Fault mode probability factor based fault-tolerant control for dissimilar redundant actuation system

This paper presents a Fault Mode Probability Factor（FMPF） based Fault-Tolerant Control（FTC） strategy for multiple faults of Dissimilar Redundant Actuation System（DRAS）composed of Hydraulic Actuator（HA） and Electro-Hydrostatic Actuator（EHA）. The long-term service and severe working conditions can result in multiple gradual faults which can ultimately degrade the system performance, resulting in the system model drift into the fault state characterized with parameter uncertainty. The paper proposes to address this problem by using the historical statistics of the multiple gradual faults and the proposed FMPF to amend the system model with parameter uncertainty. To balance the system model precision and computation time, a Moving Window（MW） method is used to determine the applied historical statistics. The FMPF based FTC strategy is developed for the amended system model where the system estimation and Linear Quadratic Regulator（LQR） are updated at the end of system sampling period. The simulations of DRAS system subjected to multiple faults have been performed and the results indicate the effectiveness of the proposed approach.

A method to calculate displacement factors using SVM

In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive analysis of factors affecting the displacement factor, such as mechanical properties of the cover rock, the ratio of mining depth to seam thickness, dip angle of the coal seam and the thickness of loose layer. Data of 63 typical observation stations were used as a training and testing sample set. A SVM regression model of the displacement factor and the factors affecting it was established with a kernel function, an insensitive loss factor and a properly selected penalty factor. Given an accurate calculation algorithm for testing and analysis, the results show that an SVM regression model can calcu- late displacement factor precisely and reliable precision can be obtained which meets engineering requirements. The experimental results show that the method to calculation of the displacement factor, based on the SVM method, is feasible. The many factors affecting the displacement factor can be consid- ered with this method. The research provides an efficient and accurate approach for the calculation of displacement in mining subsidence orediction.

Data fusion of radar and IFF for aircraft identification

The problem of identification of friend-or-foe aircraft in the actual application condition is addressed.A hybrid algorithm combining fuzzy neutral network with probability factor（FNNP）,multi-level fuzzy comprehensive evaluation and the DempsterShafer（D-S） theory is proposed.This hybrid algorithm constructs a complete process from generating the fuzzy database to the final identification,realizes the identification of friend-or-foe automatically if the training samples or expert’s experience can be obtained,and reduces the effect of uncertainties in the process of identification.At the same time,the whole algorithm can update the identification result with the augment of observations.The performance of the proposed algorithm is assessed by simulations.Results show that the proposed algorithm can successfully deduce the aircraft’s identity even if the observations have measurement errors.

A Novel Collaborative Filtering Algorithm and its Application for Recommendations in E-Commerce

With the rapid development of the Internet,the amount of data recorded on the Internet has increased dramatically.It is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of data.In this study,we propose a novel collaborative filtering algorithm for generating recommendations in e-commerce.This study has two main innovations.First,we propose a mechanismthat embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or item.Second,we propose a novel collaborative filtering algorithm by injecting the neighbor set into probability matrix factorization.We compared the proposed method with several state-of-the-art alternatives on real datasets.The experimental results show that our proposed method outperforms the prevailing approaches.

Determination and stability analysis of ultimate open-pit slope under geomechanical uncertainty

In open-pit mines,pit slope as one of the important parameters affects the mine economy and total minable reserve,and it is also affected by different uncertainties which arising from many sources.One of the most critical sources of uncertainty effects on the pit slope design is rock mass geomechanical properties.By comparing the probability of failure resulted from deterministic procedure and probabilistic one,this paper investigated the effects of aforesaid uncertainties on open-pit slope stability in metal mines.In this way,to reduce the effect of variance,it implemented Latin Hypercube Sampling(LHS)technique.Furthermore,a hypothesis test was exerted to compare the effects on two cases in Middle East.Subsequently,the investigation approved high influence of geomechanical uncertainties on overall pit steepness and stability in both iron and copper mines,though on the first case the effects were just over.