion of the stored training signals may be cross-correlated to the transmitted data signal. The acoustic signal may comprise a notification signal for notifying the adaptive equalizer of a training session.

The present invention discloses a computer vision-based express parcel violent sorting identification method. The method comprises the following steps of: a depth camera-based pose estimation: a human pose estimation problem is converted into a problem of classifying depth image pixels captured by a depth camera, and human body pose estimation is realized by using a random forest method; human body three-dimensional pose relative spatial-temporal feature extraction: relative spatial-temporal positions of geometric elements of points, lines and surfaces formed by joints in three-dimensional poses and the measures of the change of the relative spatial-temporal positions are extracted and are adopted as the feature representations of the poses; and recurrent neural network-based violent sorting identification: modeling training is performed on the pose spatial-temporal relative features which are continuous in time and are extracted from the human body three-dimensional poses through a long and short memory model (LSTM), so that the identification of express parcel violent sorting behaviors can be realized.

A spoken language understanding (SLU) system receives a sequence of words corresponding to one or more spoken utterances of a user, which is passed through a spoken language understanding module to produce a sequence of intentions. The sequence of words are passed through a first subnetwork of a multi-scale recurrent neural network (MSRNN), and the sequence of intentions are passed through a second subnetwork of the multi-scale recurrent neural network (MSRNN). Then, the outputs of the first subnetwork and the second subnetwork are combined to predict a goal of the user.

Systems and methods for time series prediction are described. The systems and methods include encoding driving series into encoded hidden states, the encoding including adaptively prioritizing driving series at each timestamp using input attention, the driving series including data sequences collected from sensors. The systems and methods further includes decoding the encoded hidden states to generate a predicting model, the decoding including adaptively prioritizing encoded hidden states using temporal attention. The systems and methods further include generating predictions of future events using the predicting model based on the data sequences. The systems and methods further include generating signals for initiating an action to devices based on the predictions.