Abstract: The present disclosure discloses an image haze removal method and apparatus, and a device. The method includes: acquiring a hazy image to be processed; and obtaining a haze-free image corresponding to the hazy image by inputting the hazy image into a pre-trained haze removal model. The present disclosure uses the residual dual attention fusion modules as basic modules of the neural network, so that each feature map can obtain pixel features while enhancing the global dependence, thus improving the image dehazing effect.

Abstract: Disclosed are an indoor non-contact human activity recognition method and system. The method comprises: collecting an indoor reflected signal by using an antenna array; filtering the reflected signal to obtain a noise-removed reflection signal; and inputting the noise-removed reflected signal to a pre-trained human activity recognition model, and determining a human activity category, the human activity recognition model being a pre-trained CNN network model based on a transfer learning algorithm. The recognition method and system have the advantages that: the antenna array is configured for collecting human actions to carry out activity recognition indoors, which can be applied to home-based care scenes; original data is denoised, so that most of high-frequency noises can be removed, and a phase change of the signal is reserved; a CNN structure is adopted for training so as to reduce a complexity of the system location-free sensing.

Abstract: A medical image segmentation method based on a U-Net, including: sending real segmentation image and original image to a generative adversarial network for data enhancement to generate a composite image with a label; then putting the composite image into original data set to obtain an expanded data set, and sending the expanded data set to improved multi-feature fusion segmentation network for training. A Dilated Convolution Module is added between the shallow and deep feature skip connections of the segmentation network to obtain receptive fields with different sizes, which enhances the fusion of detail information and deep semantics, improves the adaptability to the size of the segmentation target, and improves the medical image segmentation accuracy. The over-fitting problem that occurs when training the segmentation network is alleviated by using the expanded data set of the generative adversarial network.

Abstract: Disclosed are a network traffic classification method and system based on an improved K-means algorithm. The method comprises: judging whether a total number NIC of network traffic data points in an initial clustering center set reaches an expected number k of network traffic clusters, if the k is not reached, calculating candidate metric values of network traffic data points in a high-density network traffic data point set, selecting a network traffic data point having the maximum candidate metric value, adding same into an initial clustering center set, removing the network traffic data point from the high-density network traffic data point set, then repeating the step until the total number NIC of network traffic data points in the initial clustering center set reaches the k, and ending the step. The method and system can ensure high network traffic classification accuracy.

Abstract: Disclosed are an image dehazing method and system based on CycleGAN. The method comprises: acquiring a to-be-processed hazy image; and inputting the image into a pre-trained densely connected CycleGAN, and outputting a clear image. The densely connected CycleGAN comprises a generator, the generator comprises an encoder, a converter and a decoder, the encoder comprises a densely connected layer for extracting features of an input image, the converter comprises a transition layer for combining the features extracted at the encoder stage, the decoder comprises a densely connected layer and a scaled convolutional neural network layer, the densely connected layer is used for restoring original features of the image, and the scaled convolutional neural network layer is used for removing a checkerboard effect of the restored original features to obtain a finally output clear image.

Abstract: The present disclosure discloses a workflow scheduling method and system based on a multi-target particle swarm algorithm, and a storage medium. The method comprises the following steps that first, the difference between the frequency reduction characteristic and the execution time of each server in a cluster is considered; a multi-target comprehensive evaluation model covering workflow execution overhead, execution time and cluster load balance is constructed on the basis of a traditional model; second, a multi-target particle swarm algorithm is provided for workflow scheduling, and an efficient solving method is provided. The method alleviates the defects of premature convergence and low species diversity of the particle swarm algorithm, reduces the execution overhead and execution time of the workflow on the cluster server, and better balances the load of the cluster server.

Abstract: A medical image segmentation method include: 1) acquiring a medical image data set; 2) acquiring, from the medical image data set, an original image and a real segmentation image of a target region in the original image in pair to serve as an input data set of a pre-built constant-scaling segmentation network, the input data set including a training set, a verification set, and a test set; 3) training the constant-scaling segmentation network by using the training set to obtain a trained segmentation network model, and verifying the constant-scaling segmentation network by using the verification set, the constant-scaling segmentation network including a feature extraction module and a resolution amplifying module; and 4) inputting the original image to be segmented into the segmentation network model for segmentation to obtain a real segmentation image.

Abstract: An indoor target positioning method based on an improved convolutional neural network (CNN) model includes acquiring and preprocessing target camera serial interface (CSI) data of a to-be-positioned target and matching the preprocessed target CSI data with fingerprints in a positioning fingerprint database to obtain coordinate information of the to-be-positioned target. The generation method of the positioning fingerprint database includes: collecting indoor WiFi signals by a software defined radio (SDR) platform to obtain indoor CSI data corresponding to the WiFi signals, and preprocessing the indoor CSI data; partitioning the preprocessed indoor CSI data into a plurality of data subsets through a clustering algorithm; training an improved CNN model by the data subsets to obtain a trained improved CNN model; and generating the positioning fingerprint database by the trained improved CNN model and the preprocessed indoor CSI data.

Abstract: The present disclosure discloses a chess self-learning method and device based on machine learning, a move selection output layer and a value evaluation output layer of the method share the same input layer and hidden layer of a neural network, and a Monte Carlo tree search tree is used to construct a strategy optimizer. The training process of the method is divided into two parts, namely data generation and neural network training, so that an error between a value scalar outputted by a neural network and a final result of self-play is as small as possible, and a move vector outputted by the neural network is as close as possible to a decision vector given by a Monte Carlo tree for each search step. The present disclosure aims to construct an Al chess player for people to play chess.

Abstract: The present disclosure provides a deep neural network (DNN)-based reconstruction method and apparatus for compressive video sensing (CVS). The method divides a video signal into a key frame and a non-key frame. The key frame is reconstructed by using an existing image reconstruction method. The non-key frame is reconstructed by using a special DNN according to the present disclosure. The neural network includes an adaptive sampling module, a multi-hypothesis prediction module, and a residual reconstruction module. The neural network makes full use of a spatio-temporal correlation of the video signal to sample and reconstruct the video signal. This ensures low time complexity of an algorithm while improving reconstruction quality. Therefore, the method in the present disclosure is applicable to a video sensing system with limited resources on a sampling side and high requirements for reconstruction quality and real-time performance.

Abstract: Disclosed are a network traffic classification method and system based on an improved K-means algorithm. The method comprises: judging whether a total number NIC of network traffic data points in an initial clustering center set reaches an expected number k of network traffic clusters, if the k is not reached, calculating candidate metric values of network traffic data points in a high-density network traffic data point set, selecting a network traffic data point having the maximum candidate metric value, adding same into an initial clustering center set, removing the network traffic data point from the high-density network traffic data point set, then repeating the step until the total number NIC of network traffic data points in the initial clustering center set reaches the k, and ending the step. The method and system can ensure high network traffic classification accuracy.

Abstract: A medical image segmentation method based on a U-Net, including: sending real segmentation image and original image to a generative adversarial network for data enhancement to generate a composite image with a label; then putting the composite image into original data set to obtain an expanded data set, and sending the expanded data set to improved multi-feature fusion segmentation network for training. A Dilated Convolution Module is added between the shallow and deep feature skip connections of the segmentation network to obtain receptive fields with different sizes, which enhances the fusion of detail information and deep semantics, improves the adaptability to the size of the segmentation target, and improves the medical image segmentation accuracy. The over-fitting problem that occurs when training the segmentation network is alleviated by using the expanded data set of the generative adversarial network.

Abstract: A distributed container scheduling method includes: monitoring a container creation event in a Kubernetes API-Server in real time, and validating a container created once a new container creation event is detected; updating a container scheduling queue with containers passing the validation; when the container scheduling queue is empty, performing no operation until the containers passing the validation are added to the queue; when the container scheduling queue is not empty, reading the containers to be scheduled from the container scheduling queue in sequence, and selecting, from a Kubernetes cluster, an optimal node corresponding to the containers to be scheduled to generate a container scheduling two-tuple; and scheduling, based on the container scheduling two-tuple, the containers to be scheduled to the optimal node to finish the distributed container scheduling operation.

Abstract: A method of reconstruction of super-resolution of video frame includes inputting a first video frame with a first resolution and a plurality of consecutive frames thereof into a pre-trained super-resolution reconstruction network, and outputting, by the pre-trained super-resolution reconstruction network, a second video frame with a second resolution corresponding to the first video frame. The second resolution is higher than the first resolution. The super-resolution reconstruction network includes a feature extraction subnetwork, a spatial-temporal non-local alignment subnetwork, an attention progressive fusion subnetwork, and an up-sampling subnetwork which are connected in sequence.

Abstract: A vehicle clustering method includes: acquiring vehicle dynamic information, the vehicle dynamic information including vehicle positions and vehicle speeds; clustering vehicles based on the vehicle dynamic information; calculating, based on the vehicle dynamic information, a link reliability and a link stability of the vehicles in a cluster; calculating, based on the link reliability and the link stability, a selection priority index for a cluster head; and selecting a vehicle with a largest selection priority index in the cluster as a cluster head.

Abstract: Disclosed in the present invention are a distributed container image construction scheduling system and method. The system includes a construction node and a management node. The construction node includes an image constructor for executing a construction task issued by the management node. The management node include, a console and a scheduler. The console is responsible for acquiring the relevant parameters such as a development dependency library and system configuration required by a user, and generating tasks with these parameters and sending same to the scheduler. The scheduler is used for receiving the tasks sent by the console, detecting the legitimacy of the tasks, and sending the tasks to the corresponding construction node to be run.

Abstract: A method for recognizing a fog concentration of a hazy image includes inputting a target hazy image into a pre-trained directed acyclic graph (DAG) support vector machine to acquire a fog concentration of the target hazy image. The fog concentration of the target hazy image is represented based on a prebuilt multi-feature model, and the feature vector in the multi-feature model includes at least one of a color feature, a dark channel feature, an information quantity feature and a contrast feature.

Abstract: The present disclosure provides a deep neural network (DNN)-based reconstruction method and apparatus for compressive video sensing (CVS). The method divides a video signal into a key frame and a non-key frame. The key frame is reconstructed by using an existing image reconstruction method. The non-key frame is reconstructed by using a special DNN according to the present disclosure. The neural network includes an adaptive sampling module, a multi-hypothesis prediction module, and a residual reconstruction module. The neural network makes full use of a spatio-temporal correlation of the video signal to sample and reconstruct the video signal. This ensures low time complexity of an algorithm while improving reconstruction quality. Therefore, the method in the present disclosure is applicable to a video sensing system with limited resources on a sampling side and high requirements for reconstruction quality and real-time performance.

Abstract: A method for detecting comprehensive GPU-related factors of a distributed cluster, the method including: (1): checking whether there is a configuration file content of an operating node; (2): reading a mode parameter in an environment variable of the operating node, and correspondingly switching an operating mode according to the mode parameter; (3): reading a timer frequency value from the environment variable of the operating node so as to set a time period for reading a GPU information parameter according to the timer frequency value; (4): calculating the maximum value of the GPU information parameter of the operating node, and storing the maximum value into the GPU information list cache; and (5): initializing the transmitted information; determining whether there is a GPU in the GPU information list cache of the operating node.

Abstract: A multidimensional resource scheduling method in a Kubernetes cluster architecture system is provided. For a computing-intensive service, each server node in the cluster is scored according to CPU idleness and memory idleness; for an ordinary service, each server node in the cluster is scored according to resource requirements of a scheduling task, a resource priority of each server node and resource balance of each server node. The pod scheduling task is bound to a server node with a highest score for execution. This scheduling method meets diverse resource requests of various services, thereby enhancing the flexibility and expandability of the system.