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To achieve the safe, reliable autonomous operation of spacecraft, research on the fault diagnosis of control systems has attracted the attention of engineers and academicians throughout the aerospace field. Diagnosability can characterize the fault diagnosis capability of control systems. Connecting diagnosability analysis to the design of a spacecraft control system’s structure and diagnosis method can fundamentally improve the system’s fault diagnosis capability, improving the safety and reliability of autonomous spacecraft operation. In this paper, the diagnosability of spacecraft control systems is systematically studied from five perspectives: necessity, the current research status, the connotation, a novel index system and current development trends of diagnosability. First, the current status of spacecraft reliability analysis and reliability-based design is briefly reviewed, and existing problems are described, highlighting the advantages and importance of diagnosability research. Furthermore, the basic concepts of diagnosability are briefly introduced. By analyzing the current status of existing research on the diagnosability of both general and spacecraft control systems, the application scope of the diagnosability of spacecraft control systems is summarized. Moreover, the definition and influencing factors of the diagnosability of spacecraft control systems are presented to refine existing concepts, and a universal evaluation index system is proposed for the diagnosability of spacecraft control systems to further enhance the applicability of diagnosability evaluation and diagnosability-based design to spacecraft. Finally, the existing shortcomings and future development trends of diagnosability research for spacecraft control systems are discussed.

Working on product lifetime data is of significant importance for evaluating safety and reliability, predicting remaining useful life and formulating maintenance strategy or replacement policy. In practical applications, observed datasets often consist of failure data and randomly censored data, which are referred as general censored data. Meanwhile, inverse Gaussian (IG) distribution has been widely adopted to depict lifetime data because it not only can possess flexible expression formats but also can explain the mechanism of first hitting time from a soft failure viewpoint. Motivated by these two regards, this paper develops a novel method on best linear unbiased estimations (BLUEs) for general censored data. A three-parameter IG distribution type is adopted. A novel method is established to optimize the skewness parameter. Then, BLUEs of mean and standard deviation can be obtained. The proposed method can construct closed-form parameter estimations in linear functions of order statistics. The computation process has been further simplified for flexible applications. The frequently utilized maximum likelihood estimation method is also introduced as a reference for a better understanding. Comparative results of both comprehensive simulation study and empirical application illustrate that the proposed method can significantly enhance the estimation accuracy and keep a stable performance, because more life information can be extracted and adopted from the censored datasets.

In accelerated degradation tests, products are usually exposed to several environmental variables or operating conditions. This motivates the need for developing an accelerated degradation model involving multiple accelerating variables. Among the current literature, the conventional accelerated degradation models involving multiple accelerating variables have not considered the measurement error, which inevitably exists in practical degradation datasets. Therefore, a Wiener process-based accelerated degradation model that simultaneously considering multiple accelerating variables and measurement error is proposed. Then approximate closed-form expressions for the failure time distribution (FTD) and its percentiles are derived. The expectation maximization (EM) algorithm is adopted to estimate unknown parameters. Moreover, a multivariate normality testing method is developed to test the fitting goodness of the degradation model. Finally, a comprehensive simulation study and a real application are given to validate the proposed method. The result shows that the proposed model can provide precise estimates even for small sample size of approximately five, and the estimated mean square errors (MSEs) of the mean time to failure (MTTF) and the FTD percentile of the proposed model can be improved by at least 70 % compared with those of the reference methods when the sample size is same.

Fault diagnosis of satellite attitude control system is an important task to ensure the safe and reliable operation of on-orbit satellites. At present, most fault diagnosis methods are to diagnose independent identically distributed(i.i.d) task objects. However, even if the same device works under different working conditions, the distribution domain of the collected data almost always changes. At the same time, the training of fault diagnosis model under full working conditions can increase the model complexity and training time, and there may unknown working conditions. In view of the above situation, this paper proposed a domain adaptive adversarial deep neural network based fault diagnosis method. By combining the feature extractor, label classifier and domain classifier with the convolutional neural network and gradient inversion layer (GRL), the effective label classification can be achieved while the resolution of different domains can be reduced. We achieved feature extraction of the classification learning task in the source domain and transfer of the classification task between the two domains. The effectiveness of the diagnosis model is verified in the ground simulation data of a certain satellite under different conditions.

A fault diagnosis method of control moment gyroscope (CMG) based on K-means algorithm is proposed to improve the fault diagnosis accuracy by considering the hidden correlation between multidimensional telemetry data. Principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are used to extract features of CMG digital and physical data. On this basis, the fault diagnosis process of CMG based on K-means algorithm is supplied. Finally, the effectiveness of the proposed method is verified by using the actual in-orbit CMG data.

Highly transparent and submicrometer-grained Yb:Y2O3 ceramics were obtained by hot-pressing method with ZrO2 sintering additive. Hot-pressing method provides extra driving force to promote the densification of Yb:Y2O3 transparent ceramics, resulting in high transmittance achieved. The transmittances of all samples were ranging from 80.5-81.8% at 1100 nm and 72.7-77.7% at 600 nm. The ceramic samples in this work were sintered at a low temperature of 1600 ℃ for 3 h, providing dense microstructures with average grain size less than 1μm, which are smaller than those of Yb:Y2O3 ceramics prepared by other traditional sintering techniques. The microhardness and fracture toughness were ranging from 8.16 to 7.99 GPa, and from 0.94 to 0.88 MPa·m1/2, respectively, which are higher than the comparison samples fabricated by pressureless sintering. The absorption cross-sections of the 1 at.% Yb-doped sample at 950 and 976 nm were 0.93 × 10-20 and 1.22 × 10-20 cm2, respectively. The fluorescence decay lifetime at 1032 nm was in the range of 0.79-1.36 ms. According to the available literature, this is the first report about Yb:Y2O3 transparent ceramics fabricated by hot-pressing sintering without any combined sintering process. The ceramics obtained may have potential use for solid-state lasers partly replacing single crystals.

Reconfigurability refers to the ability of the system to overcome all faults or restore some of its performance by changing the structure or control algorithm under the condition of resource constraints and operating conditions within a certain period of time to ensure the security when the control system fails. The establishment of reconfigurability evaluation and design theoretical system is of great significance for improving the operational reliability and service life of the whole spacecraft. Research projects are being conducted worldwide regarding reconfiguration control technology. We summarize the performance factors that affect system reconfigurability based on several typical reconfiguration methods by analyzing the constraints that the system can satisfy through fault-tolerant approaches. Since the reconfigurability evaluation index reveals the limitations and potentials of reconfigurable ability of the system, we refine the quantitative reconfigurability evaluation method based on various influencing factors. We anticipate that this work will play a guiding role in the reconfiguration strategy and design of spacecraft in-orbit to achieve the fault forward.

The Wiener process model has been widely applied in degradation analysis for long-life and highly reliable products. However, the biggest challenge lies in how to properly describe the time-varying degradation mean and variance and the unit-specific property. Meanwhile, the current Wiener process degradation models cannot properly describe the degradation processes with increasing deterioration path and decreasing dispersity which do exist in real applications. Motivated by this practical problem and based on our previous study, an improved Wiener process degradation model is proposed. The degradation model can be widely adopted to depict practical degradation procedures illustrating a linear degradation mean and a quadratic variance. A random drift coefficient is further incorporated to properly consider the heterogeneity among items. The improvements can further expand the applicable scope and improve the statistical inference accuracy. Maximum likelihood estimations of unknown parameters are derived. The mean time to failure and the percentile of the failure time distribution are also constructed. Comparative results with reference methods from comprehensive simulation studies and practical applications both illustrate that the proposed method is more reasonable and can provide superior evaluation accuracy, even in limited sample size circumstances.

Degradation analysis has been recognized as an effective means for reliability assessment of complex systems and highly reliable products because few or even no failures are expected during their life span. To further our studies on degradation analysis, a generalized Gaussian process method is proposed to model degradation procedures. A one-stage maximum likelihood method is constructed for parameter estimation. Approximated forms for median life and failure time distribution (FTD) percentile are also derived considering the concept of First Hitting Time (FHT). To illustrate the performance of the proposed method, a comprehensive simulation study is conducted. Furthermore, the proposed method is illustrated and verified via two real applications including fatigue crack growth of 2017-T4 aluminum alloy and light emitting diode (LED) deterioration. The Wiener process model with mixed effects is considered as a reference method to investigate the generality of depicting common degradation processes. Meanwhile, to show the effectiveness of considering the FHT concept, another method (that has same model parameters with the proposed approach while does not consider the FHT definition) is also adopted as a reference. Comparisons show that the proposed methodology can not only show significant advantages for time- decreasing dispersity situations where the Wiener process models cannot reasonably perform, but also guarantee an enhanced precision for time-increasing variance circumstances.

Degradation information reflecting the product or system health state plays an important role in assessing reliability and making maintenance schedule. Since degradation inspections are usually compounded and contaminated by measurement errors in real applications, the conventional Wiener process with identically distributed independent Gaussian error is usually adopted. However, in many situations, autocorrelation may probably exist among the measurement errors at sequential test points because of cyclic changes or modeling errors, especially when the time intervals are relatively short. Motivated by this practical issue, a Wiener process degradation model with one-order autoregressive (AR(1)) measurement errors is proposed for degradation analysis. Explicit forms of the probability distribution function (PDF), the cumulative distribution function (CDF) and the corresponding mean time to failure (MTTF) are derived based on the concept of first hitting time (FHT). Furthermore, maximum likelihood estimations (MLE) of unknown parameters are derived. The effects of model mis-specification regarding the estimation of MTTF are also discussed. Finally, a comprehensive simulation study and two practical applications are given to demonstrate the necessity and efficiency of the proposed model.

In recent years, there has been considerable interest on the part of scientist and engineers in effectively designing accelerated degradation test, which is very useful to assess product's life information with long-life and high-reliability properties. An effective optimal ADT design method can provide a reasonable arrangement of accelerated stress level setting, sample allocation and test time. However, existing researches can hardly optimize all the multiple decision variables simultaneously in an efficient way. In the current study, an effective stochastic process based constant stress-accelerated degradation test (CSADT) model is constructed, and then an optimization design method is proposed based on the CSADT model, where the multiple decision variables involving stress level, sample allocation and test time arrangement for each stress level can be simultaneously optimized based on genetic algorithm (GA) and Monte Carlo simulation. Meanwhile, a comprehensive sensitivity analysis method incorporating orthogonal experimental design, normalization and range analysis is constructed to illustrate the robustness of the optimal design procedure. Finally, an optimal CSADT design for self-regulating heating cables is presented, and comparative results demonstrate the rationality and effectiveness of the proposed method.

Wiener processes have received considerable attention in degradation modeling over the last two decades. In this paper, we propose a generalized Wiener process degradation model that takes unit-to-unit variation, time-correlated structure and measurement error into considerations simultaneously. The constructed methodology subsumes a series of models studied in the literature as limiting cases. A simple method is given to determine the transformed time scale forms of the Wiener process degradation model. Then model parameters can be estimated based on a maximum likelihood estimation (MLE) method. The cumulative distribution function (CDF) and the probability distribution function (PDF) of the Wiener process with measurement errors are given based on the concept of the first hitting time (FHT). The percentiles of performance degradation (PD) and failure time distribution (FTD) are also obtained. Finally, a comprehensive simulation study is accomplished to demonstrate the necessity of incorporating measurement errors in the degradation model and the efficiency of the proposed model. Two illustrative real applications involving the degradation of carbon-film resistors and the wear of sliding metal are given. The comparative results show that the constructed approach can derive a reasonable result and an enhanced inference precision.

Aiming at the sensor subsystem of spacecraft attitude control system equipped with gyroscopes, earth sensors and sun sensors, the observer-based fault detection and fault location problems are studied to ensure the decoupling of fault diagnosis results with actuator faults. The kinematics equation of spacecraft attitude control system and the measurement model of various sensors are given. Then the sensor subsystem is regarded as a virtual system with gyroscope outputs as input and earth sensor outputs and sun sensor outputs as outputs. On this basis, observers with different functions are designed according to different axes of spacecraft attitude control system, and the diagnostic logic is proposed correspondingly. Finally, the effectiveness of the algorithm is verified by the simulation of the satellite attitude control system.

Liquid lithium has been considered as a candidate material for several components of future fusion devices. Since the containment materials are usually ferrous alloys, molecular dynamics simulations were performed to study the diffusion behavior of lithium atoms along tilt grain boundaries (GB) including Σ9{114}, Σ11{113}, Σ3{112} and Σ11{332} in α-Fe. The binding energies of a Li interstitial to the GBs were calculated. The results suggest that all the GBs have strong binding effect on the Li atom. The critical temperatures for the Li atom to diffuse were determined. The diffusion process of a Li interstitial in the GBs was systematically analyzed. It turns out that the diffusion mechanism depends on the GB structures. For Σ11{113} and GB Σ9{114}, the Li atom was trapped by the Frenkel defect around the GBs at 300 K and 400 K respectively and therefore the diffusion was slowed down rapidly. For Σ3{112}, no defects were formed around GB and the Li atom diffused into Fe bulk at 700 K and above. For Σ3{112}, the diffusion process is driven by the movement of the GB. Finally, the diffusion coefficient, as well as the activation energy, was evaluated.

Accelerated degradation analysis plays an important role in assessing reliability and making maintenance schedule for highly reliable products with long lifetime. In practical engineering, degradation data, especially measured under accelerated condition, are often compounded and contaminated by measurement errors, which makes the analysis more challenging. Therefore, a Wiener process model simultaneously incorporating temporal variability, individual variation and measurement errors is proposed to analyze the accelerated degradation test (ADT). The explicit forms of the probability distribution function (PDF) and the cumulative distribution function (CDF) are derived based on the concept of first hitting time (FHT). Then, combining with the acceleration models, the maximum likelihood estimations (MLE) of the model parameters are obtained. Finally, a comprehensive simulation study involving two examples and a practical application are given to demonstrate the necessity and efficiency of the proposed model.

Degradation testing is an important technique for assessing life time information of complex systems and highly reliable products. Motivated by fatigue crack growth (FCG) data and our previous study, this paper develops a novel degradation modeling approach, in which degradation is represented by an independent increment process with linear mean and general quadratic variance functions of test time or transformed test time if necessary. Based on the constructed degradation model, closed-form expressions of failure time distribution (FTD) and its percentiles can be straightforwardly derived and calculated. A one-stage method is developed to estimate model parameters and FTD. Simulation studies are conducted to validate the proposed approach, and the results illustrate that the approach can provide reasonable estimates even for small sample size situations. Finally, the method is verified by the FCG data set given as the motivating example, and the results show that it can be considered as an effective degradation modeling approach compared with the multivariate normal model and graphic approach.

This paper studies the sensor optimization configuration which meets the fault diagnosability of affine nonlinear faulty systems. First of all, the judgment method of fault diagnosability and the relevant quantitative evaluation indexes based on the differential geometry theory are presented. Then the optimal sensor allocation model is built, and the pagerank algorithm of the web page ranking is introduced into the configuration problem. Being differ from the new intellective heuristic algorithms, the weighted pagerank algorithm shifts the focus from designing an optimal subset search algorithm of better performance out to evaluating the important degree of each measuring point, and makes use of the analytic model more efficiently rather than the direct graph method. In the end, a validation example of the point mass satellite system is presented to demonstrate the effectiveness of the proposed approach.

This paper deals with the problem of fault estimation for satellite attitude control system with typical component configuration. In order to understand sensor and actuator faults propagation in the attitude control system, we have built the overall system model firstly, which includes the attitude dynamics model, sensor measurement model, attitude determination model, controller model and the moment distribution model. The faults are then considered as unknown inputs and are estimated using a classical proportional-integral observer, more over constant or time-varying fault can be estimated. The proposed observer is derived as a solution of off-line Linear Matrix Inequality (LMI) conditions. Additionally, simulation results are presented to illustrate the efficiency of the proposed method.

This paper focuses on actuator placement joint with a state feedback controller, to minimize the H ∞ norm of the closed-loop system. The actuator placement is even subject to a class of nonlinear constraints. This actuator selection problem is first formulated to be a non-convex optimization problem. Then to overcome these non-convex constraints, several procedures are provided to convert the problem into a sequential convex optimization problem, and correspondingly an optimization algorithm is given. Thruster location selection of an earth-pointing satellite is illustrated to verify the effectiveness of the proposed algorithm.

Knowledge of achievable diagnosability performance can provide theoretical guidance for developing diagnostic algorithms and optimizing sensor placement. A data driven approach for fault diagnosability quantitative evaluation without designing any diagnosis algorithm is proposed. Fault diagnosability is converted to similarity measure of output information which is denoted by fuzzy sets under different fault states. A quantitative evaluation measure and a specific diagnosability evaluation process are given. Finally, an example is presented to demonstrate the effectiveness of the proposed methodology.

This paper investigates the actuator placement of uncertain linear systems. The designed actuator locations are subject to a class of nonlinear equality constraints, and a dynamic output feedback controller should also be derived based on measured outputs. To minimize \(H\infty \) norm of the closed-loop systems, the actuator placement problem is formulated as an uncertain non-convex optimization problem. A series of transformations are developed to convert this problem into a deterministic successive convex problem. Correspondingly, an optimization algorithm is derived based on prediction-correction procedure.

An integrated design of fault diagnosis and reconfiguration method is studied for actuator failures of satellite control system. First, the effectiveness factor and the controller gain are taken as a whole to design the combined control law, which is obtained by placing the closed-loop system poles according to the theory of regional pole assignment. And this paper uses perturbation linearization method to solve the generating bilinear matrix inequalities(BMI). Then the effectiveness factor is obtained with a two-stage Kalman filter and thus the control law can be solved. Finally, the effectiveness of the provided method is illustrated by a simulation.

Driven by the satellite's features such as closed-loop, limited storage space and computing capacity constraint, a fault detection scheme based on the coprime factorization and Youla parameterization is proposed for the on-orbit satellite attitude control system. Based on the SISO dynamic of the satellite in pitch direction and the MIMO one in roll/yaw direction, the residuals equal to y(k)–y(k) is generated by a simple computation using the plant input and control error signals without running the state observer in parallel, therefore the computing expense is reduced to a certain extent, and the related theorems are provided synchronously. Simulation results are presented to show the performance of the proposed fault detection algorithm.

Screening and nurturing algae with high productivity, high lipid content and strong stress resistance are very important in algae industry. In order to increase the lipid content, the Nitzschia sp. was irradiated with a 3 MeV C 2+ beam. The sample pretreatment method was optimized to obtain the best mutagenic condition and the survival ratio curve. The positive mutants with a significant improvement in lipid content were screened and their C 2+ mutagenic effects were analyzed by comparing the greasiness and growth characteristics with the wild type algae. Results showed that when the Nitzschia sp. was cultivated in nutritious medium containing 10% glycerol solution, and dried on the filter for 5 min after centrifugation, the realization of the microalgae heavy ion mutagenesis could be done. The survival ratio curve caused by C 2+ irradiation was proved to be “saddle-shaped”. A positive mutant was screened among 20 survivals after irradiation, the average lipid content of the mutation increased by 9.8% than the wild type after 4 generations. But the growth rate of the screened mutation didn’t change after the heavy ion implantation compared to the wild type algae.

Vibration of Large flexible appendage seriously reduces the stability and accuracy of satellite attitude control, which can be solved by adjusting controller via on-orbit system identification method. This paper focuses on large flexible close mode satellite antenna, and Observer/Kalman filter identification (OKID) method is put into practice to solve the problem of system identification. Mode parameters and displacement responses under impulse and white noise excitations are extracted by finite element method (FEM), and then the concept of singular entropy is introduced to reduce observation noise and determine system order. Markov parameters are computed from displacement response and mode order, which is adopted in minimal realization of state space based on eigensystem realization algorithm (ERA). Comparing with the results of FEM in numerical simulation, OKID method is much more accurate and efficient for large flexible close mode satellite antenna on-orbit system identification.

The satellite power system is an important piece of the satellite system; targeting on the problems of complex fault mechanisms and uncertainty between fault type and fault symptoms, the method of Bayesian network fault diagnosis in the satellite power system has been raised. In the learning process of Bayesian network structure, this algorithm adopts statistical strategy for the rule library provided by many experts, extracts causal relationship from expert knowledge base, fills in the causal relationship table, thereby sets up the fault diagnosis hierarchical structure model in the satellite power system based on Bayesian network. Simulation shows that the Bayesian network fault diagnosis model is effectively solving the uncertainties in fault diagnosis.

Background: Physical loading changes bone microstructure and may influence quantitative ultrasound (QUS) parameters. This in vitro study aims at evaluates the effect of physical loading on bone QUS measurement. Objective: Ten fresh bovine bone specimens were studied. Specimens were scanned by a micro-CT and the density and structure parameters of specimens were calculated. The QUS measurement was performed when specimens subjected to loading, which changed from 0N to 1000N with the step of 100N. Statistical analysis performed to evaluate the difference between nBUA measured with and without loading, and the relationship between nBUA and the parameters measured by micro-CT. Results: While the loading exerted on bone higher than 200N, the measured nBUA significantly higher than nBUA measured without loading. Accompany with the increasing of loading, which exerted on bone, the values of nBUA also increased. A new parameter, the slope of the linearity fitted curve of nBUA values measured under different loading condition, was introduced to evaluate BMD. The correlation coefficient between the slope and BMD is -0.869 (P=0.001). Conclusions: Physical loading substantially influences bone QUS measurement. QUS measurement performed under loading condition may be a new ultrasound method for osteoporosis diagnosis.

Ultrasound contrast agents (UCA) microbubbles have been well recognized as a potential noninvasive tool for blood pressure measurement. The effect of ambient pressure on UCA subharmonic optimal driving frequency (SODF), at which the subharmonic scattering signals were maximal, was investigated experimentally. For groups of microbubbles with 80.1%±1.6% in the diameter range of 1μm-2μm, the shift of the SODF (SSODF) was 0.6MHz between overpressures of 4mmHg and 100mmHg, which was approximately twice of the corresponding shift of the resonance frequency. The SSODF may be as a novel and sensitive index of the local blood pressure estimation based on UCA microbubbles.

Background Physical loading leads to a deformation of bone microstructure and may influence quantitative ultrasound (QUS) parameters. This study aims at evaluating the effect of physical loading on bone QUS measurement, and further, on the potential of diagnosing osteoporosis using QUS method under physical loading condition. Methods 16 healthy young females (control group) and 45 postmenopausal women (divided into 3 groups according to the years since menopause (YSM)) were studied. QUS parameters were measured at calcaneus under self-weight loading (standing) and no loading (sitting) conditions. Weight-normalized QUS parameter (QUS parameter measured under loading condition divided by the weight of the subject) was proposed to evaluate the influence of loading. T-test, One-Way analysis of variance (one way ANOVA) and receiver operating characteristic (ROC) analysis were applied for analysis. Results In QUS parameters, mainly normalized broadband ultrasound attenuation (nBUA), measured with loading significantly differed from those measured without loading (p Conclusions Physical loading substantially influenced bone QUS measurement (mainly nBUA). Weight-normalized QUS parameters can discriminate menopause more effectively. By considering the high relationship between menopause and osteoporosis, an inference was drawn that adding physical loading during measurement may be a probable way to improve the QUS based osteoporosis diagnosis.

Osteoporosis is a worldwide health problem with enormous social and economic impact. Quantitative ultrasound (QUS) method provides comprehensive information on bone mass, microstructure and mechanical properties of the bone. And the cheap, safe and portable ultrasound equipment is more suitable for public health monitoring. QUS measurement was normally performed on bone specimens without mechanical loading. But human bones are subjected to loading during routine daily activities, and physical loading leads to the changes of bone microstructure and mechanical properties. We hypothesized that bone QUS parameters measured under loading condition differ from those measured without loading because the microstructure of bone was changed when loading subjected to bone. Furthermore, when loading was subjected on bone, the loading-lead microstructure change of osteoporosis bone may larger than that of health bone. By considering the high relationship between bone microstructure and QUS parameters, the QUS parameters of osteoporosis bone may changed larger than that of health bone. So osteoporosis may be detected more effectively by the combination of QUS method and mechanical loading.

The purpose of this study was to explore the triphasic mechanical properties of osteoarthritic cartilage with different pathological grades. First, samples of cartilage from rabbits with different stages of osteoarthritis (OA) were graded. Following this, the cartilage was strained by a swelling experiment, and changes were measured using a high-frequency ultrasound system. The result, together with fixed charge density and water volume fraction of cartilage samples, was used to estimate the uniaxial modulus of the cartilage tissue, based on a triphasic model. For the control cartilage samples, the uniaxial elastic modulus on the cartilage surface was lower than those in the middle and deep layers. With an increase in the OA grade, the uniaxial elastic modulus of the surface, middle and deep layers decreased. A significant difference was found in the surface elastic modulus of different OA grades (P

Gyroscope plays an important role in navigational systems and its drift has a direct influence on the precision. However, the gyroscope random drift series in practice are not traditional stationary time series for their time-varying means and variances, and large error cannot be avoided by traditional processing method. It is found that the statistical property of most gyroscope random drift series can meet the requirements defined by correlation coefficient stationary series. The analysis method of gyroscope drift series based on correlation coefficient stationary series model is established in this paper. The correlation coefficient stationarity testing method for gyroscope random drift series is discussed firstly. And then the modeling method process is presented. The proposed modeling procedure can efficiently improve the compensating accuracy of gyroscope random drift, because the presented method satisfactorily explains the nonstationary behavior of the gyroscope random drift series commonly existing in practice.

This paper present a low-cost wearable ultrasound bladder volume measurement and alarm system based on ARM9 and DSP. The system used a new volume computing method to estimate bladder volume based on the ultrasound echo obtained by only one phased-array ultrasonic transducer. The main signal processing task of ultrasound echo are performed using a DSP chip. The estimated value of bladder volume was transmitted to an alarm system based on Zigbee radio link. The system can be used for continuous monitoring of bladder volume and nursing on unconsciousness elders, handicapped with spinal cord injury, other urological patients, and children with nocturnal enuresis through setting alarm threshold.

Ultrasound contrast agents (UCA) micro-bubbles have been well recognized as a potential noninvasive tool for blood pressure estimation. However, previous UCA indices, e.g., the shift of the resonance frequency, echo amplitude and the disappearance time, suffered from problems of low resolution, nonlinearity in the relationship with blood pressure, and only variations of the local pressure but the absolute values etc. In this paper, the effect of ambient pressure on UCA sub-harmonic optimal driving frequency (SODF) was investigated, at which the sub-harmonic scattering signals were the maximum. By applying transmit frequencies between 3MHz and 8MHz and the acoustic pressure of 300kPa, the acoustic attenuation and scattering of micro-bubbles were measured at overpressures of 4mmHg, 50mmHg and 100mmHg, comparable with the healthy human blood pressure. For groups of micro-bubbles with 80% in the diameter range of 1-2 micrometers, the shift of the SODF (SSODF) was 0.6MHz between over-pressures of 4mmHg and 100...