Wei Jiaolong

Fault diagnosis based on Grey-box Neural Network identification model

This paper presents a fault diagnosis (FD) scheme for a class of nonlinear dynamic systems using a novel Grey-Box Neural Network Model (GBNNM). In this GBNNM, a composite structure, including MLP (multi-layer perception) NN (Neural Network) and integer term, is proposed to approximate both nonlinearity and dynamics of object system. Its approximation ability is then proved theoretically. And a self-defined exciting strategy is introduced into NN training to improve NNs generalization ability. Unlike previous NN model based fault diagnosis methods, a quantitative residual, which is obtained from system output and its GBNNM model output, can accurately indicates inconsistency caused by fault, so the improved residual is not essential for our scheme. The proposed FD scheme is applied in a high-fidelity Reaction Wheel (RW) in Satellite Attitude Control System (SACS) in our case study. The results of the case study demonstrate the effectiveness and superiority of our FD scheme.

UNIFORM-PRICE AUCTION FOR BANDWIDTH ALLOCATION IN THE INTERNET*

It has been widely accepted that auctioning which is the pricing approach with minimal information requirement is a proper tool to manage scare network resources. Previous works focus on Vickrey auction which is incentive compatible in classic auction theory. In the beginning of this letter, the faults of the most representative auction-based mechanisms are discussed. And then a new method called Uniform-Price Auction (UPA), which has the simplest auction rule is proposed and its incentive compatibility in the network environment is also proved. Finally, the basic mode is extended to support applications which require minimum bandwidth guarantees for a given time period by introducing derivative market, and a market mechanism for network resource allocation which is predictable, riskless, and simple for end-users is completed.

Optimization of analog circuits test mode based on Ants Colony Algorithms

In this paper, optimization technique based on Ants Colony Algorithms (ACA) was studied for test node selection. The technique yields high faults detection and isolation rates with less number of nodes. This concept can also be applied in the domain of fault dictionary. In the technique proposed, the objective function consists of the fault detection rate defined as the number of faults detected divided by the total number of faults, and the fault isolation rate which is equal to the number of faults isolated divided by the number of fault detected. The transfer probability, also regarded as the rate of change of positions, is equal to the exponential power of - dij where dij is the difference between two points located in a two dimensional space. The updating procedure measures each solution against the last N solutions globally made by ACA. As soon as N solutions are available, their moving average X is computed; each new solution G is compared with X. If G value is lower than X value, the trail level of the last solutions move is increased, otherwise its decreased. The efficiency of ACA-based technique was proved theoretically and by means of simulation. The source code proving the suitability of ACA was developed in Matlab. The both fault detection and isolation rates were compared with the rates generated by the AdvancedSCH, a platform developed by our research team. The comparison shows that ACA-based technique best suits for test node selection.