16615 results about "Deep learning" patented technology

An artificial neural network device that utilizes one or more non-volatile memory arrays as the synapses. The synapses are configured to receive inputs and to generate therefrom outputs. Neurons are configured to receive the outputs. The synapses include a plurality of memory cells, wherein each of the memory cells includes spaced apart source and drain regions formed in a semiconductor substrate with a channel region extending there between, a floating gate disposed over and insulated from a first portion of the channel region and a non-floating gate disposed over and insulated from a second portion of the channel region. Each of the plurality of memory cells is configured to store a weight value corresponding to a number of electrons on the floating gate. The plurality of memory cells are configured to multiply the inputs by the stored weight values to generate the outputs.

Methods and apparatus for deep learning-based system design improvement are provided. An example system design engine apparatus includes a deep learning network (DLN) model associated with each component of a target system to be emulated, each DLN model to be trained using known input and known output, wherein the known input and known output simulate input and output of the associated component of the target system, and wherein each DLN model is connected as each associated component to be emulated is connected in the target system to form a digital model of the target system. The example apparatus also includes a model processor to simulate behavior of the target system and/or each component of the target system to be emulated using the digital model to generate a recommendation regarding a configuration of a component of the target system and/or a structure of the component of the target system.

The invention discloses a fundus image retinal vessel segmentation method and a fundus image retinal vessel segmentation system based on deep learning. The fundus image retinal vessel segmentation method comprises the steps of performing data amplification on a training set, enhancing an image, training a convolutional neural network by using the training set, segmenting the image by using a convolutional neural network segmentation model to obtain a segmentation result, training a random forest classifier by using features of the convolutional neural network, extracting a last layer of convolutional layer output from the convolutional neural network, using the convolutional layer output as input of the random forest classifier for pixel classification to obtain another segmentation result, and fusing the two segmentation results to obtain a final segmentation image. Compared with the traditional vessel segmentation method, the fundus image retinal vessel segmentation method uses the deep convolutional neural network for feature extraction, the extracted features are more sufficient, and the segmentation precision and efficiency are higher.

In accordance with various embodiments of the disclosed subject matter, a method and a system for vision-centric deep-learning-based road situation analysis are provided. The method can include: receiving real-time traffic environment visual input from a camera; determining, using a ROLO engine, at least one initial region of interest from the real-time traffic environment visual input by using a CNN training method; verifying the at least one initial region of interest to determine if a detected object in the at least one initial region of interest is a candidate object to be tracked; using LSTMs to track the detected object based on the real-time traffic environment visual input, and predicting a future status of the detected object by using the CNN training method; and determining if a warning signal is to be presented to a driver of a vehicle based on the predicted future status of the detected object.

The invention relates to a cascaded depth neural network-based face attribute recognition method. The method includes the following steps that: 1) a cascaded depth neural network composed of a plurality of independent convolution depth neural networks is constructed; 2) a large number of face image data are adopted to train networks at all levels in the cascaded depth neural network level by level, and the output of networks of previous levels is adopted as the input of networks of posterior levels, such that a coarse-to-fine neural network structure can be obtained; and 3) the coarse-to-fine neural network structure is adopted to recognize the attributes of an inputted face image, and final recognition results can be outputted. According to the cascaded depth neural network-based face attribute recognition method of the invention, a cascade algorithm system is adopted based on depth learning, and therefore, training time can be accelerated; and a cascaded coarse-to-fine processing process is realized, and the performance of a final network can be improved by networks of each level through utilizing information of networks of upper levels, and therefore, the speed and the accuracy of face attribute recognition can be effectively improved.

The present invention provides a deep and reinforcement learning-based real-time online path planning method. According to the method, the high-level semantic information of an image is obtained through using a deep learning method, the path planning of the end-to-end real-time scenes of an environment can be completed through using a reinforcement learning method. In a training process, image information collected in the environment is brought into a scene analysis network as a current state, so that an analytical result can be obtained; the analytical result is inputted into a designed deep cyclic neural network; and the decision-making action of each step of an intelligent body in a specific scene can be obtained through training, so that an optimal complete path can be obtained. In an actual application process, image information collected by a camera is inputted into a trained deep and reinforcement learning network, so that the direction information of the walking of the intelligent body can be obtained. With the method of the invention, obtained image information can be utilized to the greatest extent under a premise that the robustness of the method is ensured and the method slightly depends on the environment, and real-time scene walking information path planning can be realized.

The invention provides a text emotion classification method based on the joint deep learning model which relates to the text emotion classification method. The method is designed with the object of solving the problems with the dimension disaster and sparse data incurred from the existing support vector machine and other shallow layer classification methods. The method comprises: 1) processing each word in the text data; using the word2vec tool to train each processed word in the text data so as to obtain a word vector dictionary; 2) obtaining the matrix M of each sentence; training the matrix M by the LSTM layer and converting it into vector with fixed dimensions; improving the input layer; generating d-dimensional h word vectors with context semantic relations; 3) using a CNN as a trainable characteristic detector to extract characteristics from the d-dimensional h word vectors with context semantic relations; and 4) connecting the extracted characteristics in order; outputting to obtain the probability of each classification wherein the classification with the maximal probability value is the predicated classification. The invention is applied to the natural language processing field.

The invention relates to an automatic detection system for a pulmonary nodule in a chest CT (Computed Tomography) image. The system provides improvements for the problems of large calculated amount of computer aided software, inaccurate prediction and few prediction varieties. The improvement of the invention comprises the steps of acquiring a CT image; segmenting a pulmonary tissue; detecting a suspected nodular lesion area in the pulmonary tissue; classifying nidi based on a nidus classification model of deep learning; and outputting an image mark and a diagnosis report. The system has high nodule detection rate and relatively low false positive rate, acquires an accurate locating, quantitative and qualitative result of a nodular lesion and a prediction probability thereof. The end-to-end (from a CT machine end to a doctor end) nodular lesion screening is truly realized, the accuracy and operability demands of doctors are satisfied and the system has wide mar5ket application prospects.

The invention relates to an intelligent and automatic delineation method for a gross tumor volume and organs at risk, which includes the following steps: (1) tumor multi-modal (mode) image reconstruction, de-noising, enhancement, registration, fusion and other preprocessing; (2) automatic extraction of tumor image features: automatically extracting one or more pieces of tumor image group (texture feature spectrum) information from CT, CBCT, MRI, PET and/or ultrasonic multi-modal (mode) tumor medical image data; and (3) intelligently and automatically delineating a gross tumor volume and organs at risk through deep learning, machine learning, artificial intelligence, regional growing, the graph theory (random walk), a geometric level set, and/or a statistical theory method. A gross tumor volume (GTV) and organs at risk (OAR) can be delineated with high precision.

Systems and methods are disclosed for segmenting a digital image to identify an object portrayed in the digital image from background pixels in the digital image. In particular, in one or more embodiments, the disclosed systems and methods use a first neural network and a second neural network to generate image information used to generate a segmentation mask that corresponds to the object portrayed in the digital image. Specifically, in one or more embodiments, the disclosed systems and methods optimize a fit between a mask boundary of the segmentation mask to edges of the object portrayed in the digital image to accurately segment the object within the digital image.

The invention discloses an RGB-D image object detection and semantic segmentation method based on a deep convolution network, which belongs to the field of depth learning and machine vision. According to the method provided by the technical scheme of the invention, Faster-RCNN is used to replace the original slow RCNN; Faster-RCNN uses GPU, which is fast in the aspect of feature extracting, and at the same time generates a regional scheme in the network; the whole training process is training from end to end; FCN is used to carry out RGB-D image semantic segmentation; FCN uses a GPU and the deep convolution network to rapidly extract the deep features of an image; deconvolution is used to fuse deep features and shallow features of the image convolution; and the local semantic information of the image is integrated into the global semantic information.

Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

The invention discloses a deep convolutional neural network-based lung cancer preventing self-service health cloud service system. The system comprises a convolutional neural network used for deep learning and training identification, a segmentation module which segments out a lung region from a CT image based on a full convolutional neural network, a deep convolutional neural network used for lung cancer diagnosis classification, and a self-service health cloud service platform used for performing early prevention and treatment according to an identified suspected lung cancer type. According to the system, the automation and intelligentization level of mobile internet-based lung cancer screening can be effectively improved, more citizens can know and participate in self-service health detection, assessment and guidance, the sensitivity, specificity and accuracy of early lung cancer screening and clinical diagnosis are improved, the lung cancer can be early discovered, early diagnosed and early treated, and the self-health management capability is enhanced.

The invention discloses a remote sensing monitoring system for automotive exhaust emission of an urban road network. The system is mainly composed of a remote measurement device layer, a site selection and position arrangement layer, and a data processing layer. Through mobile, horizontal and vertical exhaust remote measurement devices, real-time data of the automotive exhaust emission in running is obtained; by adopting an advanced site selection and position arrangement method, the remote measurement devices are scientifically networked; and in combination with external data of weather, traffic, geographic information and the like, the real-time remote measurement data of the automotive exhaust emission is subjected to intelligent analysis and data mining by adopting big data processing and analysis technologies such as deep learning and the like, and key indexes and statistical data with optimal identification performance are obtained, so that effective support is provided for government departments to make related decisions.

The invention discloses a face recognition method, device, system and apparatus based on a convolutional neural network (CNN). The method comprises the following steps: S1, face detection: using a multilayer CNN feature architecture; S2, key point positioning: obtaining the key point positions of the face by using connecting reference frame regression networks in a cascaded way in deep learning; S3, preprocessing: obtaining a face image with a fixed size; S4, feature extraction: obtaining a feature representative vector by means of a feature extraction model; and S5: feature comparison: determining the similarity according to a threshold or giving a face recognition result according to distance sorting. The method adds a multilayer CNN feature combination to a traditional CNN single-layer feature architecture to treat different imaging conditions, trains a face detection network with good robustness in the monitoring environment from massive image data sets based on a deep convolution neural network algorithm, reduces a false detection rate, and improves detection response speed.

Systems and techniques are provided for tracking locations of inventory items in an area of real space including inventory display structures. A plurality of cameras are disposed above the inventory display structures. The cameras in the plurality of cameras produce respective sequences of images in corresponding fields of view in the real space. A memory stores a map of the area of real space identifying inventory locations on inventory display structures. The system is coupled to a plurality of cameras and uses the sequences of images produced by at least two cameras in the plurality of cameras to find a location of an inventory event in three dimensions in the area of real space. The system matches the location of the inventory event with an inventory location.

The invention discloses a cross-camera pedestrian detection tracking method based on depth learning, which comprises the steps of: by training a pedestrian detection network, carrying out pedestrian detection on an input monitoring video sequence; initializing tracking targets by a target box obtained by pedestrian detection, extracting shallow layer features and deep layer features of a region corresponding to a candidate box in the pedestrian detection network, and implementing tracking; when the targets disappear, carrying out pedestrian re-identification which comprises the process of: after target disappearance information is obtained, finding images with the highest matching degrees with the disappearing targets from candidate images obtained by the pedestrian detection network and continuously tracking; and when tracking is ended, outputting motion tracks of the pedestrian targets under multiple cameras. The features extracted by the method can overcome influence of illuminationvariations and viewing angle variations; moreover, for both the tracking and pedestrian re-identification parts, the features are extracted from the pedestrian detection network; pedestrian detection, multi-target tracking and pedestrian re-identification are organically fused; and accurate cross-camera pedestrian detection and tracking in a large-range scene are implemented.

he invention discloses a medical information extraction system and method based on depth learning and distributed semantic features. The system is composed of a pretreatment module, a linguistic-model-based word vector training module, a massive medical knowledge base reinforced learning module, and a depth-artificial-neural-network-based medical term entity identification module. With a depth learning method, generation of the probability of a linguistic model is used as an optimization objective; and a primary word vector is trained by using medical text big data; on the basis of the massive medical knowledge base, a second depth artificial neural network is trained, and the massive knowledge base is combined to the feature leaning process of depth learning based on depth reinforced learning, so that distributed semantic features for the medical field are obtained; and then Chinese medical term entity identification is carried out by using the depth learning method based on the optimized statement-level maximum likelihood probability. Therefore, the word vector is generated by using lots of unmarked linguistic data, so that the tedious feature selection and optimization adjustment process during medical natural language process can be avoided.

Provided are systems and methods for a cloud security system that learns patterns of user behavior and uses the patterns to detect anomalous behavior in a network. Techniques discussed herein include obtaining activity data from a service provider system. The activity data describes actions performed during use of a cloud service over a period of time. A pattern corresponding to a series of actions performed over a subset of time can be identified. The pattern can be added a model associated with the cloud service. The model represents usage of the cloud service by the one or more users. Additional activity data can be obtained from the service provider system. Using the model, a set of actions can be identified in the additional activity data that do not correspond to the model. The set of actions and an indicator that identifies the set of actions as anomalous can be output.

The invention discloses an automatic driving system based on enhanced learning and multi-sensor fusion. The system comprises a perception system, a control system and an execution system. The perception system high-efficiently processes a laser radar, a camera and a GPS navigator through a deep learning network so as to realize real time identification and understanding of vehicles, pedestrians, lane lines, traffic signs and signal lamps surrounding a running vehicle. Through an enhanced learning technology, the laser radar and a panorama image are matched and fused so as to form a real-time three-dimensional streetscape map and determination of a driving area. The GPS navigator is combined to realize real-time navigation. The control system adopts an enhanced learning network to process information collected by the perception system, and the people, vehicles and objects of the surrounding vehicles are predicted. According to vehicle body state data, the records of driver actions are paired, a current optimal action selection is made, and the execution system is used to complete execution motion. In the invention, laser radar data and a video are fused, and driving area identification and destination path optimal programming are performed.

In various embodiments, the systems and methods described herein relate to generative models. The generative models may be trained using machine learning approaches, with training sets comprising chemical compounds and biological or chemical information that relate to the chemical compounds. Deep learning architectures may be used. In various embodiments, the generative models are used to generate chemical compounds that have desired characteristics, e.g. activity against a selected target. The generative models may be used to generate chemical compounds that satisfy multiple requirements.

The present invention relates to the field of deep learning techniques, in particular to a deep convolution neural network training method and a device. The deep convolution neural network training method and the device comprise the steps of a, pretraining the DCNN on a large scale data set, and pruning the DCNN; b, performing the migration learning on the pruned DCNN; c, performing the model compression and the pruning on the migrated DCNN with the small-scale target data set, In the process of migrating learning of large-scale source data set to small-scale target data set, the model compression and the pruning are performed on the DCNN by the migration learning method and the advantages of model compression technology, so as to improve the migration learning ability to reduce the risk of overfitting and the deployment difficulty on the small-scale target data set and improve the prediction ability of the model on the target data set.

The invention relates to an image retrieval method based on deep learning and Hash. According to the image retrieval method, on the basis of powerful learning capacity of a deep convolutional neural network, deep features of images are extracted, and the problems of weak feature expression capacity and low retrieval precision caused by use of lower features of the images in the prior art are solved; a Hash layer is introduced for construction of a Hash function, learning of the deep features of the images and the construction of the Hash function are completed in the same process, an internal relation of the image features and the Hash function is explored, and the accuracy rate of the image retrieval is greatly increased; quantization error loss is added to a loss layer of the deep convolutional neural network, the expression capacity of Hash codes is enhanced, by means of a Softmax classifier loss module and a quantization error loss module, quantization errors caused by binaryzation in the Hash function are effectively reduced, and the accuracy rate of the image retrieval is further increased.

A method for creating an interpretable model for healthcare predictions includes training, by a deep learning processor, a neural network to predict health information by providing training data, including multiple combinations of measured or observed health metrics and corresponding medical results, to the neural network. The method also includes determining, by the deep learning processor and using the neural network, prediction data including predicted results for the measured or observed health metrics for each of the multiple combinations of the measured or observed health metrics based on the training data. The method also includes training, by the deep learning processor or a learning processor, an interpretable machine learning model to make similar predictions as the neural network by providing mimic data, including combinations of the measured or observed health metrics and corresponding predicted results of the prediction data, to the interpretable machine learning model.

The invention relates to the object verification technology in the machine vision field, and especially relates to a multi-scale small object detection method based on deep-learning hierarchical feature fusion; for solving the defects that the existing object detection is low in detection precision under real scene, constrained by scale size and different for small object detection, the invention puts forward a multi-scale small object detection method based on deep-learning hierarchical feature fusion. The detection method comprises the following steps: taking an image under the real scene as a research object, extracting the feature of the input image by constructing the convolution neural network, producing less candidate regions by using a candidate region generation network, and then mapping candidate region to a feature image generated by the convolution neural network to obtain the feature of each candidate region, obtaining the feature with fixed size and fixed dimension after passing a pooling layer to input to the full-connecting layer, wherein two branches behind the full-connecting layer respectively output the recognition type and the returned position. The method disclosed by the invention is suitable for the object verification in the machine vision field.

The invention relates to a method for Hash image retrieval based on deep learning and local feature fusion. The method comprises a step (1) of preprocessing an image; a step (2) of using a convolutional neural network to train images containing category tags; a step (3) of using a binarization mode to generate Hash codes of the images and extract 1024-dimensional floating-point type local polymerization vectors; a step (4) of using the Hash codes to perform rough retrieval; and a step (5) of using the local polymerization vectors to perform fine retrieval. According to the method for Hash image retrieval based on deep learning and local feature fusion, an approximate nearest neighbor search strategy is utilized to perform image retrieval after two features are extracted, the retrieval accuracy is high, and the retrieval speed is quick.

The invention discloses a video description method based on deep learning and text summarization. The video description method comprises the following steps: through a traditional image data set, training a convolutional neural network model according to an image classification task; extracting a video frame sequence of a video, utilizing the convolutional neural network model to extract convolutional neural network characteristics to form a <video frame sequence, text description sequence> pair which is used as the input of a recurrent neural network model, and training to obtain the recurrent neural network model; describing the video frame sequence of the video to be described through the recurrent neural network model obtained by training to obtain description sequences; and through a method that graph-based vocabulary centrality is used as the significance of the text summarization, sorting the description sequences, and outputting a final description result of the video. An event which happens in one video and object attributes associated with the event are described through a natural language so as to achieve a purpose that video contents are described and summarized.

The invention discloses a dialogue automatic reply system based on deep learning and reinforcement learning. The dialogue automatic reply system comprises a user interaction module which receives question information inputted by a user in a dialogue system interface; a session management module which records the active state of the user, wherein the active state includes historical dialogue information, user position transformation information and user emotion change information; a user analysis module which analyzes registration information and the active state of the user and portraits for the user so as to obtain user portrait information; a dialogue module which generates reply information through a language module according to the question information of the user with combination of the portrait of the user; and a model learning module which updates the language model through the reinforcement learning technology according to the reply information generated by the language model. According to the dialogue automatic reply system based on deep learning and reinforcement learning, the reply of the dialogue meeting the personality of the user can be given according to the dialogue text inputted by the user with combination of context information, the personality characteristics of the user and the intentions in the dialogue.

The invention discloses a method for forecasting the traffic flow in the urban area based on depth learning. This method can forecast the traffic flow in each region of the city by extracting the high-dimensional space-time characteristics of traffic flow changes, which provides a new idea for urban traffic flow forecasting. Firstly, according to data of LPR equipment, the historical flow of each time period of the urban area is calculated; then the traffic flow forecasting model is designed by ConvLSTM and CNN, and the flow data of the critical time period which affects the forecast period is extracted as the input training model; and finally, the trained model is used to forecast the traffic flow of the urban area.

Systems and methods are disclosed for automatically segmenting a heart chamber from medical images of a patient. The system may include one or more hardware processors configured to: obtain image data including at least a representation of the patient's heart; obtain a region of interest from the image data; organize the region of interest into an input vector; apply the input vector through a trained graph; obtain an output vector representing a refined region of interest corresponding to the heart based on the application of the input vector through the trained graph; apply a deformable model on the obtained output vector representing the refined region of interest; and identify a segment of a heart chamber from the application of the deformable model on the obtained output vector.