Huanzhang Ling’s research while affiliated with Harbin Engineering University and other places

Our study investigated an algorithm for a second-order ordinary differential equation for the frequency-domain Green’s function of free-surface waves in water of infinite depth. A power-series method is introduced if the wave frequency (Formula presented.); otherwise, if (Formula presented.), then the trigonometrically fitted block Numerov-type method (TBNM) is employed. The calculation precision of the power-series method and the TBNM reached (Formula presented.) and (Formula presented.), respectively. The two methods have a high calculation efficiency compared with calculating the Green’s function using the series expansion representation approach. The calculation speed for these two methods is 15 times faster using the same computing codes.

To effectively monitor the atmospheric quality of small-scale areas, it is necessary to optimize the locations of the monitoring sites. This study combined geographic parameters extraction by GIS with fuzzy matter-element analysis. Geographic coordinates were extracted by GIS and transformed into rectangular coordinates. These coordinates were input into the Gaussian plume model to calculate the pollutant concentration at each site. Fuzzy matter-element analysis, which is used to solve incompatible problems, was used to select the locations of sites. The matter element matrices were established according to the concentration parameters. The comprehensive correlation functions KA (xj) and KB (xj), which reflect the degree of correlation among monitoring indices, were solved for each site, and a scatter diagram of the sites was drawn to determine the final positions of the sites based on the functions. The sites could be classified and ultimately selected by the scatter diagram. An actual case was tested, and the results showed that 5 positions can be used for monitoring, and the locations conformed to the technical standard. In the results of this paper, the hierarchical clustering method was used to improve the methods. The sites were classified into 5 types, and 7 locations were selected. Five of the 7 locations were completely identical to the sites determined by fuzzy matter-element analysis. The selections according to these two methods are similar, and these methods can be used in combination. In contrast to traditional methods, this study monitors the isolated point pollutant source within a small range, which can reduce the cost of monitoring.

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data.

In this chapter, a meticulous restricted equivalent transformation on singular systems is proposed. It can simplify different variables in complex singular systems and turn the complex singular system to be more simple and clear. This method gives the complexity of the singular system equivalent to more singular systems so that we approach the new phenomenon of altering the traditional restricted equivalent transformation provided the future analysis foundation on the practical system.

This paper studies the problem of robustly stochastic stability of uncertain stochastic systems with time-varying delays, where the system is subjected to both Brownian motion and Markovian parameter jumping. By using some zero equations, neither model transformation nor bounding for cross terms is required to obtain the delay-dependent results. The problem of robustly stochastic stability addressed in this paper is to design a state feedback controller such that, for all admissible uncertainties, the closed-loop system is robustly stochastically stable. A criterion for the existence of such controller is derived based on the Lyapunov function combined with linear matrix inequalities (LMI).

State-space method is a main method which study generalized state-space system. In this paper, we researched the generalized system of state-space description and obtained the form of constrained equivalent by the standardization of multiple matrices synchronously.