Swarm intention identification via dynamic distribution probability image

In the realm of decision-making for defense and security applications,it is paramount to swiftly and accurately identify the intentions of incoming swarms.Conventional identification methods predominantly focus on single-target applications and overlook the perturbations introduced by measurement noise.In this study,we propose a novel concept:the Dynamic Distribution Probability(DDP)image,which is constructed using the estimated state and its covariance matrix.Each grayscale pixel value within the image signifies the probability of the presence of the agent within the swarm.Our proposed identification scheme integrates the use of Extended Kalman Filter(EKF),Convolutional Neural Network(CNN),Back Propagation(BP)network,and Gated Recurrent Unit(GRU)network.Specifically,the DDP image is processed through a CNN to distill the formation characteristics,and the estimated swarm state from EKF is inputted into a BP network to deduce the kinematic information.The outputs from both networks are summed and subsequently channeled into a GRU network to capture temporal dynamics.Extensive numerical simulations and flight experiments are presented to demonstrate the robust anti-noise capability of the proposed scheme compared with conventional methods,as well as its superior training efficiency.

Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit(GRU)neural network is established. The scenario of the model is that an incoming missile(called missile)attacks a target aircraft(called aircraft) using Proportional Navigation(PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism(MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.

CRISPR-edited hepatic organoids as drug screening platform for non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease(NAFLD),is a chronic liver disease caused by a build-up of intrahepatic fat.Metabolic comorbidities associated with NAFLD included obesity,type 2 diabetes,hyperlipidemia,hypertension,and metabolic syndrome(1-3).With changes in diet and lifestyle,the incidence of NAFLD is rapidly increasing to an estimated 30%of global population(4).Histologic manifestations of NAFLD range from the mild stage of simple steatosis,to the advanced stage of steatosis accompanied with necroinflammation or fibrosis,so called nonalcoholic non-alcoholic steatohepatitis(NASH).