A new method is proposed to detect fault signal in power system using wavelet transform and neural network. The wavelet transform can decompose the original signal into several other signals with different levels of resolution. From these decomposed signals, the original time-domain signal can be recovered without losing any information. In order to increase the signal-noise-ratio, statistic rule is used to determine the wavelet decomposition level and threshold. Considering the inter relationship of wavelet decomposition coefficients, the signal features obtained from wavelet transform are presented for fault pattern classification. According to threshold value of each type of fault signal in each frequency band, the correlation between the type of signal and the signal features can be figured to fault pattern classification. As to this model, the advantages of morphological filter and wavelet transform are used to extract the fault feature meanwhile restraining various noises. The simulation results demonstrate that the proposed approach is verified to be effective.

To improve the performance of power quality (PQ) disturbances classification, a novel approach using wavelet neural network is proposed. The wavelet transform can accurately localizes signal characteristics in time-frequency domains The wavelet transform is utilized to extract feature vectors of different PQ disturbances. These feature vectors then are applied to the neural network for training and disturbance pattern classification. By comparing with classic neural network, it is concluded that the proposed neural network has better data driven learning and local interconnections performance. The research results between the proposed method and the other existing method are discussed and the proposed method can provide accurate classification results. The simulation results demonstrate the proposed method gives a new way for identification and classification of power quality disturbances.

Poor information means incomplete and insufficient information for the characteristic presented in the subject investigated. The qualitative fusion technique is proposed by this paper to solve some problems of statistics under the condition of poor information. Based on the grey system theory which allows the probability distribution unknown and the data number small, the concept, qualitative fusion, is defined and its calculating method is described via factor analysis of the influence on the performances of rolling bearings. Experiments on factor analysis for vibration of the tapered roller bearing show that the main factors of the influence on vibration of the tapered roller bearing are linearity of the outer ring raceway, squareness of the bore surface, waviness of the outer ring raceway, roundness of the inner ring raceway, and roughness of the inner ring raceway. Hereby vibration of the tapered roller bearing can be improved by means of controlling over these 5 quality indexes of the rolling bearing rings.

For some methods of stability analysis of a system using statistics, it is difficult to resolve the problems of unknown probability distribution and small sample. Therefore, a novel method is proposed in this paper to resolve these problems. This method is independent of probability distribution, and is useful for small sample systems. After rearrangement of the original data series, the order difference and two polynomial membership functions are introduced to estimate the true value, the lower bound and the supper bound of the system using fuzzy-set theory. Then empirical distribution function is investigated to ensure confidence level above 95%, and the degree of similarity is presented to evaluate stability of the system. Cases of computer simulation investigate stable systems with various probability distribution, unstable systems with linear systematic errors and periodic systematic errors and some mixed systems. The method of analysis for systematic stability is approved.

A model of the autoregressive model of weak electric signals in two plants was set up for the first time. The result of the AR model to forecast 10 values of the weak electric signals is well. It will construct a standard set of the AR model coefficient of the plant electric signal and the environmental factor, and can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on agricultural productions.

Evaluating the data acquisition board sampling uncertainty is a difficult problem in the field of signal sampling. This paper analyzes the sources of dada acquisition board sampling uncertainty in the first, then introduces a simulation theory of dada acquisition board sampling uncertainty evaluation based on Monte Carlo method and puts forward a relation model of sampling uncertainty results, sampling numbers and simulation times. In the case of different sample numbers and different signal scopes, the author establishes a random sampling uncertainty evaluation program of a PCI-6024E data acquisition board to execute the simulation. The results of the proposed Monte Carlo simulation method are in a good agreement with the GUM ones, and the validities of Monte Carlo method are represented.

In order to design a high precision machine, because of considering disadvantages of traditional design methods, the authors use novel professional design software CAXA to carry out Three-Dimensional solid modeling for a precision machine, and then through the interface between CAXA and ANSYS, import the model into ANSYS which is a large scale general finite element analysis software with abroad application, perform Finite Element Analysis and Optimization Design in use of ANSYS for some main precision mechanical structures. This design can make up for the disadvantage of ANSYS on modeling, short the machine design period, improve the accuracy, make some change according to the need. Finally, according to design a special high precision Brinell Hardness Tester, the authors demonstrate this novel precision machine design method successfully. This novel is good enough to be used as a reference when engineering technicians perform the mechanical modernized designing.

There are a wide variety of condition monitoring techniques currently used for the recognition and diagnosis of machinery faults. Tool wear often results in chaotics on milling process. Little research has been carried out about the occurrence and detection of chaotic behavior in time series signal of tool vibration. In the paper the vibration acceleration signal based on the operating stages of tool wear is established for the analysis of the correlation dimension of the operating stages of tool wear. Correlation dimension is calculated to recognize the tool wear operating conditions. Finally ,some experimental results from the fractal characteristic show that there are distinct differences in the correlation dimension in different tool wear conditions and close the correlation dimension in same tool wear conditions. The correlation dimension not only can be used as important scientific basis for monitoring tool wear, but also complement of other characteristic picking up method.

The maximum entropy bootstrap method is proposed to resolve the problem about parameter estimation under the condition that the number of test times is small while every time the number of test data is much, in test analysis for stochastic processes with unknown probability distribution. First the maximum entropy distribution of each test data is established, then information about the maximum entropy distributions of all tests is fused by bootstrap resampling, and lastly the inference of attribute of population is drawn from identifying the parameters of every individual, describing the stable state of population. Simulation experiment shows that the estimated results using the maximum entropy bootstrap method are coincident with the real truth of the subject investigated, with the relative errors of 0.14%~2.39% and the better results by a 9%~20% decrease of the relative errors as compared with the maximum entropy method and the bootstrap method.

A novel approach combining wavelet transform with neural network is proposed for vibration fault diagnosis of turbo-generator set in power system. The multi-resolution analysis technology is used to acquire the feature vectors which are applied to train and test the neural network. Feature extraction involves preliminary processing of measurements to obtain suitable parameters which reveal weather an interesting pattern is emerging. The feature extraction technique is needed for preliminary processing of recorded time-series vibrations over a long period of time to obtain suitable parameters. The neural network parameters are determined by means of the recursive orthogonal least squares algorithm. In network training procedure, much simulation and practical samples are utilized to verify and test the network performance. And according to the output result, the fault pattern can be recognized. The actual applications show that the method is effective for detection and diagnosis of rotating machine fault and the experiment result is correct.

In this paper fault-tolerant control is investigated for a class of nonlinear systems with model errors or external disturbance. Firstly a fault diagnosis method is proposed to estimate the faults based on the adaptive state observer. Then a novel fault-tolerant control scheme is designed to stabilize the nonlinear systems via the feedback linearization technique. The main advantage of the approach is that it is easy to implement and in most cases it can place the poles of the closed-loop systems arbitrarily. The effectiveness of the proposed scheme is demonstrated through the numerical simulation.

A general inference algorithm which based on exact algorithm of clique tree and importance sampling principle was put forward this article. It applied advantages of two algorithms, made information transfer from one clique to another, but don't calculate exact interim result. It calculated and dealt with the information using approximate algorithm, calculated the information from one clique to another using current potential. Because this algorithm was an iterative course of improvement, this continuous ran could increases potential of each clique, and produced much more exact information. Hybrid Bayesian Networks inference algorithm based on general softmax function could deal whit any function for CPD, and could be applicable for any models. Simulation test proved that the effect of classification was fine.

A new method combining wavelet transform with fuzzy theory is proposed to improve the limitation of traditional fault diagnosis technology of electric generator. In order to determine the threshold of each order of wavelet space and the decomposition level adaptively, the statistic rule is brought forward to increase the signal-noise-ratio. The wavelet transform is used to acquire the effective feature components and the proposed fuzzy diagnosis equation is used to complete classify fault pattern. The fault diagnosis model of electric generator is established and the network parameters training are fulfilled by the improved least squares algorithm. The input nodes include the information representing the fault characters. On basis of experiments data to train the fault diagnosis mode, the accurate classification results can be achieved in accordance with expert experience. In view of actual applications, the proposed method can effectively diagnose the fault pattern of electric generator.

High-resolution imaging can be achieved by optical aperture synthesis. Using a sparse array of smaller aperture, one can synthesize an aperture with the resolution of the equivalent filled aperture while reducing the size and weight of system. The environment and vibration factors of synthetic aperture arrays lead to errors in phase measurement and cause a severe degradation in the image quality. Real time phase calibration of synthetic aperture arrays is very important to produce images of consistently good quality. Redundant spacings calibration allows continuous calibration of the instrument with errors without requiring the model of the source.

The law of precision loss about dynamic measurement system is studied in this paper. Its total loss accuracy can be obtained by transformation law of its errors in different measuring phases of a certain measuring period. Based on the total precision loss and its structural characteristic, WNN is used to decompose systemic total precision loss and trace to its inside structural units, and build precision loss function model of structural units. So precision loss disciplinarian with all units and infection of different unit to total precision loss is definitude, the principal infection unit to systemic total precision can be distinguished. In the end of the paper an application examples have been introduced.

For the flight formation problem under the danger mode, a method based on the control architecture and decomposition strategy for obstacle avoidance of formation is proposed. Constructing and searching a Voronoi diagram, the reference flight paths for choice are generated based on K shortest path algorithm, then the cooperative function is established, the collision avoidance path is designed for each UAV, which meets both the request of cooperative time and optimal (or second optimal) cost of the whole formation.

Aim of research: Human body segment inertial parameters are the basic physical quantities in the study of human body in motion. Through careful calculation, inertial parameters such as the position of center of mass and moment of inertia of the total and segmental human body in motion in random postures have been obtained. Research method: Based upon the basic inertial parameters derived from Hanavan human body model and from Barter regression equation, upon position vector, moment and moment of inertia of the human body and segment relative to inertial reference frame by means of transformation matrix, and upon the resultant moment theorem and the parallel-axis theorem, inertial parameters such as the position of center of mass and moment of inertia of human body in random posture in motion are thus obtained. Result and conclusion: The research findings are in accordance with those of the balance plate and trilinear pendulum. The characteristics of individual and random posture of human body inertial parameter in motion are presented in this paper.

This paper is an in-depth study on the basis of the passed summary of relevant technologies. Multi-channel ring electrode has been developed to point impedance testing of the human body for the first time. Here, we build such a system, which bases on the hardware platform of AT89C52 combined with M82C54-2, besides, the integrated software development tools micro-soft visual c++ and the technical advantages such as multi-threading, databases, serial communication and the characteristics of real-time supported by Windows XP are used in here too. Except for the point impedance testing of the human body, the conductive volume of the human meridian and infrared-photoelectric absorption properties of physical quantities can also be detected by such an on-site data acquisition, analysis, display, record and communicate with the PC portable System. Currently, the system was being in the testing phase, we have collected some real data of human body with this vehicle, whose results are expected to be more satisfactory in the near future.

The feature extraction is one of key steps in fault diagnosis. A method of least square support vector machine (LSSVM) is put forward based on genetic optimization to restrain the end effect of empirical mode decomposition (EMD). Based on the method, a method of feature extraction is put forward. The energy of intrinsic mode functions (IMF) generated from EMD is to be the feature to distinguishing faults. The result of feature extraction experiment of analog circuit shows that the method is effective.

By combining wavelet transform (WT) with neural network theory, a novel approach is put forward to detect transient fault and analyze voltage stability. The application of signal denoising based on the statistic rule is proposed to determine the threshold of each order of wavelet space. In a view of the inter relationship of wavelet transform and neural network, the whole and local fractal exponents obtained from WT coefficients as features are presented for extracting signal features. The effectiveness of the new algorithm used to extract the characteristic signal is described, which can be realized by the value of those types of transient signal. This model incorporates the advantages of morphological filter and multi-scale WT to extract the feature of fault signal meanwhile restraining various noises. Besides, it can be implemented in real time using the available hardware. The effectiveness of this model was verified with the voltage stability analysis of simulation results.

For a model reference adaptive switching control scheme, we proposed a novel concept local overlapped switched system in this paper. Afterwards, we partitioned the closed-loop switched system, which is constructed upon reference switched system and nonlinear adaptive control law set, into finite local overlapped switched systems. This disposal is helpful to obtain a less conservative sufficient condition for global uniformly stability of the closed-loop switched system. Finally, based on local overlapped switched systems, we deduced a sufficient condition for uniformly boundedness of the closed-loop switched system which is essentially a nonlinear switched system.

The application of error separation techniques to double flank gear testing is investigated theoretically and experimentally. The mathematic model for error separation is established with the theory of Fourier series. The limitation of two-gear error separation method based on multi-step error separation technique is indicated. It can only separate the total radial composite error, but do nothing with the tooth-to-tooth radial composite error. The accuracy of a three-gear error separation method is also explored. In comparison with the two-gear error separation method, the three-gear method can separate both the total and tooth-to-tooth errors. The theoretical error caused by gear's particular structure is discussed. Experimental results show that the mathematic model is correct and the three-gear error separation method performs better than the two-gear's.

A novel marker based watershed through image enhancement is proposed to segment the dim infrared target. The dim infrared target is firstly enhanced by CB top-hat transformation and image quantization. Then, the accurate marker of the target can be easily obtained through image binarisation and marker filtering. To calculate an efficient gradient image of the dim target for the watershed segmentation, the gradient image is firstly calculated through Sobel operator and then efficiently enhanced through pseudo top-hat transformation and gradient quantization. Because of the enhancement of the dim target and the gradient image, the watershed can efficiently segment the dim infrared image. Experimental results show that the proposed algorithm is much efficient for dim infrared target segmentation.

The paper aiming at the influence factor of round grating dividing error, rolling-wheel produce eccentricity and surface shape errors provides an amendment method based on rolling-wheel to get the composite error model which includes all influence factors above, and then corrects the non-circle measurement angle error of the rolling-wheel. We make soft simulation verification and have experiment; the result indicates that the composite error amendment method can improve the diameter measurement accuracy with rolling-wheel theory. It has wide application prospect for the measurement accuracy higher than 5 μm/m.

A batch back-propagation neural networks (BBPNN) approach was presented to design general two-dimensional (2-D) quasi-equripple zero-phase finite-impulse response (FIR) digital filters. By minimizing the frequency-domain weighted error function, the BBPNN design method was obtained. The solution was presented as a parallel algorithm to approximate the desired frequency response specification. Thus, the method makes a fast calculation of the filter's coefficients possible. It is shown that the method leads to an optimal solution for the filter coefficients. The implementation of the approach was described together with some design guidelines, and some optimal design examples were given to demonstrate the effectiveness of the proposed approach.

A new method for chirp signal detection based on Fractional Fourier Transform (FRFT) is described in order to improve the performance of power line communication system. A character of chirp signal is that its energy can be concentrated properly when the fractional Fourier angle is suitable, but noise does not have this feature, signal mixed with noise can be separated in specific fractional Fourier domain, and then narrow-band filter is adopted for the signal obtained in the fractional Fourier domain, this character can be used for signal detection and then demodulation. Finally, simulation experiments indicate that the method is feasible and the capacity of the system's anti-noisy disturbance is improved.

Factor analysis (FA) is a multivariate statistical method for studying the correlation among a group of variables, and is able to minimize data redundancy and reveal hidden patterns. We used FA to reduce the dimension of feature vector and support vector machine to classify the audio data sets in the proposed audio steganalysis system. Then the system is utilized to detect the stego-audio signals embedded by wavelet domain LSB, quantization index method (QIM) and addition method (AM). The dimension of feature vector is decreased from 36 to 7 (LSB), 6 (QIM), 6 (AM) respectively. The detection rates are all greater than 91% and the detecting performance is maintained.

The azimuth error of inclinometer based on gyrocompass changes with different azimuths and incline angles. It has good repeatablility, but it is hard to be described in analytical expression. So a two-dimension look-up table method is proposed to compensate the azimuth error. The testing data in turn-table is used to form the two-dimension data table, which reflects the relationship among the azimuth error, the azimuth and incline angle. And the error characteristic of azimuth error is expressed exactly. Combined with the look-up table method and interpolation method, the azimuth error can be solved exactly and continuously. Turn-table experiments show that this method can increase the measurement accuracy of azimuth greatly, and it has been used for practical orientation in oilfield successfully.

A global calibration method of multi-sensor vision system based on flexible 3d target is proposed to solve the calibration problem of multi-sensor vision system with a large inspection range. The flexible 3d target is a form of target consisted of several planar targets, called sub-targets, which are placed flexibly according to the sensors' positions. The coordinate frame of one of the vision sensor is selected as the global coordinate frame. Using the invariance of the relative positions between sub-targets in the flexible 3d target, the closed solution of the transformation from the local coordinate frame of each sensor to the global coordinate frame can be computed. The result is refined by the nonlinear optimization method, and maximum likelihood estimation of the translation matrixes can be achieved. Experimental result demonstrates high accuracy of proposed calibration method. The proposed method greatly simplifies the process and reduces the cost of global calibration, for it does not need high-accuracy 3d measuring equipment or special 3d target as most the traditional global calibration methods do, and only needs to combine several planar targets to carry out the global calibration. It is applicable for the global calibration of multi-sensor vision system at working location.

To assist the angular accelerometer manufacturing and acceptance, a reduced model equation is deduced based on the principle of the angular accelerometer, an automated test system for electrical measurements is created, according to the practical operating requirements in the application, with multifunctional single-axis rate table and DAS module control by PC, and the software is developed in C++ Builder using multithread technology for fully automated testing through signal acquisition and processing. Test procedure is suited to static tests and dynamic testing. In this paper, principles of the automatic test system, application, and results are discussed.

Conventional P2P networks do not provide incentives for users. Therefore, users can easily obtain information without themselves contributing any information or service to a P2P community. This leads to the well known free-riding problem. Reputation systems may impose penalties on free-riders to identify and distinguish them from contributors, but these systems are vulnerable to the whitewashing attack. This paper analyzed the Tit-for-Tat protocol in the presence of whitewashers using a game theoretic approach. The Tit-for-Tat protocol is not an evolutionary stable strategy against invasion of whitewashing, but imposing an appropriate identity cost helps in preventing invasion by whitewashers. Simulation results demonstrated the theoretic analysis, and showed that the percentage of cooperating nodes in P2P networks with an appropriate identity cost is nearly four times bigger than without any identity cost.

The paper introduces a new current-mode biquadratic filter with three input terminals and one output terminal. The filter proposed in this paper uses one plus-type second-generation current conveyor (CCII+), one dual-output second-generation current conveyor (DOCCII), two grounded capacitors and three resistors. The circuit can realize all the standard filter functions: high-pass, band-pass, low-pass, notch-pass, and all-pass by choosing appropriate input branches without changing the passive elements. It has very low active and passive sensitivities, and can tune the circuit pole parameters orthogonally. The performance of the proposed biquadratic filter was examined by PSPICE simulation.

Flow-rate characteristics of pneumatic component are represented by sonic conductance C and critical pressure ratio b as specified by ISO 6538. In this method, Components are considered having ellipse flow-rate characteristics. However, since there are many of pneumatic components that ISO 6358 method can't represent proximately, regardless of whether or not calculated component is independent or composed, the flow-rate characteristics curve is not always like an ellipse, and the accuracy of sequential composition itself is questioned to air flow which passes though multiple components simultaneously. For this reason, the modification of composition method is present which ISO/WD 6358-2 is. In the paper, Error comparison between ISO/WD 6358-2 and ISO 6358:1989 have been done, the results show that error are small between them, but ISO/WD 6358-2 extends the applicable range of standard.

Knowing the position of the chute of cargo loading machinery situated in its working environment is a critical element for effectively accomplishing auto-loading of bulk cargo. This paper presents a novel approach to measure the position of the chute as well as the status of ship being loaded. A measuring device was designed according to the approach based on laser scanner and the measuring method was implemented. The system has been installed in actual cargo loading environment and proved successfully and is believed that it is a good starting point to this kind of usage.

The uncertainty evaluation of calibration instrument is an important step to evaluate the performance of instrument. The radon chamber is a standard instrument used to calibrate the radon monitors. Because the radioactivity decay and statistical fluctuation of radioactivity measurement, the uncertainty evaluation of radioactivity measurement instrument is different from the general instrument, the uncertainty evaluation of radon chamber should trace to the national reference radium source. In this paper, the two different uncertainty evaluation model of radon concentration based the radon source and radon monitor is described. The uncertainty of different input variables include radon concentration, radioactivity statistical fluctuation, the temperature and humidity is evaluated in evaluation standard Type A or Type B. According to the contribution of different factor to the uncertainty of radon concentration, the expanded and relativity expanded uncertainty of radon chamber is calculated and discussed in detail. Through comparison, the uncertainty evaluation of radon concentration based on radon monitor is better than the model based on radon source.

A novel approach was presented to solve the problem of fiber optic gyro(FOG) loop eigenfrequency and half-wavevoltage measuring. The method comes from the mechanism of reciprocal shift modulation, insteads of measuring loop eigenfrequency it measures the optics power of ineffective-modulation that having a peak; and insteads of measuring half-wavevoltage it measures the optics power of effective-modulation that having a low plain part. The modulation square wave whose frequence and amplitude could be programmed is made of the technique of direct digital synthesis. The scheme was tested by Matlab simulation, and implemented in circuit. The result of the loop eigenfrequency and half-wavevoltage is much more preciser than handwork test, what's more, it takes much less time.

Near infrared spectral analysis is a kind of indirect measuring technology, that is, calibration model is established by using sample spectra and the reference value of the interested concentration, and then predict the interested concentration of the unknown sample by using its spectrum and calibration model. Generally, the quality of calibration model is evaluated by the deviation between the predicted value and the reference value of the measured concentration. Reference value measurement error of the calibration set samples and model error are the main error source of multivariate calibration. When there was reference value measurement error, the regression model would cause great prediction deviation. However in practical application, apparent prediction error caused by reference value measurement error is always considered as the faultiness of calibration model. So it is falsely regarded that the prediction error of multivariate calibration model would be greater than that of reference method. In this paper, effect of reference value measurement error on prediction error was analyzed, and the relationship among apparent prediction error, actual prediction error and reference value measurement error was discussed. At the same time, the applicability and rationality of a computation formula was also discussed, which could estimate the true prediction error in a more precise way. It was proved that the prediction accuracy of an excellent calibration model was greater than that of the reference method by analyzing the reference value measurement error. An experiment of glucose aqueous solution samples was given in this paper. In this example, the effect of reference value measurement error on the prediction error of multivariate calibration model was investigated, and the estimating formula of the corrected prediction error was evaluated. The conclusion could be universally applied to not only multivariate calibration MLR, PCR, PLS and so on, but also in much more complex samples.

To improve the performance of fault diagnosis technology for vibrant faults of aeroengine, a novel approach combining the wavelet transform with fuzzy theory is proposed. The method with statistic rule is used to determine the threshold of each order of wavelet space and the decomposition level adaptively, increasing the signal-noise-ratio. The effective eigenvectors are acquired by wavelet transform and the fault patterns are classified by fuzzy diagnosis equation based on correlation matrix. The fault diagnosis model of aeroengine is established and the extended Kalman filter algorithm is used to fulfill the network structure. Also the robustness of fault diagnosis equation is discussed. By means of choosing enough samples to train the fault diagnosis equation, the type of fault can be determined on basis of the input information representing the faults. The actual applications show that the proposed method can effectively diagnose vibration fault of aeroengine.

A novel particle swarm optimization algorithm, called APSO_RW is presented. Random inertia weight improves its global optimization performance and an adaptive reinitialize mechanism is used when the global best particle is detected to be trapped. The new algorithm is tested on a set of benchmark functions and experimental results show its efficiency. APSO_RW is later applied in UAV (Unmanned Aerial Vehicle) path planning.

Power quality (PQ) is becoming prevalent and of critical importance for power industry recently. The quantitative detection of two distortions of voltage waveform, i.e., voltage sag and voltage swell, is conducted and on this base a novel approach based on wavelet transform (WT) to detect and locate the PQ disturbances is proposed. The signal containing noise is de-noised by wavelet transform to obtain a signal with higher signal-to-noise ratio, and then is input to the wavelet network; the synthesized method of recursive orthogonal least squares algorithm and improved Givens transform is used to fulfill the network structure; the fundamental component of the signal is estimated to extract the mixed information using wavelet network, and then the disturbance is acquired by subtracting the fundamental component; the principle of singularity detection using WT modulus maxima is presented and a dyadic wavelet transform approach for the detection and localization of the power quality disturbance is proposed. The simulation results demonstrate that the proposed method is effective.

When the statistics of the system noise and the observation noise are unknown, or almost unknown, the state estimation error computed by Kalman Filtering will be much bigger, or the Kalman Filter may become divergence. In order to avoid this demerit, a Least Square Filtering is presented. It weighs the observation data adaptively only without the requirement of the statistics of the noise. This algorithm is used to Strapdown Inertial Navigation System (SINS) initial alignment and compared with the Kalman Filtering. The simulation results show that the Least Square (LS) Filtering has faster convergent speed than the Kalman Filtering.

Measurement of the oil-water flow is of great importance in the industrial process. In the paper, a soft-measurement method for oil-water mass flow-rate was brought forward, in which the V-cone differential pressure meter was used and the adaptive wavelet network was developed to achieve the soft-measurement of the mass flow-rate. The multi-input single output model of adaptive wavelet network was adopted to approximate the mass flow-rate of oil-water. The paper focused on the experimental measurement of the homogeneous model of oil-water two-phase flow in horizontal pipe. Experimental results showed that the soft-measurement method combined differential pressure with adaptive wavelet network could satisfy the demand of the mass flow-rate measurement and the measurement error of the mass flow-rate was relatively small. The measurement error of the total mass flow-rate was acceptable.

An efficient distributed algorithm for mobile nodes of wireless sensor network (WSN) is proposed. A smart mobile robot is designed as the special node of WSN, and the nodes of WSN are deployed in the environment as signposts for the robot to follow. We use the RSSI value between the robot and other static nodes as the input of the navigation control system. The mobile robot state and navigation space are denoted by the RSSI potential field. Navigation directions are computed by using fuzzy logic method. To reduce the communication expense, each node within one hop communication range of robot is a distributed navigation unites. Then the fuzzy logic control centre will collect the control outputs from every beacon nodes and calculate the final outputs for mobile robot based on data fusion. The experimental results confirm that the navigation system based on WSN successfully achieved their assigned tasks.

Single ended traveling wave fault location technology has been commonly used for testing cable for years. The waveform attenuation and distortion of the reflected pulse is an inherent problem to define the arrival time and the propagation velocity of traveling wave correctly. Aim at obtaining the required information of the incident and reflected pulse waveform, a high-speed data acquisition system is designed based on the technology of random equivalent sampling (RES). And the problem of data storage and communication caused by high sampling rate is solved. The high-speed data acquisition system consists of fine time interval measurement module with 65ps resolution which is the key technology of RES, relatively low-speed A/D converter, Field Programmable Gate Array (FPGA), high-speed Synchronous Dual-Port RAM buffering storage and high performance programmable digital signal processors (DSP). The data acquisition and storing of the system is realized with FPGA and DSP as the core. The system promises 15.4GHz equivalent sampling rates with 125MHz real-time sampling rate. Experiment proved that the high-speed data acquisition system is stable and accurate.

The optical stabilized platform is important equipment for observing on vehicle. A kind of stabilized platform was introduced in this paper. In order to overcome the limitation of traditional compensation control, the double close loop based on displacement feedback loop and speed feedback loop was presented. The PID was used in speed feedback loop, and the fuzzy neural network (FNN) is used in displacement feedback control. The method united the fuzzy control and neural network, fuzzy control needn't build precise object model and can simulate the human experiences, and the neural network has the capabilities of self-learning and self-adaptation. So the FNN can adapt the change of parameters and disturbance. In order to prove the effectiveness of this controller, simulations have been done.

Structural health monitoring for bridge is becoming a more widely accepted way to improve bridge management. To implementation the above objective, this paper presents a GPRS-based bridge remote data acquisition and monitoring system. It is a kind of unmanned automation, intelligent data acquisition system. The network structure of the system, data transmission, information collection and data-processing model methods provide a common, advanced and effective information acquisition model for other supervisory control and data acquisition systems. This system are highly portable with little changes can be applied to other systems. These will not only improve the automation degree of monitoring, and also a lot of human and financial resources are saved with a high practical value and economic value.

In order to improve the integration of control moment gyro (CMG) gimbal servo system, this paper proposes a resolver excitation and resolver-to-digital conversion system based on digital signal processor (DSP). Only PWM, A/D functions of one DSP and a few peripheral circuits can afford to supply excitation to resolver, demodulate the resolver outputs and transform them into digital form. For decoding the resolver outputs, a simple inverse tangent method using a short lookup table is introduced. Sallen-Key low-pass filter, FIR band-pass filter, coarseness-precision combing with its error correction and a close-loop control method can improve angle solution and obtain velocity. Then angle and velocity are fed back to angle loop and velocity loop of the gimbal servo system to make the whole system very integrated. The main features include: easy realization, small bulk, light weight, low cost, adjustable parameters and wide uses. Experimental result shows that the angle precision can reach 17 bits.

Obtaining labeled training examples for some classification tasks is often expensive, such as text classification, mail filtering, while gathering large quantities of unlabeled examples is usually very cheap. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, then selecting the most informative ones with respect to a given cost function for a human to label. MBBNTree algorithm, which integrates the advantage of Markov Blanket Bayesian Networks (MBBN) and Decision Tree, would behave better performance than other Bayesian Networks for classification. But the available training samples with actual classes are not enough for building MBBNTree classifier in practice. In this paper, the MBBNTree classifier algorithm based on the Query-by-Committee of active learning would be presented to solve the problem of learning MBBNTree classifier from unlabeled samples. Experimental results show that the proposed algorithm can reach the same accuracy as passive learning with few labeled training examples.

In this study, a biological process of bone remodeling was considered as a closed loop feedback control system, which enables bone to optimize and renew itself over a lifetime. A novel idea of combining strain-adaptive and damage-induced remodeling algorithms at Basic Multicellular Unit (BMU) level was introduced. In order to make the outcomes get closer to clinical observation, the stochastic occurrence of microdamage was involved and a hypothesis that remodeling activation probability is related to the value of damage rate was assumed. Integrated with Finite Element Analysis (FEA), the changes of trabecular bone in morphology and material properties were simulated in the course of five years. The results suggest that deterioration and anisotropy of trabecluar bone are inevitable with natural aging, and that compression rather than tension can be applied to strengthen the ability of resistance to fracture. This investigation helps to gain more insight the mechanism of bone loss and identify improved treatment and prevention for osteoporosis or stress fracture.

In block motion estimation, search patterns have a large impact on the searching speed and quality of performance. Based on motion vector distribution characteristics of real world video sequences, we propose a new cross-diamond search algorithm (NCDS) using cross-search patterns before large/small diamond search patterns in this paper. NCDS employs halfway technique to achieve significant speedup on sequence with (quasi-) stationary blocks. NCDS employs Modified Partial Distortion Criterion (MPDC), which results in fewer search points with similar distortion. Experimental results show that the improvements of NCDS over CDS can be up to a 16% gain on speedup while similar prediction accuracy is maintained, and NCDS provides faster searching speed and smaller distortions than other popular fast block-matching algorithms.

How to comprehensively assess precision of the Multi-parameter measurement instruments is becoming a hot research topic in academy. In this paper, the precision of the multi-parameter measurement instruments is assessed using the methods of MTS (Mahalanobis-Taguchi System) and grey incidence degree analysis through practice case study. The study illustrates that both methods are applicable in the assessment of the Multi-parameter measurement instrument precision. Through the comparison and contrast analysis, this paper summarizes the advantages and disadvantages of the two methods, which provides good references or guidelines to properly apply the methods under different situations.

A three line calibration method for error quantification in a prismatic joint of a machine tool was proposed and implemented by using a laser interferometer as a working standard. It greatly simplified the measurement setup requirements and accelerated the calibration of prismatic joints. Moreover, it was highly economical by reducing the calibration time and eliminating the use of complex optics. The methodology was implemented on prismatic joints of a three axis CNC machine tool as per standard procedures and guide lines. Cubic spline technique was implemented as error modeling and results obtained were reported for its further use to compensate the errors for improving the accuracy in prismatic joints.

With the development of the military field, there is growing interest in extremely high sensitivity. For meeting this desire, we design a novel high sensitivity MOEMS (Micro-Opto-Electro-Mechanical-System) accelerometer which is based on laterally deformable nano-grating. Calculations predict that this sensitivity could be improved by up to two orders of magnitude in future design and it can reach as high as 10-9g. These sensors consist of two offset layers of sub-wavelength polysilicon nano-gratings. They modulate the near-field intensity and polarization of an incident light source in response to relative motion of the nano-gratings. The reflected/transmitted optical beam intensity from the nano-gratings is measured as a function of the relative lateral positions of the nano-gratings. By using rigorous coupled-wave analysis (RCWA) and Fourier transform methods, we research the theory mechanism about nano-grating accelerometer. The accelerometer belongs to displacement sensors. For small changes in the spacing of the grating elements, a large change in the optical reflection and transmission amplitude observed. An in-plane motion detection sensitivity of 160fm/√HZ has been measured, and its displacement sensitivities will be as low as 12fm/mg. This sensitivity compares favorably to that of any other MEMS (Micro-Electro-Mechanical-System) transducer.

The use of context is important in Human-Computer Interface services and become the focus of research on Pragmatic Web embedded in instrumentation where the user's context is changing rapidly. We are interested in the content, type and role of pragmatic-context, as swell how to use it in services embedded in instrumentations. In this paper, we propose an overview of the pragmatic-context definition, description and, in particular, a generic model and framework that deal with pragmatic-context in order to provide users with more tailored personalized services.

In order to track aerial target using data from radars, we improved an adaptive extended Kalman filtering algorithm based on "Current" Statistical Model. In this improved algorithm, the interval of acceleration probability distribution is adjusted, and Singer model is adopted instead of "Current" Statistical Model when target in the low-level maneuver. According to the real-time estimation of acceleration and its change rate, self-adaption of extremum of acceleration is achieved. Based on the Monte Carlo simulation, we got the conclusion that aerial target in different maneuver situation can be tracked quickly, especially in the low-level maneuver, the improvement of tracking precision is notable.

FIR filter has some advantages, such as system stability, simple implement, and linear phase. It has been widely used in digital signal processing and other relative fields. Clonal selection algorithm has been applied successfully in solving problems like memory acquisition, multi-modal optimization and traveling salesman problem. This paper proposes a novel FIR filter design method. It combines clonal selection algorithm and window function method to achieve optimization. In the design process, float coding is adopted to increase the convergence precision. Some simulation experiments are carried out to verify the performance of the presented algorithm. The results show that the introduced method is able to design some FIR filter which is difficult for other methods. The filter design approach discussed in this paper is universal and easy to implement.

The gamma ray scattering energy spectrum detected by one detector was presented to distinguish the gas liquid two-phase flow regime of vertical pipe. The simulation geometries of the gamma ray scattering measurement were built using Monte Carlo software Geant4. Computer simulations were carried out with homogeneous flow, annular flow and slug flow. The results show that the scattering energy characters of homogeneous flow and annular flow have significantly different. The scattering spectrum of slug flow has a little similar to homogeneous flow or annular flow while gas slug exists too short or too long in measuring cycle. The RBF neural networks were used to predict the flow regime. The results show that the homogeneous flow and annular flow can be completely distinguished and the mostly slug flows were exactly recognized by the neural network. It is demonstrated that the method of one detector scattering energy spectrum has the ability to identify the typical gas liquid flow regime of vertical pipe and fit the applications in engineering. The void fraction precision was improved by the flow regime compensation.

In order to test the rheological property of biological fluid more simply and more accurately test, a cone plate speed attenuation method is put forward on the base of spinning cone plate method. The cone plate is firstly driven by an eddy coil to a preset value. Then the cone plate attenuates freely under the action of a fluid viscosity force. In the system, standard viscosity oil is applied to calibrate measurement parameters and thus to eliminate systematic errors. The system employs an upper computer and a lower microprocessor and adopts broadcast communication in a master/slave manner based on RS485 serial links. The microprocessor is a LPC2131 for measuring the angular speed and the angular acceleration of the cone plate and for controlling sampling and cleaning. The upper computer is programmed with the language of Visual c++ 6.0, for data processing, data storage and data displaying. The repeatability error of the system, in which the measurement is conducted with a standard non-Newtonian fluid, is 0.57%.

In case of initial self-alignment for SINS on stationary base, when the level accelerometer outputs are taken as the measurement value for Kalman filter, the convergence speed of azimuth misalignment angle is limited by the observability of SINS. A fine alignment method for SINS is introduced firstly, which can do the accelerometer-based leveling alignment and gyro-based heading alignment simultaneously. The observability analysis by Singular Value Decomposition for this method shows that, the degree of observability of SINS is improved by adding observable extern information. Based on the analysis, a fast initial alignment method is proposed, which employs a reduced order alignment Kalman filter and takes both the level accelerometer outputs and east gyroscope output as the measurement value. Besides, the model error compensation method is given. Simulation and experimental comparison results between this fast alignment method and traditional one illustrate that, the convergence speed of azimuth misalignment angle is improved greatly with good real-time performance.

The pulse oximetry is a kind of electronic instrument that measures the oxygen saturation of arterial blood and pulse rate by non-invasive techniques. It enables prompt recognition of hypoxemia. In a conventional transmittance type pulse oximeter, the absorption of light by oxygenated and reduced hemoglobin is measured at two wavelength 660nm and 940nm. But the accuracy and measuring range of the pulse oximeter can not meet the requirement of clinical application. There are limitations in the theory of pulse oximetry, which is proved by Monte Carlo method. The mean paths are calculated in the Monte Carlo simulation. The results prove that the mean paths are not the same between the different wavelengths.

This paper introduces a field testing and calibrating system that serves the national industrial standards of production safety. The system supervises the monitoring systems of hazardous sources by measuring and evaluating them in the field. The system is designed as a cabinet that can be carried by an automobile. The front end parts of the system are designed as intrinsically safe handset instruments, so that they can be moved into Zone 0 independently and measure the supervised devices as close as possible. Measurement accuracy of the instruments is guaranteed by design. The system also provides a calibration interface with upper standard devices to facilitate periodically automatic calibration itself.

The neutral Hopfield neural network (NHNN) model is considered in this paper, which can be regarded as an extension of the classical Hopfield neural network (HNN). By introducing the neutral term into the classical HNN model, the inspiration and associate memory phenomenon can be well described and explained. Based on LMI techniques, the delay-dependent stability criteria of NHNN are obtained using the free-weighting matrices method, where the free weighting matrices are applied to express the relationships between the terms in the Leibniz-Newton formula. The optimization on the free weighting matrices by means of solving linear matrix inequalities (LMIs) can be employed such that the less conservative results can be obtained than that in Zhang, et al 2005. The numerical examples demonstrate that the proposed stability criterion is less conservative than the previous works.

Based on the logarithmic relationship between photovoltaic voltage and incident light intensity of a photo-detector, a simple and low-cost method is proposed to linearize the measurement of nonlinear variables related to light intensity. Theoretical analysis shows that this linear measurement method is feasible in a large measurement range if we choose a proper load resistance for the photodiode. Based on the Lambert-Beer law and the photovoltaic voltage-based measurement method, the linear measurements of liquid level and liquor concentration have been achieved experimentally by directly detecting the output voltage of a solar cell, and their nonlinearities are both less than 2.0%.

AFVISAR (all fiber velocity interferometer system for any reflector), which is developed from laser Doppler method, has been proved to be effective and powerful technique in acceleration (and velocity) measurement. The measuring error of AFVISAR system must be studies in order to achieve accurate measure result. However, the existing theoretical research about this problem is inadequate. In this paper, firstly, the principle of AFVISAR is analyzed with Doppler frequency shift theory and the mathematic equations for acceleration calculating are concluded. The theoretical model of the measuring error is put forward by considering object moving direction bias angle and corresponding expression of the deviation is established. Then, an example is taken to illustrate the effect that the bias angle brings to acceleration calculation. In the following paragraph, the error creating by ignoring the second-order Doppler frequency shift is analyzed and the calculation formulas are demonstrated. Finally, the effect of the spectral characteristics of fiber laser are studied, and the relative error is calculated.

With summarizing the stability assessment data over 10 years to the atmospheric pressure standard (Ruska2465-754, SN45158) established by National Center for Meteorological Standard Metrology, it is proven that the mass equipped by instrument is with good stability, the effective area of piston is about stable, the pressure reproducibility and measurement capability is stable for over 10 years and obviously better than nominal accuracy specification.

The insufficiency of single-flank testing is analyzed. Today's solutions and their existing problems are discussed. Based on this, the gear pair integration error (GPIE) is used to efficiently eliminate the insufficiency of the single-flank testing. GPIE takes all errors of a gear pair as a set, and this set is represented by the gear pair integration error curve (GPIEC) which is formed in the way that the deviations of all points on conjugate tooth flanks of the gear pair are marked with the same base zero and are arranged along the action line of the gear according to the meshing sequence of the correspondent mesh points. An introduction of the deduction of this theory and its definition formula, as well as the synthesis method of the GPIEC are presented. Finally, the feasibility and efficiency of the theory are demonstrated in resolving the insufficiency of the single-flank testing and analyzing the sources of transmission quality and gear noise through the comparison between the GPIEC of a gear pair and its transmission error curve. The significance of the theory are summarized with respect to their influence upon formulating standards for gear tolerance, analyzing errors of gear-cutting machines and transmission quality of the gear pair, and innovating ways to the research of the dynamic characteristics of gears and their noise.

The study of accelerated life test on cylinders for pneumatic industry is covered in this paper. Accelerated life testing is valuable tools to get information quickly on life distribution which is achieved by subjecting the test units to conditions that are more severe than the normal ones. Long lifetime pneumatic cylinders are selected as the test object. Section I covers the fault mechanism analysis, the piston and piston pole parking are the weak units as they are movable parts as well as leakage sources which easily result in the cylinders failure. Liquid temperature and operating frequency are chosen as stresses since their accelerating potentials are available for the specific cylinders. The complete ALT data is presented using Weibull distribution. Estimation for the parameters of failure model and other characteristics of cylinders population life distribution are done successfully. From the 3-axis graph plotted, the effects of each stresses to cylinders can be seen clearly which provide useful results for cylinders researcher and developer.

The autonomous star trackers, using charge couple device CCD camera, or active pixel sensor APS, as natural sensors for optical input, has assumed a permanent position. This is due to their highly accurate attitude determination, small size, light weight and simple functionality, making them devices of choice in nearly all modern space vehicles especially for Nano-satellites. In all star trackers, star centroiding is a fundamental process necessary for pattern recognition. In this paper, a new method is presented, which predicts centers of regions of star locations, in successive image frames, from the current knowledge of boresight direction, which is updated successively. This method is very attractive for hardware implementation using APS imagers known for their random accessibility feature lacked in CCD's. The big advantage of this method is that it does not need pattern recognition, thus making it fast. This reduction of computational budget, power consumption and time, added to the capability of incorporating them in APS imagers, is appealing to Nano-satellites navigational instrumentation.

In order to improve the initial alignment accuracy of Strapdown Inertial Navigation System (SINS) in case of large attitude errors, a combination of nonlinear Unscented Kalman Filter (UKF) and the linear Kalman Filter (KF) has been considered. For large initial attitude errors, both in heading and the level plane, application of the KF to the linearized error model of SINS does not yield good initial attitude estimation. To overcome this problem, the UKF has been employed. Initially state feedback has not been applied to maintain the nonlinear nature of the system. It is applied at the end of estimation using UKF. Simulation results for stable base SINS alignment have shown high level of accuracy for heading estimation but not equally so for the level plane. However, after employing state feedback at this point linearized approximation of the error model is justified. The superior performance of KF for linear systems is then exploited. Simulation results show marked improvement as compared to the single use of UKF or the KF.

Traditionally, manufacturers have ensured part accuracy by linear calibration of each machine tool axis. The conventional definition of the 3-D volumetric positioning error is the root mean square of the three-axis displacement error. 20 years ago, the dominate error is the lead screw pitch error of 3 axes. This definition is adequate. However, now the machine accuracy has been improved with better lead screw, linear encoder and compensation, the dominate errors become the squareness errors and straightness errors. Hence the above definition is inadequate. During the past years, the industry has seen demand emerge for the "volumetric accuracy" specification on machine tools. One hurdle remains: a standard definition so that everyone measures volumetric accuracy with the same yardstick. The issue has been discussed in many Standards Committees, machine tool builders and the metrology community. Reported here are, a new 3D volumetric positioning error measurement and compensation technique, proposed definitions or measures of 3 D volumetric positioning errors of a CNC machine tool, and its verification.

The statistical characteristics of the rolling element bearing friction torque are analyzed to find its evolvement law, via the experiment on the friction torque of two kinds of the bearings code-named HKTA and HKTB under the condition of different rotational speeds. A concept, relative increment of the variation coefficient, is proposed for an in-depth study of the evolvement course. According to the statistical calculating results for the experimental data, the higher the rotational speed of the rolling element bearing is, the severer the variation of its friction torque is. Under the condition of the low rotational speed range from 3r/min to 15r/min, the maximum of the relative increment of the variation coefficient can be over 25%. This uncovers new evidence that the degree of the evolvement of the friction torque will become stronger and stronger consequentially with the increase of the rotational speed of the rolling element bearing.

A novel video sequence compression scheme is proposed in order to realize the efficient and economical transmission of video sequence, and also the region-based functionality of MPEG-4. The CPM and NCIM fractal coding scheme is applied on each region independently by a prior image segmentation map (alpha plane) which is exactly the same as defined in MPEG-4. The first n frames of video sequence are encoded as a "set" using the Circular Prediction Mapping (CPM) and encode the remaining frames using the Non Contractive Interframe Mapping (NCIM). The CPM and NCIM accomplish the motion estimation and compensation, which can exploit the high temporal correlations between the adjacent frames of video sequence. The experimental results with the monocular video sequences provide promising performances at low bit rate coding, such as the application in video conference. We believe the proposed fractal video codec will be a powerful and efficient technique for the region-based video sequence coding.

A post-processing algorithm of video object segmentation that is based on region compensation in fixed time interval is proposed on the base of the traditional spatial-temporal video object segmentation algorithm. Firstly, an image variation detection template is calculated by noise suppression and dilatation linking of frame difference images; Secondly, the morphological gradient is calculated by the simplification of morphological opening and closing reconstruction of the original image. The accurate boundary of the object can be obtained by watershed algorithm through the nonlinear transformation and gradient grade classification of the morphological gradient image. The initial binary template of the video object can be extracted through proportion calculation; Thirdly, the final complete video object template can be obtained through region compensation in fixed time interval. Experimental results indicate the correctness and validity of the proposed video object segmentation algorithm.

The test technology of electrical safety performance based on IEC international standards is put forward in the paper, including mainly four test parameters: leakage current, high-voltage withstand, insulation resistance and ground resistance. The definitions, the types, the testing purposes and methods of these parameters are also proposed. Based on the technology, we construct a system for data collection, processing and controlling with a PC, a high-performance microcomputer ADμC842 and a FPGA, and adopts a VI technology to develop an integrated testing system for electrical safety performance. The system can be applied to both the certification testing of products and quality control in manufacturing and provides an authenticating measure for the domestic electrical equipments to enter international markets.

The working principle, design and implementation are described for a new design of leakage current test system, which is composed of human body's impedance network, detector switch circuit, the circuit including MCU and CPLD for control and measurement, can achieve peak detection, true RMS detection, real-time display of waveform. According to many international testing standards such as IEC, UL, GB, the system has a broader measuring scope of frequency and higher accuracy with good human-computer interface and calibration function.

A dynamic decoupling nonlinear control method for MIMO system is presented in this paper. The dynamic inversion method is used to decouple the multivariable system. The nonlinear control method is used to overcome the poor decoupling effect when the system model is inaccurate. The nonlinear control method has correcting function and is expressed in analytic form, it is easy to adjust the parameters of the controller and optimize the design of the control system. The method is used to design vertical transition mode of active control aircraft for gust alleviation. Simulation results show that the designed vertical transition mode improves the gust alleviation effect about 34% comparing with the normal aircraft.

Design method of eigenvalue assignment based on standard characteristic polynomial, as well as mathematical solving process of the method, is proposed in this paper so as to resolve the uncertainty of ideal eigenvalue choice in modern control theory and the difficultly in engineering implementation of modern control system design methods. Longitudinal stability holding control system of an aircraft was designed and simulated by employing proposed method. Dynamic character and robust performance simulation of the system are given. Simulation results show that the method achieves the control quality and has better robustness than another method. There is only one design parameter which is easy to calculate. So, the method is characterized as simple design, logical structure, easy programming and convenient for engineering implementation.

The structures and missions of modern satellites are very complicated, so the reliability of satellites is becoming increasingly important. This paper proposed a fault-tolerant attitude control system for a satellite based on Fuzzy Global Sliding Mode Control (FGSMC) algorithm. We designed a controller for the nonlinear model of a satellite. By designing a global sliding surface, this controller can ensure that the response of the system has global robustness against the uncertainties of system and external disturbances. In this paper attitude control is performed by four reaction flywheels. The attitude control system distributed the three control torques to the four reaction flywheels according to the distribution matrix. We deduced the formula to calculate the distribution matrix. Paper proved the stability of the designed control law, and simulated the attitude control system. The simulation results show that the attitude control law has high accuracy and robustness.

An effective approach for fault diagnosis of aeroengine based on integration of wavelet analysis and neural networks is presented. The wavelet transform can accurately localizes the characteristics of a signal in time-frequency domains and in a view of the inter relationship of wavelet transform between exponent theory, the whole and local exponents obtained from wavelet transform coefficients as features are presented for extracting fault signals, which are inputted into radial basis function for fault pattern recognition. The fault diagnosis model of aero-engine is established and the improved Levenberg-Marquardt training algorithm is used to fulfill the network structure and parameter identification. By choosing enough samples to train the fault diagnosis network and the information representing the faults input into the neural network, the fault pattern can be determined. The robustness of wavelet neural network for fault diagnosis is discussed. The practical fault diagnosis for aeroengine vibration approves to be accurate and comprehensive.

A new adaptive second-order sliding mode control method is proposed for the attitude control of flexible spacecraft during slew maneuver. Compared with traditional sliding mode control, the second sliding mode control approach guarantees the same robustness and precision, feature higher order accuracy and can be exploited to eliminate the chattering phenomenon. In the present paper, a second-order sliding mode controller is designed by treating the input of the system as a new state variable, while using it's derivate as the actual control. A fuzzy-neural network is adopted to approximate the strong coupling uncertain nonlinear dynamics, and the on-line adaptive weights tuning law is derived, the global stability of the system is guaranteed. Simulation results show the effectiveness of the proposed method.

Discrete adaptive sliding mode control (ASMC) with exponential reaching law is proposed to alleviate the influence of the factors such as the periodical fluctuation torque of motor, nonlinear friction, and other disturbance which will deteriorate the tracking performance of a DC torque motor driven inner axis for a high precision flight motion simulator, considering the limited compensating ability of the ASMC for these uncertainty, an equivalent friction advance compensator based on Stribeck model is also presented for extra-low speed servo of the system. Firstly, the way direct using the available parts of the inner axis itself to ascertain the parameters for Stribeck model is listed. Secondly, adaptive approach is used to overcome the difficulty of choice the key parameter for exponential reaching law, and the stability of the algorithm is analyzed. Lastly, comparable experiments are carried out to verify the valid of the combined approach. The experiments results show with a stable 0.00006°/s speed response, 95% of time the tracking error is within 0.0002°, other servos such as sine wave tracking are also with high precision.

The flight motion simulator is one kind of servo system with uncertainties and disturbances. To obtain high performance and good robustness for the flight simulator, we present a robust compound controller base on Backstepping controller and BP neural network. Firstly, the design procedure of the robust Backstepping controller is described and correlative problems are proposed. Secondly, the principle and the design process of BP neural network are analyzed and expatiated respectively. Finally, simulation results on the flight simulator show that the BP neural network can compensate external disturbances including system input and output disturbance and the system performance can be improved. Therefore both robustness and high performance of the flight simulator are achieved. It is an applied technology for the control of servo system, such as the flight motion simulator.

Present scheduled flights are arranged between two airports as a pair of flights. This paper proposed a novel and innovative method that one flight executes more scheduled flight plans over more airports and then come back to the departure airport and at the same time another flight execute the opposite scheduled flight from the same departure airport and come back to the airport from the opposite flight route. After a comparative analysis of various heuristic algorithms^[1], ant colony algorithm is selected to solve this problem. In this paper, choose eight airports as the research objects and obtain the results with the help of computer. The outcome indicates the validity of the improved ant colony algorithm.

The chief aim of this paper is to propose a simple on-line estimation and compensation method of GPS/SINS measurement delay. The causes of time delay for GPS/SINS integration are analyzed in this paper. New Kalman filter state equations augmented by measurement delay and modified measurement equations are derived. Based on an open-loop Kalman filter, several simulations are run, results of which show that by the proposed method, the estimation and compensation error of measurement delay is below 0.1s.

In satellites attitude measurement, Infrared Earth Sensor (IES) is one of the key components. In order to accomplish the testing and calibration of IES, a signal stimulated source is required to provide electrical earth-wave signal, so the system design of Electrical Signal Source of Infrared Earth-Wave (ESSIEW) is very important during the R&D and test of IES. Traditional system design of ESSIEW used digital circuits to implement so that its precision and security and testing efficiency are low. By analyzing the task and requirement of testing and calibration in the uninstalled IES, a new system design is proposed. The new system design includes two parts: hardware system and software system. The hardware system implements by FPGA technology and DAC transformation technology, and the software system implements by main-control software on CPU board and FPGA solidified software. The experiment and application results show that the system of new design presented in this article has characteristics of high precision and high security and full functions, improves the testing performance and efficiency of IES and satisfies with signal testing requirement of the electrical signal source.

In the land vehicle positioning and tracking system, the vehicle moving route is sometimes positioned outside the vehicle running road because of the GPS positioning errors. It may cause the tracking system unavailable. In order to improve the positioning accuracy of vehicle tracking system, a current statistical model is employed as the vehicle moving model. At the same time, the map-matching algorithm with the nearest location and the suitable moving angle is proposed to amend GPS measured data. The algorithm can determine the best matching road by making full use of the road geographic information of electronic map. Simulation results show that the vehicle tracking system has high positioning accuracy besides the navigation function.

In this paper formation configuration control of distributed satellites is studied based on continuous micro-thrust. The nonlinear model for the Leader/Follower relative position case is derived. A sliding mode relative position controller is designed based on the dynamic equations, which is proved to be able to eliminate the influence of J2-perturbation. As it is difficult to develop a globe control law for all the missions during the formation, a switching-mechanism is adopted according to the hierarchical architecture of the system. A supervisor in the upper layer controls the transition between the modes. Dwell time and safe mode are applied to ensure the stabilization of the switched system. Simulation results show that the switched system proposed in this paper can control configuration of the formation to desired patterns effectively and can still work even if the Leader satellite is malfunctioning.

In this paper, a 6-DOF motion mathematical model is discussed. It is consisted of body dynamics and kinematics block, aero dynamics block and atmosphere block. Based on Simulink, the whole rocket trajectory mathematical model is developed. In this model, dynamic system simulation becomes easy and visual. The method of modularization design gives more convenience to transplant. At last, relevant data is given to be validated by Monte Carlo means. Simulation results show that the flight trajectory of the rocket can be simulated preferably by means of this model, and it also supplies a necessary simulating tool for the development of control system.

The development of Un-manned Aerial Vehicles (UAV) is analyzed, and an integrated MEMS-SINS /GPS/ Pressure Altimeter / Magnetic Compass is presented as an airborne equipment for UAV. In this low cost integrated navigation system, the position and velocity precision can be ensured due to GPS, and the pitch and roll precision can be ensured due to accelerometers. The heading's observability is very weak, in order to eliminate this divergent trend, the method that dead reckoning revises the heading of SINS is put forward. The simulation shows that this method can effectively eliminate the divergent trend of heading.

Because of the costly price and the error accumulation of high precise Inertial Navigation Systems (INS) and the vulnerability of Global Navigation Satellite Systems (GNSS), the geomagnetic navigation technology, a passive autonomous navigation method, is paid attention again. Geomagnetic field is a natural spatial physical field, and is a function of position and time in near earth space. The navigation technology based on geomagnetic field is researched in a wide range of commercial and military applications. This paper presents the main features and the state-of-the-art of Geomagnetic Navigation System (GMNS). Geomagnetic field models and reference maps are described. Obtaining, modeling and updating accurate Anomaly Magnetic Field information is an important step for high precision geomagnetic navigation. In addition, the errors of geomagnetic measurement using strapdown magnetometers are analyzed. The precise geomagnetic data is obtained by means of magnetometer calibration and vehicle magnetic field compensation. According to the measurement data and reference map or model of geomagnetic field, the vehicle's position and attitude can be obtained using matching algorithm or state-estimating method. The tendency of geomagnetic navigation in near future is introduced at the end of this paper.

In order to design full-envelop flight control law for a certain helicopter, a novel method based on multi-objective genetic algorithm (MOGA) is put forward. In this method, the design of flight control law is viewed as a multi-objective optimization problem(MOP) where obtaining optimal performances in each designed flight envelop are treated as sub-objective and that in all designed flight envelop as objective, flight control law's parameters to be searched as decision variables, corresponding performance criteria as constraints. For the MOP, MOGA is used to get the optimal parameters of flight control law in all designed flight envelops. Finally, the novel method is applied to the flight control law's design for the helicopter's pitch motion, the simulation results show that the parameters are feasible and the performances are satisfactory, which further prove that the method is effective.

The low Reynolds numbers of small UAV will result in unfavorable aerodynamic conditions to support controlled flight. And as operated near ground, the small UAV will be affected seriously by low-frequency interference caused by atmospheric disturbance. Therefore, the GNC system needs high frequency of attitude estimation and control to realize the steady of the UAV. In company with the dimensional of small UAV dwindling away, its GNC system is more and more taken embedded designing technology to reach the purpose of compactness, light weight and low power consumption. At the same time, the operational capability of GNC system also gets limit in a certain extent. Therefore, a kind of high speed navigation algorithm design becomes the imminence demand of GNC system. Aiming at such requirement, a kind of non-linearity matrix reduction approach is adopted in this paper to create a new high speed navigation algorithm which holds the radius of meridian circle and prime vertical circle as constant and linearizes the position matrix calculation formulae of navigation equation. Compared with normal navigation algorithm, this high speed navigation algorithm decreases 17.3% operand. Within small UAV"s mission radius (20km), the accuracy of position error is less than 0.13m. The results of semi-physical experiments and small UAV's auto pilot testing proved that this algorithm can realize high frequency attitude estimation and control. It will avoid low-frequency interference caused by atmospheric disturbance properly.

The model of uniform bus alignment satellite power system, which based on Simulink technology, Adopts Three-Domain control, considers the influence to the power system by the output of solar array and different load., computes the energy changing, simulate the uniform bus alignment satellite power system and energy balance, gives the result. The result of simulation indicates that the high current/small current/trickle charge schedule was implemented in charging regulator; the bus voltage was regulated to about 28V all the time; the power, current and voltage output of the solar array and the battery meet the requirements of the satellite power supply in orbit.

Dual-band GPS occultation receiver is designed to provide highly accurate measurements for atmosphere and ionosphere. The instrument flying on low-Earth orbit (LEO) receives the signals that have crossed the atmosphere and ionosphere at varying altitudes. It contains antennas, Radio Frequency Conditioning Units (RFCU) and central Electronic Unit. Besides the usual real-time navigation, the instrument must accurately track a sufficient number of GPS Space Vehicles (SV) for Radio Occultation (RO) and for precise orbit determination (POD), providing precise dual-frequency carrier and code phase measurements for the RO application. Plenty of environmental data can be deduced by way of further processing. This paper shows some data derived by mountain based observation experiment.

Satellite Surface Contamination (OSC) has unneglectable effects on solar-array output power, connections between solar-array, thermal blanket's functions, payloads' and optical sensors' performances onboard the Low Earth Orbit (LEO) satellites (especially long lifetime satellites). It can even influence their actual life. There are many kinds of examples in our country and overseas. In order to ensure satellites for long time working normally, we have set out to monitor and analyze the satellite surface gas. To be an effective instrument, Satellite Surface Gas Component Detector (SSGCD) is used to monitor the "in-situ" surface gas for the emergence and the variety with the change of time and position, as well as to monitor and identify the gas component and its density. Through the data processing with Ground Data Processing Unit (GDPU), we can evaluate the degree of the influence resulted from OSC, and provide services for orbital fault analysis and disposal.

Solar Extreme Ultravoilet (EUV) irradiance is one of the most important energy input source for the upper atmosphere. It's responsible for the variation of the composition of upper atmosphere, vertical profile of the atmosphere density and ionosphere's TEC. In order to investigate the solar EUV irradiance in different time scales, it's better to launch EUV grating spectrograph covering the whole solar EUV spectrum. Because of the high contrast between the solar EUV emission lines, it is possible to characterize the whole solar EUV spectrum by monitoring individual bright EUV emission lines, such as the Lyman alpha emission line (121.6nm in wavelength) and the He-II emission line (30.4nm in wavelength). The solar EUV instruments described in this paper are broad wavelength passband EUV monitors respectively covering the emission lines of 30.4nm and 121.6nm. Additional important emission lines are optional, such as 12.1nm, 58.4nm and 85.6nm. Each instrument covers just one emission line selected by a group of optic components - a multi-metal-film filter selecting the EUV wavelength passband and a spherical multilayer-coated reflecting mirror collecting the solar EUV irradiance from different incidence angle and improving the wavelength resolution. This kind of design will be used in the case high accuracy solar pointing system is unavailable, and the strong background of energetic charged particles is serious, such in the earth geostationary orbit, which is located in the limb of outer radiation belt.

High energy particle detector is one of the common payloads in Chinese spacecraft. Its purpose is to detect the flux of the high energy particle in space environment. This paper gives some information about the high energy particle detector.

An integrated system of test data management and monitoring (ISTDMM) for liquid rocket engine (LRE) ground test is designed to meet the demand of the LRE test station and development unit according to the LRE test information and test process. It is an opening, distributing and highly integrating application platform, mainly includes the test data management systems, the real-time fault detection systems and data display and playback system. It can manage and analyze the test data and simulation data of the LRE, can monitor the LRE test condition in real-time and the test process in long-distance by network, and can playback the engine test process and simulate the engine work process, and can test and evaluate the fault detection algorithms and systems of LRE. It is well advanced, reliable, and practical.

A satellite navigation performance evaluation and test system framework structure based on simulation has been conceived and developed targeting the windows operating system, so as to extract and analyze he key performance of satellite navigation system The evaluation methods of Ionospheric model errors, Space vehicles (SVs) clock errors and Ephemeris prediction errors were presented. While testing physical receiver performance, the mathematical simulation satellite navigation subsystem except receiver was used to help. It can provide references for satellite navigation performance evaluation and test.