An Intelligent Agent Approach to Adaptive Project Management

This paper presents the initial steps to integrating a distributed discrete event simulation system with a framework for intelligent software agents. The resulting system has a simulation component that is based on the high-level architecture (HLA) and an agent component that implements the belief-desire-intention (BDI) approach to agent modelling. The architecture is connected to a real-time information source. The framework was successfully applied to a real-life monitoring system for a tunnel-boring machine excavation project that helped with forecasting and managing the project timelines in response to the changes in the uncertain excavation environment. This project is presented as a test case and demonstrates encouraging results for integrative modelling of large-scale problems with elements of uncertainty.

Generalized projective synchronization of chaotic systems via adaptive learning control

In this paper, a learning control approach is applied to the generalized projective synchronisation （GPS） of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov--Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme.

Projective synchronisation with non-delayed and delayed coupling in complex networks consisting of identical nodes and different nodes

This paper first investigates the projective synchronisation problem with non-delayed and delayed coupling between drive-response dynamical networks consisting of identical nodes and different nodes. Based on Lyapunov stability theory, several nonlinear controllers are applied to achieve the projective synchronisation between the drive-response dynamical networks; simultaneously the topological structure of the drive dynamical complex networks can be exactly identified. Moreover, numerical examples are presented to verify the feasibility and effectiveness of the theorems.

A New Model for Network Security Situation Assessment of the Industrial Internet

To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

An Adaptive 2D Planar Projection and Its Application in Geoscience Studies

A linear projection approach is developed to present geoscience research result in planar coordinate system projected from spherical coordinate system. Here, the sphere is intersected by a plane and its surface is projected onto the plane. In order to keep the projected coordinate system orthogonal, and minimize the distortion, one axis of the planar coordinate system is chosen in our projection based on the shape of the region to be projected, and the other axes can be chosen arbitrarily or based on the constraint of the orthogonality. In the new method the projection is self-contained. The forward projection can be fully projected backward without loss of precision. The central area of the sphere will be projected to the planar system without distortion, and the latitudinal length in the rotated spherical system keeps constant during the projecting process. Only the longitudinal length in the rotated spherical system changes with the rotated latitude. The distortion of the projection therefore, overall, is small and suitable for geoscience studies.

Construction of a new adaptive wavelet network and its learning algorithm

A new adaptive learning algorithm for constructing and training wavelet networks is proposed based on the time-frequency localization properties of wavelet frames and the adaptive projection algorithm. The exponential convergence of the adaptive projection algorithm in finite-dimensional Hilbert spaces is constructively proved, with exponential decay ratios given with high accuracy. The learning algorithm can sufficiently utilize the time-frequency information contained in the training data, iteratively determines the number of the hidden layer nodes and the weights of wavelet networks, and solves the problem of structure optimization of wavelet networks. The algorithm is simple and efficient, as illustrated by examples of signal representation and denoising.