Lian-hua Zhang | Search PubMed

PubMed2005

[Malfunction simulation by spaceflight training simulator].

OBJECTIVE: To implement malfunction simulation in spaceflight training simulator. METHOD: The principle of malfunction simulation was defined according to spacecraft malfunction predict and its countermeasures. The malfunction patterns were classified, and malfunction type was confirmed. A malfunction simulation model was established, and the malfunction simulation was realized by math simulation. RESULT: According to the requirement of astronaut training, a spacecraft subsystem malfunction simulation model was established and realized, such as environment control and life support, GNC, push, power supply, heat control, data management, measure control and communication, structure and so on. CONCLUSION: The malfunction simulation function implemented in the spaceflight training simulator satisfied the requirements for astronaut training.

Accidents, Aviation↗

PubMed2004

Intrusion detection using rough set classification.

Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of "IF-THEN" rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

Algorithms↗