In the conditions of magnetic dipole model, this paper proposed forward a centroid localization algorithm on magnetic anomaly target based on wireless sensor network node which distribution are random and the improved the weighted centroid localization algorithm based on magnetic induction intensity. According to the fluctuation of magnetic field intensity which detected by magnetic sensors, that can detect the existence of magnetic anomaly target and its location. Established an experimental system of the wireless sensor network for magnetic anomaly detection whose core designs including the HMC1043 three-axis magnetic resistance sensor and the CC2530 Zigbee RF chip. The experimental results show that the algorithm can accurately positioning the magnetic anomaly target within the network.

Abstract. There are many factors affecting dam deformation, and the time series of deformation data is directly modeled without considering the seasonality and periodicity of each influencing factor, the Ensemble Empirical Mode Decomposition (EEMD) and the Seasonal Autoregressive Integrated Moving Average (SARIMA) is proposed for prediction in this paper. Firstly, the time series of deformation data is decomposed by EEMD, which weakens its volatility to some extent, and decomposes various factors affecting dam deformation, so as to obtain a series of Intrinsic Mode Function (IMF) with different frequencies; secondly, according to the seasonal characteristics and periodic characteristics of each IMF, the SARIMA model was established respectively for rolling prediction; thirdly, the final forecast results can be obtained by superimposing the forecast results of each IMF. It is verified by experiments and compared with Gray Model, Kalman Filter Model and SARIMA model that EEMD-SARIMA model has higher prediction accuracy, and it has better fitting degree, which means that it is an effective method for dam deformation prediction.

In this article, Hilbert-Huang transform (HHT) is applied to the tank bottom corrosion acoustic emission signal de-noising processing and decomposition, the effective signals are explored and extracted, and a new method of noise reduction for acoustic emission signal is proposed. The acoustic emission signal of corrosion of tank bottom plate is processed and decomposed by Hilbert-Huang transform, and the energy ratio of each sub-signal in the original signal is calculated, the source data of each sub-signal is extracted, and the effective acoustic emission signal in the original signal is extracted. Using the calculation formula of acoustic emission amplitude, the amplitude of each sub-signal is calculated respectively. The results show that the amplitude of each sub-signal is smaller than that of the original signal. This method can be used to reduce noise and extract effective acoustic emission signals. And it provides the foundation support, to form the method of noise reduction processing and effective signal extraction for corrosion acoustic emission signal on the tank bottom plate. It has some application value to improve the accuracy of the evaluation results of the corrosion state of the tank bottom plate.

Presently, many attentions have been paid on low-noise pre-amplifier circuits and steady signal processing methods, but seldom on the combination of two technologies. In this paper, a small size low noise pre-amplifier circuit with 110dB Common Mode Rejection Ratio（CMRR）has been developed for giant magnetoresistance sensors(GMR) and its equivalent input noise voltage density is about . In addition, we proposed a new signal processing method for the sensors. In the method, we defined the quotient between the complex multiplex computation times and the output data num as a new figure of merit to evaluate that algorithm efficiency in signal detection, and name that quotient the computation times -to- output data num ratio (CTOR). Simulation results showed that the new method realized better parameters evaluation precision and higher efficiency than Modified Rife method, could be implemented easily in embedded systems.

Bias error and linearity error influence the measurement precision of three axis fluxgate magnetometers. So, it is important to obtain and calibrate these errors. Bias error and linearity error of a German made DM-060 digital magnetometer are measured by precise electromagnetic devices. Firstly, bias of each axis is obtained by an alternating weak electromagnetic standard equipment. A method by comparing mean absolute error before and after calibration is proposed to demonstrate the bias results credibility. Then, the rotating method is proposed to measure bias by a horizontal barrel shield equipment. Lastly, linearity error of each axis is obtained by a low DC electromagnetic field standard system, and linearity coefficients are calculated by linearity fitting. Experimental results show that bias of X, Y and Z axis is -2.9 nT, -1.4 nT and 3.9 nT by the alternating weak electromagnetic standard equipment, respectively. Bias of each axis changes to -5 nT, -2.5 nT and 10.5 nT by the horizontal barrel shield equipment a year later. Linearity error of X axis, Y axis and Z axis is 3.78-e005, 4.51-e005 and 7.92-e005, respectively. These experimental results show that there is a promising application to use high precise electromagnetic devices for measuring and calibrating bias error and linearity error.Keywords-three axis fluxgate magnetometer; bias error; linearity error; alternating weak electromagnetic standard equipment; horizontal barrel shield equipment; low DC electromagnetic field standard system I.