Biblio

Secret-related carrier intelligent management and control system uses the Internet of Things and artificial intelligence to solve the transformation of secret-related carrier management and control from manual operation to automatic detection, precise monitoring, and intelligent decision-making, and use technical means to resolve security risks. However, the coexistence of multiple heterogeneous networks will lead to various network security problems in the secret carrier intelligent management and control. Aiming at the actual requirements of the intelligent management and control of secret-related carriers, this paper proposes a system structure including device domain, network domain, platform domain and user domain, and conducts a detailed system security analysis, and introduces intelligent agent technology, and proposes a distributed system. The hierarchical system structure of the secret-related carrier intelligent management and control security protection model has good robustness and portability.

The work is devoted to the synthesis of an aircraft control system using a synergetic control theory. The paper contains a general description of the apparatus and its control system, a synthesis of control laws, and a computer simulation. The relevance of the work consists in the need to create a vertically take-off aircraft of the “flying platform” type in order to increase the efficiency of rescue operations in disaster zones where helicopters and other modern means can't cope with the task. The scientific novelty of the work consists in the application of synergetic approaches to the development of a hierarchical system for balancing the vehicle spatial position and to the coordinating energy-saving control of electric motors that receive energy from a turbine generator.

Hierarchical approaches for representation learning have the ability to encode relevant features at multiple scales or levels of abstraction. However, most hierarchical approaches exploit only the last level in the hierarchy, or provide a multiscale representation that holds a significant amount of redundancy. We argue that removing redundancy across the multiple levels of abstraction is important for an efficient representation of compositionality in object-based representations. With the perspective of feature learning as a data compression operation, we propose a new greedy inference algorithm for hierarchical sparse coding. Convolutional matching pursuit with a L0-norm constraint was used to encode the input signal into compact and non-redundant codes distributed across levels of the hierarchy. Simple and complex synthetic datasets of temporal signals were created to evaluate the encoding efficiency and compare with the theoretical lower bounds on the information rate for those signals. Empirical evidence have shown that the algorithm is able to infer near-optimal codes for simple signals. However, it failed for complex signals with strong overlapping between objects. We explain the inefficiency of convolutional matching pursuit that occurred in such case. This brings new insights about the NP-hard optimization problem related to using L0-norm constraint in inferring optimally compact and distributed object-based representations.