The target detection model of anesthesia resuscitation is often used to help medical staff to perform resuscitation detection on anesthetized patients. The targets of facial actions during patient resuscitation are small and are not obvious， and the existing Single Shot multibox Detector （SSD） is difficult to accurately detect the facial micro-action features of patients in real time. Aiming at the problem that the original model has low detection speed and is easy to have missed detection， an anesthesia resuscitation object detection method based on improved SSD was proposed. Firstly， the backbone network VGG （Visual Geometry Group）16 of the original SSD was replaced by the lightweight backbone network MobileNetV2， and the standard convolutions were replaced by the depthwise separable convolutions. At the same time， the calculation method of first increasing and then reducing the dimension of the extracted features from patient photos was used to reduce computational cost， thereby improving detection speed of the model. Secondly， the Coordinate Attention （CA） mechanism was integrated into the feature layers with different scales extracted by the SSD， and the ability of the feature map to extract key information was improved by weighting the channel and location information， so that the network positioning and classification performance was optimized. Finally， comparative experiments were carried out on three datasets： CEW（Closed Eyes in the Wild）， LFW（Labeled Faces in the Wild）， and HAPF（Hospital Anesthesia Patient Facial）. Experimental results show that the mean Average Precision （AP） of the proposed model reaches 95.23%， and the detection rate of photos is 24 frames per second， which are 1.39 percentage points higher and 140% higher than those of the original SSD model respectively. Therefore， the improved model has the effect of real-time accurate detection in anesthesia resuscitation detection， and can assist medical staff in resuscitation detection.
In recent years， the Grid-based distributed Xin’anjiang hydrological Model （GXM） has played an important role in flood forecasting， but when simulating the flooding process， due to the vast amount of data and calculation of the model， the computing time of GXM increases exponentially with the increase of the model warm-up period， which seriously affects the computational efficiency of GXM. Therefore， a parallel computing algorithm of GXM based on grid flow direction division and dynamic priority Directed Acyclic Graph （DAG） scheduling was proposed. Firstly， the model parameters， model components， and model calculation process were analyzed. Secondly， a parallel algorithm of GXM based on grid flow direction division was proposed from the perspective of spatial parallelism to improve the computational efficiency of the model. Finally， a DAG task scheduling algorithm based on dynamic priority was proposed to reduce the occurrence of data skew in model calculation by constructing the DAG of grid computing nodes and dynamically updating the priorities of computing nodes to achieve task scheduling during GXM computation. Experimental results on Dali River basin of Shaanxi Province and Tunxi basin of Anhui Province show that compared with the traditional serial computing method， the maximum speedup ratio of the proposed algorithm reaches 4.03 and 4.11， respectively， the computing speed and resource utilization of GXM were effectively improved when the warm-up period is 30 days and the data resolution is 1 km.
Current research on Person Re-Identification （Re-ID） mainly concentrates on short-term situations with person’s clothing usually unchanged. However， more common practical cases are long-term situations， in which a person has higher possibility to change his clothes， which should be considered by Re-ID models. Therefore， a method of person re-identification with cloth changing based on joint loss capsule network was proposed. The proposed method was based on ReIDCaps， a capsule network for cloth-changing person re-identification. In the method， vector-neuron capsules that contain more information than traditional scalar neurons were used. The length of the vector-neuron capsule was used to represent the identity information of the person， and the direction of the capsule was used to represent the clothing information of the person. Soft Embedding Attention （SEA） was used to avoid the model over-fitting. Feature Sparse Representation （FSR） mechanism was adopted to extract discriminative features. The joint loss of label smoothing regularization cross-entropy loss and Circle Loss was added to improve the generalization ability and robustness of the model. Experimental results on three datasets including Celeb-reID， Celeb-reID-light and NKUP prove that the proposed method has certain advantages compared with the existing person re-identification methods.
