Deep Learning based Quasi-consciousness Training for Robot Intelligent Model

This paper explores a deep learning based robot intelligent model that renders robots learn and reason for complex tasks. First, by constructing a network of environmental factor matrix to stimulate the learning process of the robot intelligent model, the model parameters must be subjected to coarse & fine tuning to optimize the loss function for minimizing the loss score, meanwhile robot intelligent model can fuse all previously known concepts together to represent things never experienced before, which need robot intelligent model can be generalized extensively. Secondly, in order to progressively develop a robot intelligent model with primary consciousness, every robot must be subjected to at least 1~3 years of special school for training anthropomorphic behaviour patterns to understand and process complex environmental information and make rational decisions. This work explores and delivers the potential application of deep learning-based quasi-consciousness training in the field of robot intelligent model.

A real-time battle situation intelligent awareness system based on Meta-learning & RNN

In modern warfare, real-time and accurate battle situation analysis is crucial for making strategic and tactical decisions. The proposed real-time battle situation intelligent awareness system (BSIAS) aims at meta-learning analysis and stepwise RNN (recurrent neural network) modeling, where the former carries out the basic processing and analysis of battlefield data, which includes multi-steps such as data cleansing, data fusion, data mining and continuously updates, and the latter optimizes the battlefield modeling by stepwise capturing the temporal dependencies of data set. BSIAS can predict the possible movement from any side of the fence and attack routes by taking a simulated battle as an example, which can be an intelligent support platform for commanders to make scientific decisions during wartime. This work delivers the potential application of integrated BSIAS in the field of battlefield command & analysis engineering.