32results about How to "Fast inference" patented technology

An Active Random Field is presented, in which a Markov Random Field (MRF) or a Conditional Random Field (CRF) is trained together with a fast inference algorithm using pairs of input and desired output and a benchmark error measure.

A method for controlling, during metal processing, the input energy from an energy point source that directs focused emitted energy onto a metal workpiece having a geometry, wherein the directed focused emitted energy creates a melt pool and hot zone on the workpiece that emit radiation during the process. The method comprises determining a wavelength range for the emitted radiation that is within a spectral range of radiation emitted by the hot zone during processing that is comparatively high in amount in relation to the amount of radiation emitted by the melt pool in that spectral range during processing; directing the beam onto the workpiece to generate a melt pool and hot zone on the structure; measuring the intensity of radiation within the determined wavelength range; and adjusting the input energy from the energy point source based upon the measured intensity of radiation within the determined wavelength range.

Given an image and an aligned depth map of an object, the invention predicts the 3D location, 3D orientation and opening width or area of contact for an end of arm tooling (EOAT) without requiring a physical model.

The invention discloses a bridge disease real-time detection method based on model pruning, and the method comprises the following steps: firstly obtaining a bridge apparent damage image through an unmanned plane, and dividing a data set into a training set, a verification set and a test set according to a proportion; increasing the number of data sets by applying an image enhancement method; constructing a yolov3 network framework, adding L1 regularization constraints of gamma parameter items to each batch of normalization layers (BN) of the network, setting a sparsity adjustment strategy ofprogressive local attenuation, and training the reconstructed network to obtain a training model; after the gamma parameters of all the trained BN layers are obtained, performing model pruning of thelayers and the channels according to the sizes of the gamma parameters between the layers and in the layers, deleting the channel with the small weight, and obtaining a pruned model; and carrying outbridge apparent disease automatic identification by using the trained model. The invention is high in efficiency and low in cost, and compared with a traditional manual label marking training method,the method has more obvious automation and high efficiency.

The invention provides an animal motion state video analysis method based on a deep learning algorithm. The method comprises the following steps: segmenting each frame of a mouse motion video, adjusting the size of a picture, and generating an image of a mouse motion state; carrying out a small amount of manual annotation on the images of the mouse motion state, and making an image data set required by the training model; training a mouse motion state analysis model based on the manufactured data set; and inputting the mouse motion video to be analyzed into the trained mouse motion state analysis model to obtain an analysis result of the mouse motion state. According to the mouse motion state analysis method, through a convolutional neural network technology and a transfer learning technology in deep learning, under the condition that only a small amount of video data is labeled and data distribution is unbalanced, and therefore, an end-to-end mouse motion state analysis model with high training speed, high accuracy, high inference speed and highdeduction speed is constructed.

The invention discloses an HSV and CNN-based FPC defect detection method and system and a storage medium. The detection method comprises the steps of extracting a detection region in a to-be-detected original image according to color and brightness; converting the to-be-detected original image from an RGB color space to an HSV color space, and preliminarily judging candidate defect areas from the HSV color space according to preset defect parameters; taking an intersection between the candidate defect area and the detection area as a final defect area; comparing the final defect area with a preset threshold value, and judging whether the currently detected FPC is a good product, a defective product or an uncertain product; and identifying the detection area of the uncertain product by using a pre-trained deep learning model, and predicting whether the uncertain product belongs to a good product or a defective product. According to the invention, defect detection is carried out on the FPC to be detected in a mode of combining image processing and a deep learning model, and the method has the advantages of low cost and high precision.

The invention provides a terminal equipment heterogeneous processor inference acceleration method under temperature constraint, aims at intelligent terminal equipment equipped with a plurality of heterogeneous processors in an industrial production environment, and solves the problem of low terminal equipment inference efficiency caused by deep neural network interlayer heterogeneity, processor heterogeneity and environment temperature. According to the method, firstly, the environment temperature of industrial production and the power of a terminal device processor are considered, a terminal device dynamic frequency model under temperature constraint is established, and a temperature sensing dynamic frequency algorithm is used for setting the device frequency; secondly, designing a deep neural network single-layer parallel method according to calculation modes and structural characteristics of different layers in the deep neural network; and finally, a heterogeneous processor in the terminal equipment is utilized to design a deep neural network single-layer calculation task allocation method oriented to the heterogeneous processor, and low delay and robustness of collaborative inference of the heterogeneous processor of the terminal equipment are guaranteed.

The invention discloses an aircraft detection and tracking method based on multi-scale self-adaption and side domain attention, and the method comprises the steps: extracting an original feature map of a preprocessed original aircraft image through constructing a basic feature extraction network, and extracting a small-target feature map in the original feature map through combining with a small-size target branch network model; and obtaining a detection target feature map set and feature vectors corresponding to the detection target feature maps according to the small target feature maps by using a target prediction model, and performing aircraft detection and tracking by using a multi-aircraft tracking algorithm. Fusion transmission of shallow texture features and deeper semantic features of the feature map is optimized by using a coding and decoding structure and residual connection, the inference speed is improved, information fusion is more sufficient, and the feature extraction capability of a network model is effectively improved by combining a side domain attention mechanism network; and the small-size target branch network model is utilized to reduce the information loss degree, the small-size target detection accuracy is effectively optimized, and the management efficiency of airport scene aircrafts is improved.

The invention provides a convolutional neural network INT8 quantification method, system and device and a storage medium, and the method comprises the steps: obtaining a parameter quantification coefficient of a convolution layer, carrying out the parameter nonlinear mapping of a parameter of the convolution layer, and obtaining a quantified parameter; obtaining an input quantization coefficient of the convolution layer, and establishing an input quantization function of the convolution layer, the input quantization function being used for performing nonlinear mapping on input data of the convolution layer to obtain quantized input data; and obtaining an output quantization coefficient of the convolution layer, and establishing an output quantization function of the convolution layer according to the quantized parameter and the input quantization function, the output quantization function being used for performing nonlinear mapping on output data of the convolution layer to obtain quantized output data. By the adoption of the method and device, off-line nonlinear quantization is conducted on model parameters, input and output, pure integer operation of the whole model is achieved,and quantization precision is improved.

The invention belongs to the field of computer science, and discloses a convolutional neural network compression method based on a global feature relationship. According to the method, a global feature relationship (GFR) submodule is added behind a Batch Normalization (BN) layer of the convolutional neural network, so that the aims of extracting a channel relationship and suppressing an unimportant channel are fulfilled. The GFR sub-module mainly relates to pooling, full connection layer and moving average operation. Then, the importance degree of each channel is evaluated by combining the channel relationship and the channel scale factor of the BN layer, and the index can help the model compression method to screen out important channels in the model more accurately. And finally, by utilizing a channel pruning algorithm and a grid search technology, the compression of the network model is completed by consuming less time and computing resources without finely adjusting the model, so the running memory and the storage memory of the model are remarkably reduced, and the deduction speed of the model is increased, so the possibility of deploying the convolutional neural network on thesmall mobile device is greatly improved.

The invention relates to the technical field of natural language processing in the field of artificial intelligence, and discloses a dynamic vocabulary enhancement combined model distillation method, which comprises the following steps: on the basis of an ALBert language model, adjusting the language model by combining a fine adjustment technology with a dynamic vocabulary enhancement technology to obtain a finely adjusted language model, and taking the finely adjusted language model as a teacher model; different from the conventional fine adjustment logic, when the language model is finely adjusted, in the fine adjustment process, combining the characteristics of the dictionary information with the output characteristics of the language model, and then performing fine adjustment; and after fine adjustment is finished, distilling the teacher model, and taking an obtained model prediction result as a training basis of the student model. According to the model distillation method provided by the invention, the dictionary information is introduced as the key information, so that the model can still capture the dictionary information as a feature under the condition of greatly reducing the size, thereby achieving the purposes of greatly reducing the size of the model and accelerating the inference speed under the condition of not sacrificing the extraction accuracy.

The invention discloses a deep network waveform synthesis method and device based on filter bank frequency distinguishing. The method comprises the following steps: designing a plurality of filter banks of any frequency passbands by adopting an analytical method; voice signals output by the generator are fed into a filter bank in parallel, and signals of a plurality of narrow frequency bands are obtained; the method comprises the following steps of: respectively inputting a narrow-band signal into each sub-discriminator for processing, training parameters of a generative adversarial network by synthesizing a loss function of the sub-discriminator, feeding a test text into a given acoustic model front-end network to generate a test Mel spectrum, and inputting the test Mel spectrum into a generator to generate a voice signal. The device comprises a processor and a memory. The voice waveform synthesis GAN network provided by the invention solves the problem of aliasing failure of a high-frequency part, and greatly reduces the spectrum distortion of a high-frequency band.

The invention provides a mechanical and electrical product assembly error online prediction method based on an auto-encoder and a Boosting-OSKELM algorithm, and the method comprises the steps: firstly constructing a corresponding sample data set for a certain assembly process in combination with the assembly technology and assembly error historical assembly data; secondly, carrying out dimensionality reduction on the input process data of each sample point based on an auto-encoder with a Fine-Tuning skill, and forming new order reduction representation data by the process data after dimensionality reduction and original assembly error data; thirdly, inputting the process data subjected to dimension reduction into a Boosting-KELM model, and determining a lifting sequence of a kernel function and a corresponding hyper-parameter in combination with prediction performance on a test set; and finally, according to an incremental learning recursion formula of the online sequential kernel extreme learning machine, forming a Boosting-OSKELM model so as to realize online prediction of the assembly error. According to the method, the assembly error of the mechanical and electrical product can be predicted online.

The invention provides a method for robustly classifying pictures by using a sparse network based on a retention dynamic process, which combines a neural tangent kernel theory and an adversarial training dynamic process, obtains respective adversarial samples of the sparse network and a dense network by using adversarial attacks, and obtains the sparse network suitable for adversarial training. And adversarial training is carried out on the picture set by using the sparse network to obtain a classifier, so that the adversarial attack can be effectively resisted. The performance of the sparse network obtained by the method is equivalent to the performance of the original dense network, the adversarial robustness is superior to that of the recently proposed Inverse Weight Ineritance2020, the target sparse network is found during initialization, the adversarial training-pruning process does not need to be iteratively carried out like the existing method, the training time is greatly shortened, and the training efficiency is improved. Therefore, the method is superior to an existing same-task method, and deployment of the robust adversarial neural network on resource limited equipment becomes possible.